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 3078beacc..310c3d182 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -12,12 +12,15 @@ Checks: > -readability-implicit-bool-conversion, -readability-magic-numbers, -readability-uppercase-literal-suffix, + -readability-simplify-boolean-expr, clang-analyzer-*, -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/cloud-v-pipeline b/.devops/cloud-v-pipeline index f3a4944f8..af8c0cea6 100644 --- a/.devops/cloud-v-pipeline +++ b/.devops/cloud-v-pipeline @@ -15,7 +15,7 @@ node('x86_runner1'){ // Running on x86 runner containing latest vecto stage('Running llama.cpp'){ sh'''#!/bin/bash module load gnu-bin2/0.1 # loading latest versions of vector qemu and vector gcc - qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./main -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64 + qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./llama-cli -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64 cat llama_log.txt # Printing results ''' } diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile new file mode 100644 index 000000000..522ee8147 --- /dev/null +++ b/.devops/cpu.Dockerfile @@ -0,0 +1,92 @@ +ARG UBUNTU_VERSION=22.04 + +FROM ubuntu:$UBUNTU_VERSION AS build + +ARG TARGETARCH + +ARG GGML_CPU_ARM_ARCH=armv8-a + +RUN apt-get update && \ + apt-get install -y build-essential git cmake libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN if [ "$TARGETARCH" = "amd64" ]; then \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \ + elif [ "$TARGETARCH" = "arm64" ]; then \ + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \ + else \ + echo "Unsupported architecture"; \ + exit 1; \ + fi && \ + cmake --build build -j $(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile new file mode 100644 index 000000000..974dd78a8 --- /dev/null +++ b/.devops/cuda.Dockerfile @@ -0,0 +1,94 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG CUDA_VERSION=12.6.0 +# Target the CUDA build image +ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} + +ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_CUDA_DEV_CONTAINER} AS build + +# CUDA architecture to build for (defaults to all supported archs) +ARG CUDA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 + +WORKDIR /app + +COPY . . + +RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ + fi && \ + 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) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_CUDA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile deleted file mode 100644 index 77a9ddc14..000000000 --- a/.devops/full-cuda.Dockerfile +++ /dev/null @@ -1,34 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=11.7.1 - -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} as build - -# Unless otherwise specified, we make a fat build. -ARG CUDA_DOCKER_ARCH=all - -RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip git - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} -# Enable cuBLAS -ENV LLAMA_CUBLAS=1 - -RUN make - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile deleted file mode 100644 index 8b9633dc4..000000000 --- a/.devops/full-rocm.Dockerfile +++ /dev/null @@ -1,45 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} as build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH=\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV GPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV LLAMA_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ - -RUN make - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile deleted file mode 100644 index cef1297d3..000000000 --- a/.devops/full.Dockerfile +++ /dev/null @@ -1,22 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION as build - -RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip git - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -RUN make - -ENV LC_ALL=C.utf8 - -ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile new file mode 100644 index 000000000..af783f5e9 --- /dev/null +++ b/.devops/intel.Dockerfile @@ -0,0 +1,91 @@ +ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 + +## Build Image + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build + +ARG GGML_SYCL_F16=OFF +RUN apt-get update && \ + apt-get install -y git libcurl4-openssl-dev + +WORKDIR /app + +COPY . . + +RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ + echo "GGML_SYCL_F16 is set" \ + && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ + fi && \ + echo "Building with dynamic libs" && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +### Full +FROM base AS full + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/lib/ /app +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] + diff --git a/.devops/llama-cli-cann.Dockerfile b/.devops/llama-cli-cann.Dockerfile new file mode 100644 index 000000000..02dce501c --- /dev/null +++ b/.devops/llama-cli-cann.Dockerfile @@ -0,0 +1,44 @@ +ARG ASCEND_VERSION=8.0.rc2.alpha003-910b-openeuler22.03-py3.8 + +FROM ascendai/cann:$ASCEND_VERSION AS build + +WORKDIR /app + +COPY . . + +RUN yum install -y gcc g++ cmake make +ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest +ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH} +ENV PYTHONPATH=${ASCEND_TOOLKIT_HOME}/python/site-packages:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe:${PYTHONPATH} +ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${ASCEND_TOOLKIT_HOME}/compiler/ccec_compiler/bin:${PATH} +ENV ASCEND_AICPU_PATH=${ASCEND_TOOLKIT_HOME} +ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp +ENV TOOLCHAIN_HOME=${ASCEND_TOOLKIT_HOME}/toolkit +ENV ASCEND_HOME_PATH=${ASCEND_TOOLKIT_HOME} + +# find libascend_hal.so, because the drive hasn`t been mounted. +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH + +RUN echo "Building with static libs" && \ + source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \ + cmake --build build --config Release --target llama-cli + +# TODO: use image with NNRT +FROM ascendai/cann:$ASCEND_VERSION AS runtime +COPY --from=build /app/build/bin/llama-cli /llama-cli + +ENV LC_ALL=C.utf8 + +ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest +ENV LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:$LIBRARY_PATH +ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/lib64/plugin/opskernel:${ASCEND_TOOLKIT_HOME}/lib64/plugin/nnengine:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH} +ENV PYTHONPATH=${ASCEND_TOOLKIT_HOME}/python/site-packages:${ASCEND_TOOLKIT_HOME}/opp/built-in/op_impl/ai_core/tbe:${PYTHONPATH} +ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${ASCEND_TOOLKIT_HOME}/compiler/ccec_compiler/bin:${PATH} +ENV ASCEND_AICPU_PATH=${ASCEND_TOOLKIT_HOME} +ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp +ENV TOOLCHAIN_HOME=${ASCEND_TOOLKIT_HOME}/toolkit +ENV ASCEND_HOME_PATH=${ASCEND_TOOLKIT_HOME} + +ENTRYPOINT ["/llama-cli" ] diff --git a/.devops/llama-cpp-clblast.srpm.spec b/.devops/llama-cpp-clblast.srpm.spec deleted file mode 100644 index 076f29695..000000000 --- a/.devops/llama-cpp-clblast.srpm.spec +++ /dev/null @@ -1,84 +0,0 @@ -# SRPM for building from source and packaging an RPM for RPM-based distros. -# https://fedoraproject.org/wiki/How_to_create_an_RPM_package -# Built and maintained by John Boero - boeroboy@gmail.com -# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal - -# Notes for llama.cpp: -# 1. Tags are currently based on hash - which will not sort asciibetically. -# We need to declare standard versioning if people want to sort latest releases. -# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies. -# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed. -# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo -# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries. -# It is up to the user to install the correct vendor-specific support. - -Name: llama.cpp-clblast -Version: %( date "+%%Y%%m%%d" ) -Release: 1%{?dist} -Summary: OpenCL Inference of LLaMA model in C/C++ -License: MIT -Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz -BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel -Requires: clblast -URL: https://github.com/ggerganov/llama.cpp - -%define debug_package %{nil} -%define source_date_epoch_from_changelog 0 - -%description -CPU inference for Meta's Lllama2 models using default options. - -%prep -%setup -n llama.cpp-master - -%build -make -j LLAMA_CLBLAST=1 - -%install -mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llamaclblast -cp -p server %{buildroot}%{_bindir}/llamaclblastserver -cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple - -mkdir -p %{buildroot}/usr/lib/systemd/system -%{__cat} < %{buildroot}/usr/lib/systemd/system/llamaclblast.service -[Unit] -Description=Llama.cpp server, CPU only (no GPU support in this build). -After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target - -[Service] -Type=simple -EnvironmentFile=/etc/sysconfig/llama -ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS -ExecReload=/bin/kill -s HUP $MAINPID -Restart=never - -[Install] -WantedBy=default.target -EOF - -mkdir -p %{buildroot}/etc/sysconfig -%{__cat} < %{buildroot}/etc/sysconfig/llama -LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin" -EOF - -%clean -rm -rf %{buildroot} -rm -rf %{_builddir}/* - -%files -%{_bindir}/llamaclblast -%{_bindir}/llamaclblastserver -%{_bindir}/llamaclblastsimple -/usr/lib/systemd/system/llamaclblast.service -%config /etc/sysconfig/llama - - -%pre - -%post - -%preun -%postun - -%changelog diff --git a/.devops/llama-cpp-cublas.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec similarity index 79% rename from .devops/llama-cpp-cublas.srpm.spec rename to .devops/llama-cpp-cuda.srpm.spec index f847ebb1e..7425d3a9d 100644 --- a/.devops/llama-cpp-cublas.srpm.spec +++ b/.devops/llama-cpp-cuda.srpm.spec @@ -1,5 +1,5 @@ # SRPM for building from source and packaging an RPM for RPM-based distros. -# https://fedoraproject.org/wiki/How_to_create_an_RPM_package +# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages # Built and maintained by John Boero - boeroboy@gmail.com # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal @@ -12,7 +12,7 @@ # 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries. # It is up to the user to install the correct vendor-specific support. -Name: llama.cpp-cublas +Name: llama.cpp-cuda Version: %( date "+%%Y%%m%%d" ) Release: 1%{?dist} Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) @@ -32,16 +32,16 @@ CPU inference for Meta's Lllama2 models using default options. %setup -n llama.cpp-master %build -make -j LLAMA_CUBLAS=1 +make -j GGML_CUDA=1 %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llamacppcublas -cp -p server %{buildroot}%{_bindir}/llamacppcublasserver -cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple +cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli +cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server +cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple mkdir -p %{buildroot}/usr/lib/systemd/system -%{__cat} < %{buildroot}/usr/lib/systemd/system/llamacublas.service +%{__cat} < %{buildroot}/usr/lib/systemd/system/llamacuda.service [Unit] Description=Llama.cpp server, CPU only (no GPU support in this build). After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target @@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t [Service] Type=simple EnvironmentFile=/etc/sysconfig/llama -ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS +ExecStart=/usr/bin/llama-cuda-server $LLAMA_ARGS ExecReload=/bin/kill -s HUP $MAINPID Restart=never @@ -67,10 +67,10 @@ rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llamacppcublas -%{_bindir}/llamacppcublasserver -%{_bindir}/llamacppcublassimple -/usr/lib/systemd/system/llamacublas.service +%{_bindir}/llama-cuda-cli +%{_bindir}/llama-cuda-server +%{_bindir}/llama-cuda-simple +/usr/lib/systemd/system/llamacuda.service %config /etc/sysconfig/llama %pre diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec index 446213d69..4d5560089 100644 --- a/.devops/llama-cpp.srpm.spec +++ b/.devops/llama-cpp.srpm.spec @@ -1,5 +1,5 @@ # SRPM for building from source and packaging an RPM for RPM-based distros. -# https://fedoraproject.org/wiki/How_to_create_an_RPM_package +# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages # Built and maintained by John Boero - boeroboy@gmail.com # In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal @@ -38,9 +38,9 @@ make -j %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llama -cp -p server %{buildroot}%{_bindir}/llamaserver -cp -p simple %{buildroot}%{_bindir}/llamasimple +cp -p llama-cli %{buildroot}%{_bindir}/llama-cli +cp -p llama-server %{buildroot}%{_bindir}/llama-server +cp -p llama-simple %{buildroot}%{_bindir}/llama-simple mkdir -p %{buildroot}/usr/lib/systemd/system %{__cat} < %{buildroot}/usr/lib/systemd/system/llama.service @@ -51,7 +51,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t [Service] Type=simple EnvironmentFile=/etc/sysconfig/llama -ExecStart=/usr/bin/llamaserver $LLAMA_ARGS +ExecStart=/usr/bin/llama-server $LLAMA_ARGS ExecReload=/bin/kill -s HUP $MAINPID Restart=never @@ -69,9 +69,9 @@ rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llama -%{_bindir}/llamaserver -%{_bindir}/llamasimple +%{_bindir}/llama-cli +%{_bindir}/llama-server +%{_bindir}/llama-simple /usr/lib/systemd/system/llama.service %config /etc/sysconfig/llama diff --git a/.devops/main-cuda.Dockerfile b/.devops/main-cuda.Dockerfile deleted file mode 100644 index 2b7faf7c1..000000000 --- a/.devops/main-cuda.Dockerfile +++ /dev/null @@ -1,32 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=11.7.1 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the CUDA runtime image -ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} as build - -# Unless otherwise specified, we make a fat build. -ARG CUDA_DOCKER_ARCH=all - -RUN apt-get update && \ - apt-get install -y build-essential git - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} -# Enable cuBLAS -ENV LLAMA_CUBLAS=1 - -RUN make - -FROM ${BASE_CUDA_RUN_CONTAINER} as runtime - -COPY --from=build /app/main /main - -ENTRYPOINT [ "/main" ] diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile deleted file mode 100644 index 572e5d8ea..000000000 --- a/.devops/main-intel.Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 - -FROM intel/oneapi-basekit:$ONEAPI_VERSION as build - -ARG LLAMA_SYCL_F16=OFF -RUN apt-get update && \ - apt-get install -y git - -WORKDIR /app - -COPY . . - -RUN mkdir build && \ - cd build && \ - if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ - echo "LLAMA_SYCL_F16 is set" && \ - export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ - fi && \ - cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ - cmake --build . --config Release --target main - -FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime - -COPY --from=build /app/build/bin/main /main - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/main" ] diff --git a/.devops/main-rocm.Dockerfile b/.devops/main-rocm.Dockerfile deleted file mode 100644 index 0a706dc73..000000000 --- a/.devops/main-rocm.Dockerfile +++ /dev/null @@ -1,45 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} as build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH=\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV GPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV LLAMA_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ - -RUN make - -ENTRYPOINT [ "/app/main" ] diff --git a/.devops/main-vulkan.Dockerfile b/.devops/main-vulkan.Dockerfile deleted file mode 100644 index bca460365..000000000 --- a/.devops/main-vulkan.Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -ARG UBUNTU_VERSION=jammy - -FROM ubuntu:$UBUNTU_VERSION as build - -# Install build tools -RUN apt update && apt install -y git build-essential cmake wget - -# Install Vulkan SDK -RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ - apt update -y && \ - apt-get install -y vulkan-sdk - -# Build it -WORKDIR /app -COPY . . -RUN mkdir build && \ - cd build && \ - cmake .. -DLLAMA_VULKAN=1 && \ - cmake --build . --config Release --target main - -# Clean up -WORKDIR / -RUN cp /app/build/bin/main /main && \ - rm -rf /app - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/main" ] diff --git a/.devops/main.Dockerfile b/.devops/main.Dockerfile deleted file mode 100644 index 3ab1decd6..000000000 --- a/.devops/main.Dockerfile +++ /dev/null @@ -1,20 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION as build - -RUN apt-get update && \ - apt-get install -y build-essential git - -WORKDIR /app - -COPY . . - -RUN make - -FROM ubuntu:$UBUNTU_VERSION as runtime - -COPY --from=build /app/main /main - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/main" ] diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile new file mode 100644 index 000000000..bfd7fc1c1 --- /dev/null +++ b/.devops/musa.Dockerfile @@ -0,0 +1,108 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG MUSA_VERSION=rc3.1.0 +# Target the MUSA build image +ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION} + +ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_MUSA_DEV_CONTAINER} AS build + +# MUSA architecture to build for (defaults to all supported archs) +ARG MUSA_DOCKER_ARCH=default + +RUN apt-get update && \ + apt-get install -y \ + build-essential \ + cmake \ + python3 \ + python3-pip \ + git \ + libcurl4-openssl-dev \ + libgomp1 + +COPY requirements.txt requirements.txt +COPY requirements requirements + +RUN pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt + +WORKDIR /app + +COPY . . + +# Use the default MUSA archs if not specified +RUN if [ "${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) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_MUSA_RUN_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + && pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/nix/apps.nix b/.devops/nix/apps.nix index b8a12cc0a..0ecf19fc5 100644 --- a/.devops/nix/apps.nix +++ b/.devops/nix/apps.nix @@ -6,11 +6,10 @@ let inherit (config.packages) default; binaries = [ - "llama" + "llama-cli" "llama-embedding" "llama-server" - "quantize" - "train-text-from-scratch" + "llama-quantize" ]; mkApp = name: { type = "app"; diff --git a/.devops/nix/devshells.nix b/.devops/nix/devshells.nix index 1862f0f08..bfd304af1 100644 --- a/.devops/nix/devshells.nix +++ b/.devops/nix/devshells.nix @@ -1,13 +1,52 @@ +{ inputs, ... }: + { perSystem = - { config, lib, ... }: + { + config, + lib, + system, + ... + }: { devShells = - lib.concatMapAttrs - (name: package: { - ${name} = package.passthru.shell; - ${name + "-extra"} = package.passthru.shell-extra; - }) - config.packages; + let + pkgs = import inputs.nixpkgs { inherit system; }; + stdenv = pkgs.stdenv; + scripts = config.packages.python-scripts; + in + lib.pipe (config.packages) [ + (lib.concatMapAttrs ( + name: package: { + ${name} = pkgs.mkShell { + name = "${name}"; + inputsFrom = [ package ]; + shellHook = '' + echo "Entering ${name} devShell" + ''; + }; + "${name}-extra" = + if (name == "python-scripts") then + null + else + pkgs.mkShell { + name = "${name}-extra"; + inputsFrom = [ + package + scripts + ]; + # Extra packages that *may* be used by some scripts + packages = [ + pkgs.python3Packages.tiktoken + ]; + shellHook = '' + echo "Entering ${name} devShell" + addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib stdenv.cc.cc}/lib" + ''; + }; + } + )) + (lib.filterAttrs (name: value: value != null)) + ]; }; } diff --git a/.devops/nix/nixpkgs-instances.nix b/.devops/nix/nixpkgs-instances.nix index 4a2f81c4b..90d683a71 100644 --- a/.devops/nix/nixpkgs-instances.nix +++ b/.devops/nix/nixpkgs-instances.nix @@ -26,16 +26,14 @@ config.cudaSupport = true; config.allowUnfreePredicate = p: - builtins.all - ( - license: - license.free - || builtins.elem license.shortName [ - "CUDA EULA" - "cuDNN EULA" - ] - ) - (p.meta.licenses or [ p.meta.license ]); + builtins.all ( + license: + license.free + || builtins.elem license.shortName [ + "CUDA EULA" + "cuDNN EULA" + ] + ) (p.meta.licenses or [ p.meta.license ]); }; # Ensure dependencies use ROCm consistently pkgsRocm = import inputs.nixpkgs { diff --git a/.devops/nix/package-gguf-py.nix b/.devops/nix/package-gguf-py.nix new file mode 100644 index 000000000..cca2f36a5 --- /dev/null +++ b/.devops/nix/package-gguf-py.nix @@ -0,0 +1,36 @@ +{ + lib, + llamaVersion, + numpy, + tqdm, + sentencepiece, + pyyaml, + poetry-core, + buildPythonPackage, + pytestCheckHook, +}: + +buildPythonPackage { + pname = "gguf"; + version = llamaVersion; + pyproject = true; + nativeBuildInputs = [ poetry-core ]; + propagatedBuildInputs = [ + numpy + tqdm + sentencepiece + pyyaml + ]; + src = lib.cleanSource ../../gguf-py; + pythonImportsCheck = [ + "numpy" + "gguf" + ]; + nativeCheckInputs = [ pytestCheckHook ]; + doCheck = true; + meta = with lib; { + description = "Python package for writing binary files in the GGUF format"; + license = licenses.mit; + maintainers = [ maintainers.ditsuke ]; + }; +} diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 815db6a2d..043c4364b 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -1,36 +1,47 @@ { lib, + glibc, config, stdenv, - mkShell, + runCommand, cmake, ninja, pkg-config, git, - python3, mpi, - openblas, # TODO: Use the generic `blas` so users could switch between alternative implementations + blas, cudaPackages, + autoAddDriverRunpath, darwin, rocmPackages, vulkan-headers, vulkan-loader, - clblast, - useBlas ? builtins.all (x: !x) [ - useCuda - useMetalKit - useOpenCL - useRocm - useVulkan - ], + curl, + shaderc, + useBlas ? + builtins.all (x: !x) [ + useCuda + useMetalKit + useRocm + useVulkan + ] + && blas.meta.available, useCuda ? config.cudaSupport, - useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL, - useMpi ? false, # Increases the runtime closure size by ~700M - useOpenCL ? false, + useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin, + # Increases the runtime closure size by ~700M + useMpi ? false, useRocm ? config.rocmSupport, + rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets, + enableCurl ? true, useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake -}@inputs: + + # It's necessary to consistently use backendStdenv when building with CUDA support, + # otherwise we get libstdc++ errors downstream. + effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv, + enableStatic ? effectiveStdenv.hostPlatform.isStatic, + precompileMetalShaders ? false, +}: let inherit (lib) @@ -38,51 +49,29 @@ let cmakeFeature optionals strings - versionOlder ; - # It's necessary to consistently use backendStdenv when building with CUDA support, - # otherwise we get libstdc++ errors downstream. stdenv = throw "Use effectiveStdenv instead"; - effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv; suffices = lib.optionals useBlas [ "BLAS" ] ++ lib.optionals useCuda [ "CUDA" ] ++ lib.optionals useMetalKit [ "MetalKit" ] ++ lib.optionals useMpi [ "MPI" ] - ++ lib.optionals useOpenCL [ "OpenCL" ] ++ lib.optionals useRocm [ "ROCm" ] ++ lib.optionals useVulkan [ "Vulkan" ]; pnameSuffix = strings.optionalString (suffices != [ ]) "-${strings.concatMapStringsSep "-" strings.toLower suffices}"; - descriptionSuffix = - strings.optionalString (suffices != [ ]) - ", accelerated with ${strings.concatStringsSep ", " suffices}"; + descriptionSuffix = strings.optionalString ( + suffices != [ ] + ) ", accelerated with ${strings.concatStringsSep ", " suffices}"; - # TODO: package the Python in this repository in a Nix-like way. - # It'd be nice to migrate to buildPythonPackage, as well as ensure this repo - # is PEP 517-compatible, and ensure the correct .dist-info is generated. - # https://peps.python.org/pep-0517/ - llama-python = python3.withPackages ( - ps: [ - ps.numpy - ps.sentencepiece - ] - ); - - # TODO(Green-Sky): find a better way to opt-into the heavy ml python runtime - llama-python-extra = python3.withPackages ( - ps: [ - ps.numpy - ps.sentencepiece - ps.tiktoken - ps.torchWithoutCuda - ps.transformers - ] - ); + xcrunHost = runCommand "xcrunHost" { } '' + mkdir -p $out/bin + ln -s /usr/bin/xcrun $out/bin + ''; # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64 # separately @@ -96,16 +85,9 @@ let ++ optionals useMetalKit [ MetalKit ]; cudaBuildInputs = with cudaPackages; [ - cuda_cccl.dev # - - # A temporary hack for reducing the closure size, remove once cudaPackages - # have stopped using lndir: https://github.com/NixOS/nixpkgs/issues/271792 - cuda_cudart.dev - cuda_cudart.lib - cuda_cudart.static - libcublas.dev - libcublas.lib - libcublas.static + cuda_cudart + cuda_cccl # + libcublas ]; rocmBuildInputs = with rocmPackages; [ @@ -117,174 +99,149 @@ let vulkanBuildInputs = [ vulkan-headers vulkan-loader + shaderc ]; in -effectiveStdenv.mkDerivation ( - finalAttrs: { - pname = "llama-cpp${pnameSuffix}"; - version = llamaVersion; +effectiveStdenv.mkDerivation (finalAttrs: { + pname = "llama-cpp${pnameSuffix}"; + version = llamaVersion; - # Note: none of the files discarded here are visible in the sandbox or - # affect the output hash. This also means they can be modified without - # triggering a rebuild. - src = lib.cleanSourceWith { - filter = - name: type: - let - noneOf = builtins.all (x: !x); - baseName = baseNameOf name; - in - noneOf [ - (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths - (lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths - (lib.hasPrefix "." baseName) # Skip hidden files and directories - (baseName == "flake.lock") - ]; - src = lib.cleanSource ../../.; - }; - - postPatch = '' - substituteInPlace ./ggml-metal.m \ - --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" - - # TODO: Package up each Python script or service appropriately. - # If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`, - # we could make those *.py into setuptools' entrypoints - substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python" - ''; - - nativeBuildInputs = - [ - cmake - ninja - pkg-config - git - ] - ++ optionals useCuda [ - cudaPackages.cuda_nvcc - - # TODO: Replace with autoAddDriverRunpath - # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged - cudaPackages.autoAddOpenGLRunpathHook + # Note: none of the files discarded here are visible in the sandbox or + # affect the output hash. This also means they can be modified without + # triggering a rebuild. + src = lib.cleanSourceWith { + filter = + name: type: + let + noneOf = builtins.all (x: !x); + baseName = baseNameOf name; + in + noneOf [ + (lib.hasSuffix ".nix" name) # Ignore *.nix files when computing outPaths + (lib.hasSuffix ".md" name) # Ignore *.md changes whe computing outPaths + (lib.hasPrefix "." baseName) # Skip hidden files and directories + (baseName == "flake.lock") ]; + src = lib.cleanSource ../../.; + }; - buildInputs = - optionals effectiveStdenv.isDarwin darwinBuildInputs - ++ optionals useCuda cudaBuildInputs - ++ optionals useMpi [ mpi ] - ++ optionals useOpenCL [ clblast ] - ++ optionals useRocm rocmBuildInputs - ++ optionals useVulkan vulkanBuildInputs; + postPatch = '' + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ + --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ + --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";" + ''; - cmakeFlags = - [ - (cmakeBool "LLAMA_NATIVE" false) - (cmakeBool "LLAMA_BUILD_SERVER" true) - (cmakeBool "BUILD_SHARED_LIBS" true) - (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) - (cmakeBool "LLAMA_BLAS" useBlas) - (cmakeBool "LLAMA_CLBLAST" useOpenCL) - (cmakeBool "LLAMA_CUBLAS" useCuda) - (cmakeBool "LLAMA_HIPBLAS" useRocm) - (cmakeBool "LLAMA_METAL" useMetalKit) - (cmakeBool "LLAMA_MPI" useMpi) - (cmakeBool "LLAMA_VULKAN" useVulkan) - ] - ++ optionals useCuda [ - ( - with cudaPackages.flags; - cmakeFeature "CMAKE_CUDA_ARCHITECTURES" ( - builtins.concatStringsSep ";" (map dropDot cudaCapabilities) - ) + # With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015, + # `default.metallib` may be compiled with Metal compiler from XCode + # and we need to escape sandbox on MacOS to access Metal compiler. + # `xcrun` is used find the path of the Metal compiler, which is varible + # and not on $PATH + # see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion + __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders; + + nativeBuildInputs = + [ + cmake + ninja + pkg-config + git + ] + ++ optionals useCuda [ + cudaPackages.cuda_nvcc + + autoAddDriverRunpath + ] + ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ glibc.static ] + ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ]; + + buildInputs = + optionals effectiveStdenv.isDarwin darwinBuildInputs + ++ optionals useCuda cudaBuildInputs + ++ optionals useMpi [ mpi ] + ++ optionals useRocm rocmBuildInputs + ++ optionals useBlas [ blas ] + ++ optionals useVulkan vulkanBuildInputs + ++ optionals enableCurl [ curl ]; + + cmakeFlags = + [ + (cmakeBool "LLAMA_BUILD_SERVER" true) + (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic)) + (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true) + (cmakeBool "LLAMA_CURL" enableCurl) + (cmakeBool "GGML_NATIVE" false) + (cmakeBool "GGML_BLAS" useBlas) + (cmakeBool "GGML_CUDA" useCuda) + (cmakeBool "GGML_HIP" useRocm) + (cmakeBool "GGML_METAL" useMetalKit) + (cmakeBool "GGML_VULKAN" useVulkan) + (cmakeBool "GGML_STATIC" enableStatic) + ] + ++ optionals useCuda [ + ( + with cudaPackages.flags; + cmakeFeature "CMAKE_CUDA_ARCHITECTURES" ( + builtins.concatStringsSep ";" (map dropDot cudaCapabilities) ) - ] - ++ optionals useRocm [ - (cmakeFeature "CMAKE_C_COMPILER" "hipcc") - (cmakeFeature "CMAKE_CXX_COMPILER" "hipcc") + ) + ] + ++ optionals useRocm [ + (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang") + (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets) + ] + ++ optionals useMetalKit [ + (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") + (cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders)) + ]; - # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM - # in https://github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt - # and select the line that matches the current nixpkgs version of rocBLAS. - # Should likely use `rocmPackages.clr.gpuTargets`. - "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102" - ] - ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") ] - ++ optionals useBlas [ (lib.cmakeFeature "LLAMA_BLAS_VENDOR" "OpenBLAS") ]; + # Environment variables needed for ROCm + env = optionals useRocm { + ROCM_PATH = "${rocmPackages.clr}"; + HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode"; + }; - # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, - # if they haven't been added yet. - postInstall = '' - mv $out/bin/main $out/bin/llama - mv $out/bin/server $out/bin/llama-server - mkdir -p $out/include - cp $src/llama.h $out/include/ - ''; + # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, + # if they haven't been added yet. + postInstall = '' + mkdir -p $out/include + cp $src/include/llama.h $out/include/ + ''; - # Define the shells here, but don't add in the inputsFrom to avoid recursion. - passthru = { - inherit - useBlas - useCuda - useMetalKit - useMpi - useOpenCL - useRocm - useVulkan - ; + meta = { + # Configurations we don't want even the CI to evaluate. Results in the + # "unsupported platform" messages. This is mostly a no-op, because + # cudaPackages would've refused to evaluate anyway. + badPlatforms = optionals useCuda lib.platforms.darwin; - shell = mkShell { - name = "shell-${finalAttrs.finalPackage.name}"; - description = "contains numpy and sentencepiece"; - buildInputs = [ llama-python ]; - inputsFrom = [ finalAttrs.finalPackage ]; - shellHook = '' - addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib effectiveStdenv.cc.cc}/lib" - ''; - }; + # Configurations that are known to result in build failures. Can be + # overridden by importing Nixpkgs with `allowBroken = true`. + broken = (useMetalKit && !effectiveStdenv.isDarwin); - shell-extra = mkShell { - name = "shell-extra-${finalAttrs.finalPackage.name}"; - description = "contains numpy, sentencepiece, torchWithoutCuda, and transformers"; - buildInputs = [ llama-python-extra ]; - inputsFrom = [ finalAttrs.finalPackage ]; - }; - }; + description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; + homepage = "https://github.com/ggerganov/llama.cpp/"; + license = lib.licenses.mit; - meta = { - # Configurations we don't want even the CI to evaluate. Results in the - # "unsupported platform" messages. This is mostly a no-op, because - # cudaPackages would've refused to evaluate anyway. - badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; + # Accommodates `nix run` and `lib.getExe` + mainProgram = "llama-cli"; - # Configurations that are known to result in build failures. Can be - # overridden by importing Nixpkgs with `allowBroken = true`. - broken = (useMetalKit && !effectiveStdenv.isDarwin); + # These people might respond, on the best effort basis, if you ping them + # in case of Nix-specific regressions or for reviewing Nix-specific PRs. + # Consider adding yourself to this list if you want to ensure this flake + # stays maintained and you're willing to invest your time. Do not add + # other people without their consent. Consider removing people after + # they've been unreachable for long periods of time. - description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; - homepage = "https://github.com/ggerganov/llama.cpp/"; - license = lib.licenses.mit; + # Note that lib.maintainers is defined in Nixpkgs, but you may just add + # an attrset following the same format as in + # https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix + maintainers = with lib.maintainers; [ + philiptaron + SomeoneSerge + ]; - # Accommodates `nix run` and `lib.getExe` - mainProgram = "llama"; - - # These people might respond, on the best effort basis, if you ping them - # in case of Nix-specific regressions or for reviewing Nix-specific PRs. - # Consider adding yourself to this list if you want to ensure this flake - # stays maintained and you're willing to invest your time. Do not add - # other people without their consent. Consider removing people after - # they've been unreachable for long periods of time. - - # Note that lib.maintainers is defined in Nixpkgs, but you may just add - # an attrset following the same format as in - # https://github.com/NixOS/nixpkgs/blob/f36a80e54da29775c78d7eff0e628c2b4e34d1d7/maintainers/maintainer-list.nix - maintainers = with lib.maintainers; [ - philiptaron - SomeoneSerge - ]; - - # Extend `badPlatforms` instead - platforms = lib.platforms.all; - }; - } -) + # Extend `badPlatforms` instead + platforms = lib.platforms.all; + }; +}) diff --git a/.devops/nix/python-scripts.nix b/.devops/nix/python-scripts.nix new file mode 100644 index 000000000..56ea18278 --- /dev/null +++ b/.devops/nix/python-scripts.nix @@ -0,0 +1,66 @@ +{ + lib, + stdenv, + buildPythonPackage, + poetry-core, + mkShell, + python3Packages, + gguf-py, +}@inputs: + +let + llama-python-deps = with python3Packages; [ + numpy + sentencepiece + transformers + protobuf + torchWithoutCuda + gguf-py + tqdm + + # for scripts/compare-llama-bench.py + gitpython + tabulate + + # for examples/pydantic-models-to-grammar-examples.py + docstring-parser + pydantic + + ]; + + llama-python-test-deps = with python3Packages; [ + # Server bench + matplotlib + + # server tests + openai + pytest + prometheus-client + ]; +in + +buildPythonPackage ({ + pname = "llama-scripts"; + version = "0.0.0"; + pyproject = true; + + # NOTE: The files filtered out here are not visible in the build sandbox, neither + # do they affect the output hash. They can be modified without triggering a rebuild. + src = lib.cleanSourceWith { + filter = + name: type: + let + any = builtins.any (x: x); + baseName = builtins.baseNameOf name; + in + any [ + (lib.hasSuffix ".py" name) + (baseName == "README.md") + (baseName == "pyproject.toml") + ]; + src = lib.cleanSource ../../.; + }; + nativeBuildInputs = [ poetry-core ]; + nativeCheckInputs = llama-python-test-deps; + dependencies = llama-python-deps; +}) diff --git a/.devops/nix/scope.nix b/.devops/nix/scope.nix index 78530c9e8..478e8c422 100644 --- a/.devops/nix/scope.nix +++ b/.devops/nix/scope.nix @@ -1,19 +1,41 @@ { lib, newScope, + python3, llamaVersion ? "0.0.0", }: +let + pythonPackages = python3.pkgs; + buildPythonPackage = pythonPackages.buildPythonPackage; + numpy = pythonPackages.numpy; + tqdm = pythonPackages.tqdm; + sentencepiece = pythonPackages.sentencepiece; + pyyaml = pythonPackages.pyyaml; + poetry-core = pythonPackages.poetry-core; + pytestCheckHook = pythonPackages.pytestCheckHook; +in + # We're using `makeScope` instead of just writing out an attrset # because it allows users to apply overlays later using `overrideScope'`. # Cf. https://noogle.dev/f/lib/makeScope -lib.makeScope newScope ( - self: { - inherit llamaVersion; - llama-cpp = self.callPackage ./package.nix { }; - docker = self.callPackage ./docker.nix { }; - docker-min = self.callPackage ./docker.nix { interactive = false; }; - sif = self.callPackage ./sif.nix { }; - } -) +lib.makeScope newScope (self: { + inherit llamaVersion; + gguf-py = self.callPackage ./package-gguf-py.nix { + inherit + buildPythonPackage + numpy + tqdm + sentencepiece + poetry-core + pyyaml + pytestCheckHook + ; + }; + python-scripts = self.callPackage ./python-scripts.nix { inherit buildPythonPackage poetry-core; }; + llama-cpp = self.callPackage ./package.nix { }; + docker = self.callPackage ./docker.nix { }; + docker-min = self.callPackage ./docker.nix { interactive = false; }; + sif = self.callPackage ./sif.nix { }; +}) diff --git a/.devops/nix/sif.nix b/.devops/nix/sif.nix index 7535ca0f3..7a5e1dd0f 100644 --- a/.devops/nix/sif.nix +++ b/.devops/nix/sif.nix @@ -7,7 +7,7 @@ }: let - optionalInt = cond: x: if cond then x else 0; + optionalInt = cond: x: if cond then x else 0; in singularity-tools.buildImage rec { inherit (llama-cpp) name; diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile new file mode 100644 index 000000000..a8088ea00 --- /dev/null +++ b/.devops/rocm.Dockerfile @@ -0,0 +1,113 @@ +ARG UBUNTU_VERSION=24.04 + +# This needs to generally match the container host's environment. +ARG ROCM_VERSION=6.3 +ARG AMDGPU_VERSION=6.3 + +# Target the CUDA build image +ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete + +### Build image +FROM ${BASE_ROCM_DEV_CONTAINER} AS build + +# Unless otherwise specified, we make a fat build. +# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 +# This is mostly tied to rocBLAS supported archs. +# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported +# gfx906 is deprecated +#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html + +#ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102' +ARG ROCM_DOCKER_ARCH=gfx1100 + +# Set nvcc architectured +ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH} +# Enable ROCm +# ENV CC=/opt/rocm/llvm/bin/clang +# ENV CXX=/opt/rocm/llvm/bin/clang++ + +RUN apt-get update \ + && apt-get install -y \ + build-essential \ + cmake \ + git \ + libcurl4-openssl-dev \ + curl \ + libgomp1 + +WORKDIR /app + +COPY . . + +RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \ + && cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib \ + && find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ${BASE_ROCM_DEV_CONTAINER} AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl\ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3-pip \ + python3 \ + python3-wheel\ + && pip install --break-system-packages --upgrade setuptools \ + && pip install --break-system-packages -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile deleted file mode 100644 index 4f83904bc..000000000 --- a/.devops/server-cuda.Dockerfile +++ /dev/null @@ -1,32 +0,0 @@ -ARG UBUNTU_VERSION=22.04 -# This needs to generally match the container host's environment. -ARG CUDA_VERSION=11.7.1 -# Target the CUDA build image -ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} -# Target the CUDA runtime image -ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} - -FROM ${BASE_CUDA_DEV_CONTAINER} as build - -# Unless otherwise specified, we make a fat build. -ARG CUDA_DOCKER_ARCH=all - -RUN apt-get update && \ - apt-get install -y build-essential git - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} -# Enable cuBLAS -ENV LLAMA_CUBLAS=1 - -RUN make - -FROM ${BASE_CUDA_RUN_CONTAINER} as runtime - -COPY --from=build /app/server /server - -ENTRYPOINT [ "/server" ] diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile deleted file mode 100644 index 312f2df80..000000000 --- a/.devops/server-intel.Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 - -FROM intel/oneapi-basekit:$ONEAPI_VERSION as build - -ARG LLAMA_SYCL_F16=OFF -RUN apt-get update && \ - apt-get install -y git - -WORKDIR /app - -COPY . . - -RUN mkdir build && \ - cd build && \ - if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ - echo "LLAMA_SYCL_F16 is set" && \ - export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ - fi && \ - cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ - cmake --build . --config Release --target server - -FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime - -COPY --from=build /app/build/bin/server /server - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/server" ] diff --git a/.devops/server-rocm.Dockerfile b/.devops/server-rocm.Dockerfile deleted file mode 100644 index e9a31647c..000000000 --- a/.devops/server-rocm.Dockerfile +++ /dev/null @@ -1,45 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -# This needs to generally match the container host's environment. -ARG ROCM_VERSION=5.6 - -# Target the CUDA build image -ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete - -FROM ${BASE_ROCM_DEV_CONTAINER} as build - -# Unless otherwise specified, we make a fat build. -# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 -# This is mostly tied to rocBLAS supported archs. -ARG ROCM_DOCKER_ARCH=\ - gfx803 \ - gfx900 \ - gfx906 \ - gfx908 \ - gfx90a \ - gfx1010 \ - gfx1030 \ - gfx1100 \ - gfx1101 \ - gfx1102 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt - -WORKDIR /app - -COPY . . - -# Set nvcc architecture -ENV GPU_TARGETS=${ROCM_DOCKER_ARCH} -# Enable ROCm -ENV LLAMA_HIPBLAS=1 -ENV CC=/opt/rocm/llvm/bin/clang -ENV CXX=/opt/rocm/llvm/bin/clang++ - -RUN make - -ENTRYPOINT [ "/app/server" ] diff --git a/.devops/server-vulkan.Dockerfile b/.devops/server-vulkan.Dockerfile deleted file mode 100644 index e0add6fc3..000000000 --- a/.devops/server-vulkan.Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -ARG UBUNTU_VERSION=jammy - -FROM ubuntu:$UBUNTU_VERSION as build - -# Install build tools -RUN apt update && apt install -y git build-essential cmake wget - -# Install Vulkan SDK -RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ - apt update -y && \ - apt-get install -y vulkan-sdk - -# Build it -WORKDIR /app -COPY . . -RUN mkdir build && \ - cd build && \ - cmake .. -DLLAMA_VULKAN=1 && \ - cmake --build . --config Release --target server - -# Clean up -WORKDIR / -RUN cp /app/build/bin/server /server && \ - rm -rf /app - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/server" ] diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile deleted file mode 100644 index 134588fe2..000000000 --- a/.devops/server.Dockerfile +++ /dev/null @@ -1,20 +0,0 @@ -ARG UBUNTU_VERSION=22.04 - -FROM ubuntu:$UBUNTU_VERSION as build - -RUN apt-get update && \ - apt-get install -y build-essential git - -WORKDIR /app - -COPY . . - -RUN make - -FROM ubuntu:$UBUNTU_VERSION as runtime - -COPY --from=build /app/server /server - -ENV LC_ALL=C.utf8 - -ENTRYPOINT [ "/server" ] diff --git a/.devops/tools.sh b/.devops/tools.sh index 3a7d274e4..41a6b1e55 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -8,36 +8,40 @@ arg1="$1" shift if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then - python3 ./convert.py "$@" + exec python3 ./convert_hf_to_gguf.py "$@" elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then - ./quantize "$@" + exec ./llama-quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then - ./main "$@" -elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then - ./finetune "$@" + exec ./llama-cli "$@" +elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then + exec ./llama-bench "$@" +elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then + exec ./llama-perplexity "$@" elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then echo "Converting PTH to GGML..." - for i in `ls $1/$2/ggml-model-f16.bin*`; do + for i in $(ls $1/$2/ggml-model-f16.bin*); do if [ -f "${i/f16/q4_0}" ]; then echo "Skip model quantization, it already exists: ${i/f16/q4_0}" else echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..." - ./quantize "$i" "${i/f16/q4_0}" q4_0 + exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0 fi done elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then - ./server "$@" + exec ./llama-server "$@" else echo "Unknown command: $arg1" echo "Available commands: " echo " --run (-r): Run a model previously converted into ggml" echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512" + echo " --bench (-b): Benchmark the performance of the inference for various parameters." + echo " ex: -m model.gguf" + echo " --perplexity (-p): Measure the perplexity of a model over a given text." + echo " ex: -m model.gguf -f file.txt" echo " --convert (-c): Convert a llama model into ggml" echo " ex: --outtype f16 \"/models/7B/\" " echo " --quantize (-q): Optimize with quantization process ggml" echo " ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2" - echo " --finetune (-f): Run finetune command to create a lora finetune of the model" - echo " See documentation for finetune for command-line parameters" echo " --all-in-one (-a): Execute --convert & --quantize" echo " ex: \"/models/\" 7B" echo " --server (-s): Run a model on the server" diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile new file mode 100644 index 000000000..9064f3838 --- /dev/null +++ b/.devops/vulkan.Dockerfile @@ -0,0 +1,89 @@ +ARG UBUNTU_VERSION=24.04 + +FROM ubuntu:$UBUNTU_VERSION AS build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK and cURL +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk libcurl4-openssl-dev curl + +# Build it +WORKDIR /app + +COPY . . + +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ + cmake --build build --config Release -j$(nproc) + +RUN mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; + +RUN mkdir -p /app/full \ + && cp build/bin/* /app/full \ + && cp *.py /app/full \ + && cp -r gguf-py /app/full \ + && cp -r requirements /app/full \ + && cp requirements.txt /app/full \ + && cp .devops/tools.sh /app/full/tools.sh + +## Base image +FROM ubuntu:$UBUNTU_VERSION AS base + +RUN apt-get update \ + && apt-get install -y libgomp1 curl libvulkan-dev \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +COPY --from=build /app/lib/ /app + +### Full +FROM base AS full + +COPY --from=build /app/full /app + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y \ + git \ + python3 \ + python3-pip \ + python3-wheel \ + && pip install --break-system-packages --upgrade setuptools \ + && pip install --break-system-packages -r requirements.txt \ + && apt autoremove -y \ + && apt clean -y \ + && rm -rf /tmp/* /var/tmp/* \ + && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \ + && find /var/cache -type f -delete + +ENTRYPOINT ["/app/tools.sh"] + +### Light, CLI only +FROM base AS light + +COPY --from=build /app/full/llama-cli /app + +WORKDIR /app + +ENTRYPOINT [ "/app/llama-cli" ] + +### Server, Server only +FROM base AS server + +ENV LLAMA_ARG_HOST=0.0.0.0 + +COPY --from=build /app/full/llama-server /app + +WORKDIR /app + +HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] + +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.dockerignore b/.dockerignore index 633bbc3a9..064b7c7be 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,7 +1,7 @@ *.o *.a .cache/ -.git/ +# Do not ignore .git directory, otherwise the reported build number will always be 0 .github/ .gitignore .vs/ @@ -12,8 +12,8 @@ build*/ models/* -/main -/quantize +/llama-cli +/llama-quantize arm_neon.h compile_commands.json diff --git a/.ecrc b/.ecrc index a3351f4e6..c68877ec2 100644 --- a/.ecrc +++ b/.ecrc @@ -1,5 +1,5 @@ { - "Exclude": ["^\\.gitmodules$"], + "Exclude": ["^\\.gitmodules$", "stb_image\\.h"], "Disable": { "IndentSize": true } diff --git a/.editorconfig b/.editorconfig index 16d16b3b5..5d63d0a51 100644 --- a/.editorconfig +++ b/.editorconfig @@ -24,5 +24,27 @@ 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 + +[examples/cvector-generator/*.txt] +trim_trailing_whitespace = unset +insert_final_newline = unset + +[models/templates/*.jinja] +indent_style = unset +indent_size = unset +end_of_line = unset +charset = unset +trim_trailing_whitespace = unset +insert_final_newline = unset diff --git a/.flake8 b/.flake8 index 18fba2c15..d64c2564a 100644 --- a/.flake8 +++ b/.flake8 @@ -1,3 +1,17 @@ [flake8] max-line-length = 125 -ignore = W503 +ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503 +exclude = + # Do not traverse examples + examples, + # Do not include package initializers + __init__.py, + # No need to traverse our git directory + .git, + # There's no value in checking cache directories + __pycache__, + # No need to include the build path + build, + # This contains builds that we don't want to check + dist # This is generated with `python build .` for package releases +# max-complexity = 10 diff --git a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml new file mode 100644 index 000000000..b85bf5741 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml @@ -0,0 +1,87 @@ +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: command + attributes: + label: Compile command + description: > + Please provide the exact command you used to compile llama.cpp. For example: `cmake -B ...`. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including 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..1904e31fd --- /dev/null +++ b/.github/ISSUE_TEMPLATE/019-bug-misc.yml @@ -0,0 +1,91 @@ +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: command + attributes: + label: Command line + description: > + Please provide the exact commands you entered, if applicable. For example: `llama-server -m ... -c ...`, `llama-cli -m ...`, etc. + This will be automatically formatted into code, so no need for backticks. + render: shell + 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 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/020-enhancement.yml b/.github/ISSUE_TEMPLATE/020-enhancement.yml new file mode 100644 index 000000000..02dd4f575 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/020-enhancement.yml @@ -0,0 +1,51 @@ +name: Enhancement +description: Used to request enhancements for llama.cpp. +title: "Feature Request: " +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas) + + - type: checkboxes + id: prerequisites + attributes: + label: Prerequisites + description: Please confirm the following before submitting your enhancement request. + options: + - label: I am running the latest code. Mention the version if possible as well. + required: true + - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). + required: true + - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). + required: true + - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share. + required: true + + - type: textarea + id: feature-description + attributes: + label: Feature Description + description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. + placeholder: Detailed description of the enhancement + validations: + required: true + + - type: textarea + id: motivation + attributes: + label: Motivation + description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. + placeholder: Explanation of why this feature is needed and its benefits + validations: + required: true + + - type: textarea + id: possible-implementation + attributes: + label: Possible Implementation + description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. + placeholder: Detailed description of potential implementation + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/030-research.yml b/.github/ISSUE_TEMPLATE/030-research.yml new file mode 100644 index 000000000..18975dbbf --- /dev/null +++ b/.github/ISSUE_TEMPLATE/030-research.yml @@ -0,0 +1,52 @@ +name: Research +description: Track new technical research area. +title: "Research: " +labels: ["research 🔬"] +body: + - type: markdown + attributes: + value: | + Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22) + + - type: checkboxes + id: research-stage + attributes: + label: Research Stage + description: Track general state of this research ticket + options: + - label: Background Research (Let's try to avoid reinventing the wheel) + - label: Hypothesis Formed (How do you think this will work and it's effect?) + - label: Strategy / Implementation Forming + - label: Analysis of results + - label: Debrief / Documentation (So people in the future can learn from us) + + - type: textarea + id: background + attributes: + label: Previous existing literature and research + description: Whats the current state of the art and whats the motivation for this research? + + - type: textarea + id: hypothesis + attributes: + label: Hypothesis + description: How do you think this will work and it's effect? + + - type: textarea + id: implementation + attributes: + label: Implementation + description: Got an approach? e.g. a PR ready to go? + + - type: textarea + id: analysis + attributes: + label: Analysis + description: How does the proposed implementation behave? + + - 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/040-refactor.yml b/.github/ISSUE_TEMPLATE/040-refactor.yml new file mode 100644 index 000000000..b6e6ab36d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/040-refactor.yml @@ -0,0 +1,28 @@ +name: Refactor (Maintainers) +description: Used to track refactoring opportunities. +title: "Refactor: " +labels: ["refactor"] +body: + - type: markdown + attributes: + value: | + Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered. + Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too. + + - type: textarea + id: background-description + attributes: + label: Background Description + description: Please provide a detailed written description of the pain points you are trying to solve. + placeholder: Detailed description behind your motivation to request refactor + validations: + required: true + + - type: textarea + id: possible-approaches + attributes: + label: Possible Refactor Approaches + description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list. + placeholder: Your idea of possible refactoring opportunity/approaches + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md deleted file mode 100644 index 49812832c..000000000 --- a/.github/ISSUE_TEMPLATE/bug.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -name: Bug template -about: Used to report bugs in llama.cpp -labels: ["bug-unconfirmed"] -assignees: '' - ---- - -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. - -If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests). diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 000000000..eb8c4b472 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,11 @@ +blank_issues_enabled: true +contact_links: + - name: Got an idea? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas + about: Pop it there. It may then become an enhancement ticket. + - name: Got a question? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a + about: Ask a question there! + - name: Want to contribute? + url: https://github.com/ggerganov/llama.cpp/wiki/contribute + about: Head to the contribution guide page of the wiki for areas you can help with diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md deleted file mode 100644 index dcffda750..000000000 --- a/.github/ISSUE_TEMPLATE/enhancement.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -name: Enhancement template -about: Used to request enhancements for llama.cpp -labels: ["enhancement"] -assignees: '' - ---- - -# Prerequisites - -Please answer the following questions for yourself before submitting an issue. - -- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now. -- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). -- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed). -- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share. - -# Feature Description - -Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. - -# Motivation - -Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. - -# Possible Implementation - -If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 000000000..1b47bc968 --- /dev/null +++ b/.github/labeler.yml @@ -0,0 +1,86 @@ +# https://github.com/actions/labeler +Kompute: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-kompute.h + - 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/** + - README-metal.md +SYCL: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-sycl.h + - ggml/src/ggml-sycl/** + - docs/backend/SYCL.md + - examples/sycl/** +Nvidia GPU: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-cuda.h + - ggml/src/ggml-cuda/** +Vulkan: + - changed-files: + - any-glob-to-any-file: + - ggml/include/ggml-vulkan.h + - ggml/src/ggml-vulkan/** +documentation: + - changed-files: + - any-glob-to-any-file: + - docs/** + - media/** +testing: + - changed-files: + - any-glob-to-any-file: + - tests/** +build: + - changed-files: + - any-glob-to-any-file: + - cmake/** + - CMakeLists.txt + - CMakePresets.json +examples: + - changed-files: + - any-glob-to-any-file: examples/** +devops: + - changed-files: + - any-glob-to-any-file: + - .devops/** + - .github/** + - ci/** +python: + - changed-files: + - any-glob-to-any-file: + - "**/*.py" + - requirements/** + - gguf-py/** + - .flake8 +script: + - changed-files: + - any-glob-to-any-file: + - scripts/** +android: + - changed-files: + - any-glob-to-any-file: + - examples/llama.android/** +server: + - changed-files: + - any-glob-to-any-file: + - examples/server/** +ggml: + - changed-files: + - any-glob-to-any-file: + - ggml/** +nix: + - changed-files: + - any-glob-to-any-file: + - "**/*.nix" + - .github/workflows/nix-*.yml + - .devops/nix/nixpkgs-instances.nix +embedding: + - changed-files: + - any-glob-to-any-file: examples/embedding/ diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 000000000..d9f5bdc23 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1 @@ +*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/bench.yml.disabled b/.github/workflows/bench.yml.disabled new file mode 100644 index 000000000..1c8787ef7 --- /dev/null +++ b/.github/workflows/bench.yml.disabled @@ -0,0 +1,315 @@ +# TODO: there have been some issues with the workflow, so disabling for now +# https://github.com/ggerganov/llama.cpp/issues/7893 +# +# Benchmark +name: Benchmark + +on: + workflow_dispatch: + inputs: + gpu-series: + description: 'Azure GPU series to run with' + required: true + type: choice + options: + - Standard_NC4as_T4_v3 + - Standard_NC24ads_A100_v4 + - Standard_NC80adis_H100_v5 + sha: + description: 'Commit SHA1 to build' + required: false + type: string + duration: + description: 'Duration of the bench' + type: string + default: 10m + + push: + branches: + - master + paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp'] + pull_request_target: + types: [opened, synchronize, reopened] + paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp'] + schedule: + - cron: '04 2 * * *' + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }} + cancel-in-progress: true + +jobs: + bench-server-baseline: + runs-on: Standard_NC4as_T4_v3 + env: + RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it + N_USERS: 8 + DURATION: 10m + + strategy: + matrix: + model: [phi-2] + ftype: [q4_0, q8_0, f16] + include: + - model: phi-2 + ftype: q4_0 + pr_comment_enabled: "true" + + if: | + inputs.gpu-series == 'Standard_NC4as_T4_v3' + || ( + github.event_name == 'schedule' + && github.ref_name == 'master' + && github.repository_owner == 'ggerganov' + ) + || github.event_name == 'pull_request_target' + || ( + github.event_name == 'push' + && github.event.ref == 'refs/heads/master' + && github.repository_owner == 'ggerganov' + ) + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: Install python env + id: pipenv + run: | + cd examples/server/bench + python3 -m venv venv + source venv/bin/activate + pip install -r requirements.txt + + - name: Prometheus + id: install_prometheus + run: | + wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz + tar xzf prometheus*.tar.gz --strip-components=1 + ./prometheus --config.file=examples/server/bench/prometheus.yml & + while ! nc -z localhost 9090; do + sleep 0.1 + done + + - name: Set up Go + uses: actions/setup-go@v5 + with: + go-version: '1.21' + + - name: Install k6 and xk6-sse + id: k6_installation + run: | + cd examples/server/bench + go install go.k6.io/xk6/cmd/xk6@latest + xk6 build master \ + --with github.com/phymbert/xk6-sse + + - name: Build + id: cmake_build + run: | + set -eux + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DLLAMA_CURL=ON \ + -DLLAMA_CUBLAS=ON \ + -DCUDAToolkit_ROOT=/usr/local/cuda \ + -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \ + -DCMAKE_CUDA_ARCHITECTURES=75 \ + -DLLAMA_FATAL_WARNINGS=OFF \ + -DLLAMA_ALL_WARNINGS=OFF \ + -DCMAKE_BUILD_TYPE=Release; + cmake --build build --config Release -j $(nproc) --target llama-server + + - name: Download the dataset + id: download_dataset + run: | + cd examples/server/bench + wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json + + - name: Server bench + id: server_bench + env: + HEAD_REF: ${{ github.head_ref || github.ref_name }} + run: | + set -eux + + cd examples/server/bench + source venv/bin/activate + python bench.py \ + --runner-label ${{ env.RUNNER_LABEL }} \ + --name ${{ github.job }} \ + --branch $HEAD_REF \ + --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \ + --scenario script.js \ + --duration ${{ github.event.inputs.duration || env.DURATION }} \ + --hf-repo ggml-org/models \ + --hf-file ${{ matrix.model }}/ggml-model-${{ matrix.ftype }}.gguf \ + --model-path-prefix /models \ + --parallel ${{ env.N_USERS }} \ + -ngl 33 \ + --batch-size 2048 \ + --ubatch-size 256 \ + --ctx-size 16384 \ + --n-prompts 1000 \ + --max-prompt-tokens 1024 \ + --max-tokens 2048 + + cat results.github.env >> $GITHUB_ENV + + # Remove dataset as we do not want it in the artefact + rm ShareGPT_V3_unfiltered_cleaned_split.json + + - uses: actions/upload-artifact@v4 + with: + name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }} + compression-level: 9 + path: | + examples/server/bench/*.jpg + examples/server/bench/*.json + examples/server/bench/*.log + + - name: Commit status + uses: Sibz/github-status-action@v1 + with: + authToken: ${{secrets.GITHUB_TOKEN}} + sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }} + context: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }} + description: | + ${{ env.BENCH_RESULTS }} + state: 'success' + + - name: Upload benchmark images + uses: devicons/public-upload-to-imgur@v2.2.2 + continue-on-error: true # Important as it looks unstable: 503 + id: imgur_step + with: + client_id: ${{secrets.IMGUR_CLIENT_ID}} + path: | + examples/server/bench/prompt_tokens_seconds.jpg + examples/server/bench/predicted_tokens_seconds.jpg + examples/server/bench/kv_cache_usage_ratio.jpg + examples/server/bench/requests_processing.jpg + + - name: Extract mermaid + id: set_mermaid + run: | + set -eux + + cd examples/server/bench + PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid) + echo "PROMPT_TOKENS_SECONDS<> $GITHUB_ENV + echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid) + echo "PREDICTED_TOKENS_SECONDS<> $GITHUB_ENV + echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid) + echo "KV_CACHE_USAGE_RATIO<> $GITHUB_ENV + echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + REQUESTS_PROCESSING=$(cat requests_processing.mermaid) + echo "REQUESTS_PROCESSING<> $GITHUB_ENV + echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV + + - name: Extract image url + id: extract_image_url + continue-on-error: true + run: | + set -eux + + echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV + echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV + echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV + echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV + + - name: Comment PR + uses: mshick/add-pr-comment@v2 + id: comment_pr + if: ${{ github.event.pull_request != '' && matrix.pr_comment_enabled == 'true' }} + with: + message-id: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }} + message: | +

+ + 📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_ for `${{ matrix.model }}`-`${{ matrix.ftype }}`: **${{ env.BENCH_ITERATIONS}} iterations** 🚀 + +

+ +
+ + Expand details for performance related PR only + + - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }} + - HTTP request : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms p(95)=${{ env.HTTP_REQ_DURATION_P_95_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }} + - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_P_95_ }}tk/s + - Token generation (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_TOKENS_SECOND_P_95_ }}tk/s + - ${{ env.BENCH_GRAPH_XLABEL }} + + +

+ + prompt_tokens_seconds + +

+ + More + + ```mermaid + ${{ env.PROMPT_TOKENS_SECONDS }} + ``` + +
+ + predicted_tokens_seconds + +
+ More + + ```mermaid + ${{ env.PREDICTED_TOKENS_SECONDS }} + ``` + +
+ +

+ +
+ + Details + +

+ + kv_cache_usage_ratio + +

+ More + + ```mermaid + ${{ env.KV_CACHE_USAGE_RATIO }} + ``` + +
+ + requests_processing + +
+ More + + ```mermaid + ${{ env.REQUESTS_PROCESSING }} + ``` + +
+ +

+
+
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 9144f9266..6841ba589 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -10,65 +10,133 @@ on: push: branches: - master - paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m'] + paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] pull_request: types: [opened, synchronize, reopened] - paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m'] + paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp'] + +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: + contents: write # for creating release env: BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GGML_NLOOP: 3 GGML_N_THREADS: 1 + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 jobs: - ubuntu-focal-make: - runs-on: ubuntu-20.04 + macOS-latest-cmake-arm64: + runs-on: macos-14 steps: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-arm64 + evict-old-files: 1d - name: Dependencies id: depends + continue-on-error: true run: | - sudo apt-get update - sudo apt-get install build-essential gcc-8 - - - 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-latest-cmake: - runs-on: ubuntu-latest - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v3 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential + brew update - name: Build id: cmake_build run: | - mkdir build + sysctl -a + cmake -B build \ + -DCMAKE_BUILD_RPATH="@loader_path" \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + + - name: Test + id: cmake_test + run: | cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON - cmake --build . --config Release -j $(nproc) + ctest -L 'main|curl' --verbose --timeout 900 + + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + cp LICENSE ./build/bin/ + cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp + zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip + name: llama-bin-macos-arm64.zip + + macOS-latest-cmake-x64: + runs-on: macos-13 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-x64 + evict-old-files: 1d + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + 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 \ + -DCMAKE_BUILD_RPATH="@loader_path" \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_METAL=OFF \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - name: Test id: cmake_test @@ -76,6 +144,110 @@ jobs: cd build ctest -L main --verbose --timeout 900 + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + cp LICENSE ./build/bin/ + cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp + zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip + name: llama-bin-macos-x64.zip + + ubuntu-cpu-cmake: + runs-on: ubuntu-22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-cpu-cmake + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libcurl4-openssl-dev + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L 'main|curl' --verbose --timeout 900 + + - name: Test llama2c conversion + id: llama2c_test + run: | + cd build + echo "Fetch tokenizer" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin + echo "Fetch llama2c model" + wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin + ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf + ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + cp LICENSE ./build/bin/ + cp examples/run/linenoise.cpp/LICENSE ./build/bin/LICENSE.linenoise.cpp + zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip + name: llama-bin-ubuntu-x64.zip + ubuntu-latest-cmake-sanitizer: runs-on: ubuntu-latest @@ -84,12 +256,59 @@ jobs: strategy: matrix: sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug, Release] + build_type: [Debug] steps: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }} + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential + + - name: Build + id: cmake_build + if: ${{ matrix.sanitizer != 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DGGML_OPENMP=OFF + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + ubuntu-latest-llguidance: + runs-on: ubuntu-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 - name: Dependencies id: depends @@ -102,8 +321,10 @@ jobs: run: | mkdir build cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} - cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + cmake .. \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_LLGUIDANCE=ON + cmake --build . --config Release -j $(nproc) - name: Test id: cmake_test @@ -111,33 +332,34 @@ jobs: cd build ctest -L main --verbose --timeout 900 - ubuntu-latest-cmake-mpi: + ubuntu-latest-cmake-rpc: runs-on: ubuntu-latest continue-on-error: true - strategy: - matrix: - mpi_library: [mpich, libopenmpi-dev] - steps: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-rpc + evict-old-files: 1d - name: Dependencies id: depends run: | sudo apt-get update - sudo apt-get install build-essential ${{ matrix.mpi_library }} + sudo apt-get install build-essential - name: Build id: cmake_build run: | - mkdir build - cd build - cmake -DLLAMA_MPI=ON .. - cmake --build . --config Release -j $(nproc) + cmake -B build \ + -DGGML_RPC=ON + cmake --build build --config Release -j $(nproc) - name: Test id: cmake_test @@ -151,21 +373,101 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-vulkan + evict-old-files: 1d + + - name: Dependencies + id: depends + run: | + wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add - + sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + sudo apt-get update -y + sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk + + - name: Build + id: cmake_build + run: | + cmake -B build \ + -DGGML_VULKAN=ON + cmake --build build --config Release -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + # This is using llvmpipe and runs slower than other backends + ctest -L main --verbose --timeout 1800 + + ubuntu-22-cmake-hip: + runs-on: ubuntu-22.04 + container: rocm/dev-ubuntu-22.04:6.0.2 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 - name: Dependencies id: depends run: | sudo apt-get update - sudo apt-get install build-essential libvulkan-dev + sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev - - name: Build + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-hip + evict-old-files: 1d + + - name: Build with native CMake HIP support id: cmake_build run: | - mkdir build - cd build - cmake -DLLAMA_VULKAN=ON .. - cmake --build . --config Release -j $(nproc) + cmake -B build -S . \ + -DCMAKE_HIP_COMPILER="$(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_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: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-musa + evict-old-files: 1d + + - 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 @@ -173,7 +475,7 @@ jobs: continue-on-error: true steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: add oneAPI to apt shell: bash @@ -197,16 +499,23 @@ jobs: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-sycl + evict-old-files: 1d - name: Build id: cmake_build run: | source /opt/intel/oneapi/setvars.sh - mkdir build - cd build - cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx .. - cmake --build . --config Release -j $(nproc) + cmake -B build \ + -DGGML_SYCL=ON \ + -DCMAKE_C_COMPILER=icx \ + -DCMAKE_CXX_COMPILER=icpx + cmake --build build --config Release -j $(nproc) ubuntu-22-cmake-sycl-fp16: runs-on: ubuntu-22.04 @@ -214,7 +523,7 @@ jobs: continue-on-error: true steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: add oneAPI to apt shell: bash @@ -238,79 +547,24 @@ jobs: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-22-cmake-sycl-fp16 + evict-old-files: 1d - name: Build id: cmake_build run: | source /opt/intel/oneapi/setvars.sh - mkdir build - cd build - cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON .. - cmake --build . --config Release -j $(nproc) - - # TODO: build with LLAMA_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@v3 - - - name: Dependencies - id: depends - continue-on-error: true - run: | - brew update - - - name: Build - id: make_build - env: - LLAMA_FATAL_WARNINGS: 1 - run: | - LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu) - - - name: Test - id: make_test - run: | - LLAMA_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu) - LLAMA_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu) - - # TODO: build with LLAMA_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 - # would be great if we fix these - macOS-latest-cmake: - runs-on: macos-latest - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v3 - - - name: Dependencies - id: depends - continue-on-error: true - run: | - brew update - - - name: Build - id: cmake_build - run: | - sysctl -a - mkdir build - cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF .. - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose --timeout 900 + cmake -B build \ + -DGGML_SYCL=ON \ + -DCMAKE_C_COMPILER=icx \ + -DCMAKE_CXX_COMPILER=icpx \ + -DGGML_SYCL_F16=ON + cmake --build build --config Release -j $(nproc) macOS-latest-cmake-ios: runs-on: macos-latest @@ -318,7 +572,13 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-ios + evict-old-files: 1d - name: Dependencies id: depends @@ -330,15 +590,16 @@ jobs: id: cmake_build run: | sysctl -a - mkdir build - cd build - cmake -G Xcode .. \ + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ -DCMAKE_SYSTEM_NAME=iOS \ - -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO macOS-latest-cmake-tvos: runs-on: macos-latest @@ -346,7 +607,13 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-cmake-tvos + evict-old-files: 1d - name: Dependencies id: depends @@ -358,15 +625,16 @@ jobs: id: cmake_build run: | sysctl -a - mkdir build - cd build - cmake -G Xcode .. \ + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ -DLLAMA_BUILD_SERVER=OFF \ -DCMAKE_SYSTEM_NAME=tvOS \ - -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO macOS-latest-swift: runs-on: macos-latest @@ -378,7 +646,13 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: macOS-latest-swift + evict-old-files: 1d - name: Dependencies id: depends @@ -386,83 +660,130 @@ jobs: run: | brew update + - name: Build llama.cpp with CMake + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) + sudo cmake --install build --config Release + - name: xcodebuild for swift package id: xcodebuild run: | - xcodebuild -scheme llama -destination "${{ matrix.destination }}" + xcodebuild -scheme llama-Package -destination "${{ matrix.destination }}" - - name: Build Swift Example - id: make_build_swift_example + windows-msys2: + runs-on: windows-latest + + strategy: + fail-fast: false + matrix: + include: + - { sys: UCRT64, env: ucrt-x86_64, build: Release } + - { sys: CLANG64, env: clang-x86_64, build: Release } + + steps: + - name: Clone + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-msys2 + variant: sccache + evict-old-files: 1d + + - name: Setup ${{ matrix.sys }} + uses: msys2/setup-msys2@v2 + with: + update: true + msystem: ${{matrix.sys}} + install: >- + base-devel + git + mingw-w64-${{matrix.env}}-toolchain + mingw-w64-${{matrix.env}}-cmake + mingw-w64-${{matrix.env}}-openblas + + - name: Build using CMake + shell: msys2 {0} run: | - make swift + cmake -B build + cmake --build build --config ${{ matrix.build }} -j $(nproc) + + - name: Clean after building using CMake + shell: msys2 {0} + run: | + rm -rf build + + - name: Build using CMake w/ OpenBLAS + shell: msys2 {0} + run: | + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS + cmake --build build --config ${{ matrix.build }} -j $(nproc) windows-latest-cmake: runs-on: windows-latest env: OPENBLAS_VERSION: 0.3.23 - OPENCL_VERSION: 2023.04.17 - CLBLAST_VERSION: 1.6.0 SDE_VERSION: 9.33.0-2024-01-07 VULKAN_VERSION: 1.3.261.1 strategy: matrix: include: - - build: 'noavx' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF -DBUILD_SHARED_LIBS=ON' - - build: 'avx2' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' - - build: 'avx' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON' - - build: 'avx512' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON' - - build: 'clblast' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"' - - build: 'openblas' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - - build: 'kompute' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' - - build: 'vulkan' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' + - build: 'noavx-x64' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF' + - build: 'avx2-x64' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON' + - build: 'avx-x64' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF' + - build: 'avx512-x64' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON' + - build: 'openblas-x64' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' + - build: 'kompute-x64' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON' + - build: 'vulkan-x64' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON' + - build: 'llvm-arm64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' + - build: 'msvc-arm64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' + - build: 'llvm-arm64-opencl-adreno' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON' steps: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-${{ matrix.build }} + variant: sccache + evict-old-files: 1d + - name: Clone Kompute submodule id: clone_kompute - if: ${{ matrix.build == 'kompute' }} + if: ${{ matrix.build == 'kompute-x64' }} run: | - git submodule update --init kompute - - - name: Download OpenCL SDK - id: get_opencl - if: ${{ matrix.build == 'clblast' }} - run: | - curl.exe -o $env:RUNNER_TEMP/opencl.zip -L "https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip" - mkdir $env:RUNNER_TEMP/opencl - tar.exe -xvf $env:RUNNER_TEMP/opencl.zip --strip-components=1 -C $env:RUNNER_TEMP/opencl - - - name: Download CLBlast - id: get_clblast - if: ${{ matrix.build == 'clblast' }} - run: | - curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z" - curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE" - 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/clblast.7z - rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast - foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) { - $txt = Get-Content -Path $f -Raw - $txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8 - } + git submodule update --init ggml/src/ggml-kompute/kompute - name: Download OpenBLAS id: get_openblas - if: ${{ matrix.build == 'openblas' }} + if: ${{ matrix.build == 'openblas-x64' }} run: | curl.exe -o $env:RUNNER_TEMP/openblas.zip -L "https://github.com/xianyi/OpenBLAS/releases/download/v${env:OPENBLAS_VERSION}/OpenBLAS-${env:OPENBLAS_VERSION}-x64.zip" curl.exe -o $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt -L "https://github.com/xianyi/OpenBLAS/raw/v${env:OPENBLAS_VERSION}/LICENSE" @@ -475,38 +796,54 @@ jobs: - name: Install Vulkan SDK id: get_vulkan - if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} + if: ${{ matrix.build == 'kompute-x64' || matrix.build == 'vulkan-x64' }} run: | curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe" & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}" Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin" + - name: Install Ninja + id: install_ninja + run: | + choco install ninja + + - name: Install OpenCL Headers and Libs + id: install_opencl + if: ${{ matrix.build == 'llvm-arm64-opencl-adreno' }} + run: | + git clone https://github.com/KhronosGroup/OpenCL-Headers + cd OpenCL-Headers + cmake -B build ` + -DBUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_TESTING=OFF ` + -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build --target install + git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader + cd OpenCL-ICD-Loader + cmake -B build-arm64-release ` + -A arm64 ` + -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" ` + -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release" + cmake --build build-arm64-release --target install --config release + - name: Build id: cmake_build run: | - mkdir build - cd build - cmake .. ${{ matrix.defines }} - cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} - - - name: Add clblast.dll - id: add_clblast_dll - if: ${{ matrix.build == 'clblast' }} - run: | - cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release - cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt + cmake -S . -B build ${{ matrix.defines }} + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} - name: Add libopenblas.dll id: add_libopenblas_dll - if: ${{ matrix.build == 'openblas' }} + if: ${{ matrix.build == 'openblas-x64' }} run: | cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt - name: Check AVX512F support id: check_avx512f - if: ${{ matrix.build == 'avx512' }} + if: ${{ matrix.build == 'avx512-x64' }} continue-on-error: true run: | cd build @@ -520,14 +857,14 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 - name: Test (Intel SDE) id: cmake_test_sde - if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation + if: ${{ matrix.build == 'avx512-x64' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation run: | curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz" # for some weird reason windows tar doesn't like sde tar.xz @@ -535,6 +872,7 @@ jobs: 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe) cd build + $env:LLAMA_SKIP_TESTS_SLOW_ON_EMULATOR = 1 & $sde -future -- ctest -L main -C Release --verbose --timeout 900 - name: Determine tag name @@ -555,44 +893,147 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt - 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\* + Copy-Item .\examples\run\linenoise.cpp\LICENSE .\build\bin\Release\linenoise.cpp.txt + 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\* - name: Upload artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - path: | - llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip + path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip + name: llama-bin-win-${{ matrix.build }}.zip - windows-latest-cmake-cublas: - runs-on: windows-latest + 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 + with: + fetch-depth: 0 + + - name: Install dependencies + env: + DEBIAN_FRONTEND: noninteractive + run: | + apt update + apt install -y cmake build-essential ninja-build libgomp1 git + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ubuntu-latest-cmake-cuda + evict-old-files: 1d + + - name: Build with CMake + run: | + cmake -S . -B build -G Ninja \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_CUDA_ARCHITECTURES=89-real \ + -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DGGML_NATIVE=OFF \ + -DGGML_CUDA=ON + cmake --build build + + windows-2019-cmake-cuda: + runs-on: windows-2019 strategy: matrix: - cuda: ['12.2.0', '11.7.1'] - build: ['cublas'] + cuda: ['12.4', '11.7'] + build: ['cuda'] steps: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: - fetch-depth: 0 + fetch-depth: 0 - - uses: Jimver/cuda-toolkit@v0.2.11 - id: cuda-toolkit + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2.16 with: - cuda: ${{ matrix.cuda }} - method: 'network' - sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]' + key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }} + variant: sccache + evict-old-files: 1d + + - 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 Ninja + id: install_ninja + run: | + choco install ninja - name: Build id: cmake_build + shell: cmd run: | - mkdir build - cd build - cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON -DBUILD_SHARED_LIBS=ON - 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" ^ + -DLLAMA_BUILD_SERVER=ON ^ + -DGGML_NATIVE=OFF ^ + -DGGML_CUDA=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 @@ -615,56 +1056,254 @@ jobs: - name: Upload artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - path: | - llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip + path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip + name: llama-bin-win-cu${{ 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 if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - path: | - cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip + path: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip + name: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip windows-latest-cmake-sycl: runs-on: windows-latest + defaults: run: shell: bash env: - WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/62641e01-1e8d-4ace-91d6-ae03f7f8a71f/w_BaseKit_p_2024.0.0.49563_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 id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-sycl + variant: sccache + evict-old-files: 1d + - name: Install - run: scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + run: | + scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL - name: Build id: cmake_build run: examples/sycl/win-build-sycl.bat + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - 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.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 "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin + cp "${{ 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 the release package + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip + name: llama-bin-win-sycl-x64.zip + + windows-latest-cmake-hip: + if: ${{ github.event.inputs.create_release != 'true' }} + runs-on: windows-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Install + id: depends + run: | + $ErrorActionPreference = "Stop" + write-host "Downloading AMD HIP SDK Installer" + Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" + write-host "Installing AMD HIP SDK" + Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait + write-host "Completed AMD HIP SDK installation" + + - name: Verify ROCm + id: verify + run: | + & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: ${{ github.job }} + evict-old-files: 1d + + - 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" ` + -DCMAKE_BUILD_TYPE=Release ` + -DGGML_HIP=ON ` + -DGGML_RPC=ON + cmake --build build -j ${env:NUMBER_OF_PROCESSORS} + + windows-latest-cmake-hip-release: + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + runs-on: windows-latest + + strategy: + matrix: + gpu_target: [gfx1100, gfx1101, gfx1030] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: windows-latest-cmake-hip-release + evict-old-files: 1d + + - name: Install + id: depends + run: | + $ErrorActionPreference = "Stop" + write-host "Downloading AMD HIP SDK Installer" + Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" + write-host "Installing AMD HIP SDK" + Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait + write-host "Completed AMD HIP SDK installation" + + - name: Verify ROCm + id: verify + run: | + & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + + - 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" ` + -DCMAKE_BUILD_TYPE=Release ` + -DAMDGPU_TARGETS=${{ matrix.gpu_target }} ` + -DGGML_HIP=ON ` + -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\" + cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\" + cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\" + + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + run: | + 7z a llama-${{ steps.tag.outputs.name }}-bin-win-hip-x64-${{ matrix.gpu_target }}.zip .\build\bin\* + + - name: Upload artifacts + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-win-hip-x64-${{ matrix.gpu_target }}.zip + name: llama-bin-win-hip-x64-${{ matrix.gpu_target }}.zip + ios-xcode-build: runs-on: macos-latest steps: - name: Checkout code - uses: actions/checkout@v3 + uses: actions/checkout@v4 + + - name: Build + id: cmake_build + run: | + sysctl -a + cmake -B build -G Xcode \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \ + -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO + sudo cmake --install build --config Release + + - name: xcodebuild for swift package + id: xcodebuild + run: | + xcodebuild -scheme llama-Package -destination 'generic/platform=iOS' - name: Build Xcode project run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build @@ -674,7 +1313,13 @@ jobs: steps: - name: Clone - uses: actions/checkout@v3 + uses: actions/checkout@v4 + + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: android-build + evict-old-files: 1d - name: Set up JDK uses: actions/setup-java@v3 @@ -693,43 +1338,32 @@ jobs: ./gradlew build --no-daemon -# freeBSD-latest: -# runs-on: macos-12 -# steps: -# - name: Clone -# uses: actions/checkout@v3 -# -# - name: Build -# uses: cross-platform-actions/action@v0.19.0 -# with: -# operating_system: freebsd -# version: '13.2' -# hypervisor: 'qemu' -# run: | -# sudo pkg update -# sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas -# gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu` - release: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} runs-on: ubuntu-latest needs: - - ubuntu-focal-make - - ubuntu-latest-cmake - - macOS-latest-make - - macOS-latest-cmake + - ubuntu-cpu-cmake - windows-latest-cmake - - windows-latest-cmake-cublas + - windows-2019-cmake-cuda + - windows-latest-cmake-hip-release + - macOS-latest-cmake-arm64 + - macOS-latest-cmake-x64 steps: - name: Clone id: checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: fetch-depth: 0 + - name: ccache + uses: hendrikmuhs/ccache-action@v1.2.16 + with: + key: release + evict-old-files: 1d + - name: Determine tag name id: tag shell: bash @@ -745,11 +1379,17 @@ jobs: - name: Download artifacts id: download-artifact - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 + with: + path: ./artifact + + - name: Move artifacts + id: move_artifacts + run: mkdir -p ./artifact/release && mv ./artifact/*/*.zip ./artifact/release - name: Create release id: create_release - uses: anzz1/action-create-release@v1 + uses: ggml-org/action-create-release@v1 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} with: @@ -764,7 +1404,7 @@ jobs: const path = require('path'); const fs = require('fs'); const release_id = '${{ steps.create_release.outputs.id }}'; - for (let file of await fs.readdirSync('./artifact')) { + for (let file of await fs.readdirSync('./artifact/release')) { if (path.extname(file) === '.zip') { console.log('uploadReleaseAsset', file); await github.repos.uploadReleaseAsset({ @@ -772,7 +1412,7 @@ jobs: repo: context.repo.repo, release_id: release_id, name: file, - data: await fs.readFileSync(`./artifact/${file}`) + data: await fs.readFileSync(`./artifact/release/${file}`) }); } } @@ -786,7 +1426,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v3 +# uses: actions/checkout@v4 # # - name: Dependencies # run: | @@ -810,7 +1450,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v3 +# uses: actions/checkout@v4 # # - name: Dependencies # run: | @@ -834,7 +1474,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v3 +# uses: actions/checkout@v4 # # - name: Dependencies # run: | @@ -864,7 +1504,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v3 +# uses: actions/checkout@v4 # # - name: Add msbuild to PATH # uses: microsoft/setup-msbuild@v1 @@ -880,7 +1520,7 @@ jobs: # msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }} # # - name: Upload binaries -# uses: actions/upload-artifact@v1 +# uses: actions/upload-artifact@v4 # with: # name: llama-bin-${{ matrix.arch }} # path: build/bin/${{ matrix.build }} @@ -903,7 +1543,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v3 +# uses: actions/checkout@v4 # # - name: Add msbuild to PATH # uses: microsoft/setup-msbuild@v1 @@ -935,7 +1575,7 @@ jobs: # # - name: Upload binaries # if: matrix.blas == 'ON' -# uses: actions/upload-artifact@v1 +# uses: actions/upload-artifact@v4 # with: # name: llama-blas-bin-${{ matrix.arch }} # path: build/bin/${{ matrix.build }} @@ -949,7 +1589,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v3 +# uses: actions/checkout@v4 # # - name: Dependencies # run: | @@ -969,3 +1609,37 @@ jobs: # popd # emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }} # make + + openEuler-latest-cmake-cann: + if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }} + defaults: + run: + shell: bash -el {0} + runs-on: ubuntu-24.04-arm + strategy: + matrix: + cann: + - '8.0.rc3.beta1-910b-openeuler22.03-py3.10' + device: + - 'ascend910b3' + build: + - 'Release' + container: ascendai/cann:${{ matrix.cann }} + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Dependencies + run: | + yum update -y + yum install -y git gcc gcc-c++ make cmake + + - name: Build + run: | + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${{ matrix.build }} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${{ matrix.device }} + cmake --build build -j $(nproc) diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml new file mode 100644 index 000000000..276a217d4 --- /dev/null +++ b/.github/workflows/close-issue.yml @@ -0,0 +1,28 @@ +name: Close inactive issues +on: + schedule: + - cron: "42 0 * * *" + +# 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: + issues: write + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v5 + with: + exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap" + days-before-issue-stale: 30 + days-before-issue-close: 14 + stale-issue-label: "stale" + close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." + days-before-pr-stale: -1 + days-before-pr-close: -1 + operations-per-run: 10000 + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml deleted file mode 100644 index 392db8a08..000000000 --- a/.github/workflows/code-coverage.yml +++ /dev/null @@ -1,36 +0,0 @@ -name: Code Coverage -on: [push, pull_request] - -env: - GGML_NLOOP: 3 - GGML_N_THREADS: 1 - -jobs: - run: - runs-on: ubuntu-20.04 - steps: - - name: Checkout - uses: actions/checkout@v3 - - - name: Dependencies - run: | - sudo apt-get update - sudo apt-get install build-essential gcc-8 lcov - - - name: Build - run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests - - - name: Run tests - run: CC=gcc-8 make test - - - name: Generate coverage report - run: | - make coverage - make lcov-report - - - name: Upload coverage to Codecov - uses: codecov/codecov-action@v3 - env: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} - with: - files: lcov-report/coverage.info diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 94f9161fc..6955a7dc8 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -10,45 +10,50 @@ name: Publish Docker image on: - pull_request: - push: - branches: - - master + 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 }} + 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: + packages: write jobs: push_to_registry: name: Push Docker image to Docker Hub - if: github.event.pull_request.draft == false - runs-on: ubuntu-latest + runs-on: ubuntu-22.04 env: COMMIT_SHA: ${{ github.sha }} strategy: + fail-fast: false matrix: config: - - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "server", dockerfile: ".devops/server.Dockerfile", platforms: "linux/amd64,linux/arm64" } - # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I - # have disabled them for now until the reason why - # is understood. - - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-cuda", dockerfile: ".devops/server-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" } + # Multi-stage build + - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false} + - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false} + # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete + #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true } steps: - name: Check out the repo - uses: actions/checkout@v3 + uses: actions/checkout@v4 + with: + fetch-depth: 0 # preserve git history, so we can determine the build number - name: Set up QEMU - uses: docker/setup-qemu-action@v2 + uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx - uses: docker/setup-buildx-action@v2 + uses: docker/setup-buildx-action@v3 - name: Log in to Docker Hub uses: docker/login-action@v2 @@ -57,9 +62,45 @@ jobs: username: ${{ github.repository_owner }} password: ${{ secrets.GITHUB_TOKEN }} - # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}" # to lower case + REPO_NAME="${{ github.event.repository.name }}" + + # determine tag name postfix (build number, commit hash) + if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then + TAG_POSTFIX="-b${BUILD_NUMBER}" + else + SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-') + TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}" + fi + # list all tags possible + if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then + TYPE="" + else + TYPE="-${{ matrix.config.tag }}" + fi + PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:" + FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}" + LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}" + SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}" + echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT + echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT + echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT + echo "full_output_tags=$FULLTAGS" # print out for debugging + echo "light_output_tags=$LIGHTTAGS" # print out for debugging + echo "server_output_tags=$SERVERTAGS" # print out for debugging + env: + GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }} + GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}' + - name: Free Disk Space (Ubuntu) - uses: jlumbroso/free-disk-space@main + if: ${{ matrix.config.free_disk_space == true }} + uses: ggml-org/free-disk-space@v1.3.1 with: # this might remove tools that are actually needed, # if set to "true" but frees about 6 GB @@ -74,34 +115,59 @@ jobs: docker-images: true swap-storage: true - - name: Determine tag name - id: tag - shell: bash - run: | - BUILD_NUMBER="$(git rev-list --count HEAD)" - SHORT_HASH="$(git rev-parse --short=7 HEAD)" - if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then - echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT - else - SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') - echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT - fi - - - name: Build and push Docker image (versioned) - if: github.event_name == 'push' - uses: docker/build-push-action@v4 + - name: Build and push Full Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }} + uses: docker/build-push-action@v6 with: context: . push: true platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" + # tag list is generated from step above + tags: ${{ steps.tag.outputs.full_output_tags }} file: ${{ matrix.config.dockerfile }} + target: full + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache - - name: Build and push Docker image (tagged) - uses: docker/build-push-action@v4 + - name: Build and push Light Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }} + uses: docker/build-push-action@v6 with: context: . - push: ${{ github.event_name == 'push' }} + push: true platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" + # tag list is generated from step above + tags: ${{ steps.tag.outputs.light_output_tags }} file: ${{ matrix.config.dockerfile }} + target: light + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache + + - name: Build and push Server Docker image (tagged + versioned) + if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }} + uses: docker/build-push-action@v6 + with: + context: . + push: true + platforms: ${{ matrix.config.platforms }} + # tag list is generated from step above + tags: ${{ steps.tag.outputs.server_output_tags }} + file: ${{ matrix.config.dockerfile }} + target: server + provenance: false + # using github experimental cache + cache-from: type=gha + cache-to: type=gha,mode=max + # return to this if the experimental github cache is having issues + #cache-to: type=local,dest=/tmp/.buildx-cache + #cache-from: type=local,src=/tmp/.buildx-cache diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml index 0e0993cd4..f02b7c219 100644 --- a/.github/workflows/editorconfig.yml +++ b/.github/workflows/editorconfig.yml @@ -14,10 +14,16 @@ on: branches: - master +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + jobs: editorconfig: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 - - uses: editorconfig-checker/action-editorconfig-checker@main + - uses: actions/checkout@v4 + - uses: editorconfig-checker/action-editorconfig-checker@v2 + with: + version: v3.0.3 - run: editorconfig-checker diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml index 57db17512..3ca4d3058 100644 --- a/.github/workflows/gguf-publish.yml +++ b/.github/workflows/gguf-publish.yml @@ -24,9 +24,9 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: '3.9.x' - name: Install dependencies diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml new file mode 100644 index 000000000..368dbdbe5 --- /dev/null +++ b/.github/workflows/labeler.yml @@ -0,0 +1,17 @@ +name: "Pull Request Labeler" +on: +- pull_request_target + +jobs: + labeler: + permissions: + contents: read + pull-requests: write + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + repository: "ggerganov/llama.cpp" + - uses: actions/labeler@v5 + with: + configuration-path: '.github/labeler.yml' diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml deleted file mode 100644 index 8d0a3fd7f..000000000 --- a/.github/workflows/nix-ci-aarch64.yml +++ /dev/null @@ -1,61 +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'] - -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 01c5a9d5a..000000000 --- a/.github/workflows/nix-ci.yml +++ /dev/null @@ -1,68 +0,0 @@ -name: Nix CI - -on: - workflow_dispatch: # allows manual triggering - push: - branches: - - master - pull_request: - types: [opened, synchronize, reopened] - -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-check-requirements.yml b/.github/workflows/python-check-requirements.yml index 92e1108b3..46e80aecd 100644 --- a/.github/workflows/python-check-requirements.yml +++ b/.github/workflows/python-check-requirements.yml @@ -3,16 +3,20 @@ name: Python check requirements.txt on: push: paths: + - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - - 'requirements.txt' - - 'requirements/*.txt' + - '**/requirements*.txt' pull_request: paths: + - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - - 'requirements.txt' - - 'requirements/*.txt' + - '**/requirements*.txt' + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true jobs: python-check-requirements: @@ -20,10 +24,10 @@ jobs: name: check-requirements steps: - name: Check out source repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up Python environment - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: "3.11" - name: Run check-requirements.sh script - run: bash scripts/check-requirements.sh nocleanup + run: bash scripts/check-requirements.sh diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index ea0a05ea1..ddfdf73b8 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -1,6 +1,17 @@ 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 }} + cancel-in-progress: true jobs: flake8-lint: @@ -8,13 +19,12 @@ jobs: name: Lint steps: - name: Check out source repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up Python environment - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: "3.11" - name: flake8 Lint uses: py-actions/flake8@v2 with: - ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503" - exclude: "examples/*,examples/*/**,*/**/__init__.py" + plugins: "flake8-no-print" diff --git a/.github/workflows/python-type-check.yml b/.github/workflows/python-type-check.yml new file mode 100644 index 000000000..373bb6010 --- /dev/null +++ b/.github/workflows/python-type-check.yml @@ -0,0 +1,40 @@ +name: Python Type-Check + +on: + push: + paths: + - '.github/workflows/python-type-check.yml' + - 'pyrightconfig.json' + - '**.py' + - '**/requirements*.txt' + pull_request: + paths: + - '.github/workflows/python-type-check.yml' + - 'pyrightconfig.json' + - '**.py' + - '**/requirements*.txt' + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +jobs: + python-type-check: + runs-on: ubuntu-latest + name: pyright type-check + steps: + - name: Check out source repository + uses: actions/checkout@v4 + - name: Set up Python environment + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install Python dependencies + # TODO: use a venv + run: pip install -r requirements/requirements-all.txt + - name: Type-check with Pyright + uses: jakebailey/pyright-action@v2 + with: + version: 1.1.382 + level: warning + warnings: true diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 0b6f6669b..3a29107d0 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -3,13 +3,32 @@ name: Server on: workflow_dispatch: # allows manual triggering + inputs: + sha: + description: 'Commit SHA1 to build' + required: false + type: string + slow_tests: + description: 'Run slow tests' + required: true + type: boolean push: branches: - master - paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] pull_request: types: [opened, synchronize, reopened] - paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + +env: + LLAMA_LOG_COLORS: 1 + LLAMA_LOG_PREFIX: 1 + LLAMA_LOG_TIMESTAMPS: 1 + LLAMA_LOG_VERBOSITY: 10 + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true jobs: server: @@ -17,67 +36,204 @@ jobs: strategy: matrix: - sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug, Release] + sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken + build_type: [RelWithDebInfo] include: - build_type: Release sanitizer: "" - exclude: - - build_type: Release - sanitizer: ADDRESS - - build_type: Release - sanitizer: THREAD - - build_type: Release - sanitizer: UNDEFINED - - container: - image: ubuntu:latest - ports: - - 8888 - options: --cpus 4 + fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken steps: - - name: Clone - id: checkout - uses: actions/checkout@v3 - - name: Dependencies id: depends run: | - apt-get update - apt-get -y install \ + sudo apt-get update + sudo apt-get -y install \ build-essential \ + xxd \ git \ cmake \ - python3-pip \ + curl \ wget \ - psmisc + language-pack-en \ + libcurl4-openssl-dev - - name: Build - id: cmake_build - run: | - mkdir build - cd build - cmake .. \ - -DLLAMA_NATIVE=OFF \ - -DLLAMA_BUILD_SERVER=ON \ - -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ - -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; - cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: Python setup + id: setup_python + uses: actions/setup-python@v5 + with: + python-version: '3.11' - name: Tests dependencies id: test_dependencies run: | pip install -r examples/server/tests/requirements.txt - - name: Download models - id: download_models + # Setup nodejs (to be used for verifying bundled index.html) + - uses: actions/setup-node@v4 + with: + node-version: '22.11.0' + + - name: WebUI - Install dependencies + id: webui_lint run: | - cd examples/server/tests - ../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf + cd examples/server/webui + npm ci + + - name: WebUI - Check code format + id: webui_format + run: | + git config --global --add safe.directory $(realpath .) + cd examples/server/webui + git status + + npm run format + git status + modified_files="$(git status -s)" + echo "Modified files: ${modified_files}" + if [ -n "${modified_files}" ]; then + echo "Files do not follow coding style. To fix: npm run format" + echo "${modified_files}" + exit 1 + fi + + - name: Verify bundled index.html + id: verify_server_index_html + run: | + git config --global --add safe.directory $(realpath .) + cd examples/server/webui + git status + + npm run build + git status + modified_files="$(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 + + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DLLAMA_CURL=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \ + -DGGML_OPENMP=OFF ; + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + + - name: Build (sanitizers) + id: cmake_build_sanitizers + if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }} + run: | + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DLLAMA_CURL=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server + + - name: Build (sanitizers) + id: cmake_build + if: ${{ matrix.sanitizer == '' }} + run: | + cmake -B build \ + -DGGML_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DLLAMA_CURL=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ; + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server - name: Tests - id: server_integration_test + id: server_integration_tests + if: ${{ matrix.sanitizer == '' }} run: | cd examples/server/tests - PORT=8888 ./tests.sh + ./tests.sh + + - name: Tests (sanitizers) + id: server_integration_tests_sanitizers + if: ${{ matrix.sanitizer != '' }} + run: | + cd examples/server/tests + LLAMA_SANITIZE=1 ./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 + SLOW_TESTS=1 ./tests.sh + + + server-windows: + runs-on: windows-2019 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + + - name: libCURL + id: get_libcurl + env: + CURL_VERSION: 8.6.0_6 + run: | + curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip" + mkdir $env:RUNNER_TEMP/libcurl + tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl + + - name: Build + id: cmake_build + run: | + cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include" + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server + + - name: Python setup + id: setup_python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Tests dependencies + id: test_dependencies + run: | + pip install -r examples/server/tests/requirements.txt + + - name: Copy Libcurl + id: prepare_libcurl + run: | + cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll + + - name: Tests + id: server_integration_tests + if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} + run: | + cd examples/server/tests + $env:PYTHONIOENCODING = ":replace" + pytest -v -x -m "not slow" + + - 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 + $env:SLOW_TESTS = "1" + pytest -v -x diff --git a/.github/workflows/tidy-post.yml b/.github/workflows/tidy-post.yml deleted file mode 100644 index 03652760c..000000000 --- a/.github/workflows/tidy-post.yml +++ /dev/null @@ -1,20 +0,0 @@ -name: clang-tidy review post comments - -on: - workflow_dispatch: - workflows: ["clang-tidy-review"] - types: - - completed - -jobs: - build: - runs-on: ubuntu-latest - - steps: - - uses: ZedThree/clang-tidy-review/post@v0.13.0 - # lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup - with: - # adjust options as necessary - lgtm_comment_body: '' - annotations: false - max_comments: 25 diff --git a/.github/workflows/tidy-review.yml b/.github/workflows/tidy-review.yml deleted file mode 100644 index a4bc8d976..000000000 --- a/.github/workflows/tidy-review.yml +++ /dev/null @@ -1,23 +0,0 @@ -name: clang-tidy-review - -on: - pull_request: - branches: - - master - -jobs: - clang-tidy-review: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - - - uses: ZedThree/clang-tidy-review@v0.13.0 - id: review - with: - lgtm_comment_body: '' - build_dir: build - cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on - split_workflow: true - - - uses: ZedThree/clang-tidy-review/upload@v0.13.0 diff --git a/.github/workflows/zig-build.yml b/.github/workflows/zig-build.yml deleted file mode 100644 index 68a698ab9..000000000 --- a/.github/workflows/zig-build.yml +++ /dev/null @@ -1,25 +0,0 @@ -name: Zig CI - -on: - pull_request: - push: - branches: - - master - -jobs: - build: - strategy: - fail-fast: false - matrix: - runs-on: [ubuntu-latest, macos-latest, windows-latest] - runs-on: ${{ matrix.runs-on }} - steps: - - uses: actions/checkout@v3 - with: - submodules: recursive - fetch-depth: 0 - - uses: goto-bus-stop/setup-zig@v2 - with: - version: 0.11.0 - - name: Build Summary - run: zig build --summary all -freference-trace diff --git a/.gitignore b/.gitignore index 62b6b8b1a..694f36e04 100644 --- a/.gitignore +++ b/.gitignore @@ -1,94 +1,145 @@ -*.o +# Extensions + *.a -*.so -*.gguf -*.bin -*.exe -*.dll -*.log -*.gcov -*.gcno -*.gcda -*.dot *.bat +*.bin +*.d +*.dll +*.dot +*.etag +*.exe +*.gcda +*.gcno +*.gcov +*.gguf +*.gguf.json +*.lastModified +*.log *.metallib -.DS_Store -.build/ +*.o +*.so +*.swp +*.tmp + +# IDE / OS + .cache/ .ccls-cache/ .direnv/ +.DS_Store .envrc +.idea/ .swiftpm -.venv -.clang-tidy .vs/ .vscode/ -.idea/ +nppBackup + + +# Coverage -lcov-report/ gcovr-report/ +lcov-report/ +# Build Artifacts + +tags +.build/ build* +!build-info.cmake +!build-info.cpp.in +!build-info.sh +!build.zig +!docs/build.md +/libllama.so +/llama-* +/vulkan-shaders-gen +android-ndk-* +arm_neon.h cmake-build-* +CMakeSettings.json +compile_commands.json +ggml-metal-embed.metal +llama-batched-swift +/rpc-server out/ tmp/ +autogen-*.md + +# Deprecated + +/main +/server + +# CI + +!.github/workflows/*.yml + +# Models models/* models-mnt +!models/.editorconfig +!models/ggml-vocab-*.gguf* -/Pipfile -/baby-llama -/beam-search -/benchmark-matmult -/convert-llama2c-to-ggml -/embd-input-test -/embedding -/gguf -/gguf-llama-simple -/imatrix -/infill -/libllama.so -/llama-bench -/llava-cli -/lookahead -/lookup -/main -/metal -/passkey -/perplexity -/q8dot -/quantize -/quantize-stats -/result -/save-load-state -/server -/simple -/batched -/batched-bench -/export-lora -/finetune -/speculative -/parallel -/train-text-from-scratch -/tokenize -/vdot -/common/build-info.cpp -arm_neon.h -compile_commands.json -CMakeSettings.json - -__pycache__ -dist - +# Zig zig-out/ zig-cache/ +# Logs + ppl-*.txt qnt-*.txt perf-*.txt -examples/jeopardy/results.txt +# Examples -poetry.lock +examples/jeopardy/results.txt +examples/server/*.css.hpp +examples/server/*.html.hpp +examples/server/*.js.hpp +examples/server/*.mjs.hpp +!build_64.sh +!examples/*.bat +!examples/*/*.kts +!examples/*/*/*.kts +!examples/sycl/*.bat +!examples/sycl/*.sh + +# Server Web UI temporary files +node_modules +examples/server/webui/dist + +# Python + +/.venv +__pycache__/ +*/poetry.lock poetry.toml -nppBackup + +# Nix +/result + +# Test binaries +/tests/test-backend-ops +/tests/test-double-float +/tests/test-grad0 +/tests/test-grammar-parser +/tests/test-llama-grammar +/tests/test-opt +/tests/test-quantize-fns +/tests/test-quantize-perf +/tests/test-rope +/tests/test-sampling +/tests/test-tokenizer-0 +/tests/test-tokenizer-1-bpe +/tests/test-tokenizer-1-spm + +# Scripts +!/scripts/install-oneapi.bat + +# Test models for lora adapters +/lora-tests + +# Local scripts +/run-vim.sh +/run-chat.sh diff --git a/.gitmodules b/.gitmodules index b7e8b8ff2..23ce5ff05 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "kompute"] - path = kompute + path = ggml/src/ggml-kompute/kompute url = https://github.com/nomic-ai/kompute.git diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 65796fe2e..91d791628 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,13 +3,14 @@ exclude: prompts/.*.txt repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v3.2.0 + rev: v4.6.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-added-large-files - repo: https://github.com/PyCQA/flake8 - rev: 6.0.0 + rev: 7.0.0 hooks: - id: flake8 + additional_dependencies: [flake8-no-print] diff --git a/AUTHORS b/AUTHORS new file mode 100644 index 000000000..6796b2941 --- /dev/null +++ b/AUTHORS @@ -0,0 +1,1047 @@ +# date: Tue Feb 4 13:04:05 EET 2025 +# this file is auto-generated by scripts/gen-authors.sh + +0cc4m +0xspringtime <110655352+0xspringtime@users.noreply.github.com> +20kdc +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 +Aaron Miller +Aaryaman Vasishta +Abheek Gulati +Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> +Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com> +Adithya Balaji +AdithyanI +Adrian +Adrian Hesketh +Adrien Gallouët +Adrien Gallouët +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> +Ali Nehzat +Ali Tariq +Alon +AlpinDale <52078762+AlpinDale@users.noreply.github.com> +Amir +AmirAli Mirian <37371367+amiralimi@users.noreply.github.com> +Ananta Bastola +Anas Ahouzi <112881240+aahouzi@users.noreply.github.com> +András Salamon +Andreas (Andi) Kunar +Andreas Kieslinger <47689530+aendk@users.noreply.github.com> +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 +Atsushi Tatsuma +Austin <77757836+teleprint-me@users.noreply.github.com> +AustinMroz +BADR +Bach Le +Bailey Chittle <39804642+bachittle@users.noreply.github.com> +BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com> +Bartowski +Behnam M <58621210+ibehnam@users.noreply.github.com> +Ben Ashbaugh +Ben Garney +Ben Siraphob +Ben Williams +Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com> +Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com> +Benson Wong +Bernat Vadell +Bernhard M. 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<112617865+garrnizon@users.noreply.github.com> diff --git a/CMakeLists.txt b/CMakeLists.txt index 48880f720..7b2a1845e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,7 +1,10 @@ -cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. +cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. project("llama.cpp" C CXX) include(CheckIncludeFileCXX) +#set(CMAKE_WARN_DEPRECATED YES) +set(CMAKE_WARN_UNUSED_CLI YES) + set(CMAKE_EXPORT_COMPILE_COMMANDS ON) if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE) @@ -9,11 +12,17 @@ if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE) set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo") endif() +# Add path to modules +list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/") + set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) set(LLAMA_STANDALONE ON) + include(git-vars) + # configure project version # TODO else() @@ -32,1120 +41,138 @@ else() endif() endif() - -# -# Option list -# - -if (APPLE) - set(LLAMA_METAL_DEFAULT ON) -else() - set(LLAMA_METAL_DEFAULT OFF) -endif() - -# general -option(BUILD_SHARED_LIBS "build shared libraries" OFF) -option(LLAMA_STATIC "llama: static link libraries" OFF) -option(LLAMA_NATIVE "llama: enable -march=native flag" ON) -option(LLAMA_LTO "llama: enable link time optimization" OFF) -option(LLAMA_CCACHE "llama: use ccache if available" ON) - -# debug -option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON) -option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF) -option(LLAMA_GPROF "llama: enable gprof" OFF) - -# build -option(LLAMA_FATAL_WARNINGS "llama: enable -Werror flag" OFF) - -# sanitizers -option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF) -option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF) -option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF) - -# instruction set specific -if (LLAMA_NATIVE) - set(INS_ENB OFF) -else() - set(INS_ENB ON) -endif() - -option(LLAMA_AVX "llama: enable AVX" ${INS_ENB}) -option(LLAMA_AVX2 "llama: enable AVX2" ${INS_ENB}) -option(LLAMA_AVX512 "llama: enable AVX512" OFF) -option(LLAMA_AVX512_VBMI "llama: enable AVX512-VBMI" OFF) -option(LLAMA_AVX512_VNNI "llama: enable AVX512-VNNI" OFF) -option(LLAMA_FMA "llama: enable FMA" ${INS_ENB}) -# in MSVC F16C is implied with AVX2/AVX512 -if (NOT MSVC) - option(LLAMA_F16C "llama: enable F16C" ${INS_ENB}) -endif() +option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT}) if (WIN32) - set(LLAMA_WIN_VER "0x602" CACHE STRING "llama: Windows Version") + add_compile_definitions(_CRT_SECURE_NO_WARNINGS) endif() +if (MSVC) + add_compile_options("$<$:/utf-8>") + add_compile_options("$<$:/utf-8>") + add_compile_options("$<$:/bigobj>") + add_compile_options("$<$:/bigobj>") +endif() + +# +# option list +# + +# debug +option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON) +option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF) + +# build +option(LLAMA_FATAL_WARNINGS "llama: enable -Werror flag" OFF) + +# sanitizers +option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF) +option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF) +option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF) + +# utils +option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE}) + +# extra artifacts +option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) +option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) +option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE}) + # 3rd party libs -option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) -option(LLAMA_BLAS "llama: use BLAS" OFF) -set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") -option(LLAMA_CUBLAS "llama: use CUDA" OFF) -#option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF) -option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF) -option(LLAMA_CUDA_FORCE_MMQ "llama: use mmq kernels instead of cuBLAS" OFF) -set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels") -set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels") -option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some calculations" OFF) -set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") -set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING - "llama: max. batch size for using peer access") -option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) -option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) -option(LLAMA_CLBLAST "llama: use CLBlast" OFF) -option(LLAMA_VULKAN "llama: use Vulkan" OFF) -option(LLAMA_VULKAN_CHECK_RESULTS "llama: run Vulkan op checks" OFF) -option(LLAMA_VULKAN_DEBUG "llama: enable Vulkan debug output" OFF) -option(LLAMA_VULKAN_VALIDATE "llama: enable Vulkan validation" OFF) -option(LLAMA_VULKAN_RUN_TESTS "llama: run Vulkan tests" OFF) -option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) -option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) -option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) -option(LLAMA_METAL_EMBED_LIBRARY "llama: embed Metal library" OFF) -option(LLAMA_KOMPUTE "llama: use Kompute" OFF) -option(LLAMA_MPI "llama: use MPI" OFF) -option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) -option(LLAMA_SYCL "llama: use SYCL" OFF) -option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) -option(LLAMA_CPU_HBM "llama: use memkind for CPU HBM" OFF) - -option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) -option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) -option(LLAMA_BUILD_SERVER "llama: build server example" ON) - -# add perf arguments -option(LLAMA_PERF "llama: enable perf" OFF) +option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) +option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF) # Required for relocatable CMake package -include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) +include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake) +include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake) -# -# Compile flags -# +# override ggml options +set(GGML_ALL_WARNINGS ${LLAMA_ALL_WARNINGS}) +set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS}) -if (LLAMA_SYCL) - set(CMAKE_CXX_STANDARD 17) -else() - set(CMAKE_CXX_STANDARD 11) +# change the default for these ggml options +if (NOT DEFINED GGML_LLAMAFILE) + set(GGML_LLAMAFILE_DEFAULT ON) endif() -set(CMAKE_CXX_STANDARD_REQUIRED true) -set(CMAKE_C_STANDARD 11) -set(CMAKE_C_STANDARD_REQUIRED true) -set(THREADS_PREFER_PTHREAD_FLAG ON) - -find_package(Threads REQUIRED) -include(CheckCXXCompilerFlag) - -# enable libstdc++ assertions for debug builds -if (CMAKE_SYSTEM_NAME MATCHES "Linux") - add_compile_definitions($<$:_GLIBCXX_ASSERTIONS>) +if (NOT DEFINED GGML_CUDA_GRAPHS) + set(GGML_CUDA_GRAPHS_DEFAULT ON) endif() +# transition helpers +function (llama_option_depr TYPE OLD NEW) + if (${OLD}) + message(${TYPE} "${OLD} is deprecated and will be removed in the future.\nUse ${NEW} instead\n") + set(${NEW} ON PARENT_SCOPE) + endif() +endfunction() + +llama_option_depr(FATAL_ERROR LLAMA_CUBLAS GGML_CUDA) +llama_option_depr(WARNING LLAMA_CUDA GGML_CUDA) +llama_option_depr(WARNING LLAMA_KOMPUTE GGML_KOMPUTE) +llama_option_depr(WARNING LLAMA_METAL GGML_METAL) +llama_option_depr(WARNING LLAMA_METAL_EMBED_LIBRARY GGML_METAL_EMBED_LIBRARY) +llama_option_depr(WARNING LLAMA_NATIVE GGML_NATIVE) +llama_option_depr(WARNING LLAMA_RPC GGML_RPC) +llama_option_depr(WARNING LLAMA_SYCL GGML_SYCL) +llama_option_depr(WARNING LLAMA_SYCL_F16 GGML_SYCL_F16) +llama_option_depr(WARNING LLAMA_CANN GGML_CANN) + if (NOT MSVC) if (LLAMA_SANITIZE_THREAD) + message(STATUS "Using -fsanitize=thread") + add_compile_options(-fsanitize=thread) link_libraries (-fsanitize=thread) endif() if (LLAMA_SANITIZE_ADDRESS) + message(STATUS "Using -fsanitize=address") + add_compile_options(-fsanitize=address -fno-omit-frame-pointer) link_libraries (-fsanitize=address) endif() if (LLAMA_SANITIZE_UNDEFINED) + message(STATUS "Using -fsanitize=undefined") + add_compile_options(-fsanitize=undefined) link_libraries (-fsanitize=undefined) endif() endif() -if (APPLE AND LLAMA_ACCELERATE) - find_library(ACCELERATE_FRAMEWORK Accelerate) - if (ACCELERATE_FRAMEWORK) - message(STATUS "Accelerate framework found") +# +# 3rd-party +# - add_compile_definitions(GGML_USE_ACCELERATE) - add_compile_definitions(ACCELERATE_NEW_LAPACK) - add_compile_definitions(ACCELERATE_LAPACK_ILP64) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK}) - else() - message(WARNING "Accelerate framework not found") - endif() -endif() - -if (LLAMA_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) - - message(STATUS "Metal framework found") - set(GGML_HEADERS_METAL ggml-metal.h) - set(GGML_SOURCES_METAL ggml-metal.m) - - add_compile_definitions(GGML_USE_METAL) - if (LLAMA_METAL_NDEBUG) - add_compile_definitions(GGML_METAL_NDEBUG) - endif() - - # get full path to the file - #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") - - # copy ggml-metal.metal to bin directory - configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) - - if (LLAMA_METAL_EMBED_LIBRARY) - enable_language(ASM) - add_compile_definitions(GGML_METAL_EMBED_LIBRARY) - - set(METALLIB_SOURCE "${CMAKE_SOURCE_DIR}/ggml-metal.metal") - file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") - set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s") - - add_custom_command( - OUTPUT ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".section __DATA,__ggml_metallib" > ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".globl _ggml_metallib_start" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo "_ggml_metallib_start:" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".incbin \\\"${METALLIB_SOURCE}\\\"" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo ".globl _ggml_metallib_end" >> ${EMBED_METALLIB_ASSEMBLY} - COMMAND echo "_ggml_metallib_end:" >> ${EMBED_METALLIB_ASSEMBLY} - DEPENDS ${METALLIB_SOURCE} - COMMENT "Generate assembly for embedded Metal library" - ) - - set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${EMBED_METALLIB_ASSEMBLY}) - endif() - - if (LLAMA_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) - if (LLAMA_QKK_64) - set(XC_FLAGS ${XC_FLAGS} -DQK_K=64) - 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 - DEPENDS ggml-metal.metal - COMMENT "Compiling Metal kernels" - ) - - add_custom_target( - ggml-metal ALL - DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - ) - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} - ${FOUNDATION_LIBRARY} - ${METAL_FRAMEWORK} - ${METALKIT_FRAMEWORK} - ) -endif() -if (LLAMA_BLAS) - if (LLAMA_STATIC) - set(BLA_STATIC ON) - endif() - if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22) - set(BLA_SIZEOF_INTEGER 8) - endif() - - set(BLA_VENDOR ${LLAMA_BLAS_VENDOR}) - find_package(BLAS) - - if (BLAS_FOUND) - message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}") - - if ("${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 (${LLAMA_BLAS_VENDOR} MATCHES "Generic") - pkg_check_modules(DepBLAS REQUIRED blas) - elseif (${LLAMA_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 REQUIRED openblas) - endif() - elseif (${LLAMA_BLAS_VENDOR} MATCHES "FLAME") - pkg_check_modules(DepBLAS REQUIRED blis) - elseif (${LLAMA_BLAS_VENDOR} MATCHES "ATLAS") - pkg_check_modules(DepBLAS REQUIRED blas-atlas) - elseif (${LLAMA_BLAS_VENDOR} MATCHES "FlexiBLAS") - pkg_check_modules(DepBLAS REQUIRED flexiblas_api) - elseif (${LLAMA_BLAS_VENDOR} MATCHES "Intel") - # all Intel* libraries share the same include path - pkg_check_modules(DepBLAS REQUIRED mkl-sdl) - elseif (${LLAMA_BLAS_VENDOR} MATCHES "NVHPC") - # this doesn't provide pkg-config - # suggest to assign BLAS_INCLUDE_DIRS on your own - if ("${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}) - - add_compile_definitions(GGML_USE_OPENBLAS) - - if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${LLAMA_BLAS_VENDOR} MATCHES "Generic" OR ${LLAMA_BLAS_VENDOR} MATCHES "Intel")) - add_compile_definitions(GGML_BLAS_USE_MKL) - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES}) - set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS}) - else() - message(WARNING "BLAS not found, please refer to " - "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" - " to set correct LLAMA_BLAS_VENDOR") - endif() -endif() - -if (LLAMA_QKK_64) - add_compile_definitions(GGML_QKK_64) -endif() - -if (LLAMA_CUBLAS) - cmake_minimum_required(VERSION 3.17) - - find_package(CUDAToolkit) - if (CUDAToolkit_FOUND) - message(STATUS "cuBLAS found") - - enable_language(CUDA) - - set(GGML_HEADERS_CUDA ggml-cuda.h) - set(GGML_SOURCES_CUDA ggml-cuda.cu) - - add_compile_definitions(GGML_USE_CUBLAS) - if (LLAMA_CUDA_FORCE_DMMV) - add_compile_definitions(GGML_CUDA_FORCE_DMMV) - endif() - if (LLAMA_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_CUDA_FORCE_MMQ) - endif() - add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) - if (DEFINED LLAMA_CUDA_DMMV_Y) - add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility - endif() - if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16) - add_compile_definitions(GGML_CUDA_F16) - endif() - add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) - add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE}) - - if (LLAMA_STATIC) - if (WIN32) - # As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt) - else () - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) - endif() - else() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt) - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) - - if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) - # 52 == lowest CUDA 12 standard - # 60 == f16 CUDA intrinsics - # 61 == integer CUDA intrinsics - # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster - if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16) - set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics - else() - set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics - #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work - endif() - endif() - message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") - - else() - message(WARNING "cuBLAS not found") - endif() -endif() - -if (LLAMA_MPI) - cmake_minimum_required(VERSION 3.10) - find_package(MPI) - if (MPI_C_FOUND) - message(STATUS "MPI found") - - set(GGML_HEADERS_MPI ggml-mpi.h) - set(GGML_SOURCES_MPI ggml-mpi.c) - - add_compile_definitions(GGML_USE_MPI) - add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS}) - - if (NOT MSVC) - add_compile_options(-Wno-cast-qual) - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_C_LIBRARIES}) - set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS}) - - # Even if you're only using the C header, C++ programs may bring in MPI - # C++ functions, so more linkage is needed - if (MPI_CXX_FOUND) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_CXX_LIBRARIES}) - endif() - else() - message(WARNING "MPI not found") - endif() -endif() - -if (LLAMA_CLBLAST) - find_package(CLBlast) - if (CLBlast_FOUND) - message(STATUS "CLBlast found") - - set(GGML_HEADERS_OPENCL ggml-opencl.h) - set(GGML_SOURCES_OPENCL ggml-opencl.cpp) - - add_compile_definitions(GGML_USE_CLBLAST) - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast) - else() - message(WARNING "CLBlast not found") - endif() -endif() - -if (LLAMA_VULKAN) - find_package(Vulkan) - if (Vulkan_FOUND) - message(STATUS "Vulkan found") - - set(GGML_HEADERS_VULKAN ggml-vulkan.h) - set(GGML_SOURCES_VULKAN ggml-vulkan.cpp) - - add_compile_definitions(GGML_USE_VULKAN) - - if (LLAMA_VULKAN_CHECK_RESULTS) - add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) - endif() - - if (LLAMA_VULKAN_DEBUG) - add_compile_definitions(GGML_VULKAN_DEBUG) - endif() - - if (LLAMA_VULKAN_VALIDATE) - add_compile_definitions(GGML_VULKAN_VALIDATE) - endif() - - if (LLAMA_VULKAN_RUN_TESTS) - add_compile_definitions(GGML_VULKAN_RUN_TESTS) - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} Vulkan::Vulkan) - else() - message(WARNING "Vulkan not found") - endif() -endif() - -if (LLAMA_HIPBLAS) - list(APPEND CMAKE_PREFIX_PATH /opt/rocm) - - if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang") - endif() - - if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") - endif() - - find_package(hip REQUIRED) - find_package(hipblas REQUIRED) - find_package(rocblas REQUIRED) - - message(STATUS "HIP and hipBLAS found") - - set(GGML_HEADERS_ROCM ggml-cuda.h) - set(GGML_SOURCES_ROCM ggml-cuda.cu) - - add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) - - if (LLAMA_HIP_UMA) - add_compile_definitions(GGML_HIP_UMA) - endif() - - if (LLAMA_CUDA_FORCE_DMMV) - add_compile_definitions(GGML_CUDA_FORCE_DMMV) - endif() - - if (LLAMA_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_CUDA_FORCE_MMQ) - endif() - - add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) - add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) - - set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX) - - if (LLAMA_STATIC) - message(FATAL_ERROR "Static linking not supported for HIP/ROCm") - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device PUBLIC hip::host roc::rocblas roc::hipblas) -endif() - -if (LLAMA_SYCL) - if ( NOT DEFINED ENV{ONEAPI_ROOT}) - message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh") - endif() - #todo: AOT - - find_package(IntelSYCL REQUIRED) - - message(STATUS "SYCL found") - - add_compile_definitions(GGML_USE_SYCL) - - if (LLAMA_SYCL_F16) - add_compile_definitions(GGML_SYCL_F16) - endif() - - add_compile_options(-I./) #include DPCT - add_compile_options(-I/${SYCL_INCLUDE_DIR}) - - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib") - - set(GGML_HEADERS_SYCL ggml-sycl.h) - set(GGML_SOURCES_SYCL ggml-sycl.cpp) - - if (WIN32) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib) - else() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) - endif() -endif() - -if (LLAMA_KOMPUTE) - add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1) - find_package(Vulkan COMPONENTS glslc REQUIRED) - find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc) - if (NOT glslc_executable) - message(FATAL_ERROR "glslc not found") - endif() - - function(compile_shader) - set(options) - set(oneValueArgs) - set(multiValueArgs SOURCES) - cmake_parse_arguments(compile_shader "${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_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_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 ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) - - add_compile_definitions(GGML_USE_KOMPUTE) - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute) - set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR}) - else() - message(WARNING "Kompute not found") - endif() -endif() - -if (LLAMA_CPU_HBM) - find_library(memkind memkind REQUIRED) - - add_compile_definitions(GGML_USE_CPU_HBM) - - target_link_libraries(ggml PUBLIC memkind) -endif() - -if (LLAMA_PERF) - add_compile_definitions(GGML_PERF) -endif() - -function(get_flags CCID CCVER) - set(C_FLAGS "") - set(CXX_FLAGS "") - - if (CCID MATCHES "Clang") - set(C_FLAGS -Wunreachable-code-break -Wunreachable-code-return) - set(CXX_FLAGS -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi) - - if ( - (CCID STREQUAL "Clang" AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR - (CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0) - ) - list(APPEND C_FLAGS -Wdouble-promotion) - endif() - elseif (CCID STREQUAL "GNU") - set(C_FLAGS -Wdouble-promotion) - set(CXX_FLAGS -Wno-array-bounds) - - if (CCVER VERSION_GREATER_EQUAL 7.1.0) - list(APPEND CXX_FLAGS -Wno-format-truncation) - endif() - if (CCVER VERSION_GREATER_EQUAL 8.1.0) - list(APPEND CXX_FLAGS -Wextra-semi) - endif() - endif() - - set(GF_C_FLAGS ${C_FLAGS} PARENT_SCOPE) - set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE) -endfunction() - -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) - 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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 (WIN32) - add_compile_definitions(_CRT_SECURE_NO_WARNINGS) - - if (BUILD_SHARED_LIBS) - set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON) - endif() -endif() - -if (LLAMA_LTO) - include(CheckIPOSupported) - check_ipo_supported(RESULT result OUTPUT output) - if (result) - set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE) - else() - message(WARNING "IPO is not supported: ${output}") - endif() -endif() - -if (LLAMA_CCACHE) - find_program(LLAMA_CCACHE_FOUND ccache) - if (LLAMA_CCACHE_FOUND) - set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache) - set(ENV{CCACHE_SLOPPINESS} time_macros) - message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.") - else() - message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF") - endif () -endif() - -# this version of Apple ld64 is buggy -execute_process( - COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v - ERROR_VARIABLE output - OUTPUT_QUIET -) - -if (output MATCHES "dyld-1015\.7") - add_compile_definitions(HAVE_BUGGY_APPLE_LINKER) -endif() - -# Architecture specific -# TODO: probably these flags need to be tweaked on some architectures -# feel free to update the Makefile for your architecture and send a pull request or issue -message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}") -if (MSVC) - string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR) - message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}") -else () - set(CMAKE_GENERATOR_PLATFORM_LWR "") -endif () - -if (NOT MSVC) - if (LLAMA_STATIC) - add_link_options(-static) - if (MINGW) - add_link_options(-static-libgcc -static-libstdc++) - endif() - endif() - if (LLAMA_GPROF) - add_compile_options(-pg) - endif() -endif() - -set(ARCH_FLAGS "") - -if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR - (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND - CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) - 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() { 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() - 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 (LLAMA_NATIVE) - include(cmake/FindSIMD.cmake) - endif () - if (LLAMA_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 (LLAMA_AVX512_VBMI) - add_compile_definitions($<$:__AVX512VBMI__>) 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(${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le") - list(APPEND ARCH_FLAGS -mcpu=powerpc64le) - else() - list(APPEND ARCH_FLAGS -mcpu=native -mtune=native) - #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) - endif() -else() - message(STATUS "Unknown architecture") -endif() - -add_compile_options("$<$:${ARCH_FLAGS}>") -add_compile_options("$<$:${ARCH_FLAGS}>") - -if (LLAMA_CUBLAS) - 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=${LLAMA_WIN_VER}) +if (NOT TARGET ggml) + add_subdirectory(ggml) + # ... otherwise assume ggml is added by a parent CMakeLists.txt endif() # -# POSIX conformance +# build the library # -# clock_gettime came in POSIX.1b (1993) -# 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) -endif() - -# Data types, macros and functions related to controlling CPU affinity and -# some memory allocation are available on Linux through GNU extensions in libc -if (CMAKE_SYSTEM_NAME MATCHES "Linux") - add_compile_definitions(_GNU_SOURCE) -endif() - -# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1, -# and on macOS its availability depends on enabling Darwin extensions -# similarly on DragonFly, enabling BSD extensions is necessary -if ( - CMAKE_SYSTEM_NAME MATCHES "Darwin" OR - CMAKE_SYSTEM_NAME MATCHES "iOS" OR - CMAKE_SYSTEM_NAME MATCHES "tvOS" OR - CMAKE_SYSTEM_NAME MATCHES "DragonFly" -) - add_compile_definitions(_DARWIN_C_SOURCE) -endif() - -# alloca is a non-standard interface that is not visible on BSDs when -# POSIX conformance is specified, but not all of them provide a clean way -# to enable it in such cases -if (CMAKE_SYSTEM_NAME MATCHES "FreeBSD") - add_compile_definitions(__BSD_VISIBLE) -endif() -if (CMAKE_SYSTEM_NAME MATCHES "NetBSD") - add_compile_definitions(_NETBSD_SOURCE) -endif() -if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD") - add_compile_definitions(_BSD_SOURCE) -endif() +add_subdirectory(src) # -# libraries +# utils, programs, examples and tests # -# ggml - -add_library(ggml OBJECT - ggml.c - ggml.h - ggml-alloc.c - ggml-alloc.h - ggml-backend.c - ggml-backend.h - ggml-quants.c - ggml-quants.h - ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} - ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} - ${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} - ) - -target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) -target_compile_features (ggml PUBLIC c_std_11) # don't bump - -target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS}) - -add_library(ggml_static STATIC $) - -if (BUILD_SHARED_LIBS) - set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON) - add_library(ggml_shared SHARED $) - target_link_libraries(ggml_shared PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS}) - install(TARGETS ggml_shared LIBRARY) +if (LLAMA_BUILD_COMMON) + add_subdirectory(common) endif() -# llama - -add_library(llama - llama.cpp - llama.h - ) - -target_include_directories(llama PUBLIC .) -target_compile_features (llama PUBLIC cxx_std_11) # don't bump - -target_link_libraries(llama PRIVATE - ggml - ${LLAMA_EXTRA_LIBS} - ) - -if (BUILD_SHARED_LIBS) - set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD) - if (LLAMA_METAL) - set_target_properties(llama PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") - endif() +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION) + include(CTest) + add_subdirectory(tests) endif() +if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES) + add_subdirectory(examples) + add_subdirectory(pocs) +endif() # # install @@ -1154,46 +181,43 @@ endif() include(GNUInstallDirs) include(CMakePackageConfigHelpers) -set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} - CACHE PATH "Location of header files") -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") -set(LLAMA_BUILD_NUMBER ${BUILD_NUMBER}) -set(LLAMA_BUILD_COMMIT ${BUILD_COMMIT}) +set(LLAMA_BUILD_NUMBER ${BUILD_NUMBER}) +set(LLAMA_BUILD_COMMIT ${BUILD_COMMIT}) set(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER}) -get_directory_property(LLAMA_TRANSIENT_DEFINES COMPILE_DEFINITIONS) + +set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header files") +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") + +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( - ${CMAKE_CURRENT_SOURCE_DIR}/scripts/LlamaConfig.cmake.in - ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake - INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama + ${CMAKE_CURRENT_SOURCE_DIR}/cmake/llama-config.cmake.in + ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake + INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama PATH_VARS LLAMA_INCLUDE_INSTALL_DIR LLAMA_LIB_INSTALL_DIR LLAMA_BIN_INSTALL_DIR ) write_basic_package_version_file( - ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake + ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake VERSION ${LLAMA_INSTALL_VERSION} COMPATIBILITY SameMajorVersion) -install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake - ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake - DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) - -set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" - "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") - -set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") -install(TARGETS ggml PUBLIC_HEADER) - -set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/llama.h) -install(TARGETS llama LIBRARY PUBLIC_HEADER) +install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake + ${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake + DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama) install( - FILES convert.py + FILES convert_hf_to_gguf.py PERMISSIONS OWNER_READ OWNER_WRITE @@ -1203,40 +227,10 @@ install( WORLD_READ WORLD_EXECUTE DESTINATION ${CMAKE_INSTALL_BINDIR}) -install( - FILES convert-lora-to-ggml.py - PERMISSIONS - OWNER_READ - OWNER_WRITE - OWNER_EXECUTE - GROUP_READ - GROUP_EXECUTE - WORLD_READ - WORLD_EXECUTE - DESTINATION ${CMAKE_INSTALL_BINDIR}) -if (LLAMA_METAL) - install( - FILES ggml-metal.metal - PERMISSIONS - OWNER_READ - OWNER_WRITE - GROUP_READ - WORLD_READ - DESTINATION ${CMAKE_INSTALL_BINDIR}) -endif() -# -# programs, examples and tests -# +configure_file(cmake/llama.pc.in + "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + @ONLY) -add_subdirectory(common) - -if (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION) - include(CTest) - add_subdirectory(tests) -endif () - -if (LLAMA_BUILD_EXAMPLES) - add_subdirectory(examples) - add_subdirectory(pocs) -endif() +install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig) diff --git a/CMakePresets.json b/CMakePresets.json new file mode 100644 index 000000000..13bdd7907 --- /dev/null +++ b/CMakePresets.json @@ -0,0 +1,97 @@ +{ + "version": 4, + "configurePresets": [ + { + "name": "base", + "hidden": true, + "generator": "Ninja", + "binaryDir": "${sourceDir}/build-${presetName}", + "cacheVariables": { + "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", + "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." + } + }, + { + "name": "sycl-base", + "hidden": true, + "generator": "Ninja", + "binaryDir": "${sourceDir}/build-${presetName}", + "cacheVariables": { + "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", + "CMAKE_CXX_COMPILER": "icx", + "CMAKE_C_COMPILER": "cl", + "GGML_SYCL": "ON", + "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." + } + }, + { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, + { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } }, + { "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, + { "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } }, + { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } }, + { "name": "vulkan", "hidden": true, "cacheVariables": { "GGML_VULKAN": "ON" } }, + + { + "name": "x64-windows-llvm", "hidden": true, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake" + } + }, + + { + "name": "arm64-windows-msvc", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake" + } + }, + + { + "name": "arm64-windows-llvm", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake" + } + }, + + { + "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-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-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] }, + { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] }, + { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] }, + { "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] }, + + { "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-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-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..72d594b46 --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1,11 @@ +# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs + +/ci/ @ggerganov +/.devops/*.Dockerfile @ngxson +/examples/server/ @ngxson +/ggml/src/ggml-cuda/fattn* @JohannesGaessler +/ggml/src/ggml-cuda/mmq.* @JohannesGaessler +/ggml/src/ggml-cuda/mmv.* @JohannesGaessler +/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler +/ggml/src/ggml-opt.cpp @JohannesGaessler +/ggml/src/gguf.cpp @JohannesGaessler diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..8d411982b --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,125 @@ +# Pull requests (for contributors) + +- Test your changes: + - Execute [the full CI locally on your machine](ci/README.md) before publishing + - 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 + +# Pull requests (for collaborators) + +- 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 + +- Avoid adding third-party dependencies, extra files, extra headers, etc. +- Always consider cross-compatibility with other operating systems and architectures +- Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple +- Vertical alignment makes things more readable and easier to batch edit +- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` +- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets +- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo` + - In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary + ```cpp + // OK + llama_context * ctx; + const llama_rope_type rope_type; + + // not OK + struct llama_context * ctx; + const enum llama_rope_type rope_type; + ``` + + _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_ + +- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` to format the added code +- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines) +- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices +- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$ + +![matmul](media/matmul.png) + +# Naming guidelines + +- Use `snake_case` for function, variable and type names +- Naming usually optimizes for longest common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963) + + ```cpp + // not OK + int small_number; + int big_number; + + // OK + int number_small; + int number_big; + ``` + +- Enum values are always in upper case and prefixed with the enum name + + ```cpp + enum llama_vocab_type { + LLAMA_VOCAB_TYPE_NONE = 0, + LLAMA_VOCAB_TYPE_SPM = 1, + LLAMA_VOCAB_TYPE_BPE = 2, + LLAMA_VOCAB_TYPE_WPM = 3, + LLAMA_VOCAB_TYPE_UGM = 4, + LLAMA_VOCAB_TYPE_RWKV = 5, + }; + ``` + +- The general naming pattern is `_`, with `` being `_` + + ```cpp + llama_model_init(); // class: "llama_model", method: "init" + llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove" + llama_sampler_get_seed(); // class: "llama_sampler", method: "get_seed" + llama_set_embeddings(); // class: "llama_context", method: "set_embeddings" + llama_n_threads(); // class: "llama_context", method: "n_threads" + llama_adapter_lora_free(); // class: "llama_adapter_lora", method: "free" + ``` + + - The `get` `` can be omitted + - The `` can be omitted if not necessary + - The `_context` suffix of the `` is optional. Use it to disambiguate symbols when needed + - Use `init`/`free` for constructor/destructor `` + +- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else + + ```cpp + typedef struct llama_context * llama_context_t; + + enum llama_pooling_type llama_pooling_type(const llama_context_t ctx); + ``` + + _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_ + +- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension +- Python filenames are all lowercase with underscores + +- _(TODO: abbreviations usage)_ + +# Preprocessor directives + +- _(TODO: add guidelines with examples and apply them to the codebase)_ + + ```cpp + #ifdef FOO + #endif // FOO + ``` + +# Documentation + +- Documentation is a community effort +- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference +- When you notice incorrect or outdated documentation, please update it + +# Resources + +The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects: + +https://github.com/ggerganov/llama.cpp/projects diff --git a/LICENSE b/LICENSE index 76f67efdc..acb96ce78 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 Georgi Gerganov +Copyright (c) 2023-2024 The ggml authors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/Makefile b/Makefile index 4f26c0463..dc3de3cb1 100644 --- a/Makefile +++ b/Makefile @@ -1,18 +1,161 @@ +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 = \ - main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ - simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ - speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o + libllava.a \ + llama-batched \ + llama-batched-bench \ + llama-bench \ + llama-cli \ + llama-convert-llama2c-to-ggml \ + llama-embedding \ + llama-eval-callback \ + llama-export-lora \ + llama-gbnf-validator \ + llama-gguf \ + llama-gguf-hash \ + llama-gguf-split \ + llama-gritlm \ + llama-imatrix \ + llama-infill \ + llama-llava-cli \ + llama-minicpmv-cli\ + llama-qwen2vl-cli\ + llama-lookahead \ + llama-lookup \ + llama-lookup-create \ + llama-lookup-merge \ + llama-lookup-stats \ + llama-parallel \ + llama-passkey \ + llama-perplexity \ + llama-q8dot \ + llama-quantize \ + llama-quantize-stats \ + llama-retrieval \ + llama-save-load-state \ + llama-server \ + llama-simple \ + llama-simple-chat \ + llama-run \ + llama-speculative \ + llama-tokenize \ + llama-vdot \ + llama-cvector-generator \ + llama-gen-docs \ + tests/test-c.o # Binaries only useful for tests TEST_TARGETS = \ - tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \ - tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \ - tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope \ - tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease + tests/test-arg-parser \ + tests/test-autorelease \ + tests/test-backend-ops \ + tests/test-chat \ + tests/test-chat-template \ + tests/test-double-float \ + tests/test-grammar-integration \ + tests/test-grammar-parser \ + tests/test-json-schema-to-grammar \ + tests/test-llama-grammar \ + tests/test-log \ + tests/test-model-load-cancel \ + tests/test-quantize-fns \ + tests/test-quantize-perf \ + tests/test-rope \ + tests/test-sampling \ + tests/test-tokenizer-0 \ + tests/test-tokenizer-1-bpe \ + tests/test-tokenizer-1-spm +# tests/test-opt \ -# Code coverage output files -COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report +# 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 \ + simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \ + retrieval speculative infill tokenize parallel export-lora lookahead lookup passkey gritlm + +# Legacy build targets that were renamed in #7809, but we want to build binaries that for them that output a deprecation warning if people try to use them. +# We don't want to clutter things too much, so we only build replacements for the most commonly used binaries. +LEGACY_TARGETS_BUILD = main quantize perplexity embedding server + +# Deprecation aliases +ifdef LLAMA_CUBLAS +$(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.) +endif + +ifdef LLAMA_CUDA +GGML_CUDA := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_KOMPUTE +GGML_KOMPUTE := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_METAL +GGML_METAL := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_RPC +GGML_RPC := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_SYCL +GGML_SYCL := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_SYCL_F16 +GGML_SYCL_F16 := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_OPENBLAS +GGML_OPENBLAS := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_OPENBLAS64 +GGML_OPENBLAS64 := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_BLIS +GGML_BLIS := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_LLAMAFILE +GGML_NO_LLAMAFILE := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_ACCELERATE +GGML_NO_ACCELERATE := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_OPENMP +GGML_NO_OPENMP := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_NO_METAL +GGML_NO_METAL := 1 +DEPRECATE_WARNING := 1 +endif + +ifdef LLAMA_DISABLE_LOGS +REMOVE_WARNING := 1 +endif + +ifdef LLAMA_SERVER_VERBOSE +REMOVE_WARNING := 1 +endif ifndef UNAME_S UNAME_S := $(shell uname -s) @@ -26,13 +169,26 @@ ifndef UNAME_M UNAME_M := $(shell uname -m) endif +# In GNU make default CXX is g++ instead of c++. Let's fix that so that users +# of non-gcc compilers don't have to provide g++ alias or wrapper. +DEFCC := cc +DEFCXX := c++ +ifeq ($(origin CC),default) +CC := $(DEFCC) +endif +ifeq ($(origin CXX),default) +CXX := $(DEFCXX) +endif + # Mac OS + Arm can report x86_64 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789 ifeq ($(UNAME_S),Darwin) - ifndef LLAMA_NO_METAL - LLAMA_METAL := 1 + ifndef GGML_NO_METAL + GGML_METAL := 1 endif + GGML_NO_OPENMP := 1 + ifneq ($(UNAME_P),arm) SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null) ifeq ($(SYSCTL_M),1) @@ -43,16 +199,33 @@ ifeq ($(UNAME_S),Darwin) endif endif -default: $(BUILD_TARGETS) +ifdef GGML_METAL + GGML_METAL_EMBED_LIBRARY := 1 +endif + +ifdef GGML_RPC + BUILD_TARGETS += rpc-server +endif + +ifdef GGML_VULKAN + BUILD_TARGETS += vulkan-shaders-gen +endif + +default: $(BUILD_TARGETS) $(LEGACY_TARGETS_BUILD) test: $(TEST_TARGETS) @failures=0; \ for test_target in $(TEST_TARGETS); do \ - if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \ - ./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \ - elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \ + if [ "$$test_target" = "tests/test-tokenizer-0" ]; then \ + ./$$test_target $(CURDIR)/models/ggml-vocab-llama-spm.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \ ./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \ - elif [ "$$test_target" = "tests/test-tokenizer-1-llama" ]; then \ + ./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \ + elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \ continue; \ elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \ continue; \ @@ -73,19 +246,7 @@ test: $(TEST_TARGETS) fi @echo 'All tests passed.' -all: $(BUILD_TARGETS) $(TEST_TARGETS) - -coverage: ## Run code coverage - gcov -pb tests/*.cpp - -lcov-report: coverage ## Generate lcov report - mkdir -p lcov-report - lcov --capture --directory . --output-file lcov-report/coverage.info - genhtml lcov-report/coverage.info --output-directory lcov-report - -gcovr-report: coverage ## Generate gcovr report - mkdir -p gcovr-report - gcovr --root . --html --html-details --output gcovr-report/coverage.html +all: $(BUILD_TARGETS) $(TEST_TARGETS) $(LEGACY_TARGETS_BUILD) ifdef RISCV_CROSS_COMPILE CC := riscv64-unknown-linux-gnu-gcc @@ -96,34 +257,28 @@ endif # Compile flags # -# keep standard at C11 and C++11 -MK_CPPFLAGS = -I. -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 -# -Ofast tends to produce faster code, but may not be available for some compilers. -ifdef LLAMA_FAST -MK_CFLAGS += -Ofast -HOST_CXXFLAGS += -Ofast -MK_NVCCFLAGS += -O3 -else -MK_CFLAGS += -O3 -MK_CXXFLAGS += -O3 -MK_NVCCFLAGS += -O3 +ifdef LLAMA_NO_CCACHE +GGML_NO_CCACHE := 1 +DEPRECATE_WARNING := 1 endif -ifndef LLAMA_NO_CCACHE +ifndef GGML_NO_CCACHE CCACHE := $(shell which ccache) ifdef CCACHE export CCACHE_SLOPPINESS = time_macros -$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.) +$(info I ccache found, compilation results will be cached. Disable with GGML_NO_CCACHE.) CC := $(CCACHE) $(CC) CXX := $(CCACHE) $(CXX) else $(info I ccache not found. Consider installing it for faster compilation.) endif # CCACHE -endif # LLAMA_NO_CCACHE +endif # GGML_NO_CCACHE # clock_gettime came in POSIX.1b (1993) # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional @@ -142,6 +297,7 @@ 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, @@ -167,16 +323,24 @@ ifeq ($(UNAME_S),OpenBSD) MK_CPPFLAGS += -D_BSD_SOURCE endif +ifdef GGML_SCHED_MAX_COPIES + MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(GGML_SCHED_MAX_COPIES) +endif + ifdef LLAMA_DEBUG - MK_CFLAGS += -O0 -g - MK_CXXFLAGS += -O0 -g - MK_LDFLAGS += -g + MK_CFLAGS += -O0 -g + MK_CXXFLAGS += -O0 -g + MK_LDFLAGS += -g + MK_NVCCFLAGS += -O0 -g ifeq ($(UNAME_S),Linux) MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS endif else - MK_CPPFLAGS += -DNDEBUG + MK_CPPFLAGS += -DNDEBUG + MK_CFLAGS += -O3 -g + MK_CXXFLAGS += -O3 -g + MK_NVCCFLAGS += -O3 -g endif ifdef LLAMA_SANITIZE_THREAD @@ -197,24 +361,36 @@ ifdef LLAMA_SANITIZE_UNDEFINED MK_LDFLAGS += -fsanitize=undefined -g endif -ifdef LLAMA_SERVER_VERBOSE - MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) +ifdef LLAMA_SERVER_SSL + MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT + MK_LDFLAGS += -lssl -lcrypto endif - -ifdef LLAMA_CODE_COVERAGE - MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' +ifndef GGML_NO_CPU_AARCH64 + MK_CPPFLAGS += -DGGML_USE_CPU_AARCH64 endif -ifdef LLAMA_DISABLE_LOGS - MK_CPPFLAGS += -DLOG_DISABLE_LOGS -endif # LLAMA_DISABLE_LOGS - # warnings -WARN_FLAGS = -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -MK_CFLAGS += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int \ - -Werror=implicit-function-declaration -MK_CXXFLAGS += $(WARN_FLAGS) -Wmissing-declarations -Wmissing-noreturn +WARN_FLAGS = \ + -Wall \ + -Wextra \ + -Wpedantic \ + -Wcast-qual \ + -Wno-unused-function + +MK_CFLAGS += \ + $(WARN_FLAGS) \ + -Wshadow \ + -Wstrict-prototypes \ + -Wpointer-arith \ + -Wmissing-prototypes \ + -Werror=implicit-int \ + -Werror=implicit-function-declaration + +MK_CXXFLAGS += \ + $(WARN_FLAGS) \ + -Wmissing-declarations \ + -Wmissing-noreturn ifeq ($(LLAMA_FATAL_WARNINGS),1) MK_CFLAGS += -Werror @@ -259,21 +435,22 @@ ifdef LLAMA_GPROF MK_CFLAGS += -pg MK_CXXFLAGS += -pg endif -ifdef LLAMA_PERF - MK_CPPFLAGS += -DGGML_PERF -endif # Architecture specific # TODO: probably these flags need to be tweaked on some architectures # feel free to update the Makefile for your architecture and send a pull request or issue -ifndef RISCV +ifndef RISCV_CROSS_COMPILE ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) # Use all CPU extensions that are available: MK_CFLAGS += -march=native -mtune=native HOST_CXXFLAGS += -march=native -mtune=native + # Usage AMX build test + #MK_CFLAGS += -march=graniterapids -mtune=graniterapids + #HOST_CXXFLAGS += -march=graniterapids -mtune=graniterapids + # Usage AVX-only #MK_CFLAGS += -mfma -mf16c -mavx #MK_CXXFLAGS += -mfma -mf16c -mavx @@ -342,231 +519,488 @@ ifneq ($(filter ppc64le%,$(UNAME_M)),) CUDA_POWER_ARCH = 1 endif -else +ifneq ($(filter loongarch64%,$(UNAME_M)),) + MK_CFLAGS += -mlasx + MK_CXXFLAGS += -mlasx +endif + +ifneq ($(filter riscv64%,$(UNAME_M)),) MK_CFLAGS += -march=rv64gcv -mabi=lp64d MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d endif -ifdef LLAMA_QKK_64 - MK_CPPFLAGS += -DGGML_QKK_64 +else # RISC-V CROSS COMPILATION + MK_CFLAGS += -march=rv64gcv -mabi=lp64d + MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d endif -ifndef LLAMA_NO_ACCELERATE +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 - MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK - MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64 - MK_LDFLAGS += -framework Accelerate + 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 # LLAMA_NO_ACCELERATE +endif # GGML_NO_ACCELERATE -ifdef LLAMA_MPI - MK_CPPFLAGS += -DGGML_USE_MPI - MK_CFLAGS += -Wno-cast-qual - MK_CXXFLAGS += -Wno-cast-qual - OBJS += ggml-mpi.o -endif # LLAMA_MPI +ifndef GGML_NO_OPENMP + MK_CPPFLAGS += -DGGML_USE_OPENMP + MK_CFLAGS += -fopenmp + MK_CXXFLAGS += -fopenmp +endif # GGML_NO_OPENMP -ifdef LLAMA_OPENBLAS - MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) - MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) - MK_LDFLAGS += $(shell pkg-config --libs openblas) -endif # LLAMA_OPENBLAS +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_EXT += ggml/src/ggml-blas/ggml-blas.o +endif # GGML_OPENBLAS -ifdef LLAMA_BLIS - MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis - MK_LDFLAGS += -lblis -L/usr/local/lib -endif # LLAMA_BLIS +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_EXT += ggml/src/ggml-blas/ggml-blas.o +endif # GGML_OPENBLAS64 -ifdef LLAMA_CUBLAS +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_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_EXT += ggml/src/ggml-blas/ggml-blas.o +endif # GGML_NVPL + +ifndef GGML_NO_LLAMAFILE + 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_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_EXT += ggml/src/ggml-rpc.o +endif # GGML_RPC + +OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu)) +OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu)) + +ifdef GGML_CUDA_FA_ALL_QUANTS + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*.cu)) +else + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu)) + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu)) + OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-vec*f16-f16.cu)) +endif # GGML_CUDA_FA_ALL_QUANTS + +ifdef GGML_CUDA ifneq ('', '$(wildcard /opt/cuda)') CUDA_PATH ?= /opt/cuda else CUDA_PATH ?= /usr/local/cuda endif - MK_CPPFLAGS += -DGGML_USE_CUBLAS -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/usr/lib/wsl/lib - OBJS += ggml-cuda.o + + 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_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 JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT + ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo endif # LLAMA_DEBUG -ifdef LLAMA_CUDA_NVCC - NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) + +ifdef GGML_CUDA_DEBUG + MK_NVCCFLAGS += --device-debug +endif # GGML_CUDA_DEBUG + +ifdef GGML_CUDA_NVCC + NVCC = $(CCACHE) $(GGML_CUDA_NVCC) else NVCC = $(CCACHE) nvcc -endif #LLAMA_CUDA_NVCC +endif # GGML_CUDA_NVCC + ifdef CUDA_DOCKER_ARCH MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) else ifndef CUDA_POWER_ARCH MK_NVCCFLAGS += -arch=native endif # CUDA_DOCKER_ARCH -ifdef LLAMA_CUDA_FORCE_DMMV - MK_NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV -endif # LLAMA_CUDA_FORCE_DMMV -ifdef LLAMA_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_MMQ MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ -endif # LLAMA_CUDA_FORCE_MMQ -ifdef LLAMA_CUDA_DMMV_X - MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) -else - MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=32 -endif # LLAMA_CUDA_DMMV_X -ifdef LLAMA_CUDA_MMV_Y - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) -else ifdef LLAMA_CUDA_DMMV_Y - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility -else - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=1 -endif # LLAMA_CUDA_MMV_Y -ifdef LLAMA_CUDA_F16 +endif # GGML_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_CUBLAS + MK_NVCCFLAGS += -DGGML_CUDA_FORCE_CUBLAS +endif # GGML_CUDA_FORCE_CUBLAS + +ifdef GGML_CUDA_F16 MK_NVCCFLAGS += -DGGML_CUDA_F16 -endif # LLAMA_CUDA_F16 -ifdef LLAMA_CUDA_DMMV_F16 +endif # GGML_CUDA_F16 + +ifdef GGML_CUDA_DMMV_F16 MK_NVCCFLAGS += -DGGML_CUDA_F16 -endif # LLAMA_CUDA_DMMV_F16 -ifdef LLAMA_CUDA_KQUANTS_ITER - MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER) -else - MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2 -endif -ifdef LLAMA_CUDA_PEER_MAX_BATCH_SIZE - MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(LLAMA_CUDA_PEER_MAX_BATCH_SIZE) +endif # GGML_CUDA_DMMV_F16 + +ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE + MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE) else MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 -endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE -#ifdef LLAMA_CUDA_CUBLAS -# MK_NVCCFLAGS += -DGGML_CUDA_CUBLAS -#endif # LLAMA_CUDA_CUBLAS -ifdef LLAMA_CUDA_CCBIN - MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) -endif -ggml-cuda.o: ggml-cuda.cu ggml-cuda.h +endif # GGML_CUDA_PEER_MAX_BATCH_SIZE + +ifdef GGML_CUDA_NO_PEER_COPY + MK_NVCCFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # GGML_CUDA_NO_PEER_COPY + +ifdef GGML_CUDA_CCBIN + MK_NVCCFLAGS += -ccbin $(GGML_CUDA_CCBIN) +endif # GGML_CUDA_CCBIN + +ifdef GGML_CUDA_FA_ALL_QUANTS + MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # GGML_CUDA_FA_ALL_QUANTS + ifdef JETSON_EOL_MODULE_DETECT - $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -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 $@ +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 +define NVCC_COMPILE $(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ +endef # NVCC_COMPILE endif # JETSON_EOL_MODULE_DETECT -endif # LLAMA_CUBLAS -ifdef LLAMA_CLBLAST +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 + $(NVCC_COMPILE) - MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) - MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) - MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) +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) + $(NVCC_COMPILE) +endif # GGML_CUDA - # Mac provides OpenCL as a framework - ifeq ($(UNAME_S),Darwin) - MK_LDFLAGS += -lclblast -framework OpenCL - else - MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL) - endif - OBJS += ggml-opencl.o - -ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # LLAMA_CLBLAST - -ifdef LLAMA_VULKAN +ifdef GGML_VULKAN MK_CPPFLAGS += -DGGML_USE_VULKAN - MK_LDFLAGS += -lvulkan - OBJS += ggml-vulkan.o + MK_LDFLAGS += $(shell pkg-config --libs vulkan) + OBJ_GGML_EXT += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o -ifdef LLAMA_VULKAN_CHECK_RESULTS +ifdef GGML_VULKAN_CHECK_RESULTS MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS endif -ifdef LLAMA_VULKAN_DEBUG +ifdef GGML_VULKAN_DEBUG MK_CPPFLAGS += -DGGML_VULKAN_DEBUG endif -ifdef LLAMA_VULKAN_VALIDATE +ifdef GGML_VULKAN_MEMORY_DEBUG + MK_CPPFLAGS += -DGGML_VULKAN_MEMORY_DEBUG +endif + +ifdef GGML_VULKAN_PERF + MK_CPPFLAGS += -DGGML_VULKAN_PERF +endif + +ifdef GGML_VULKAN_VALIDATE MK_CPPFLAGS += -DGGML_VULKAN_VALIDATE endif -ifdef LLAMA_VULKAN_RUN_TESTS +ifdef GGML_VULKAN_RUN_TESTS MK_CPPFLAGS += -DGGML_VULKAN_RUN_TESTS endif -ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # LLAMA_VULKAN +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/ggml-vulkan/vulkan-shaders +_ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp) -ifdef LLAMA_HIPBLAS +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) + +$(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen + $(_ggml_vk_genshaders_cmd) \ + --glslc $(GLSLC_CMD) \ + --input-dir $(_ggml_vk_input_dir) \ + --target-hpp $(_ggml_vk_header) \ + --target-cpp $(_ggml_vk_source) + +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_HIP ifeq ($(wildcard /opt/rocm),) - ROCM_PATH ?= /usr - GPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) + ROCM_PATH ?= /usr + AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) else ROCM_PATH ?= /opt/rocm - GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) + AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) endif - HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc - LLAMA_CUDA_DMMV_X ?= 32 - LLAMA_CUDA_MMV_Y ?= 1 - LLAMA_CUDA_KQUANTS_ITER ?= 2 - MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS -ifdef LLAMA_HIP_UMA - MK_CPPFLAGS += -DGGML_HIP_UMA -endif # LLAMA_HIP_UMA - MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib - MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas - HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) - HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) - HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) - HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER) -ifdef LLAMA_CUDA_FORCE_DMMV - HIPFLAGS += -DGGML_CUDA_FORCE_DMMV -endif # LLAMA_CUDA_FORCE_DMMV - OBJS += ggml-cuda.o -ggml-cuda.o: ggml-cuda.cu ggml-cuda.h - $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< -endif # LLAMA_HIPBLAS -ifdef LLAMA_METAL - MK_CPPFLAGS += -DGGML_USE_METAL - MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit - OBJS += ggml-metal.o -ifdef LLAMA_METAL_NDEBUG + MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA + +ifdef GGML_HIP_UMA + MK_CPPFLAGS += -DGGML_HIP_UMA +endif # GGML_HIP_UMA + + MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64 + MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas + + HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc + + HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) + +ifdef GGML_CUDA_FORCE_MMQ + HIPFLAGS += -DGGML_CUDA_FORCE_MMQ +endif # GGML_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_CUBLAS + HIPFLAGS += -DGGML_CUDA_FORCE_CUBLAS +endif # GGML_CUDA_FORCE_CUBLAS + +ifdef GGML_CUDA_NO_PEER_COPY + HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # GGML_CUDA_NO_PEER_COPY + + 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) + $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -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 + $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< +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_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 LLAMA_METAL_EMBED_LIBRARY - MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY - OBJS += ggml-metal-embed.o +ifdef GGML_METAL_EMBED_LIBRARY + MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY + OBJ_GGML_EXT += ggml/src/ggml-metal-embed.o endif -endif # LLAMA_METAL +endif # GGML_METAL -ifdef LLAMA_METAL -ggml-metal.o: ggml-metal.m ggml-metal.h +ifdef GGML_METAL +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 LLAMA_METAL_EMBED_LIBRARY -ggml-metal-embed.o: ggml-metal.metal +ifdef GGML_METAL_EMBED_LIBRARY +ggml/src/ggml-metal-embed.o: \ + ggml/src/ggml-metal/ggml-metal.metal \ + ggml/src/ggml-metal/ggml-metal-impl.h \ + ggml/src/ggml-common.h @echo "Embedding Metal library" - $(eval TEMP_ASSEMBLY=$(shell mktemp)) - @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY) - @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY) - @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY) - @echo ".incbin \"$<\"" >> $(TEMP_ASSEMBLY) - @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY) - @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY) - @$(AS) $(TEMP_ASSEMBLY) -o $@ - @rm -f ${TEMP_ASSEMBLY} + @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/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 # LLAMA_METAL +endif # GGML_METAL -ifdef LLAMA_MPI -ggml-mpi.o: ggml-mpi.c ggml-mpi.h - $(CC) $(CFLAGS) -c $< -o $@ -endif # LLAMA_MPI +DIR_GGML = ggml +DIR_LLAMA = src +DIR_COMMON = common + +OBJ_GGML = \ + $(DIR_GGML)/src/ggml.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-hbm.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-traits.o \ + $(OBJ_GGML_EXT) + +OBJ_LLAMA = \ + $(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 = \ + $(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)/chat.o \ + $(DIR_COMMON)/build-info.o \ + $(DIR_COMMON)/json-schema-to-grammar.o + +OBJ_ALL = $(OBJ_GGML) $(OBJ_LLAMA) $(OBJ_COMMON) + +LIB_GGML = $(LIB_PRE)ggml$(DSO_EXT) +LIB_GGML_S = $(LIB_PRE)ggml.a + +LIB_LLAMA = $(LIB_PRE)llama$(DSO_EXT) +LIB_LLAMA_S = $(LIB_PRE)llama.a + +LIB_COMMON = $(LIB_PRE)common$(DSO_EXT) +LIB_COMMON_S = $(LIB_PRE)common.a + +LIB_ALL = $(LIB_GGML) $(LIB_LLAMA) $(LIB_COMMON) +LIB_ALL_S = $(LIB_GGML_S) $(LIB_LLAMA_S) $(LIB_COMMON_S) GF_CC := $(CC) include scripts/get-flags.mk @@ -580,12 +1014,17 @@ override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS) override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) # identify CUDA host compiler -ifdef LLAMA_CUBLAS +ifdef GGML_CUDA GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler include scripts/get-flags.mk CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic endif +ifdef LLAMA_CURL +override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL +override LDFLAGS := $(LDFLAGS) -lcurl +endif + # # Print build information # @@ -600,67 +1039,126 @@ $(info I NVCCFLAGS: $(NVCCFLAGS)) $(info I LDFLAGS: $(LDFLAGS)) $(info I CC: $(shell $(CC) --version | head -n 1)) $(info I CXX: $(shell $(CXX) --version | head -n 1)) -ifdef LLAMA_CUBLAS +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])') ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) + ifndef CUDA_DOCKER_ARCH ifndef CUDA_POWER_ARCH -$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via CUDA_DOCKER_ARCH) +$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus ) endif # CUDA_POWER_ARCH endif # CUDA_DOCKER_ARCH + endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) -endif # LLAMA_CUBLAS +endif # GGML_CUDA $(info ) +ifdef DEPRECATE_WARNING +$(info !!! DEPRECATION WARNING !!!) +$(info The following LLAMA_ options are deprecated and will be removed in the future. Use the GGML_ prefix instead) +$(info - LLAMA_CUDA) +$(info - LLAMA_METAL) +$(info - LLAMA_METAL_EMBED_LIBRARY) +$(info - LLAMA_OPENMP) +$(info - LLAMA_RPC) +$(info - LLAMA_SYCL) +$(info - LLAMA_SYCL_F16) +$(info - LLAMA_OPENBLAS) +$(info - LLAMA_OPENBLAS64) +$(info - LLAMA_BLIS) +$(info - LLAMA_NO_LLAMAFILE) +$(info - LLAMA_NO_ACCELERATE) +$(info - LLAMA_NO_OPENMP) +$(info - LLAMA_NO_METAL) +$(info - LLAMA_NO_CCACHE) +$(info ) +endif + +ifdef REMOVE_WARNING +$(info !!! REMOVAL WARNING !!!) +$(info The following LLAMA_ options have been removed and are no longer supported) +$(info - LLAMA_DISABLE_LOGS (https://github.com/ggerganov/llama.cpp/pull/9418)) +$(info - LLAMA_SERVER_VERBOSE (https://github.com/ggerganov/llama.cpp/pull/9418)) +$(info ) +endif + # -# Build library +# Build libraries # -ggml.o: ggml.c ggml.h ggml-cuda.h - $(CC) $(CFLAGS) -c $< -o $@ +# Libraries +LIB_GGML = libggml.so +LIB_GGML_S = libggml.a -ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h - $(CC) $(CFLAGS) -c $< -o $@ +LIB_LLAMA = libllama.so +LIB_LLAMA_S = libllama.a -ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h - $(CC) $(CFLAGS) -c $< -o $@ +LIB_COMMON = libcommon.so +LIB_COMMON_S = libcommon.a -ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h - $(CC) $(CFLAGS) -c $< -o $@ +# Targets +BUILD_TARGETS += $(LIB_GGML) $(LIB_GGML_S) $(LIB_LLAMA) $(LIB_LLAMA_S) $(LIB_COMMON) $(LIB_COMMON_S) -OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o +# Dependency files +DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d) -llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +# Default target +all: $(BUILD_TARGETS) -COMMON_H_DEPS = common/common.h common/sampling.h common/log.h -COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o +# force c++ build for source file that have same name as c file +# Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files +$(DIR_GGML)/%_cpp.o: $(DIR_GGML)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -common.o: common/common.cpp $(COMMON_H_DEPS) - $(CXX) $(CXXFLAGS) -c $< -o $@ +# Rules for building object files +$(DIR_GGML)/%.o: $(DIR_GGML)/%.c + $(CC) $(CFLAGS) -MMD -c $< -o $@ -sampling.o: common/sampling.cpp $(COMMON_H_DEPS) - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_GGML)/%.o: $(DIR_GGML)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -console.o: common/console.cpp common/console.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_LLAMA)/%.o: $(DIR_LLAMA)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_COMMON)/%.o: $(DIR_COMMON)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -train.o: common/train.cpp common/train.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -libllama.so: llama.o ggml.o $(OBJS) +# Rules for building libraries +$(LIB_GGML): $(OBJ_GGML) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) -libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) - ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS) +$(LIB_GGML_S): $(OBJ_GGML) + ar rcs $(LIB_GGML_S) $^ -clean: - rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) - find examples pocs -type f -name "*.o" -delete +$(LIB_LLAMA): $(OBJ_LLAMA) $(LIB_GGML) + $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) + +$(LIB_LLAMA_S): $(OBJ_LLAMA) + ar rcs $(LIB_LLAMA_S) $^ + +$(LIB_COMMON): $(OBJ_COMMON) $(LIB_LLAMA) $(LIB_GGML) + $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) + +$(LIB_COMMON_S): $(OBJ_COMMON) + ar rcs $(LIB_COMMON_S) $^ + +# 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 @@ -673,122 +1171,253 @@ clean: # Helper function that replaces .c, .cpp, and .cu file endings with .o: GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1)))) -main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +llama-cli: examples/main/main.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @echo - @echo '==== Run ./main -h for help. ====' + @echo '==== Run ./llama-cli -h for help. ====' @echo -infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +llama-infill: examples/infill/infill.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +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) -tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +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) -batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +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) -batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) +llama-tokenize: examples/tokenize/tokenize.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) +llama-batched: examples/batched/batched.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) +llama-batched-bench: examples/batched-bench/batched-bench.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-quantize: examples/quantize/quantize.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-quantize-stats: examples/quantize-stats/quantize-stats.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-perplexity: examples/perplexity/perplexity.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-imatrix: examples/imatrix/imatrix.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h examples/llava/llava.h examples/llava/llava.cpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2) - -gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) +llama-embedding: examples/embedding/embedding.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) +llama-gritlm: examples/gritlm/gritlm.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) +llama-save-load-state: examples/save-load-state/save-load-state.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-gguf: examples/gguf/gguf.cpp \ + $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +examples/gguf-hash/deps/sha1/sha1.o: \ + examples/gguf-hash/deps/sha1/sha1.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +examples/gguf-hash/deps/xxhash/xxhash.o: \ + examples/gguf-hash/deps/xxhash/xxhash.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +examples/gguf-hash/deps/sha256/sha256.o: \ + examples/gguf-hash/deps/sha256/sha256.c + $(CC) $(CFLAGS) -Iexamples/gguf-hash/deps -c $< -o $@ + +llama-gguf-hash: examples/gguf-hash/gguf-hash.cpp examples/gguf-hash/deps/sha1/sha1.o examples/gguf-hash/deps/xxhash/xxhash.o examples/gguf-hash/deps/sha256/sha256.o\ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Iexamples/gguf-hash/deps -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-gguf-split: examples/gguf-split/gguf-split.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-eval-callback: examples/eval-callback/eval-callback.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-cvector-generator: examples/cvector-generator/cvector-generator.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-bench: examples/llama-bench/llama-bench.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, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-retrieval: examples/retrieval/retrieval.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-speculative: examples/speculative/speculative.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-parallel: examples/parallel/parallel.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookahead: examples/lookahead/lookahead.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup: examples/lookup/lookup.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup-create: examples/lookup/lookup-create.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup-merge: examples/lookup/lookup-merge.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup-stats: examples/lookup/lookup-stats.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-passkey: examples/passkey/passkey.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +ifdef GGML_RPC +rpc-server: examples/rpc/rpc-server.cpp \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) +endif # GGML_RPC + +llama-server: \ + examples/server/server.cpp \ + examples/server/utils.hpp \ + examples/server/httplib.h \ + examples/server/index.html.hpp \ + examples/server/loading.html.hpp \ + common/chat.cpp \ + common/chat.hpp \ + common/chat-template.hpp \ + common/json.hpp \ + common/minja.hpp \ + $(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/% 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' && \ + echo "};" && \ + echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \ + ) > $@ + +llama-gen-docs: examples/gen-docs/gen-docs.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +libllava.a: examples/llava/llava.cpp \ + examples/llava/llava.h \ + examples/llava/clip.cpp \ + examples/llava/clip.h \ + common/stb_image.h \ + common/base64.hpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual -llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual - $(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp) - $(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS) +llama-llava-cli: examples/llava/llava-cli.cpp \ + examples/llava/llava.cpp \ + examples/llava/llava.h \ + examples/llava/clip.cpp \ + examples/llava/clip.h \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual -baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \ + examples/llava/llava.cpp \ + examples/llava/llava.h \ + examples/llava/clip.cpp \ + examples/llava/clip.h \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual -beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \ + examples/llava/llava.cpp \ + examples/llava/llava.h \ + examples/llava/clip.cpp \ + examples/llava/clip.h \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual ifeq ($(UNAME_S),Darwin) swift: examples/batched.swift @@ -803,7 +1432,7 @@ common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh rm $@.tmp; \ fi -build-info.o: common/build-info.cpp +common/build-info.o: common/build-info.cpp $(CXX) $(CXXFLAGS) -c $(filter-out %.h,$^) -o $@ # @@ -812,90 +1441,165 @@ build-info.o: common/build-info.cpp tests: $(TEST_TARGETS) -benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) +tests/test-arg-parser: tests/test-arg-parser.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -run-benchmark-matmult: benchmark-matmult - ./$@ - -.PHONY: run-benchmark-matmult swift - -vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS) +tests/test-llama-grammar: tests/test-llama-grammar.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS) +tests/test-log: tests/test-log.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS) +tests/test-grammar-parser: tests/test-grammar-parser.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS) +tests/test-grammar-integration: tests/test-grammar-integration.cpp \ + $(OBJ_ALL) $(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 ggml.o $(OBJS) +tests/test-double-float: tests/test-double-float.cpp $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS) +tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +tests/test-chat: tests/test-chat.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -Iexamples/server -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, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS) +tests/test-quantize-fns: tests/test-quantize-fns.cpp \ + $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS) +tests/test-quantize-perf: tests/test-quantize-perf.cpp \ + $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) +tests/test-sampling: tests/test-sampling.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) +tests/test-tokenizer-0: tests/test-tokenizer-0.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) +tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) +tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS) +tests/test-rope: tests/test-rope.cpp ggml/src/ggml.o \ + $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-c.o: tests/test-c.c llama.h +tests/test-c.o: tests/test-c.c include/llama.h $(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@ -tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS) +tests/test-backend-ops: tests/test-backend-ops.cpp \ + $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) +tests/test-model-load-cancel: tests/test-model-load-cancel.cpp tests/get-model.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) +tests/test-autorelease: tests/test-autorelease.cpp tests/get-model.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-chat-template: tests/test-chat-template.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-chat-template: tests/test-chat-template.cpp \ + $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +# +# PoCs +# + +llama-vdot: pocs/vdot/vdot.cpp ggml/src/ggml.o \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \ + $(OBJ_GGML) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +# +# 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: FORCE main quantize perplexity embedding server + +# Define the object file target +examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp + $(CXX) $(CXXFLAGS) -c $< -o $@ + +# NOTE: We currently will always build the deprecation-warning `main` and `server` binaries to help users migrate. +# Eventually we will want to remove these target from building all the time. +main: examples/deprecation-warning/deprecation-warning.o + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "NOTICE: The 'main' binary is deprecated. Please use 'llama-cli' instead." + +server: examples/deprecation-warning/deprecation-warning.o + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "NOTICE: The 'server' binary is deprecated. Please use 'llama-server' instead." + +quantize: examples/deprecation-warning/deprecation-warning.o +ifneq (,$(wildcard quantize)) + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "#########" + @echo "WARNING: The 'quantize' binary is deprecated. Please use 'llama-quantize' instead." + @echo " Remove the 'quantize' binary to remove this warning." + @echo "#########" +endif + +perplexity: examples/deprecation-warning/deprecation-warning.o +ifneq (,$(wildcard perplexity)) + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "#########" + @echo "WARNING: The 'perplexity' binary is deprecated. Please use 'llama-perplexity' instead." + @echo " Remove the 'perplexity' binary to remove this warning." + @echo "#########" +endif + +embedding: examples/deprecation-warning/deprecation-warning.o +ifneq (,$(wildcard embedding)) + $(CXX) $(CXXFLAGS) $< -o $@ $(LDFLAGS) + @echo "#########" + @echo "WARNING: The 'embedding' binary is deprecated. Please use 'llama-embedding' instead." + @echo " Remove the 'embedding' binary to remove this warning." + @echo "#########" +endif diff --git a/Package.swift b/Package.swift index b24c9204a..01c996d24 100644 --- a/Package.swift +++ b/Package.swift @@ -14,47 +14,6 @@ let package = Package( .library(name: "llama", targets: ["llama"]), ], targets: [ - .target( - name: "llama", - path: ".", - exclude: [ - "cmake", - "examples", - "scripts", - "models", - "tests", - "CMakeLists.txt", - "ggml-cuda.cu", - "ggml-cuda.h", - "Makefile" - ], - sources: [ - "ggml.c", - "llama.cpp", - "ggml-alloc.c", - "ggml-backend.c", - "ggml-quants.c", - "ggml-metal.m", - ], - resources: [ - .process("ggml-metal.metal") - ], - publicHeadersPath: "spm-headers", - cSettings: [ - .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), - .define("GGML_USE_ACCELERATE"), - .unsafeFlags(["-fno-objc-arc"]), - .define("GGML_USE_METAL"), - // 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") - ], - linkerSettings: [ - .linkedFramework("Accelerate") - ] - ) - ], - cxxLanguageStandard: .cxx11 + .systemLibrary(name: "llama", pkgConfig: "llama"), + ] ) diff --git a/README-sycl.md b/README-sycl.md deleted file mode 100644 index dd5bf9dea..000000000 --- a/README-sycl.md +++ /dev/null @@ -1,494 +0,0 @@ -# llama.cpp for SYCL - -- [Background](#background) -- [OS](#os) -- [Intel GPU](#intel-gpu) -- [Docker](#docker) -- [Linux](#linux) -- [Windows](#windows) -- [Environment Variable](#environment-variable) -- [Known Issue](#known-issue) -- [Q&A](#q&a) -- [Todo](#todo) - -## Background - -SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators—such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17. - -oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms. - -Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs. - -To avoid to re-invent the wheel, this code refer other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL. - -The llama.cpp for SYCL is used to support Intel GPUs. - -For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). - -## OS - -|OS|Status|Verified| -|-|-|-| -|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39| -|Windows|Support|Windows 11| - - -## Intel GPU - -### Verified - -|Intel GPU| Status | Verified Model| -|-|-|-| -|Intel Data Center Max Series| Support| Max 1550| -|Intel Data Center Flex Series| Support| Flex 170| -|Intel Arc Series| Support| Arc 770, 730M| -|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| -|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7| - -Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. - -### Memory - -The memory is a limitation to run LLM on GPUs. - -When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors: buffer size = 3577.56 MiB`. - -For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+. - -For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. - -## Docker - -Note: -- Only docker on Linux is tested. Docker on WSL may not work. -- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that) - -### Build the image - -You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. - - -```sh -# For F16: -#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile . - -# Or, for F32: -docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile . - -# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example -``` - -### Run - -```sh -# Firstly, find all the DRI cards: -ls -la /dev/dri -# Then, pick the card that you want to use. - -# For example with "/dev/dri/card1" -docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -``` - -## Linux - -### Setup Environment - -1. Install Intel GPU driver. - -a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html). - -Note: for iGPU, please install the client GPU driver. - -b. Add user to group: video, render. - -```sh -sudo usermod -aG render username -sudo usermod -aG video username -``` - -Note: re-login to enable it. - -c. Check - -```sh -sudo apt install clinfo -sudo clinfo -l -``` - -Output (example): - -``` -Platform #0: Intel(R) OpenCL Graphics - `-- Device #0: Intel(R) Arc(TM) A770 Graphics - - -Platform #0: Intel(R) OpenCL HD Graphics - `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49] -``` - -2. Install Intel® oneAPI Base toolkit. - -a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). - -Recommend to install to default folder: **/opt/intel/oneapi**. - -Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder. - -b. Check - -```sh -source /opt/intel/oneapi/setvars.sh - -sycl-ls -``` - -There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. - -Output (example): -``` -[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] -[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] -[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50] -[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918] - -``` - -2. Build locally: - -Note: -- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. -- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. - -```sh -mkdir -p build -cd build -source /opt/intel/oneapi/setvars.sh - -# For FP16: -#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON - -# Or, for FP32: -cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx - -# Build example/main only -#cmake --build . --config Release --target main - -# Or, build all binary -cmake --build . --config Release -v - -cd .. -``` - -or - -```sh -./examples/sycl/build.sh -``` - -### Run - -1. Put model file to folder **models** - -You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. - -2. Enable oneAPI running environment - -``` -source /opt/intel/oneapi/setvars.sh -``` - -3. List device ID - -Run without parameter: - -```sh -./build/bin/ls-sycl-device - -# or running the "main" executable and look at the output log: - -./build/bin/main -``` - -Check the ID in startup log, like: - -``` -found 4 SYCL devices: - Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, - max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 - Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, - max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 - Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - -``` - -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero running time, recommended | -|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| - -4. Set device ID and execute llama.cpp - -Set device ID = 0 by **GGML_SYCL_DEVICE=0** - -```sh -GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -``` -or run by script: - -```sh -./examples/sycl/run_llama2.sh -``` - -Note: - -- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. - - -5. Check the device ID in output - -Like: -``` -Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device -``` - -## Windows - -### Setup Environment - -1. Install Intel GPU driver. - -Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). - -Note: **The driver is mandatory for compute function**. - -2. Install Visual Studio. - -Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows. - -3. Install Intel® oneAPI Base toolkit. - -a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). - -Recommend to install to default folder: **C:\Program Files (x86)\Intel\oneAPI**. - -Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder. - -b. Enable oneAPI running environment: - -- In Search, input 'oneAPI'. - -Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" - -- In Run: - -In CMD: -``` -"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 -``` - -c. Check GPU - -In oneAPI command line: - -``` -sycl-ls -``` - -There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. - -Output (example): -``` -[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] -[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] -[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] -[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] -``` - -4. Install cmake & make - -a. Download & install cmake for Windows: https://cmake.org/download/ - -b. Download & install mingw-w64 make for Windows provided by w64devkit - -- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - -- Extract `w64devkit` on your pc. - -- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`. - -### Build locally: - -In oneAPI command line window: - -``` -mkdir -p build -cd build -@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force - -:: for FP16 -:: faster for long-prompt inference -:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON - -:: for FP32 -cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release - - -:: build example/main only -:: make main - -:: build all binary -make -j -cd .. -``` - -or - -``` -.\examples\sycl\win-build-sycl.bat -``` - -Note: - -- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. - -### Run - -1. Put model file to folder **models** - -You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. - -2. Enable oneAPI running environment - -- In Search, input 'oneAPI'. - -Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" - -- In Run: - -In CMD: -``` -"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 -``` - -3. List device ID - -Run without parameter: - -``` -build\bin\ls-sycl-device.exe - -or - -build\bin\main.exe -``` - -Check the ID in startup log, like: - -``` -found 4 SYCL devices: - Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, - max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 - Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, - max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 - Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - -``` - -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero running time, recommended | -|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| - -4. Set device ID and execute llama.cpp - -Set device ID = 0 by **set GGML_SYCL_DEVICE=0** - -``` -set GGML_SYCL_DEVICE=0 -build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -``` -or run by script: - -``` -.\examples\sycl\win-run-llama2.bat -``` - -Note: - -- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. - - -5. Check the device ID in output - -Like: -``` -Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device -``` - -## Environment Variable - -#### Build - -|Name|Value|Function| -|-|-|-| -|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.
For FP32/FP16, LLAMA_SYCL=ON is mandatory.| -|LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference.
For FP32, not set it.| -|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path| -|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path| - -#### Running - - -|Name|Value|Function| -|-|-|-| -|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output| -|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG| - -## Known Issue - -- Hang during startup - - llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. - - Solution: add **--no-mmap** or **--mmap 0**. - -## Q&A - -- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. - - Miss to enable oneAPI running environment. - - Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. - -- In Windows, no result, not error. - - Miss to enable oneAPI running environment. - -- Meet compile error. - - Remove folder **build** and try again. - -- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux. - - Please run **sudo sycl-ls**. - - If you see it in result, please add video/render group to your ID: - - ``` - sudo usermod -aG render username - sudo usermod -aG video username - ``` - - Then **relogin**. - - If you do not see it, please check the installation GPU steps again. - -## Todo - -- Support multiple cards. diff --git a/README.md b/README.md index 67717c1e3..11f3d0286 100644 --- a/README.md +++ b/README.md @@ -3,95 +3,68 @@ ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) [![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) [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) Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ -### Hot topics +## Recent API changes -- The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) -- Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) -- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 -- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 -- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216 +- [Changelog for `libllama` API](https://github.com/ggerganov/llama.cpp/issues/9289) +- [Changelog for `llama-server` REST API](https://github.com/ggerganov/llama.cpp/issues/9291) + +## Hot topics + +- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggerganov/llama.cpp/pull/11427 +- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode +- Universal tool call support in `llama-server`: https://github.com/ggerganov/llama.cpp/pull/9639 +- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim +- 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) ---- -
- Table of Contents -
    -
  1. - Description -
    2. -
  2. - Usage - -
    3. -
  3. Contributing
    4. -
  4. Coding guidelines
    5. -
  5. Docs
    6. -
-
- ## 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 -- AVX, AVX2 and AVX512 support for x86 architectures +- AVX, AVX2, AVX512 and AMX support for x86 architectures - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use -- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP) -- Vulkan, SYCL, and (partial) OpenCL backend support +- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA) +- 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 platforms:** - -- [X] Mac OS -- [X] Linux -- [X] Windows (via CMake) -- [X] Docker -- [X] FreeBSD - -**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 🦙🦙🦙 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) -- [X] Falcon +- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct) +- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) +- [X] [BERT](https://github.com/ggerganov/llama.cpp/pull/5423) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) -- [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) @@ -100,13 +73,40 @@ Typically finetunes of the base models below are supported as well. - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [Phi models](https://huggingface.co/models?search=microsoft/phi) +- [x] [PhiMoE](https://github.com/ggerganov/llama.cpp/pull/11003) - [x] [GPT-2](https://huggingface.co/gpt2) - [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118) - [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) - [x] [Gemma](https://ai.google.dev/gemma) +- [x] [Mamba](https://github.com/state-spaces/mamba) +- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf) +- [x] [Xverse](https://huggingface.co/models?search=xverse) +- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r) +- [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) +- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520) +- [x] [Smaug](https://huggingface.co/models?search=Smaug) +- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B) +- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM) +- [x] [Flan T5](https://huggingface.co/models?search=flan-t5) +- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca) +- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat) +- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966) +- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct) +- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a) +- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat) +- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) +- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) +- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1) +- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct) -**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) @@ -114,768 +114,399 @@ Typically finetunes of the base models below are supported as well. - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) +- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) +- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) +- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) +- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge) +- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) -**HTTP server** +
-[llama.cpp web server](./examples/server) 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. - -**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) - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp) +- JS/TS (Programmable Prompt Engine CLI): [offline-ai/cli](https://github.com/offline-ai/cli) - JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm) +- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) +- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs) - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) +- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client) - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) +- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html) - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) - 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: +
+UIs -- [iohub/collama](https://github.com/iohub/coLLaMA) +*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* + +- [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) -- [nat/openplayground](https://github.com/nat/openplayground) -- [Faraday](https://faraday.dev/) (proprietary) +- [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) -- [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) -- [semperai/amica](https://github.com/semperai/amica) -- [withcatai/catai](https://github.com/withcatai/catai) +- [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) -- [Msty](https://msty.app) (proprietary) -- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (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) +- [Autopen](https://github.com/blackhole89/autopen) (GPL) ---- +
-Here is a typical run using LLaMA v2 13B on M2 Ultra: +
+Tools -``` -$ make -j && ./main -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) +- [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 pre-built Mobile and Web platform wrappers and a model example) -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 +
+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 +- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server +- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale - 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 -``` +
-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: +
+Games -https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4 +- [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. -## Usage +
-Here are the end-to-end binary build and model conversion steps for most supported models. +## Supported backends -### Get the Code +| 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 | +| [HIP](docs/build.md#hip) | AMD GPU | +| [Vulkan](docs/build.md#vulkan) | GPU | +| [CANN](docs/build.md#cann) | Ascend NPU | -```bash -git clone https://github.com/ggerganov/llama.cpp -cd llama.cpp -``` +## Building the project -### Build +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: -In order to build llama.cpp you have three different options. +- 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) -- Using `make`: - - On Linux or MacOS: +## Obtaining and quantizing models - ```bash - make - ``` +The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`: - - On Windows: +- [Trending](https://huggingface.co/models?library=gguf&sort=trending) +- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf) - 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 - ``` +You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from Hugging Face by using this CLI argument: `-hf /[:quant]` -- Using `CMake`: +After downloading a model, use the CLI tools to run it locally - see below. + +`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 in conversation mode + + Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME` ```bash - mkdir build - cd build - cmake .. - cmake --build . --config Release + llama-cli -m model.gguf + + # > 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! ``` -- Using `Zig` (version 0.11 or later): - - Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C, - it's also possible to cross compile for other operating systems and architectures: - - ```bash - zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c - ``` - - The `zig targets` command will give you valid options to use. - -- 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 clinfo clover \ - opencl clblast openblas - - gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 - ``` - - **Notes:** With this packages you can build llama.cpp with OPENBLAS and - CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read - the instructions for use and activate this options in this document below. - -### 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 `LLAMA_NO_METAL=1` flag or the `LLAMA_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. - -### MPI Build - -MPI lets you distribute the computation over a cluster of machines. Because of the serial nature of LLM prediction, this won't yield any end-to-end speed-ups, but it will let you run larger models than would otherwise fit into RAM on a single machine. - -First you will need MPI libraries installed on your system. The two most popular (only?) options are [MPICH](https://www.mpich.org) and [OpenMPI](https://www.open-mpi.org). Either can be installed with a package manager (`apt`, Homebrew, MacPorts, etc). - -Next you will need to build the project with `LLAMA_MPI` set to true on all machines; if you're building with `make`, you will also need to specify an MPI-capable compiler (when building with CMake, this is configured automatically): - -- Using `make`: - - ```bash - make CC=mpicc CXX=mpicxx LLAMA_MPI=1 - ``` - -- Using `CMake`: - - ```bash - cmake -S . -B build -DLLAMA_MPI=ON - ``` - -Once the programs are built, download/convert the weights on all of the machines in your cluster. The paths to the weights and programs should be identical on all machines. - -Next, ensure password-less SSH access to each machine from the primary host, and create a `hostfile` with a list of the hostnames and their relative "weights" (slots). If you want to use localhost for computation, use its local subnet IP address rather than the loopback address or "localhost". - -Here is an example hostfile: - -``` -192.168.0.1:2 -malvolio.local:1 -``` - -The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive. - -Finally, you're ready to run a computation using `mpirun`: - -```bash -mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -``` - -### 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 and CLBlast. There are currently several different BLAS implementations available for build and use: - -- #### Accelerate Framework: - - This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions. - -- #### OpenBLAS: - - This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine. - - - Using `make`: - - On Linux: - ```bash - make LLAMA_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 LLAMA_OPENBLAS=1 - ``` - - - Using `CMake` on Linux: - - ```bash - mkdir build - cd build - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS - cmake --build . --config Release - ``` - -- #### BLIS - - Check [BLIS.md](docs/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](README-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](./README-sycl.md). - - - Using manual oneAPI installation: - By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: - ```bash - mkdir build - cd build - source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON - cmake --build . --config Release - ``` - - - Using oneAPI docker image: - If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. - - 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. - -- #### cuBLAS - - This provides BLAS 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). - - 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. - - - Using `make`: - ```bash - make LLAMA_CUBLAS=1 - ``` - - Using `CMake`: - - ```bash - mkdir build - cd build - cmake .. -DLLAMA_CUBLAS=ON - cmake --build . --config Release - ``` - - The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: - - - | Option | Legal values | Default | Description | - |--------------------------------|------------------------|---------|-------------| - | LLAMA_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. | - | LLAMA_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. | - | LLAMA_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. | - | LLAMA_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. | - | LLAMA_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. | - | LLAMA_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. | - -- #### hipBLAS - - This provides BLAS 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/en/latest/deploy/linux/quick_start.html). - - - Using `make`: - ```bash - make LLAMA_HIPBLAS=1 - ``` - - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): - ```bash - CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \ - cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ - && cmake --build build -- -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 `-DLLAMA_HIP_UMA=ON"`. - However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). - - - Using `make` (example for target gfx1030, build with 16 CPU threads): - ```bash - make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030 - ``` - - - 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% - mkdir build - cd build - cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ .. - cmake --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) - Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`. - - - 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 | - |-------------------------|------------------------|---------|-------------| - | LLAMA_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. | - | LLAMA_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. | - | LLAMA_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. | - -- #### CLBlast - - OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU. - - You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK). - - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed. - - - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page. - - -
- Installing the OpenCL SDK from source - - ```sh - git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git - mkdir OpenCL-SDK/build - cd OpenCL-SDK/build - cmake .. -DBUILD_DOCS=OFF \ - -DBUILD_EXAMPLES=OFF \ - -DBUILD_TESTING=OFF \ - -DOPENCL_SDK_BUILD_SAMPLES=OFF \ - -DOPENCL_SDK_TEST_SAMPLES=OFF - cmake --build . --config Release - cmake --install . --prefix /some/path - ``` -
- - ##### Installing CLBlast - - Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages. - - Alternatively, they may be built from source. - - -
- Windows: - - ```cmd - set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64" - git clone https://github.com/CNugteren/CLBlast.git - mkdir CLBlast\build - cd CLBlast\build - cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64 - cmake --build . --config Release - cmake --install . --prefix C:/CLBlast - ``` - - -
- Unix: - - ```sh - git clone https://github.com/CNugteren/CLBlast.git - mkdir CLBlast/build - cd CLBlast/build - cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF - cmake --build . --config Release - cmake --install . --prefix /some/path - ``` - - Where `/some/path` is where the built library will be installed (default is `/usr/local`).
- ##### Building Llama with CLBlast +-
+ Run in conversation mode with custom chat template - - Build with make: - ```sh - make LLAMA_CLBLAST=1 - ``` - - CMake (Unix): - ```sh - mkdir build - cd build - cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path - cmake --build . --config Release - ``` - - CMake (Windows): - ```cmd - set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast" - git clone https://github.com/ggerganov/llama.cpp - cd llama.cpp - mkdir build - cd build - cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64 - cmake --build . --config Release - cmake --install . --prefix C:/LlamaCPP + ```bash + # use the "chatml" template (use -h to see the list of supported templates) + llama-cli -m model.gguf -cnv --chat-template chatml + + # use a custom template + llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:' ``` - ##### Running Llama with CLBlast +
- The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does. +-
+ Run simple text completion - To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`. - The selection can be a number (starting from 0) or a text string to search: + To disable conversation mode explicitly, use `-no-cnv` - ```sh - GGML_OPENCL_PLATFORM=1 ./main ... - GGML_OPENCL_DEVICE=2 ./main ... - GGML_OPENCL_PLATFORM=Intel ./main ... - GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ... - ``` + ```bash + llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv - The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful. - Using the variables it is possible to select a CPU-based driver as well, if so desired. + # 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. + ``` - You can get a list of platforms and devices from the `clinfo -l` command, etc. +
-- #### Vulkan +-
+ Constrain the output with a custom grammar - **With docker**: + ```bash + llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' - You don't need to install Vulkan SDK. It will be installed inside the container. + # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"} + ``` - ```sh - # Build the image - docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile . + The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md). - # Then, use it: - docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 - ``` + For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/ - **Without docker**: +
- Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) - For example, on Ubuntu 22.04 (jammy), use the command below: +## [`llama-server`](examples/server) - ```bash - wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list - apt update -y - apt-get install -y vulkan-sdk - # To verify the installation, use the command below: - vulkaninfo - ``` +#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs. - Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. +-
+ Start a local HTTP server with default configuration on port 8080 - Then, build llama.cpp using the cmake command below: + ```bash + llama-server -m model.gguf --port 8080 - ```bash - mkdir -p build - cd build - cmake .. -DLLAMA_VULKAN=1 - cmake --build . --config Release - # Test the output binary (with "-ngl 33" to offload all layers to GPU) - ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 + # Basic web UI can be accessed via browser: http://localhost:8080 + # Chat completion endpoint: http://localhost:8080/v1/chat/completions + ``` - # You should see in the output, ggml_vulkan detected your GPU. For example: - # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 - ``` +
-### Prepare and Quantize +-
+ Support multiple-users and parallel decoding -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. + ```bash + # up to 4 concurrent requests, each with 4096 max context + llama-server -m model.gguf -c 16384 -np 4 + ``` -```bash -# obtain the official LLaMA model weights and place them in ./models -ls ./models -llama-2-7b tokenizer_checklist.chk tokenizer.model -# [Optional] for models using BPE tokenizers -ls ./models - vocab.json -# [Optional] for PyTorch .bin models like Mistral-7B -ls ./models - +
-# install Python dependencies -python3 -m pip install -r requirements.txt +-
+ Enable speculative decoding -# convert the model to ggml FP16 format -python3 convert.py models/mymodel/ + ```bash + # the draft.gguf model should be a small variant of the target model.gguf + llama-server -m model.gguf -md draft.gguf + ``` -# [Optional] for models using BPE tokenizers -python convert.py models/mymodel/ --vocab-type bpe +
-# quantize the model to 4-bits (using Q4_K_M method) -./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M +-
+ Serve an embedding model -# update the gguf filetype to current version if older version is now unsupported -./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY -``` + ```bash + # use the /embedding endpoint + llama-server -m model.gguf --embedding --pooling cls -ub 8192 + ``` -### Run the quantized model +
-```bash -# start inference on a gguf model -./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 -``` +-
+ Serve a reranking model -When running the larger models, make sure you have enough disk space to store all the intermediate files. + ```bash + # use the /reranking endpoint + llama-server -m model.gguf --reranking + ``` -### Running on Windows with prebuilt binaries +
-You will find prebuilt Windows binaries on the release page. +-
+ Constrain all outputs with a grammar -Simply download and extract the latest zip package of choice: (e.g. `llama-b1380-bin-win-avx2-x64.zip`) + ```bash + # custom grammar + llama-server -m model.gguf --grammar-file grammar.gbnf -From the unzipped folder, open a terminal/cmd window here and place a pre-converted `.gguf` model file. Test out the main example like so: + # JSON + llama-server -m model.gguf --grammar-file grammars/json.gbnf + ``` -``` -.\main -m llama-2-7b.Q4_0.gguf -n 128 -``` +
-### Memory/Disk Requirements -As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. +## [`llama-perplexity`](examples/perplexity) -| Model | Original size | Quantized size (Q4_0) | -|------:|--------------:|-----------------------:| -| 7B | 13 GB | 3.9 GB | -| 13B | 24 GB | 7.8 GB | -| 30B | 60 GB | 19.5 GB | -| 65B | 120 GB | 38.5 GB | +#### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text. -### Quantization +-
+ Measure the perplexity over a text file -Several quantization methods are supported. They differ in the resulting model disk size and inference speed. + ```bash + llama-perplexity -m model.gguf -f file.txt -*(outdated)* + # [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 + ``` -| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | -|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:| -| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 | -| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G | -| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 | -| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 | -| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | -| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 | -| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G | -| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 | -| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 | -| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | +
-- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) -- recent k-quants improvements and new i-quants - - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) - - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) - - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) - - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) - - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) - - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) - - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) - - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) - - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) - - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) - - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996) - - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) - - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) - - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) +-
+ Measure KL divergence -### Perplexity (measuring model quality) + ```bash + # TODO + ``` -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). +
-The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. -The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads. +[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md) +[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity) -#### How to run +## [`llama-bench`](examples/llama-bench) -1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip -2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw` -3. Output: -``` -perplexity : calculating perplexity over 655 chunks -24.43 seconds per pass - ETA 4.45 hours -[1]4.5970,[2]5.1807,[3]6.0382,... -``` -And after 4.45 hours, you will have the final perplexity. +#### Benchmark the performance of the inference for various parameters. -### Interactive mode +-
+ Run default benchmark -If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter. -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:"`. + ```bash + llama-bench -m model.gguf -Here is an example of a few-shot interaction, invoked with the command + # 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) + ``` -```bash -# default arguments using a 7B model -./examples/chat.sh +
-# advanced chat with a 13B model -./examples/chat-13B.sh +## [`llama-run`](examples/run) -# custom arguments using a 13B model -./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt -``` +#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3]. -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 `main` example program. +-
+ Run a model with a specific prompt (by default it's pulled from Ollama registry) -![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png) + ```bash + llama-run granite-code + ``` -### Persistent Interaction +
-The prompt, user inputs, and model generations can be saved and resumed across calls to `./main` 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. +[^3]: [RamaLama](https://github.com/containers/ramalama) -```bash -# Start a new chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh +## [`llama-simple`](examples/simple) -# Resume that chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh +#### A minimal example for implementing apps with `llama.cpp`. Useful for developers. -# Start a different chat with the same prompt/model -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh +-
+ Basic text completion -# 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 -``` + ```bash + llama-simple -m model.gguf -### Constrained output with grammars + # 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 + ``` -`llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only: +
-```bash -./main -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). +## Contributing -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. +- Contributors can open PRs +- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch +- Collaborators will be invited based on contributions +- Any help with managing issues, PRs and projects is very appreciated! +- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions +- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information +- 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) -### Instruct mode +## Other documentation -1. First, download and place the `ggml` model into the `./models` folder -2. Run the `main` tool like this: +- [main (cli)](examples/main/README.md) +- [server](examples/server/README.md) +- [GBNF grammars](grammars/README.md) -``` -./examples/alpaca.sh -``` +#### Development documentation -Sample run: +- [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) -``` -== Running in interactive mode. == - - Press Ctrl+C to interject at any time. - - Press Return to return control to LLaMa. - - If you want to submit another line, end your input in '\'. - - Below is an instruction that describes a task. Write a response that appropriately completes the request. - -> How many letters are there in the English alphabet? -There 26 letters in the English Alphabet -> What is the most common way of transportation in Amsterdam? -The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis -> List 5 words that start with "ca". -cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach. -> -``` - -### Obtaining and using the Facebook LLaMA 2 model - -- Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. -- Alternatively, if you want to save time and space, you can download already converted and quantized models from [TheBloke](https://huggingface.co/TheBloke), including: - - [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGUF) - - [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGUF) - - [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGUF) - - [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF) - - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) - - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) - -### 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: @@ -887,184 +518,5 @@ If your issue is with model generation quality, then please at least scan the fo - [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) -### Android +#### References -#### Building the Project using Android NDK -You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/). - -First, install the essential packages for termux: -``` -pkg install clang wget git cmake -``` -Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake: -``` -$ mkdir build-android -$ cd build-android -$ export NDK= -$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod .. -$ make -``` -Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card. -Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone: - -https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 - -#### Building the Project using Termux (F-Droid) -Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card. - -Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU. - -If you opt to utilize OpenBLAS, you'll need to install the corresponding package. -``` -apt install libopenblas -``` - -Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages: -``` -apt install ocl-icd opencl-headers opencl-clhpp clinfo -``` - -In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below: -``` -cmake . -make -cp libclblast.so* $PREFIX/lib -cp ./include/clblast.h ../llama.cpp -``` - -Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below: -``` -cp /data/data/com.termux/files/usr/include/openblas/cblas.h . -cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h . -make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice) -``` - -Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below: -``` -GGML_OPENCL_PLATFORM=0 -GGML_OPENCL_DEVICE=0 -export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH -``` - -(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ ) - -For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle. - -Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script. - -### Docker - -#### Prerequisites -* Docker must be installed and running on your system. -* Create a folder to store big models & intermediate files (ex. /llama/models) - -#### Images -We have three Docker images available for this project: - -1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) -2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) -3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) - -Additionally, there the following images, similar to the above: - -- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) -- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) -- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) -- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) - -The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now). - -#### Usage - -The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image. - -Replace `/path/to/models` below with the actual path where you downloaded the models. - -```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B -``` - -On completion, you are ready to play! - -```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 -``` - -or with a light image: - -```bash -docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 -``` - -or with a server image: - -```bash -docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 -``` - -### Docker With CUDA - -Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container. - -#### Building Locally - -```bash -docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile . -docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile . -docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile . -``` - -You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. - -The defaults are: - -- `CUDA_VERSION` set to `11.7.1` -- `CUDA_DOCKER_ARCH` set to `all` - -The resulting images, are essentially the same as the non-CUDA images: - -1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. -2. `local/llama.cpp:light-cuda`: This image only includes the main executable file. -3. `local/llama.cpp:server-cuda`: This image only includes the server executable file. - -#### Usage - -After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag. - -```bash -docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 -docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 -docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 -``` - -### Contributing - -- Contributors can open PRs -- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch -- Collaborators will be invited based on contributions -- Any help with managing issues and PRs is very appreciated! -- 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) - -### Coding guidelines - -- Avoid adding third-party dependencies, extra files, extra headers, etc. -- Always consider cross-compatibility with other operating systems and architectures -- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple -- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit -- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` -- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions -- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices -- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT` - -### Docs - -- [main](./examples/main/README.md) -- [server](./examples/server/README.md) -- [jeopardy](./examples/jeopardy/README.md) -- [BLIS](./docs/BLIS.md) -- [Performance troubleshooting](./docs/token_generation_performance_tips.md) -- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks) -- [GBNF grammars](./grammars/README.md) diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 000000000..f4322c6ee --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,67 @@ +# Security Policy + + - [**Using llama.cpp securely**](#using-llamacpp-securely) + - [Untrusted models](#untrusted-models) + - [Untrusted inputs](#untrusted-inputs) + - [Data privacy](#data-privacy) + - [Untrusted environments or networks](#untrusted-environments-or-networks) + - [Multi-Tenant environments](#multi-tenant-environments) + - [**Reporting a vulnerability**](#reporting-a-vulnerability) + +## Using llama.cpp securely + +### Untrusted models +Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources. + +*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code. + +> [!NOTE] +> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance. + +### Untrusted inputs + +Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks. + +For maximum security when handling untrusted inputs, you may need to employ the following: + +* Sandboxing: Isolate the environment where the inference happens. +* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics. +* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches. +* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as: + * Validation: Enforce strict rules on allowed characters and data types. + * Filtering: Remove potentially malicious scripts or code fragments. + * Encoding: Convert special characters into safe representations. + * Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)). + +### Data privacy + +To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors. + +### Untrusted environments or networks + +If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions: +* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value +* Encrypt your data if sending it over the network. + +### Multi-Tenant environments + +If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks. + +1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity. + +2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring. + +3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk. + +4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time. + +## Reporting a vulnerability + +Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++. + + +However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released. + +Please disclose it as a private [security advisory](https://github.com/ggerganov/llama.cpp/security/advisories/new). + +A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure. diff --git a/Sources/llama/llama.h b/Sources/llama/llama.h new file mode 100644 index 000000000..41725880e --- /dev/null +++ b/Sources/llama/llama.h @@ -0,0 +1,4 @@ +#pragma once + +#include + diff --git a/Sources/llama/module.modulemap b/Sources/llama/module.modulemap new file mode 100644 index 000000000..d010555b1 --- /dev/null +++ b/Sources/llama/module.modulemap @@ -0,0 +1,5 @@ +module llama [system] { + header "llama.h" + link "llama" + export * +} diff --git a/build.zig b/build.zig deleted file mode 100644 index c0af454dc..000000000 --- a/build.zig +++ /dev/null @@ -1,139 +0,0 @@ -// Compatible with Zig Version 0.11.0 -const std = @import("std"); -const ArrayList = std.ArrayList; -const Compile = std.Build.Step.Compile; -const ConfigHeader = std.Build.Step.ConfigHeader; -const Mode = std.builtin.Mode; -const CrossTarget = std.zig.CrossTarget; - -const Maker = struct { - builder: *std.build.Builder, - target: CrossTarget, - optimize: Mode, - enable_lto: bool, - - include_dirs: ArrayList([]const u8), - cflags: ArrayList([]const u8), - cxxflags: ArrayList([]const u8), - objs: ArrayList(*Compile), - - fn addInclude(m: *Maker, dir: []const u8) !void { - try m.include_dirs.append(dir); - } - fn addProjectInclude(m: *Maker, path: []const []const u8) !void { - try m.addInclude(try m.builder.build_root.join(m.builder.allocator, path)); - } - fn addCFlag(m: *Maker, flag: []const u8) !void { - try m.cflags.append(flag); - } - fn addCxxFlag(m: *Maker, flag: []const u8) !void { - try m.cxxflags.append(flag); - } - fn addFlag(m: *Maker, flag: []const u8) !void { - try m.addCFlag(flag); - try m.addCxxFlag(flag); - } - - fn init(builder: *std.build.Builder) !Maker { - const target = builder.standardTargetOptions(.{}); - const zig_version = @import("builtin").zig_version_string; - const commit_hash = try std.ChildProcess.exec( - .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } }, - ); - try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt( - \\int LLAMA_BUILD_NUMBER = {}; - \\char const *LLAMA_COMMIT = "{s}"; - \\char const *LLAMA_COMPILER = "Zig {s}"; - \\char const *LLAMA_BUILD_TARGET = "{s}"; - \\ - , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) })); - var m = Maker{ - .builder = builder, - .target = target, - .optimize = builder.standardOptimizeOption(.{}), - .enable_lto = false, - .include_dirs = ArrayList([]const u8).init(builder.allocator), - .cflags = ArrayList([]const u8).init(builder.allocator), - .cxxflags = ArrayList([]const u8).init(builder.allocator), - .objs = ArrayList(*Compile).init(builder.allocator), - }; - - try m.addCFlag("-std=c11"); - try m.addCxxFlag("-std=c++11"); - try m.addProjectInclude(&.{}); - try m.addProjectInclude(&.{"common"}); - return m; - } - - fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile { - const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize }); - if (o.target.getAbi() != .msvc) - o.defineCMacro("_GNU_SOURCE", null); - - if (std.mem.endsWith(u8, src, ".c")) { - o.addCSourceFiles(&.{src}, m.cflags.items); - o.linkLibC(); - } else { - o.addCSourceFiles(&.{src}, m.cxxflags.items); - if (o.target.getAbi() == .msvc) { - o.linkLibC(); // need winsdk + crt - } else { - // linkLibCpp already add (libc++ + libunwind + libc) - o.linkLibCpp(); - } - } - for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i }); - o.want_lto = m.enable_lto; - return o; - } - - fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile { - const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize }); - e.addCSourceFiles(&.{src}, m.cxxflags.items); - for (deps) |d| e.addObject(d); - for (m.objs.items) |o| e.addObject(o); - for (m.include_dirs.items) |i| e.addIncludePath(.{ .path = i }); - - // https://github.com/ziglang/zig/issues/15448 - if (e.target.getAbi() == .msvc) { - e.linkLibC(); // need winsdk + crt - } else { - // linkLibCpp already add (libc++ + libunwind + libc) - e.linkLibCpp(); - } - m.builder.installArtifact(e); - e.want_lto = m.enable_lto; - return e; - } -}; - -pub fn build(b: *std.build.Builder) !void { - var make = try Maker.init(b); - make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false; - - const ggml = make.obj("ggml", "ggml.c"); - const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c"); - const ggml_backend = make.obj("ggml-backend", "ggml-backend.c"); - const ggml_quants = make.obj("ggml-quants", "ggml-quants.c"); - const llama = make.obj("llama", "llama.cpp"); - const buildinfo = make.obj("common", "common/build-info.cpp"); - const common = make.obj("common", "common/common.cpp"); - const console = make.obj("console", "common/console.cpp"); - const sampling = make.obj("sampling", "common/sampling.cpp"); - const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); - const train = make.obj("train", "common/train.cpp"); - const clip = make.obj("clip", "examples/llava/clip.cpp"); - const llava = make.obj("llava", "examples/llava/llava.cpp"); - - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train }); - - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip, llava }); - if (server.target.isWindows()) { - server.linkSystemLibrary("ws2_32"); - } -} diff --git a/ci/run.sh b/ci/run.sh index 35eb3c7aa..77c32ce00 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -1,4 +1,4 @@ -#/bin/bash +#!/bin/bash # # sample usage: # @@ -13,6 +13,9 @@ # # with SYCL support # GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt # +# # with VULKAN support +# GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# if [ -z "$2" ]; then echo "usage: $0 " @@ -36,20 +39,25 @@ SRC=`pwd` CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON" if [ ! -z ${GG_BUILD_METAL} ]; then - CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON" fi if [ ! -z ${GG_BUILD_CUDA} ]; then - CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUBLAS=1" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native" fi if [ ! -z ${GG_BUILD_SYCL} ]; then if [ -z ${ONEAPI_ROOT} ]; then - echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:\n source /opt/intel/oneapi/setvars.sh" + echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:" + echo "source /opt/intel/oneapi/setvars.sh" exit 1 fi - CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON" +fi + +if [ ! -z ${GG_BUILD_VULKAN} ]; then + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1" fi ## helpers @@ -102,8 +110,11 @@ function gg_run_ctest_debug { set -e + # Check cmake, make and ctest are installed + gg_check_build_requirements + (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log @@ -130,8 +141,11 @@ function gg_run_ctest_release { set -e + # Check cmake, make and ctest are installed + gg_check_build_requirements + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log if [ -z ${GG_BUILD_LOW_PERF} ]; then (time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log @@ -152,13 +166,64 @@ function gg_sum_ctest_release { gg_printf '```\n' } +# test_scripts_debug + +function gg_run_test_scripts_debug { + cd ${SRC} + + set -e + + (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_debug { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in debug mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + +# test_scripts_release + +function gg_run_test_scripts_release { + cd ${SRC} + + set -e + + (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_release { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in release mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + function gg_get_model { - local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf" - local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" - if [[ -s $gguf_3b ]]; then - echo -n "$gguf_3b" - elif [[ -s $gguf_7b ]]; then - echo -n "$gguf_7b" + local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf" + local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf" + local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" + if [[ -s $gguf_0 ]]; then + echo -n "$gguf_0" + elif [[ -s $gguf_1 ]]; then + echo -n "$gguf_1" + elif [[ -s $gguf_2 ]]; then + echo -n "$gguf_2" else echo >&2 "No model found. Can't run gg_run_ctest_with_model." exit 1 @@ -207,187 +272,7 @@ function gg_sum_ctest_with_model_release { gg_printf '```\n' } -# open_llama_3b_v2 - -function gg_run_open_llama_3b_v2 { - cd ${SRC} - - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json - - gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip - unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ - head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw - - path_models="../models-mnt/open-llama/3B-v2" - path_wiki="../models-mnt/wikitext/wikitext-2-raw" - - rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release - - set -e - - (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - - python3 ../convert.py ${path_models} - - model_f16="${path_models}/ggml-model-f16.gguf" - model_q8_0="${path_models}/ggml-model-q8_0.gguf" - model_q4_0="${path_models}/ggml-model-q4_0.gguf" - model_q4_1="${path_models}/ggml-model-q4_1.gguf" - model_q5_0="${path_models}/ggml-model-q5_0.gguf" - model_q5_1="${path_models}/ggml-model-q5_1.gguf" - model_q2_k="${path_models}/ggml-model-q2_k.gguf" - model_q3_k="${path_models}/ggml-model-q3_k.gguf" - model_q4_k="${path_models}/ggml-model-q4_k.gguf" - model_q5_k="${path_models}/ggml-model-q5_k.gguf" - model_q6_k="${path_models}/ggml-model-q6_k.gguf" - - wiki_test_60="${path_wiki}/wiki.test-60.raw" - - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 - ./bin/quantize ${model_f16} ${model_q4_0} q4_0 - ./bin/quantize ${model_f16} ${model_q4_1} q4_1 - ./bin/quantize ${model_f16} ${model_q5_0} q5_0 - ./bin/quantize ${model_f16} ${model_q5_1} q5_1 - ./bin/quantize ${model_f16} ${model_q2_k} q2_k - ./bin/quantize ${model_f16} ${model_q3_k} q3_k - ./bin/quantize ${model_f16} ${model_q4_k} q4_k - ./bin/quantize ${model_f16} ${model_q5_k} q5_k - ./bin/quantize ${model_f16} ${model_q6_k} q6_k - - (time ./bin/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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/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/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/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/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/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/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/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/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/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/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/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/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - - (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - - function check_ppl { - qnt="$1" - ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - - if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then - printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" - return 20 - fi - - printf ' - %s @ %s OK\n' "$qnt" "$ppl" - return 0 - } - - check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - - cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log - - # lora - function compare_ppl { - qnt="$1" - ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - - if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then - printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2" - return 20 - fi - - printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2" - return 0 - } - - path_lora="../models-mnt/open-llama/3B-v2/lora" - path_shakespeare="../models-mnt/shakespeare" - - shakespeare="${path_shakespeare}/shakespeare.txt" - lora_shakespeare="${path_lora}/ggml-adapter-model.bin" - - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin - gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt - - python3 ../convert-lora-to-ggml.py ${path_lora} - - # f16 - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log - compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # q8_0 - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log - compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # q8_0 + f16 lora-base - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log - compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - set +e -} - -function gg_sum_open_llama_3b_v2 { - gg_printf '### %s\n\n' "${ci}" - - gg_printf 'OpenLLaMA 3B-v2:\n' - gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" - gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" - gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" - gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)" - gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" - gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" - gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" - gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" - gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" - gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" - gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" - gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" - gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" - gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" - gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" - gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" - gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" - gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" - gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" - gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)" - gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)" -} - # open_llama_7b_v2 -# requires: GG_BUILD_CUDA function gg_run_open_llama_7b_v2 { cd ${SRC} @@ -411,10 +296,10 @@ function gg_run_open_llama_7b_v2 { set -e - (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert.py ${path_models} + python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -430,44 +315,47 @@ function gg_run_open_llama_7b_v2 { wiki_test="${path_wiki}/wiki.test.raw" - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 - ./bin/quantize ${model_f16} ${model_q4_0} q4_0 - ./bin/quantize ${model_f16} ${model_q4_1} q4_1 - ./bin/quantize ${model_f16} ${model_q5_0} q5_0 - ./bin/quantize ${model_f16} ${model_q5_1} q5_1 - ./bin/quantize ${model_f16} ${model_q2_k} q2_k - ./bin/quantize ${model_f16} ${model_q3_k} q3_k - ./bin/quantize ${model_f16} ${model_q4_k} q4_k - ./bin/quantize ${model_f16} ${model_q5_k} q5_k - ./bin/quantize ${model_f16} ${model_q6_k} q6_k + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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/main --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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/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/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/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/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/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/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/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/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/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/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/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/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/save-load-state --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" @@ -496,48 +384,6 @@ function gg_run_open_llama_7b_v2 { cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log - # lora - function compare_ppl { - qnt="$1" - ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - - if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then - printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2" - return 20 - fi - - printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2" - return 0 - } - - path_lora="../models-mnt/open-llama/7B-v2/lora" - path_shakespeare="../models-mnt/shakespeare" - - shakespeare="${path_shakespeare}/shakespeare.txt" - lora_shakespeare="${path_lora}/ggml-adapter-model.bin" - - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin - gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt - - python3 ../convert-lora-to-ggml.py ${path_lora} - - # f16 - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log - compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # currently not supported by the CUDA backend - # q8_0 - #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log - #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log - #compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # q8_0 + f16 lora-base - #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log - #compare_ppl "q8_0 / f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - set +e } @@ -548,7 +394,6 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" - gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)" gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" @@ -561,11 +406,271 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" - gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" - gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" - #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" - #gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)" - #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)" +} + +# pythia_1.4b + +function gg_run_pythia_1_4b { + cd ${SRC} + + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw + + path_models="../models-mnt/pythia/1.4B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test_60="${path_wiki}/wiki.test-60.raw" + + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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} -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} -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} -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="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_1_4b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 1.4B:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" +} + +# pythia_2_8b + +function gg_run_pythia_2_8b { + cd ${SRC} + + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + + path_models="../models-mnt/pythia/2.8B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test="${path_wiki}/wiki.test.raw" + + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 -no-cnv --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 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 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.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="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_2_8b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 2.8B:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" } # bge-small @@ -574,7 +679,7 @@ function gg_run_embd_bge_small { cd ${SRC} gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json - gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/tokenizer.model + gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin @@ -592,17 +697,17 @@ function gg_run_embd_bge_small { set -e (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert-hf-to-gguf.py ${path_models} + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 - (time ./bin/embedding --model ${model_f16} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/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 } @@ -616,8 +721,92 @@ function gg_sum_embd_bge_small { gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" } +# rerank_tiny + +function gg_run_rerank_tiny { + cd ${SRC} + + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer_config.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/special_tokens_map.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/resolve/main/pytorch_model.bin + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/sentence_bert_config.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/vocab.txt + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/modules.json + gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json + + gg_wget models-mnt/rerank-tiny/1_Pooling https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/1_Pooling/config.json + + path_models="../models-mnt/rerank-tiny" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${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." -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 + # rerank score 1: 0.029 + # rerank score 2: 0.135 + + # check that the score is in the range [$3, $4] + function check_score { + qnt="$1" + score=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$score < $3" | bc) -eq 1 ] || [ $(echo "$score > $4" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: score not in range [%s, %s])\n' "$qnt" "$score" "$3" "$4" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$score" + return 0 + } + + check_score "rerank score 0" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 0")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log + check_score "rerank score 1" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 1")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log + check_score "rerank score 2" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 2")" "0.10" "0.30" | tee -a $OUT/${ci}-rk-f16.log + + set +e +} + +function gg_sum_rerank_tiny { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Rerank Tiny (Jina):\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-rk-f16.log)" +} + +function gg_check_build_requirements { + if ! command -v cmake &> /dev/null; then + gg_printf 'cmake not found, please install' + fi + + if ! command -v make &> /dev/null; then + gg_printf 'make not found, please install' + fi + + if ! command -v ctest &> /dev/null; then + gg_printf 'ctest not found, please install' + fi +} + ## main +export LLAMA_LOG_PREFIX=1 +export LLAMA_LOG_TIMESTAMPS=1 + if [ -z ${GG_BUILD_LOW_PERF} ]; then # Create symlink: ./llama.cpp/models-mnt -> $MNT/models/models-mnt rm -rf ${SRC}/models-mnt @@ -626,7 +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 @@ -640,12 +832,19 @@ test $ret -eq 0 && gg_run ctest_release if [ -z ${GG_BUILD_LOW_PERF} ]; then test $ret -eq 0 && gg_run embd_bge_small + test $ret -eq 0 && gg_run rerank_tiny + + if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then + test $ret -eq 0 && gg_run test_scripts_debug + test $ret -eq 0 && gg_run test_scripts_release + fi if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then - if [ -z ${GG_BUILD_CUDA} ]; then - test $ret -eq 0 && gg_run open_llama_3b_v2 + if [ -z ${GG_BUILD_CUDA} ] && [ -z ${GG_BUILD_VULKAN} ]; then + test $ret -eq 0 && gg_run pythia_1_4b else - test $ret -eq 0 && gg_run open_llama_7b_v2 + test $ret -eq 0 && gg_run pythia_2_8b + #test $ret -eq 0 && gg_run open_llama_7b_v2 fi test $ret -eq 0 && gg_run ctest_with_model_debug test $ret -eq 0 && gg_run ctest_with_model_release 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/arm64-windows-llvm.cmake b/cmake/arm64-windows-llvm.cmake new file mode 100644 index 000000000..802379680 --- /dev/null +++ b/cmake/arm64-windows-llvm.cmake @@ -0,0 +1,16 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-pc-windows-msvc ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) + +set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/cmake/arm64-windows-msvc.cmake b/cmake/arm64-windows-msvc.cmake new file mode 100644 index 000000000..c77631420 --- /dev/null +++ b/cmake/arm64-windows-msvc.cmake @@ -0,0 +1,6 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-pc-windows-msvc ) +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) diff --git a/scripts/build-info.cmake b/cmake/build-info.cmake similarity index 95% rename from scripts/build-info.cmake rename to cmake/build-info.cmake index ea3dc55c8..c1a456e17 100644 --- a/scripts/build-info.cmake +++ b/cmake/build-info.cmake @@ -44,7 +44,7 @@ if(MSVC) set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME}) else() execute_process( - COMMAND sh -c "$@ --version | head -1" _ ${CMAKE_C_COMPILER} + COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER} OUTPUT_VARIABLE OUT OUTPUT_STRIP_TRAILING_WHITESPACE ) 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};${GF_CXX_FLAGS}>") + else() + # todo : msvc + set(C_FLAGS "" PARENT_SCOPE) + set(CXX_FLAGS "" PARENT_SCOPE) + endif() + endif() +endfunction() diff --git a/cmake/git-vars.cmake b/cmake/git-vars.cmake new file mode 100644 index 000000000..1a4c24ebf --- /dev/null +++ b/cmake/git-vars.cmake @@ -0,0 +1,22 @@ +find_package(Git) + +# the commit's SHA1 +execute_process(COMMAND + "${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8 + WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}" + OUTPUT_VARIABLE GIT_SHA1 + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) + +# the date of the commit +execute_process(COMMAND + "${GIT_EXECUTABLE}" log -1 --format=%ad --date=local + WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}" + OUTPUT_VARIABLE GIT_DATE + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) + +# the subject of the commit +execute_process(COMMAND + "${GIT_EXECUTABLE}" log -1 --format=%s + WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}" + OUTPUT_VARIABLE GIT_COMMIT_SUBJECT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) diff --git a/cmake/llama-config.cmake.in b/cmake/llama-config.cmake.in new file mode 100644 index 000000000..90cbec5b6 --- /dev/null +++ b/cmake/llama-config.cmake.in @@ -0,0 +1,30 @@ +set(LLAMA_VERSION @LLAMA_INSTALL_VERSION@) +set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@) +set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@) +set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) + +@PACKAGE_INIT@ + +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@") + +find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake) + +find_library(llama_LIBRARY llama + REQUIRED + 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 "ggml::ggml;ggml::ggml-base;" + IMPORTED_LINK_INTERFACE_LANGUAGES "CXX" + IMPORTED_LOCATION "${llama_LIBRARY}" + INTERFACE_COMPILE_FEATURES c_std_90 + POSITION_INDEPENDENT_CODE ON) + +check_required_components(Llama) diff --git a/cmake/llama.pc.in b/cmake/llama.pc.in new file mode 100644 index 000000000..6fb58b5f6 --- /dev/null +++ b/cmake/llama.pc.in @@ -0,0 +1,10 @@ +prefix=@CMAKE_INSTALL_PREFIX@ +exec_prefix=@CMAKE_INSTALL_PREFIX@ +libdir=@CMAKE_INSTALL_FULL_LIBDIR@ +includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@ + +Name: llama +Description: Port of Facebook's LLaMA model in C/C++ +Version: @LLAMA_INSTALL_VERSION@ +Libs: -L${libdir} -lggml -lggml-base -lllama +Cflags: -I${includedir} diff --git a/cmake/x64-windows-llvm.cmake b/cmake/x64-windows-llvm.cmake new file mode 100644 index 000000000..0603d738f --- /dev/null +++ b/cmake/x64-windows-llvm.cmake @@ -0,0 +1,11 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR x86_64 ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( arch_c_flags "-march=native" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" ) + diff --git a/codecov.yml b/codecov.yml deleted file mode 100644 index a301c5b2c..000000000 --- a/codecov.yml +++ /dev/null @@ -1,14 +0,0 @@ -comment: off - -coverage: - status: - project: - default: - target: auto - threshold: 0 - base: auto - patch: - default: - target: auto - threshold: 0 - base: auto diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index f79acfef1..e61015d2a 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -1,5 +1,8 @@ # common +find_package(Threads REQUIRED) + +llama_add_compile_flags() # Build info header # @@ -19,7 +22,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") endif() endif() - set(GIT_INDEX "${GIT_DIR}/index") + if(EXISTS "${GIT_DIR}/index") + set(GIT_INDEX "${GIT_DIR}/index") + else() + message(WARNING "Git index not found in git repository.") + set(GIT_INDEX "") + endif() else() message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") set(GIT_INDEX "") @@ -31,7 +39,7 @@ add_custom_command( COMMENT "Generating build details from Git" COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} - -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake" + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake" WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.." DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX} VERBATIM @@ -42,27 +50,75 @@ if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() - set(TARGET common) add_library(${TARGET} STATIC + arg.cpp + arg.h base64.hpp - common.h + chat.cpp + chat.hpp + chat-template.hpp common.cpp - sampling.h - sampling.cpp - console.h + common.h console.cpp - grammar-parser.h - grammar-parser.cpp - train.h - train.cpp + console.h + json-schema-to-grammar.cpp + json.hpp + llguidance.cpp + log.cpp + log.h + minja.hpp + ngram-cache.cpp + ngram-cache.h + sampling.cpp + sampling.h + speculative.cpp + speculative.h ) if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() +set(LLAMA_COMMON_EXTRA_LIBS build_info) + +# Use curl to download model url +if (LLAMA_CURL) + find_package(CURL REQUIRED) + target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL) + include_directories(${CURL_INCLUDE_DIRS}) + find_library(CURL_LIBRARY curl REQUIRED) + set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY}) +endif () + +if (LLAMA_LLGUIDANCE) + include(ExternalProject) + set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source) + set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release) + ExternalProject_Add(llguidance_ext + GIT_REPOSITORY https://github.com/guidance-ai/llguidance + # v0.6.12: + GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09 + PREFIX ${CMAKE_BINARY_DIR}/llguidance + SOURCE_DIR ${LLGUIDANCE_SRC} + BUILD_IN_SOURCE TRUE + CONFIGURE_COMMAND "" + BUILD_COMMAND cargo build --release + INSTALL_COMMAND "" + BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/libllguidance.a ${LLGUIDANCE_PATH}/llguidance.h + UPDATE_COMMAND "" + ) + target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE) + + add_library(llguidance STATIC IMPORTED) + set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/libllguidance.a) + add_dependencies(llguidance llguidance_ext) + + target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH}) + set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance) +endif () + target_include_directories(${TARGET} PUBLIC .) -target_compile_features(${TARGET} PUBLIC cxx_std_11) -target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama) +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 new file mode 100644 index 000000000..152f671ab --- /dev/null +++ b/common/arg.cpp @@ -0,0 +1,2370 @@ +#include "arg.h" + +#include "log.h" +#include "sampling.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "json-schema-to-grammar.h" + +using json = nlohmann::ordered_json; + +common_arg & common_arg::set_examples(std::initializer_list examples) { + this->examples = std::move(examples); + return *this; +} + +common_arg & common_arg::set_excludes(std::initializer_list excludes) { + this->excludes = std::move(excludes); + return *this; +} + +common_arg & common_arg::set_env(const char * env) { + help = help + "\n(env: " + env + ")"; + this->env = env; + return *this; +} + +common_arg & common_arg::set_sparam() { + is_sparam = true; + return *this; +} + +bool common_arg::in_example(enum llama_example ex) { + return examples.find(ex) != examples.end(); +} + +bool common_arg::is_exclude(enum llama_example ex) { + return excludes.find(ex) != excludes.end(); +} + +bool common_arg::get_value_from_env(std::string & output) { + if (env == nullptr) return false; + char * value = std::getenv(env); + if (value) { + output = value; + return true; + } + return false; +} + +bool common_arg::has_value_from_env() { + return env != nullptr && std::getenv(env); +} + +static std::vector break_str_into_lines(std::string input, size_t max_char_per_line) { + std::vector result; + std::istringstream iss(input); + std::string line; + auto add_line = [&](const std::string& l) { + if (l.length() <= max_char_per_line) { + result.push_back(l); + } else { + std::istringstream line_stream(l); + std::string word, current_line; + while (line_stream >> word) { + if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) { + if (!current_line.empty()) result.push_back(current_line); + current_line = word; + } else { + current_line += (!current_line.empty() ? " " : "") + word; + } + } + if (!current_line.empty()) result.push_back(current_line); + } + }; + while (std::getline(iss, line)) { + add_line(line); + } + return result; +} + +std::string common_arg::to_string() { + // params for printing to console + const static int n_leading_spaces = 40; + const static int n_char_per_line_help = 70; // TODO: detect this based on current console + std::string leading_spaces(n_leading_spaces, ' '); + + std::ostringstream ss; + for (const auto arg : args) { + if (arg == args.front()) { + if (args.size() == 1) { + ss << arg; + } else { + // first arg is usually abbreviation, we need padding to make it more beautiful + auto tmp = std::string(arg) + ", "; + auto spaces = std::string(std::max(0, 7 - (int)tmp.size()), ' '); + ss << tmp << spaces; + } + } else { + ss << arg << (arg != args.back() ? ", " : ""); + } + } + if (value_hint) ss << " " << value_hint; + if (value_hint_2) ss << " " << value_hint_2; + if (ss.tellp() > n_leading_spaces - 3) { + // current line is too long, add new line + ss << "\n" << leading_spaces; + } else { + // padding between arg and help, same line + ss << std::string(leading_spaces.size() - ss.tellp(), ' '); + } + const auto help_lines = break_str_into_lines(help, n_char_per_line_help); + for (const auto & line : help_lines) { + ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n"; + } + return ss.str(); +} + +// +// utils +// + +static void common_params_handle_model_default( + std::string & model, + const std::string & model_url, + std::string & hf_repo, + std::string & hf_file, + const std::string & hf_token, + const std::string & model_default) { + if (!hf_repo.empty()) { + // short-hand to avoid specifying --hf-file -> default it to --model + if (hf_file.empty()) { + if (model.empty()) { + auto auto_detected = common_get_hf_file(hf_repo, hf_token); + if (auto_detected.first.empty() || auto_detected.second.empty()) { + exit(1); // built without CURL, error message already printed + } + hf_repo = auto_detected.first; + hf_file = auto_detected.second; + } else { + hf_file = model; + } + } + // make sure model path is present (for caching purposes) + if (model.empty()) { + // this is to avoid different repo having same file name, or same file name in different subdirs + std::string filename = hf_repo + "_" + hf_file; + // to make sure we don't have any slashes in the filename + string_replace_all(filename, "/", "_"); + model = fs_get_cache_file(filename); + } + } else if (!model_url.empty()) { + if (model.empty()) { + auto f = string_split(model_url, '#').front(); + f = string_split(f, '?').front(); + model = fs_get_cache_file(string_split(f, '/').back()); + } + } else if (model.empty()) { + model = model_default; + } +} + +const std::vector kv_cache_types = { + GGML_TYPE_F32, + GGML_TYPE_F16, + GGML_TYPE_BF16, + GGML_TYPE_Q8_0, + GGML_TYPE_Q4_0, + GGML_TYPE_Q4_1, + GGML_TYPE_IQ4_NL, + GGML_TYPE_Q5_0, + GGML_TYPE_Q5_1, +}; + +static ggml_type kv_cache_type_from_str(const std::string & s) { + for (const auto & type : kv_cache_types) { + if (ggml_type_name(type) == s) { + return type; + } + } + throw std::runtime_error("Unsupported cache type: " + s); +} + +static std::string get_all_kv_cache_types() { + std::ostringstream msg; + for (const auto & type : kv_cache_types) { + msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", "); + } + return msg.str(); +} + +// +// CLI argument parsing functions +// + +static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) { + std::string arg; + const std::string arg_prefix = "--"; + common_params & params = ctx_arg.params; + + std::unordered_map arg_to_options; + for (auto & opt : ctx_arg.options) { + for (const auto & arg : opt.args) { + arg_to_options[arg] = &opt; + } + } + + // handle environment variables + for (auto & opt : ctx_arg.options) { + std::string value; + if (opt.get_value_from_env(value)) { + try { + if (opt.handler_void && (value == "1" || value == "true")) { + opt.handler_void(params); + } + if (opt.handler_int) { + opt.handler_int(params, std::stoi(value)); + } + if (opt.handler_string) { + opt.handler_string(params, value); + continue; + } + } catch (std::exception & e) { + throw std::invalid_argument(string_format( + "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what())); + } + } + } + + // handle command line arguments + auto check_arg = [&](int i) { + if (i+1 >= argc) { + throw std::invalid_argument("expected value for argument"); + } + }; + + for (int i = 1; i < argc; i++) { + const std::string arg_prefix = "--"; + + std::string arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + if (arg_to_options.find(arg) == arg_to_options.end()) { + throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str())); + } + auto opt = *arg_to_options[arg]; + if (opt.has_value_from_env()) { + fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str()); + } + try { + if (opt.handler_void) { + opt.handler_void(params); + continue; + } + + // arg with single value + check_arg(i); + std::string val = argv[++i]; + if (opt.handler_int) { + opt.handler_int(params, std::stoi(val)); + continue; + } + if (opt.handler_string) { + opt.handler_string(params, val); + continue; + } + + // arg with 2 values + check_arg(i); + std::string val2 = argv[++i]; + if (opt.handler_str_str) { + opt.handler_str_str(params, val, val2); + continue; + } + } catch (std::exception & e) { + throw std::invalid_argument(string_format( + "error while handling argument \"%s\": %s\n\n" + "usage:\n%s\n\nto show complete usage, run with -h", + arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str())); + } + } + + postprocess_cpu_params(params.cpuparams, nullptr); + postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams); + + 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"); + } + + // TODO: refactor model params in a common struct + common_params_handle_model_default(params.model, params.model_url, params.hf_repo, params.hf_file, params.hf_token, DEFAULT_MODEL_PATH); + common_params_handle_model_default(params.speculative.model, params.speculative.model_url, params.speculative.hf_repo, params.speculative.hf_file, params.hf_token, ""); + common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file, params.hf_token, ""); + + if (params.escape) { + string_process_escapes(params.prompt); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); + for (auto & antiprompt : params.antiprompt) { + string_process_escapes(antiprompt); + } + for (auto & seq_breaker : params.sampling.dry_sequence_breakers) { + string_process_escapes(seq_breaker); + } + } + + if (!params.kv_overrides.empty()) { + params.kv_overrides.emplace_back(); + params.kv_overrides.back().key[0] = 0; + } + + if (params.reranking && params.embedding) { + throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both"); + } + + if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) { + throw std::runtime_error(string_format( + "error: the supplied chat template is not supported: %s%s\n", + params.chat_template.c_str(), + params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates" + )); + } + + return true; +} + +static void common_params_print_usage(common_params_context & ctx_arg) { + auto print_options = [](std::vector & options) { + for (common_arg * opt : options) { + printf("%s", opt->to_string().c_str()); + } + }; + + std::vector common_options; + std::vector sparam_options; + std::vector specific_options; + for (auto & opt : ctx_arg.options) { + // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + printf("----- common params -----\n\n"); + print_options(common_options); + printf("\n\n----- sampling params -----\n\n"); + print_options(sparam_options); + // TODO: maybe convert enum llama_example to string + printf("\n\n----- example-specific params -----\n\n"); + 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; +} + +static void add_rpc_devices(std::string servers) { + auto rpc_servers = string_split(servers, ','); + if (rpc_servers.empty()) { + throw std::invalid_argument("no RPC servers specified"); + } + ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC"); + if (!rpc_reg) { + throw std::invalid_argument("failed to find RPC backend"); + } + typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint); + ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device"); + if (!ggml_backend_rpc_add_device_fn) { + throw std::invalid_argument("failed to find RPC device add function"); + } + for (const auto & server : rpc_servers) { + ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str()); + if (dev) { + ggml_backend_device_register(dev); + } else { + throw std::invalid_argument("failed to register RPC device"); + } + } +} + +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 + + try { + if (!common_params_parse_ex(argc, argv, ctx_arg)) { + ctx_arg.params = params_org; + return false; + } + if (ctx_arg.params.usage) { + common_params_print_usage(ctx_arg); + if (ctx_arg.print_usage) { + ctx_arg.print_usage(argc, argv); + } + exit(0); + } + } catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + ctx_arg.params = params_org; + return false; + } + + 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.sampling.samplers) { + sampler_type_chars += common_sampler_type_to_chr(sampler); + sampler_type_names += common_sampler_type_to_str(sampler) + ";"; + } + sampler_type_names.pop_back(); + + + /** + * filter options by example + * rules: + * - all examples inherit options from LLAMA_EXAMPLE_COMMON + * - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example + * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example + */ + auto add_opt = [&](common_arg arg) { + if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) { + ctx_arg.options.push_back(std::move(arg)); + } + }; + + + add_opt(common_arg( + {"-h", "--help", "--usage"}, + "print usage and exit", + [](common_params & params) { + params.usage = true; + } + )); + add_opt(common_arg( + {"--version"}, + "show version and build info", + [](common_params &) { + fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); + exit(0); + } + )); + add_opt(common_arg( + {"--verbose-prompt"}, + string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"), + [](common_params & params) { + params.verbose_prompt = true; + } + )); + add_opt(common_arg( + {"--no-display-prompt"}, + string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"), + [](common_params & params) { + params.display_prompt = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-co", "--color"}, + string_format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"), + [](common_params & params) { + params.use_color = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); + add_opt(common_arg( + {"-t", "--threads"}, "N", + string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads), + [](common_params & params, int value) { + params.cpuparams.n_threads = value; + if (params.cpuparams.n_threads <= 0) { + params.cpuparams.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_env("LLAMA_ARG_THREADS")); + add_opt(common_arg( + {"-tb", "--threads-batch"}, "N", + "number of threads to use during batch and prompt processing (default: same as --threads)", + [](common_params & params, int value) { + params.cpuparams_batch.n_threads = value; + if (params.cpuparams_batch.n_threads <= 0) { + params.cpuparams_batch.n_threads = std::thread::hardware_concurrency(); + } + } + )); + add_opt(common_arg( + {"-C", "--cpu-mask"}, "M", + "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")", + [](common_params & params, const std::string & mask) { + params.cpuparams.mask_valid = true; + if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + )); + add_opt(common_arg( + {"-Cr", "--cpu-range"}, "lo-hi", + "range of CPUs for affinity. Complements --cpu-mask", + [](common_params & params, const std::string & range) { + params.cpuparams.mask_valid = true; + if (!parse_cpu_range(range, params.cpuparams.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + )); + add_opt(common_arg( + {"--cpu-strict"}, "<0|1>", + string_format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu), + [](common_params & params, const std::string & value) { + params.cpuparams.strict_cpu = std::stoul(value); + } + )); + add_opt(common_arg( + {"--prio"}, "N", + string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.cpuparams.priority = (enum ggml_sched_priority) prio; + } + )); + add_opt(common_arg( + {"--poll"}, "<0...100>", + string_format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll), + [](common_params & params, const std::string & value) { + params.cpuparams.poll = std::stoul(value); + } + )); + add_opt(common_arg( + {"-Cb", "--cpu-mask-batch"}, "M", + "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.cpuparams_batch.mask_valid = true; + if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + )); + add_opt(common_arg( + {"-Crb", "--cpu-range-batch"}, "lo-hi", + "ranges of CPUs for affinity. Complements --cpu-mask-batch", + [](common_params & params, const std::string & range) { + params.cpuparams_batch.mask_valid = true; + if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + )); + add_opt(common_arg( + {"--cpu-strict-batch"}, "<0|1>", + "use strict CPU placement (default: same as --cpu-strict)", + [](common_params & params, int value) { + params.cpuparams_batch.strict_cpu = value; + } + )); + add_opt(common_arg( + {"--prio-batch"}, "N", + string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.cpuparams_batch.priority = (enum ggml_sched_priority) prio; + } + )); + add_opt(common_arg( + {"--poll-batch"}, "<0|1>", + "use polling to wait for work (default: same as --poll)", + [](common_params & params, int value) { + params.cpuparams_batch.poll = value; + } + )); + add_opt(common_arg( + {"-lcs", "--lookup-cache-static"}, "FNAME", + "path to static lookup cache to use for lookup decoding (not updated by generation)", + [](common_params & params, const std::string & value) { + params.lookup_cache_static = value; + } + ).set_examples({LLAMA_EXAMPLE_LOOKUP})); + add_opt(common_arg( + {"-lcd", "--lookup-cache-dynamic"}, "FNAME", + "path to dynamic lookup cache to use for lookup decoding (updated by generation)", + [](common_params & params, const std::string & value) { + params.lookup_cache_dynamic = value; + } + ).set_examples({LLAMA_EXAMPLE_LOOKUP})); + add_opt(common_arg( + {"-c", "--ctx-size"}, "N", + string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx), + [](common_params & params, int value) { + params.n_ctx = value; + } + ).set_env("LLAMA_ARG_CTX_SIZE")); + add_opt(common_arg( + {"-n", "--predict", "--n-predict"}, "N", + string_format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict), + [](common_params & params, int value) { + params.n_predict = value; + } + ).set_env("LLAMA_ARG_N_PREDICT")); + add_opt(common_arg( + {"-b", "--batch-size"}, "N", + string_format("logical maximum batch size (default: %d)", params.n_batch), + [](common_params & params, int value) { + params.n_batch = value; + } + ).set_env("LLAMA_ARG_BATCH")); + add_opt(common_arg( + {"-ub", "--ubatch-size"}, "N", + string_format("physical maximum batch size (default: %d)", params.n_ubatch), + [](common_params & params, int value) { + params.n_ubatch = value; + } + ).set_env("LLAMA_ARG_UBATCH")); + add_opt(common_arg( + {"--keep"}, "N", + string_format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep), + [](common_params & params, int value) { + params.n_keep = value; + } + )); + add_opt(common_arg( + {"--no-context-shift"}, + string_format("disables context shift on inifinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"), + [](common_params & params) { + params.ctx_shift = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); + add_opt(common_arg( + {"--chunks"}, "N", + string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), + [](common_params & params, int value) { + params.n_chunks = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"-fa", "--flash-attn"}, + string_format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"), + [](common_params & params) { + params.flash_attn = true; + } + ).set_env("LLAMA_ARG_FLASH_ATTN")); + add_opt(common_arg( + {"-p", "--prompt"}, "PROMPT", + ex == LLAMA_EXAMPLE_MAIN + ? "prompt to start generation with\nif -cnv is set, this will be used as system prompt" + : "prompt to start generation with", + [](common_params & params, const std::string & value) { + params.prompt = value; + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--no-perf"}, + string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), + [](common_params & params) { + params.no_perf = true; + params.sampling.no_perf = true; + } + ).set_env("LLAMA_ARG_NO_PERF")); + add_opt(common_arg( + {"-f", "--file"}, "FNAME", + "a file containing the prompt (default: none)", + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + // store the external file name in params + params.prompt_file = value; + std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); + if (!params.prompt.empty() && params.prompt.back() == '\n') { + params.prompt.pop_back(); + } + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--in-file"}, "FNAME", + "an input file (repeat to specify multiple files)", + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + params.in_files.push_back(value); + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"-bf", "--binary-file"}, "FNAME", + "binary file containing the prompt (default: none)", + [](common_params & params, const std::string & value) { + std::ifstream file(value, std::ios::binary); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + // store the external file name in params + params.prompt_file = value; + std::ostringstream ss; + ss << file.rdbuf(); + params.prompt = ss.str(); + fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str()); + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-e", "--escape"}, + string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), + [](common_params & params) { + params.escape = true; + } + )); + add_opt(common_arg( + {"--no-escape"}, + "do not process escape sequences", + [](common_params & params) { + params.escape = false; + } + )); + add_opt(common_arg( + {"-ptc", "--print-token-count"}, "N", + string_format("print token count every N tokens (default: %d)", params.n_print), + [](common_params & params, int value) { + params.n_print = value; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--prompt-cache"}, "FNAME", + "file to cache prompt state for faster startup (default: none)", + [](common_params & params, const std::string & value) { + params.path_prompt_cache = value; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--prompt-cache-all"}, + "if specified, saves user input and generations to cache as well\n", + [](common_params & params) { + params.prompt_cache_all = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--prompt-cache-ro"}, + "if specified, uses the prompt cache but does not update it", + [](common_params & params) { + params.prompt_cache_ro = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-r", "--reverse-prompt"}, "PROMPT", + "halt generation at PROMPT, return control in interactive mode\n", + [](common_params & params, const std::string & value) { + params.antiprompt.emplace_back(value); + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-sp", "--special"}, + string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"), + [](common_params & params) { + params.special = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-cnv", "--conversation"}, + "run in conversation mode:\n" + "- does not print special tokens and suffix/prefix\n" + "- interactive mode is also enabled\n" + "(default: auto enabled if chat template is available)", + [](common_params & params) { + params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-no-cnv", "--no-conversation"}, + "force disable conversation mode (default: false)", + [](common_params & params) { + params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-i", "--interactive"}, + string_format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"), + [](common_params & params) { + params.interactive = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-if", "--interactive-first"}, + string_format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"), + [](common_params & params) { + params.interactive_first = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-mli", "--multiline-input"}, + "allows you to write or paste multiple lines without ending each in '\\'", + [](common_params & params) { + params.multiline_input = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--in-prefix-bos"}, + "prefix BOS to user inputs, preceding the `--in-prefix` string", + [](common_params & params) { + params.input_prefix_bos = true; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"--in-prefix"}, "STRING", + "string to prefix user inputs with (default: empty)", + [](common_params & params, const std::string & value) { + params.input_prefix = value; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); + add_opt(common_arg( + {"--in-suffix"}, "STRING", + "string to suffix after user inputs with (default: empty)", + [](common_params & params, const std::string & value) { + params.input_suffix = value; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); + add_opt(common_arg( + {"--no-warmup"}, + "skip warming up the model with an empty run", + [](common_params & params) { + params.warmup = false; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--spm-infill"}, + string_format( + "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", + params.spm_infill ? "enabled" : "disabled" + ), + [](common_params & params) { + params.spm_infill = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL})); + add_opt(common_arg( + {"--samplers"}, "SAMPLERS", + 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.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.sampling.seed, LLAMA_DEFAULT_SEED), + [](common_params & params, const std::string & value) { + params.sampling.seed = std::stoul(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--sampling-seq", "--sampler-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.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.sampling.ignore_eos = true; + } + ).set_sparam()); + add_opt(common_arg( + {"--temp"}, "N", + string_format("temperature (default: %.1f)", (double)params.sampling.temp), + [](common_params & params, const std::string & value) { + 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.sampling.top_k), + [](common_params & params, int 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.sampling.top_p), + [](common_params & params, const std::string & 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.sampling.min_p), + [](common_params & params, const std::string & 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.sampling.xtc_probability), + [](common_params & params, const std::string & 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.sampling.xtc_threshold), + [](common_params & params, const std::string & 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.sampling.typ_p), + [](common_params & params, const std::string & 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.sampling.penalty_last_n), + [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value)); + } + 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.sampling.penalty_repeat), + [](common_params & params, const std::string & 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.sampling.penalty_present), + [](common_params & params, const std::string & 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.sampling.penalty_freq), + [](common_params & params, const std::string & 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.sampling.dry_multiplier), + [](common_params & params, const std::string & 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.sampling.dry_base), + [](common_params & params, const std::string & value) { + float potential_base = std::stof(value); + if (potential_base >= 1.0f) + { + 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.sampling.dry_allowed_length), + [](common_params & params, int 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.sampling.dry_penalty_last_n), + [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\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.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 + "'"; + }).c_str()), + [](common_params & params, const std::string & value) { + static bool defaults_cleared = false; + + if (!defaults_cleared) { + params.sampling.dry_sequence_breakers.clear(); + defaults_cleared = true; + } + + if (value == "none") { + params.sampling.dry_sequence_breakers.clear(); + } else { + 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.sampling.dynatemp_range), + [](common_params & params, const std::string & 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.sampling.dynatemp_exponent), + [](common_params & params, const std::string & 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.sampling.mirostat), + [](common_params & params, int 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.sampling.mirostat_eta), + [](common_params & params, const std::string & 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.sampling.mirostat_tau), + [](common_params & params, const std::string & value) { + params.sampling.mirostat_tau = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS", + "modifies the likelihood of token appearing in the completion,\n" + "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n" + "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'", + [](common_params & params, const std::string & value) { + std::stringstream ss(value); + llama_token key; + char sign; + std::string value_str; + 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.sampling.logit_bias.push_back({key, bias}); + } else { + throw std::invalid_argument("invalid input format"); + } + } catch (const std::exception&) { + throw std::invalid_argument("invalid input format"); + } + } + ).set_sparam()); + add_opt(common_arg( + {"--grammar"}, "GRAMMAR", + 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.sampling.grammar = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--grammar-file"}, "FNAME", + "file to read grammar from", + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(params.sampling.grammar) + ); + } + ).set_sparam()); + add_opt(common_arg( + {"-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.sampling.grammar = json_schema_to_grammar(json::parse(value)); + } + ).set_sparam()); + add_opt(common_arg( + {"--pooling"}, "{none,mean,cls,last,rank}", + "pooling type for embeddings, use model default if unspecified", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; } + else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_POOLING")); + add_opt(common_arg( + {"--attention"}, "{causal,non-causal}", + "attention type for embeddings, use model default if unspecified", + [](common_params & params, const std::string & value) { + /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; } + else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--rope-scaling"}, "{none,linear,yarn}", + "RoPE frequency scaling method, defaults to linear unless specified by the model", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_env("LLAMA_ARG_ROPE_SCALING_TYPE")); + add_opt(common_arg( + {"--rope-scale"}, "N", + "RoPE context scaling factor, expands context by a factor of N", + [](common_params & params, const std::string & value) { + params.rope_freq_scale = 1.0f / std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_SCALE")); + add_opt(common_arg( + {"--rope-freq-base"}, "N", + "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)", + [](common_params & params, const std::string & value) { + params.rope_freq_base = std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_FREQ_BASE")); + add_opt(common_arg( + {"--rope-freq-scale"}, "N", + "RoPE frequency scaling factor, expands context by a factor of 1/N", + [](common_params & params, const std::string & value) { + params.rope_freq_scale = std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_FREQ_SCALE")); + add_opt(common_arg( + {"--yarn-orig-ctx"}, "N", + string_format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx), + [](common_params & params, int value) { + params.yarn_orig_ctx = value; + } + ).set_env("LLAMA_ARG_YARN_ORIG_CTX")); + add_opt(common_arg( + {"--yarn-ext-factor"}, "N", + string_format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor), + [](common_params & params, const std::string & value) { + params.yarn_ext_factor = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_EXT_FACTOR")); + add_opt(common_arg( + {"--yarn-attn-factor"}, "N", + string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor), + [](common_params & params, const std::string & value) { + params.yarn_attn_factor = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR")); + add_opt(common_arg( + {"--yarn-beta-slow"}, "N", + string_format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow), + [](common_params & params, const std::string & value) { + params.yarn_beta_slow = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_BETA_SLOW")); + add_opt(common_arg( + {"--yarn-beta-fast"}, "N", + string_format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast), + [](common_params & params, const std::string & value) { + params.yarn_beta_fast = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_BETA_FAST")); + add_opt(common_arg( + {"-gan", "--grp-attn-n"}, "N", + string_format("group-attention factor (default: %d)", params.grp_attn_n), + [](common_params & params, int value) { + params.grp_attn_n = value; + } + ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_PASSKEY})); + add_opt(common_arg( + {"-gaw", "--grp-attn-w"}, "N", + string_format("group-attention width (default: %d)", params.grp_attn_w), + [](common_params & params, int value) { + params.grp_attn_w = value; + } + ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN})); + add_opt(common_arg( + {"-dkvc", "--dump-kv-cache"}, + "verbose print of the KV cache", + [](common_params & params) { + params.dump_kv_cache = true; + } + )); + add_opt(common_arg( + {"-nkvo", "--no-kv-offload"}, + "disable KV offload", + [](common_params & params) { + params.no_kv_offload = true; + } + ).set_env("LLAMA_ARG_NO_KV_OFFLOAD")); + add_opt(common_arg( + {"-ctk", "--cache-type-k"}, "TYPE", + string_format( + "KV cache data type for K\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_k) + ), + [](common_params & params, const std::string & value) { + params.cache_type_k = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_K")); + add_opt(common_arg( + {"-ctv", "--cache-type-v"}, "TYPE", + string_format( + "KV cache data type for V\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_v) + ), + [](common_params & params, const std::string & value) { + params.cache_type_v = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_V")); + add_opt(common_arg( + {"--perplexity", "--all-logits"}, + string_format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"), + [](common_params & params) { + params.logits_all = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--hellaswag"}, + "compute HellaSwag score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.hellaswag = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--hellaswag-tasks"}, "N", + string_format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks), + [](common_params & params, int value) { + params.hellaswag_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--winogrande"}, + "compute Winogrande score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.winogrande = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--winogrande-tasks"}, "N", + string_format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks), + [](common_params & params, int value) { + params.winogrande_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--multiple-choice"}, + "compute multiple choice score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.multiple_choice = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--multiple-choice-tasks"}, "N", + string_format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks), + [](common_params & params, int value) { + params.multiple_choice_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--kl-divergence"}, + "computes KL-divergence to logits provided via --kl-divergence-base", + [](common_params & params) { + params.kl_divergence = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--save-all-logits", "--kl-divergence-base"}, "FNAME", + "set logits file", + [](common_params & params, const std::string & value) { + params.logits_file = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--ppl-stride"}, "N", + string_format("stride for perplexity calculation (default: %d)", params.ppl_stride), + [](common_params & params, int value) { + params.ppl_stride = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--ppl-output-type"}, "<0|1>", + string_format("output type for perplexity calculation (default: %d)", params.ppl_output_type), + [](common_params & params, int value) { + params.ppl_output_type = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"-dt", "--defrag-thold"}, "N", + string_format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold), + [](common_params & params, const std::string & value) { + params.defrag_thold = std::stof(value); + } + ).set_env("LLAMA_ARG_DEFRAG_THOLD")); + add_opt(common_arg( + {"-np", "--parallel"}, "N", + string_format("number of parallel sequences to decode (default: %d)", params.n_parallel), + [](common_params & params, int value) { + params.n_parallel = value; + } + ).set_env("LLAMA_ARG_N_PARALLEL")); + add_opt(common_arg( + {"-ns", "--sequences"}, "N", + string_format("number of sequences to decode (default: %d)", params.n_sequences), + [](common_params & params, int value) { + params.n_sequences = value; + } + ).set_examples({LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"-cb", "--cont-batching"}, + string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), + [](common_params & params) { + params.cont_batching = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING")); + add_opt(common_arg( + {"-nocb", "--no-cont-batching"}, + "disable continuous batching", + [](common_params & params) { + params.cont_batching = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING")); + add_opt(common_arg( + {"--mmproj"}, "FILE", + "path to a multimodal projector file for LLaVA. see examples/llava/README.md", + [](common_params & params, const std::string & value) { + params.mmproj = value; + } + ).set_examples({LLAMA_EXAMPLE_LLAVA})); + add_opt(common_arg( + {"--image"}, "FILE", + "path to an image file. use with multimodal models. Specify multiple times for batching", + [](common_params & params, const std::string & value) { + params.image.emplace_back(value); + } + ).set_examples({LLAMA_EXAMPLE_LLAVA})); + if (llama_supports_rpc()) { + add_opt(common_arg( + {"--rpc"}, "SERVERS", + "comma separated list of RPC servers", + [](common_params & params, const std::string & value) { + add_rpc_devices(value); + GGML_UNUSED(params); + } + ).set_env("LLAMA_ARG_RPC")); + } + add_opt(common_arg( + {"--mlock"}, + "force system to keep model in RAM rather than swapping or compressing", + [](common_params & params) { + params.use_mlock = true; + } + ).set_env("LLAMA_ARG_MLOCK")); + add_opt(common_arg( + {"--no-mmap"}, + "do not memory-map model (slower load but may reduce pageouts if not using mlock)", + [](common_params & params) { + params.use_mmap = false; + } + ).set_env("LLAMA_ARG_NO_MMAP")); + add_opt(common_arg( + {"--numa"}, "TYPE", + "attempt optimizations that help on some NUMA systems\n" + "- distribute: spread execution evenly over all nodes\n" + "- isolate: only spawn threads on CPUs on the node that execution started on\n" + "- numactl: use the CPU map provided by numactl\n" + "if run without this previously, it is recommended to drop the system page cache before using this\n" + "see https://github.com/ggerganov/llama.cpp/issues/1437", + [](common_params & params, const std::string & value) { + /**/ 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 { 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 &) { + std::vector rpc_devices; + std::vector all_devices; + 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) { + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev); + if (ggml_backend_reg_name(reg) == std::string("RPC")) { + rpc_devices.push_back(dev); + } else { + all_devices.push_back(dev); + } + } + } + // insert RPC devices in front + all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end()); + printf("Available devices:\n"); + for (size_t i = 0; i < all_devices.size(); ++i) { + auto * dev = all_devices[i]; + 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: 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( + {"-sm", "--split-mode"}, "{none,layer,row}", + "how to split the model across multiple GPUs, one of:\n" + "- none: use one GPU only\n" + "- layer (default): split layers and KV across GPUs\n" + "- row: split rows across GPUs", + [](common_params & params, const std::string & value) { + std::string arg_next = value; + if (arg_next == "none") { + params.split_mode = LLAMA_SPLIT_MODE_NONE; + } else if (arg_next == "layer") { + params.split_mode = LLAMA_SPLIT_MODE_LAYER; + } else if (arg_next == "row") { + params.split_mode = LLAMA_SPLIT_MODE_ROW; + } else { + throw std::invalid_argument("invalid value"); + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the split mode has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_SPLIT_MODE")); + add_opt(common_arg( + {"-ts", "--tensor-split"}, "N0,N1,N2,...", + "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1", + [](common_params & params, const std::string & value) { + std::string arg_next = value; + + // split string by , and / + const std::regex regex{ R"([,/]+)" }; + std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 }; + std::vector split_arg{ it, {} }; + if (split_arg.size() >= llama_max_devices()) { + throw std::invalid_argument( + string_format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices()) + ); + } + for (size_t i = 0; i < llama_max_devices(); ++i) { + if (i < split_arg.size()) { + params.tensor_split[i] = std::stof(split_arg[i]); + } else { + params.tensor_split[i] = 0.0f; + } + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting a tensor split has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_TENSOR_SPLIT")); + add_opt(common_arg( + {"-mg", "--main-gpu"}, "INDEX", + string_format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu), + [](common_params & params, int value) { + params.main_gpu = value; + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the main GPU has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_MAIN_GPU")); + add_opt(common_arg( + {"--check-tensors"}, + string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"), + [](common_params & params) { + params.check_tensors = true; + } + )); + add_opt(common_arg( + {"--override-kv"}, "KEY=TYPE:VALUE", + "advanced option to override model metadata by key. may be specified multiple times.\n" + "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false", + [](common_params & params, const std::string & value) { + if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) { + throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", value.c_str())); + } + } + )); + add_opt(common_arg( + {"--lora"}, "FNAME", + "path to LoRA adapter (can be repeated to use multiple adapters)", + [](common_params & params, const std::string & value) { + params.lora_adapters.push_back({ std::string(value), 1.0, nullptr }); + } + // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); + add_opt(common_arg( + {"--lora-scaled"}, "FNAME", "SCALE", + "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)", + [](common_params & params, const std::string & fname, const std::string & scale) { + params.lora_adapters.push_back({ fname, std::stof(scale), nullptr }); + } + // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); + add_opt(common_arg( + {"--control-vector"}, "FNAME", + "add a control vector\nnote: this argument can be repeated to add multiple control vectors", + [](common_params & params, const std::string & value) { + params.control_vectors.push_back({ 1.0f, value, }); + } + )); + add_opt(common_arg( + {"--control-vector-scaled"}, "FNAME", "SCALE", + "add a control vector with user defined scaling SCALE\n" + "note: this argument can be repeated to add multiple scaled control vectors", + [](common_params & params, const std::string & fname, const std::string & scale) { + params.control_vectors.push_back({ std::stof(scale), fname }); + } + )); + add_opt(common_arg( + {"--control-vector-layer-range"}, "START", "END", + "layer range to apply the control vector(s) to, start and end inclusive", + [](common_params & params, const std::string & start, const std::string & end) { + params.control_vector_layer_start = std::stoi(start); + params.control_vector_layer_end = std::stoi(end); + } + )); + add_opt(common_arg( + {"-a", "--alias"}, "STRING", + "set alias for model name (to be used by REST API)", + [](common_params & params, const std::string & value) { + params.model_alias = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ALIAS")); + add_opt(common_arg( + {"-m", "--model"}, "FNAME", + ex == LLAMA_EXAMPLE_EXPORT_LORA + ? std::string("model path from which to load base model") + : string_format( + "model path (default: `models/$filename` with filename from `--hf-file` " + "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH + ), + [](common_params & params, const std::string & value) { + params.model = value; + } + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL")); + add_opt(common_arg( + {"-mu", "--model-url"}, "MODEL_URL", + "model download url (default: unused)", + [](common_params & params, const std::string & value) { + params.model_url = value; + } + ).set_env("LLAMA_ARG_MODEL_URL")); + add_opt(common_arg( + {"-hf", "-hfr", "--hf-repo"}, "/[:quant]", + "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n" + "example: unsloth/phi-4-GGUF:q4_k_m\n" + "(default: unused)", + [](common_params & params, const std::string & value) { + params.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO")); + add_opt(common_arg( + {"-hfd", "-hfrd", "--hf-repo-draft"}, "/[:quant]", + "Same as --hf-repo, but for the draft model (default: unused)", + [](common_params & params, const std::string & value) { + params.speculative.hf_repo = value; + } + ).set_env("LLAMA_ARG_HFD_REPO")); + add_opt(common_arg( + {"-hff", "--hf-file"}, "FILE", + "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)", + [](common_params & params, const std::string & value) { + params.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE")); + add_opt(common_arg( + {"-hfv", "-hfrv", "--hf-repo-v"}, "/[:quant]", + "Hugging Face model repository for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO_V")); + add_opt(common_arg( + {"-hffv", "--hf-file-v"}, "FILE", + "Hugging Face model file for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE_V")); + add_opt(common_arg( + {"-hft", "--hf-token"}, "TOKEN", + "Hugging Face access token (default: value from HF_TOKEN environment variable)", + [](common_params & params, const std::string & value) { + params.hf_token = value; + } + ).set_env("HF_TOKEN")); + add_opt(common_arg( + {"--context-file"}, "FNAME", + "file to load context from (repeat to specify multiple files)", + [](common_params & params, const std::string & value) { + std::ifstream file(value, std::ios::binary); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + params.context_files.push_back(value); + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--chunk-size"}, "N", + string_format("minimum length of embedded text chunks (default: %d)", params.chunk_size), + [](common_params & params, int value) { + params.chunk_size = value; + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--chunk-separator"}, "STRING", + string_format("separator between chunks (default: '%s')", params.chunk_separator.c_str()), + [](common_params & params, const std::string & value) { + params.chunk_separator = value; + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--junk"}, "N", + string_format("number of times to repeat the junk text (default: %d)", params.n_junk), + [](common_params & params, int value) { + params.n_junk = value; + } + ).set_examples({LLAMA_EXAMPLE_PASSKEY})); + add_opt(common_arg( + {"--pos"}, "N", + string_format("position of the passkey in the junk text (default: %d)", params.i_pos), + [](common_params & params, int value) { + params.i_pos = value; + } + ).set_examples({LLAMA_EXAMPLE_PASSKEY})); + add_opt(common_arg( + {"-o", "--output", "--output-file"}, "FNAME", + string_format("output file (default: '%s')", + ex == LLAMA_EXAMPLE_EXPORT_LORA + ? params.lora_outfile.c_str() + : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR + ? params.cvector_outfile.c_str() + : params.out_file.c_str()), + [](common_params & params, const std::string & value) { + params.out_file = value; + params.cvector_outfile = value; + params.lora_outfile = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA})); + add_opt(common_arg( + {"-ofreq", "--output-frequency"}, "N", + string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq), + [](common_params & params, int value) { + params.n_out_freq = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--save-frequency"}, "N", + string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq), + [](common_params & params, int value) { + params.n_save_freq = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--process-output"}, + string_format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"), + [](common_params & params) { + params.process_output = true; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--no-ppl"}, + string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), + [](common_params & params) { + params.compute_ppl = false; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--chunk", "--from-chunk"}, "N", + string_format("start processing the input from chunk N (default: %d)", params.i_chunk), + [](common_params & params, int value) { + params.i_chunk = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"-pps"}, + string_format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"), + [](common_params & params) { + params.is_pp_shared = true; + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"-npp"}, "n0,n1,...", + "number of prompt tokens", + [](common_params & params, const std::string & value) { + auto p = string_split(value, ','); + params.n_pp.insert(params.n_pp.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"-ntg"}, "n0,n1,...", + "number of text generation tokens", + [](common_params & params, const std::string & value) { + auto p = string_split(value, ','); + params.n_tg.insert(params.n_tg.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"-npl"}, "n0,n1,...", + "number of parallel prompts", + [](common_params & params, const std::string & value) { + auto p = string_split(value, ','); + params.n_pl.insert(params.n_pl.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"--embd-normalize"}, "N", + string_format("normalisation for embeddings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize), + [](common_params & params, int value) { + params.embd_normalize = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--embd-output-format"}, "FORMAT", + "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix", + [](common_params & params, const std::string & value) { + params.embd_out = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--embd-separator"}, "STRING", + "separator of embeddings (default \\n) for example \"<#sep#>\"", + [](common_params & params, const std::string & value) { + params.embd_sep = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--host"}, "HOST", + string_format("ip address to listen (default: %s)", params.hostname.c_str()), + [](common_params & params, const std::string & value) { + params.hostname = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST")); + add_opt(common_arg( + {"--port"}, "PORT", + string_format("port to listen (default: %d)", params.port), + [](common_params & params, int value) { + params.port = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT")); + add_opt(common_arg( + {"--path"}, "PATH", + string_format("path to serve static files from (default: %s)", params.public_path.c_str()), + [](common_params & params, const std::string & value) { + params.public_path = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH")); + add_opt(common_arg( + {"--no-webui"}, + string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), + [](common_params & params) { + params.webui = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI")); + add_opt(common_arg( + {"--embedding", "--embeddings"}, + string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), + [](common_params & params) { + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS")); + add_opt(common_arg( + {"--reranking", "--rerank"}, + string_format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"), + [](common_params & params) { + params.reranking = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING")); + add_opt(common_arg( + {"--api-key"}, "KEY", + "API key to use for authentication (default: none)", + [](common_params & params, const std::string & value) { + params.api_keys.push_back(value); + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY")); + add_opt(common_arg( + {"--api-key-file"}, "FNAME", + "path to file containing API keys (default: none)", + [](common_params & params, const std::string & value) { + std::ifstream key_file(value); + if (!key_file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::string key; + while (std::getline(key_file, key)) { + if (!key.empty()) { + params.api_keys.push_back(key); + } + } + key_file.close(); + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--ssl-key-file"}, "FNAME", + "path to file a PEM-encoded SSL private key", + [](common_params & params, const std::string & value) { + params.ssl_file_key = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_KEY_FILE")); + add_opt(common_arg( + {"--ssl-cert-file"}, "FNAME", + "path to file a PEM-encoded SSL certificate", + [](common_params & params, const std::string & value) { + params.ssl_file_cert = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE")); + add_opt(common_arg( + {"-to", "--timeout"}, "N", + string_format("server read/write timeout in seconds (default: %d)", params.timeout_read), + [](common_params & params, int value) { + params.timeout_read = value; + params.timeout_write = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT")); + add_opt(common_arg( + {"--threads-http"}, "N", + string_format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http), + [](common_params & params, int value) { + params.n_threads_http = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP")); + add_opt(common_arg( + {"--cache-reuse"}, "N", + string_format("min chunk size to attempt reusing from the cache via KV shifting (default: %d)", params.n_cache_reuse), + [](common_params & params, int value) { + params.n_cache_reuse = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_REUSE")); + add_opt(common_arg( + {"--metrics"}, + string_format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_metrics = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS")); + add_opt(common_arg( + {"--slots"}, + string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_slots = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS")); + add_opt(common_arg( + {"--props"}, + string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_props = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS")); + add_opt(common_arg( + {"--no-slots"}, + "disables slots monitoring endpoint", + [](common_params & params) { + params.endpoint_slots = false; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS")); + add_opt(common_arg( + {"--slot-save-path"}, "PATH", + "path to save slot kv cache (default: disabled)", + [](common_params & params, const std::string & value) { + params.slot_save_path = value; + // if doesn't end with DIRECTORY_SEPARATOR, add it + if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { + params.slot_save_path += DIRECTORY_SEPARATOR; + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--jinja"}, + "use jinja template for chat (default: disabled)", + [](common_params & params) { + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA")); + add_opt(common_arg( + {"--chat-template"}, "JINJA_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) { + params.chat_template = value; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE")); + add_opt(common_arg( + {"--chat-template-file"}, "JINJA_TEMPLATE_FILE", + string_format( + "set custom jinja chat template file (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) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(params.chat_template)); + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); + add_opt(common_arg( + {"-sps", "--slot-prompt-similarity"}, "SIMILARITY", + string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity), + [](common_params & params, const std::string & value) { + params.slot_prompt_similarity = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--lora-init-without-apply"}, + string_format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"), + [](common_params & params) { + params.lora_init_without_apply = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--simple-io"}, + "use basic IO for better compatibility in subprocesses and limited consoles", + [](common_params & params) { + params.simple_io = true; + } + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); + add_opt(common_arg( + {"--positive-file"}, "FNAME", + string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), + [](common_params & params, const std::string & value) { + params.cvector_positive_file = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--negative-file"}, "FNAME", + string_format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()), + [](common_params & params, const std::string & value) { + params.cvector_negative_file = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--pca-batch"}, "N", + string_format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch), + [](common_params & params, int value) { + params.n_pca_batch = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--pca-iter"}, "N", + string_format("number of iterations used for PCA (default: %d)", params.n_pca_iterations), + [](common_params & params, int value) { + params.n_pca_iterations = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--method"}, "{pca, mean}", + "dimensionality reduction method to be used (default: pca)", + [](common_params & params, const std::string & value) { + /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; } + else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--output-format"}, "{md,jsonl}", + "output format for batched-bench results (default: md)", + [](common_params & params, const std::string & value) { + /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; } + else if (value == "md") { params.batched_bench_output_jsonl = false; } + else { std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"--log-disable"}, + "Log disable", + [](common_params &) { + common_log_pause(common_log_main()); + } + )); + add_opt(common_arg( + {"--log-file"}, "FNAME", + "Log to file", + [](common_params &, const std::string & value) { + common_log_set_file(common_log_main(), value.c_str()); + } + )); + add_opt(common_arg( + {"--log-colors"}, + "Enable colored logging", + [](common_params &) { + common_log_set_colors(common_log_main(), true); + } + ).set_env("LLAMA_LOG_COLORS")); + add_opt(common_arg( + {"-v", "--verbose", "--log-verbose"}, + "Set verbosity level to infinity (i.e. log all messages, useful for debugging)", + [](common_params & params) { + params.verbosity = INT_MAX; + common_log_set_verbosity_thold(INT_MAX); + } + )); + add_opt(common_arg( + {"-lv", "--verbosity", "--log-verbosity"}, "N", + "Set the verbosity threshold. Messages with a higher verbosity will be ignored.", + [](common_params & params, int value) { + params.verbosity = value; + common_log_set_verbosity_thold(value); + } + ).set_env("LLAMA_LOG_VERBOSITY")); + add_opt(common_arg( + {"--log-prefix"}, + "Enable prefx in log messages", + [](common_params &) { + common_log_set_prefix(common_log_main(), true); + } + ).set_env("LLAMA_LOG_PREFIX")); + add_opt(common_arg( + {"--log-timestamps"}, + "Enable timestamps in log messages", + [](common_params &) { + common_log_set_timestamps(common_log_main(), true); + } + ).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}).set_env("LLAMA_ARG_DRAFT_MAX")); + 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}).set_env("LLAMA_ARG_DRAFT_MIN")); + 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}).set_env("LLAMA_ARG_DRAFT_P_SPLIT")); + 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}).set_env("LLAMA_ARG_DRAFT_P_MIN")); + 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}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT")); + 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}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT")); + 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}).set_env("LLAMA_ARG_MODEL_DRAFT")); + + add_opt(common_arg( + {"-mv", "--model-vocoder"}, "FNAME", + "vocoder model for audio generation (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model = value; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--tts-use-guide-tokens"}, + "Use guide tokens to improve TTS word recall", + [](common_params & params) { + params.vocoder.use_guide_tokens = true; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + + // model-specific + add_opt(common_arg( + {"--tts-oute-default"}, + string_format("use default OuteTTS models (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF"; + params.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf"; + params.vocoder.hf_repo = "ggml-org/WavTokenizer"; + params.vocoder.hf_file = "WavTokenizer-Large-75-F16.gguf"; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + + add_opt(common_arg( + {"--embd-bge-small-en-default"}, + string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF"; + params.hf_file = "bge-small-en-v1.5-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--embd-e5-small-en-default"}, + string_format("use default e5-small-v2 model (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF"; + params.hf_file = "e5-small-v2-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--embd-gte-small-default"}, + string_format("use default gte-small model (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "ggml-org/gte-small-Q8_0-GGUF"; + params.hf_file = "gte-small-q8_0.gguf"; + params.pooling_type = LLAMA_POOLING_TYPE_NONE; + params.embd_normalize = 2; + params.n_ctx = 512; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + return ctx_arg; +} diff --git a/common/arg.h b/common/arg.h new file mode 100644 index 000000000..49ab8667b --- /dev/null +++ b/common/arg.h @@ -0,0 +1,80 @@ +#pragma once + +#include "common.h" + +#include +#include +#include + +// +// CLI argument parsing +// + +struct common_arg { + std::set examples = {LLAMA_EXAMPLE_COMMON}; + std::set excludes = {}; + std::vector args; + const char * value_hint = nullptr; // help text or example for arg value + const char * value_hint_2 = nullptr; // for second arg value + const char * env = nullptr; + std::string help; + bool is_sparam = false; // is current arg a sampling param? + void (*handler_void) (common_params & params) = nullptr; + void (*handler_string) (common_params & params, const std::string &) = nullptr; + void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr; + void (*handler_int) (common_params & params, int) = nullptr; + + common_arg( + const std::initializer_list & args, + const char * value_hint, + const std::string & help, + void (*handler)(common_params & params, const std::string &) + ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {} + + common_arg( + const std::initializer_list & args, + const char * value_hint, + const std::string & help, + void (*handler)(common_params & params, int) + ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {} + + common_arg( + const std::initializer_list & args, + const std::string & help, + void (*handler)(common_params & params) + ) : args(args), help(help), handler_void(handler) {} + + // support 2 values for arg + common_arg( + const std::initializer_list & args, + const char * value_hint, + const char * value_hint_2, + const std::string & help, + void (*handler)(common_params & params, const std::string &, const std::string &) + ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {} + + common_arg & set_examples(std::initializer_list examples); + common_arg & set_excludes(std::initializer_list excludes); + common_arg & set_env(const char * env); + common_arg & set_sparam(); + bool in_example(enum llama_example ex); + bool is_exclude(enum llama_example ex); + bool get_value_from_env(std::string & output); + bool has_value_from_env(); + std::string to_string(); +}; + +struct common_params_context { + enum llama_example ex = LLAMA_EXAMPLE_COMMON; + common_params & params; + std::vector options; + void(*print_usage)(int, char **) = nullptr; + common_params_context(common_params & params) : params(params) {} +}; + +// parse input arguments from CLI +// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message) +bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); + +// function to be used by test-arg-parser +common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); diff --git a/common/chat-template.hpp b/common/chat-template.hpp new file mode 100644 index 000000000..882ba41bd --- /dev/null +++ b/common/chat-template.hpp @@ -0,0 +1,529 @@ +/* + Copyright 2024 Google LLC + + Use of this source code is governed by an MIT-style + license that can be found in the LICENSE file or at + https://opensource.org/licenses/MIT. +*/ +// SPDX-License-Identifier: MIT +#pragma once + +#include "minja.hpp" +#include +#include +#include + +using json = nlohmann::ordered_json; + +namespace minja { + +struct chat_template_caps { + bool supports_tools = false; + bool supports_tool_calls = false; + bool supports_tool_responses = false; + bool supports_system_role = false; + bool supports_parallel_tool_calls = false; + bool supports_tool_call_id = false; + // meta-llama/Llama-3.1-8B-Instruct expects arguments to be an object. + // Most other templates (and OpenAI's API) expect the arguments object to be stringified. + bool requires_object_arguments = false; + // CohereForAI/c4ai-command-r-plus simple variant + bool requires_non_null_content = false; + // MiniMaxAI/MiniMax-Text-01 special + bool requires_typed_content = false; +}; + +struct chat_template_inputs { + nlohmann::ordered_json messages; + nlohmann::ordered_json tools; + bool add_generation_prompt = true; + nlohmann::ordered_json extra_context; + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); +}; + +struct chat_template_options { + bool apply_polyfills = true; + bool use_bos_token = true; + bool use_eos_token = true; + bool define_strftime_now = true; + + bool polyfill_tools = true; + bool polyfill_tool_call_examples = true; + bool polyfill_tool_calls = true; + bool polyfill_tool_responses = true; + bool polyfill_system_role = true; + bool polyfill_object_arguments = true; + bool polyfill_typed_content = true; +}; + +class chat_template { + + private: + chat_template_caps caps_; + std::string source_; + std::string bos_token_; + std::string eos_token_; + std::shared_ptr template_root_; + std::string tool_call_example_; + + std::string try_raw_render( + const nlohmann::ordered_json & messages, + const nlohmann::ordered_json & tools, + bool add_generation_prompt, + const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const + { + try { + chat_template_inputs inputs; + inputs.messages = messages; + inputs.tools = tools; + inputs.add_generation_prompt = add_generation_prompt; + inputs.extra_context = extra_context; + // Use fixed date for tests + inputs.now = std::chrono::system_clock::from_time_t(0); + + chat_template_options opts; + opts.apply_polyfills = false; + + auto prompt = apply(inputs, opts); + // fprintf(stderr, "try_raw_render: %s\n", prompt.c_str()); + return prompt; + } catch (const std::exception & e) { + // fprintf(stderr, "try_raw_render error: %s\n", e.what()); + return ""; + } + } + + public: + + chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token) + : source_(source), bos_token_(bos_token), eos_token_(eos_token) + { + template_root_ = minja::Parser::parse(source_, { + /* .trim_blocks = */ true, + /* .lstrip_blocks = */ true, + /* .keep_trailing_newline = */ false, + }); + + auto contains = [](const std::string & haystack, const std::string & needle) { + return haystack.find(needle) != std::string::npos; + }; + + const std::string user_needle = ""; + const std::string sys_needle = ""; + const json dummy_str_user_msg = {{"role", "user"}, {"content", user_needle}}; + const json dummy_typed_user_msg = {{"role", "user"}, {"content", json::array({{{"type", "text"}, {"text", user_needle}}})}}; + + caps_.requires_typed_content = + !contains(try_raw_render(json::array({dummy_str_user_msg}), {}, false), user_needle) + && contains(try_raw_render(json::array({dummy_typed_user_msg}), {}, false), user_needle); + + const auto dummy_user_msg = caps_.requires_typed_content + ? dummy_typed_user_msg + : dummy_str_user_msg; + const json needle_system_msg = { + {"role", "system"}, + {"content", caps_.requires_typed_content ? json::array({{{"type", "text"}, {"text", sys_needle}}}) : json(sys_needle)}, + }; + + caps_.supports_system_role = contains(try_raw_render({needle_system_msg, dummy_user_msg,}, {}, false), sys_needle); + + auto out = try_raw_render(json::array({ + dummy_user_msg + }), json::array({ + { + {"name", "some_tool"}, + {"type", "function"}, + {"function", { + {"name", "some_tool"}, + {"description", "Some tool."}, + {"parameters", { + {"type", "object"}, + {"properties", { + {"arg", { + {"type", "string"}, + {"description", "Some argument."}, + }}, + }}, + {"required", json::array({ "arg" })}, + }}, + }}, + }, + }), false); + caps_.supports_tools = contains(out, "some_tool"); + + auto make_tool_calls_msg = [&](const json & tool_calls) { + return json { + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", tool_calls}, + }; + }; + auto make_tool_call = [](const std::string & tool_name, const json & arguments) { + return json { + {"id", "call_1___"}, + {"type", "function"}, + {"function", { + {"arguments", arguments}, + {"name", tool_name}, + }}, + }; + }; + const json dummy_args_obj {{"argument_needle", "print('Hello, World!')"}}; + + // Note: the arguments are rendered in both cases, but may be double-escaped, which we don't want. + out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})), + }), {}, false); + auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':"); + out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})), + }), {}, false); + auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':"); + + caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments; + caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments; + auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false); + auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false); + caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle); + + if (caps_.supports_tool_calls) { + auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump()); + auto tc1 = make_tool_call("test_tool1", dummy_args); + auto tc2 = make_tool_call("test_tool2", dummy_args); + auto out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({tc1, tc2})), + }), {}, false); + caps_.supports_parallel_tool_calls = contains(out, "test_tool1") && contains(out, "test_tool2"); + + out = try_raw_render(json::array({ + dummy_user_msg, + make_tool_calls_msg(json::array({tc1})), + { + {"role", "tool"}, + {"name", "test_tool1"}, + {"content", "Some response!"}, + {"tool_call_id", "call_911_"}, + } + }), {}, false); + caps_.supports_tool_responses = contains(out, "Some response!"); + caps_.supports_tool_call_id = contains(out, "call_911_"); + } + + try { + if (!caps_.supports_tools) { + const json user_msg { + {"role", "user"}, + {"content", "Hey"}, + }; + const json args { + {"arg1", "some_value"}, + }; + const json tool_call_msg { + {"role", "assistant"}, + {"content", nullptr}, + {"tool_calls", json::array({ + { + // TODO: detect if requires numerical id or fixed length == 6 like Nemo + {"id", "call_1___"}, + {"type", "function"}, + {"function", { + {"name", "tool_name"}, + {"arguments", (caps_.requires_object_arguments ? args : json(minja::Value(args).dump(-1, /* to_json= */ true)))}, + }}, + }, + })}, + }; + std::string prefix, full; + { + chat_template_inputs inputs; + inputs.messages = json::array({user_msg}); + inputs.add_generation_prompt = true; + prefix = apply(inputs); + } + { + chat_template_inputs inputs; + inputs.messages = json::array({user_msg, tool_call_msg}); + inputs.add_generation_prompt = false; + full = apply(inputs); + } + auto eos_pos_last = full.rfind(eos_token_); + if (eos_pos_last == prefix.size() - eos_token_.size() || + (full[full.size() - 1] == '\n' && (eos_pos_last == full.size() - eos_token_.size() - 1))) { + full = full.substr(0, eos_pos_last); + } + size_t common_prefix_length = 0; + for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) { + if (prefix[i] != full[i]) { + break; + } + if (prefix[i] == '<') { + // DeepSeek R1's template (as of 20250209) adds a trailing if add_generation_prompt, + // but it removes thinking tags for past messages. + // The prefix and full strings diverge at vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <. + continue; + } + common_prefix_length = i + 1; + } + auto example = full.substr(common_prefix_length); + if (example.find("tool_name") == std::string::npos && example.find("some_value") == std::string::npos) { + fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n"); + } else { + tool_call_example_ = example; + } + } + } catch (const std::exception & e) { + fprintf(stderr, "Failed to generate tool call example: %s\n", e.what()); + } + } + + const std::string & source() const { return source_; } + const std::string & bos_token() const { return bos_token_; } + const std::string & eos_token() const { return eos_token_; } + const chat_template_caps & original_caps() const { return caps_; } + + // Deprecated, please use the form with chat_template_inputs and chat_template_options + std::string apply( + const nlohmann::ordered_json & messages, + const nlohmann::ordered_json & tools, + bool add_generation_prompt, + const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(), + bool apply_polyfills = true) + { + fprintf(stderr, "[%s] Deprecated!\n", __func__); + chat_template_inputs inputs; + inputs.messages = messages; + inputs.tools = tools; + inputs.add_generation_prompt = add_generation_prompt; + inputs.extra_context = extra_context; + inputs.now = std::chrono::system_clock::now(); + + chat_template_options opts; + opts.apply_polyfills = apply_polyfills; + + return apply(inputs, opts); + } + + std::string apply( + const chat_template_inputs & inputs, + const chat_template_options & opts = chat_template_options()) const + { + json actual_messages; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto has_tool_calls = false; + auto has_tool_responses = false; + auto has_string_content = false; + for (const auto & message : inputs.messages) { + if (message.contains("tool_calls") && !message["tool_calls"].is_null()) { + has_tool_calls = true; + } + if (message.contains("role") && message["role"] == "tool") { + has_tool_responses = true; + } + if (message.contains("content") && message["content"].is_string()) { + has_string_content = true; + } + } + + auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role; + auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools; + auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples; + auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls; + auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses; + auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments; + auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content; + + auto needs_polyfills = opts.apply_polyfills && (false + || polyfill_system_role + || polyfill_tools + || polyfill_tool_calls + || polyfill_tool_responses + || polyfill_object_arguments + || polyfill_typed_content + ); + + if (needs_polyfills) { + actual_messages = json::array(); + + auto add_message = [&](const json & msg) { + if (polyfill_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) { + actual_messages.push_back({ + {"role", msg.at("role")}, + {"content", {{ + {"type", "text"}, + {"text", msg.at("content")}, + }}}, + }); + } else { + actual_messages.push_back(msg); + } + }; + + std::string pending_system; + auto flush_sys = [&]() { + if (!pending_system.empty()) { + add_message({ + {"role", "user"}, + {"content", pending_system}, + }); + pending_system.clear(); + } + }; + + json adjusted_messages; + if (polyfill_tools) { + adjusted_messages = add_system(inputs.messages, + "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) + + (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_ + "\n\n")); + } else { + adjusted_messages = inputs.messages; + } + + for (const auto & message_ : adjusted_messages) { + auto message = message_; + if (!message.contains("role") || !message.contains("content")) { + throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump()); + } + std::string role = message.at("role"); + + if (message.contains("tool_calls")) { + if (polyfill_object_arguments || polyfill_tool_calls) { + for (auto & tool_call : message.at("tool_calls")) { + if (tool_call["type"] == "function") { + auto & function = tool_call.at("function"); + auto & arguments = function.at("arguments"); + if (arguments.is_string()) { + try { + arguments = json::parse(arguments.get()); + } catch (const std::exception & ecvt) { + fprintf(stderr, "Failed to parse arguments: %s\n", ecvt.what()); + } + } + } + } + } + if (polyfill_tool_calls) { + auto content = message.at("content"); + auto tool_calls = json::array(); + for (const auto & tool_call : message.at("tool_calls")) { + if (tool_call.at("type") != "function") { + continue; + } + const auto & function = tool_call.at("function"); + auto tc = json { + {"name", function.at("name")}, + {"arguments", function.at("arguments")}, + }; + if (tool_call.contains("id")) { + tc["id"] = tool_call["id"]; + } + tool_calls.push_back(tc); + } + auto obj = json { + {"tool_calls", tool_calls}, + }; + if (!content.is_null() && content != "") { + obj["content"] = content; + } + message["content"] = obj.dump(2); + message.erase("tool_calls"); + } + } + if (polyfill_tool_responses && role == "tool") { + message["role"] = "user"; + auto obj = json { + {"tool_response", { + {"content", message.at("content")}, + }}, + }; + if (message.contains("name")) { + obj["tool_response"]["name"] = message.at("name"); + } + if (message.contains("tool_call_id")) { + obj["tool_response"]["tool_call_id"] = message.at("tool_call_id"); + } + message["content"] = obj.dump(2); + message.erase("name"); + } + + if (!message["content"].is_null() && polyfill_system_role) { + std::string content = message.at("content"); + if (role == "system") { + if (!pending_system.empty()) pending_system += "\n"; + pending_system += content; + continue; + } else { + if (role == "user") { + if (!pending_system.empty()) { + message["content"] = pending_system + (content.empty() ? "" : "\n" + content); + pending_system.clear(); + } + } else { + flush_sys(); + } + } + } + add_message(message); + } + flush_sys(); + } else { + actual_messages = inputs.messages; + } + + auto context = minja::Context::make(json({ + {"messages", actual_messages}, + {"add_generation_prompt", inputs.add_generation_prompt}, + })); + context->set("bos_token", opts.use_bos_token ? bos_token_ : ""); + context->set("eos_token", opts.use_eos_token ? eos_token_ : ""); + if (opts.define_strftime_now) { + auto now = inputs.now; + context->set("strftime_now", Value::callable([now](const std::shared_ptr &, minja::ArgumentsValue & args) { + args.expectArgs("strftime_now", {1, 1}, {0, 0}); + auto format = args.args[0].get(); + + auto time = std::chrono::system_clock::to_time_t(now); + auto local_time = *std::localtime(&time); + std::ostringstream ss; + ss << std::put_time(&local_time, format.c_str()); + return ss.str(); + })); + } + if (!inputs.tools.is_null()) { + context->set("tools", minja::Value(inputs.tools)); + } + if (!inputs.extra_context.is_null()) { + for (auto & kv : inputs.extra_context.items()) { + context->set(kv.key(), minja::Value(kv.value())); + } + } + + auto ret = template_root_->render(context); + // fprintf(stderr, "actual_messages: %s\n", actual_messages.dump(2).c_str()); + // fprintf(stderr, "apply: %s\n\n", ret.c_str()); + return ret; + } + + static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) { + json messages_with_system = messages; + + if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") { + std::string existing_system = messages_with_system.at(0).at("content"); + messages_with_system[0] = json { + {"role", "system"}, + {"content", existing_system + "\n\n" + system_prompt}, + }; + } else { + messages_with_system.insert(messages_with_system.begin(), json { + {"role", "system"}, + {"content", system_prompt}, + }); + } + return messages_with_system; + } +}; + +} // namespace minja diff --git a/common/chat.cpp b/common/chat.cpp new file mode 100644 index 000000000..ef1c6fb3d --- /dev/null +++ b/common/chat.cpp @@ -0,0 +1,966 @@ +#include "chat.hpp" +#include "chat-template.hpp" +#include "json-schema-to-grammar.h" +#include "log.h" +#include "minja.hpp" + +std::string common_chat_format_name(common_chat_format format) { + switch (format) { + case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only"; + case COMMON_CHAT_FORMAT_GENERIC: return "Generic"; + case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo"; + case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x"; + case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools"; + case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1"; + case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2"; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2"; + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1"; + case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro"; + case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B"; + default: + throw std::runtime_error("Unknown chat format"); + } +} + +const common_grammar_options grammar_options { + /* .dotall = */ false, + /* .compact_spaces = */ false, + // /* .compact_spaces = */ true, +}; + +static bool parse_json(std::string::const_iterator & it, const std::string::const_iterator & end, json & out) { + // // https://json.nlohmann.me/features/parsing/sax_interface/ + struct json_error_locator : public nlohmann::json_sax { + std::size_t position; + bool found_error; + + json_error_locator() : position(0), found_error(false) {} + + bool parse_error(std::size_t position, const std::string &, const json::exception &) override { + this->position = position - 1; + this->found_error = true; + return false; + } + bool null() override { return true; } + bool boolean(bool) override { return true; } + bool number_integer(number_integer_t) override { return true; } + bool number_unsigned(number_unsigned_t) override { return true; } + bool number_float(number_float_t, const string_t &) override { return true; } + bool string(string_t &) override { return true; } + bool binary(binary_t &) override { return true; } + bool start_object(std::size_t) override { return true; } + bool key(string_t &) override { return true; } + bool end_object() override { return true; } + bool start_array(std::size_t) override { return true; } + bool end_array() override { return true; } + }; + json_error_locator err_loc; + json::sax_parse(it, end, &err_loc); + + std::string::const_iterator temptative_end; + if (err_loc.found_error) { + temptative_end = it + err_loc.position; + } else { + temptative_end = end; + } + std::string json_sub {it, temptative_end}; + try { + out = json::parse(json_sub); + it = temptative_end; + return true; + } catch (const std::exception &) { + return false; + } +} + + +/** + * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between. + * Aggregates the prefix, suffix and in-between text into the content. + */ +static common_chat_msg parse_json_tool_calls( + const std::string& input, + const std::optional & trigger_opt, + const std::regex & function_regex, + const std::regex & close_regex) { + std::smatch match; + + common_chat_msg result; + result.role = "assistant"; + + + auto end = input.end(); + auto it = input.begin(); + + if (trigger_opt) { + if (!std::regex_search(it, end, match, *trigger_opt)) { + result.content = input; + return result; + } + result.content = match.prefix().str(); + it = match.suffix().first; + } + + while (it != end) { + std::sregex_iterator rend; + std::sregex_iterator rit(it, end, function_regex); + if (rit == rend) { + fprintf(stderr, "No more tool calls found\n"); + result.content += std::string(it, end); + break; + } + auto name = rit->str(1); + result.content += std::string(it, rit->prefix().second); + it = rit->suffix().first; + + json arguments; + if (!parse_json(it, end, arguments)) { + throw std::runtime_error("Failed to parse json tool call arguments"); + } + if (!std::regex_search(it, end, match, close_regex)) { + throw std::runtime_error("Malformed input, missing closing pattern"); + } + it = match.suffix().first; + result.tool_calls.push_back({name, arguments.is_string() ? arguments.get() : arguments.dump(), /* id= */ ""}); + } + return result; +} + +static common_chat_msg parse_prefixed_json_tool_call_array(const std::string& input, const std::string & prefix, size_t rstrip_prefix = 0) { + auto content_end = input.find(prefix); + size_t tc_start = std::string::npos; + + common_chat_msg result; + result.role = "assistant"; + const auto process_tool_calls = [&](const json & tool_calls) { + for (const auto & tool_call : tool_calls) { + const auto & arguments = tool_call["arguments"]; + result.tool_calls.push_back({ + tool_call["name"], + arguments.is_string() ? arguments.get() : arguments.dump(), + tool_call.contains("id") ? tool_call["id"] : "", + }); + } + }; + if (content_end == std::string::npos) { + result.content = input; + } else { + tc_start = content_end + prefix.size() - rstrip_prefix; + result.content = input.substr(0, content_end); + auto tool_calls = json::parse(input.substr(tc_start)); + process_tool_calls(tool_calls); + } + return result; +} + +static void foreach_function(const json & tools, const std::function & fn) { + for (const auto & tool : tools) { + if (!tool.contains("type") || tool["type"] != "function" || !tool.contains("function")) { + LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str()); + continue; + } + fn(tool); + } +} + +static std::string apply( + const common_chat_template & tmpl, + const nlohmann::ordered_json & messages, + const nlohmann::ordered_json & tools, + bool add_generation_prompt, + const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) +{ + minja::chat_template_inputs tmpl_inputs; + tmpl_inputs.messages = messages; + tmpl_inputs.tools = tools; + tmpl_inputs.add_generation_prompt = add_generation_prompt; + tmpl_inputs.extra_context = extra_context; + // TODO: add flag to control date/time, if only for testing purposes. + // tmpl_inputs.now = std::chrono::system_clock::now(); + + minja::chat_template_options tmpl_opts; + tmpl_opts.use_bos_token = false; + tmpl_opts.use_eos_token = false; + + return tmpl.apply(tmpl_inputs, tmpl_opts); +} + +static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + common_chat_params data; + + auto tool_call_schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + auto tool_schema = json { + {"type", "object"}, + {"properties", { + {"name", { + {"type", "string"}, + {"const", function["name"]}, + }}, + {"arguments", function["parameters"]}, + }}, + {"required", json::array({"name", "arguments"})}, + }; + if (function.contains("description")) { + tool_schema["description"] = function["description"]; + } + if (inputs.parallel_tool_calls) { + tool_schema["properties"]["id"] = { + {"type", "string"}, + {"minLength", 4}, + }; + tool_schema["required"].push_back("id"); + } + tool_call_schemas.emplace_back(tool_schema); + }); + const auto tool_call = + inputs.parallel_tool_calls + ? json { + {"type", "object"}, + {"properties", { + {"tool_calls", { + {"type", "array"}, + {"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { + {"anyOf", tool_call_schemas}, + }}, + {"minItems", 1}, + }}, + }}, + {"required", json::array({"tool_calls"})}, + } + : json { + {"type", "object"}, + {"properties", { + {"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json { + {"anyOf", tool_call_schemas}, + }}, + }}, + {"required", json::array({"tool_call"})}, + }; + const auto schema = + inputs.tool_choice != "required" + ? json { + {"anyOf", json::array({ + tool_call, + { + {"type", "object"}, + {"properties", { + {"response", inputs.json_schema.is_null() + ? json {{"type", "string"}} + : inputs.json_schema + }, + }}, + {"required", json::array({"response"})}, + }, + })} + } + : tool_call; + + data.grammar_lazy = false; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + builder.add_schema("root", schema); + }, grammar_options); + + auto tweaked_messages = common_chat_template::add_system( + inputs.messages, + "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request"); + + data.prompt = apply(tmpl, tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_GENERIC; + return data; +} +static common_chat_msg common_chat_parse_generic(const std::string & input) { + json data = json::parse(input); + common_chat_msg result; + result.role = "assistant"; + if (data.contains("tool_calls")) { + for (const auto & tool_call : data["tool_calls"]) { + result.tool_calls.push_back({ + tool_call["name"], + tool_call["arguments"].dump(), + tool_call.contains("id") ? tool_call["id"] : "", + }); + } + } else if (data.contains("tool_call")) { + result.tool_calls.push_back({ + data["tool_call"]["name"], + data["tool_call"]["arguments"].dump(), + /* id= */ "", + }); + } else if (data.contains("response")) { + const auto & response = data["response"]; + result.content = response.is_string() ? response.get() : response.dump(2); + } + return result; +} + +static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + schemas.push_back({ + {"type", "object"}, + {"properties", { + // Important note: the model is probably trained to take a JSON stringified arguments value. + // It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object. + {"name", { + {"type", "string"}, + {"const", function["name"]}, + }}, + {"arguments", function["parameters"]}, + {"id", { + {"type", "string"}, + // Nemo's template expects a 9-character alphanumeric ID. + {"pattern", "^[a-zA-Z0-9]{9}$"}, + }}, + }}, + {"required", json::array({"name", "arguments", "id"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema)); + }, grammar_options); + data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true}); + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO; + return data; +} +static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input) { + return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]"); +} + +static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + schemas.push_back({ + {"type", "object"}, + {"properties", { + {"tool_call_id", { + {"type", "string"}, + // Command-R's template expects an integer string. + {"pattern", "^[0-9]{1,10}$"}, + }}, + {"tool_name", { + {"type", "string"}, + {"const", function["name"]}, + }}, + {"parameters", function["parameters"]}, + }}, + {"required", json::array({"tool_call_id", "tool_name", "parameters"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\""); + }, grammar_options); + data.grammar_triggers.push_back({"<|START_ACTION|>", /* .at_start = */ false}); + data.preserved_tokens = { + "<|START_RESPONSE|>", + "<|END_RESPONSE|>", + "<|START_THINKING|>", + "<|END_THINKING|>", + "<|END_ACTION|>", + }; + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_COMMAND_R7B; + return data; +} +static common_chat_msg common_chat_parse_command_r7b(const std::string & input) { + static std::regex response_regex("<\\|START_RESPONSE\\|>([\\s\\S\\n\\r]*?)<\\|END_RESPONSE\\|>"); + static std::regex thought_action_regex("<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|><\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>"); + std::smatch match; + + common_chat_msg result; + result.role = "assistant"; + if (std::regex_match(input, match, response_regex)) { + result.content = match[1].str(); + } else if (std::regex_match(input, match, thought_action_regex)) { + result.tool_plan = match[1].str(); + auto actions_str = match[2].str(); + auto actions = json::parse(actions_str); + for (const auto & action : actions) { + result.tool_calls.push_back({ + /* .name = */ action["tool_name"], + /* .arguments = */ action["parameters"].dump(), + /* .id = */ action["tool_call_id"], + }); + } + } else { + LOG_ERR("Failed to parse command_r output"); + result.content = input; + } + return result; +} + +static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector & expected_properties) { + if (!parameters.is_object() || !parameters.contains("type") || parameters["type"] != "object" || !parameters.contains("properties") || !parameters.contains("required")) { + throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties"); + } + const auto & parameters_properties = parameters.at("properties"); + const auto & parameters_required = parameters.at("required"); + for (const auto & prop : expected_properties) { + if (!parameters_properties.contains(prop)) { + throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop); + } + if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) { + throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop); + } + } + if (parameters_properties.size() != expected_properties.size()) { + throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", ")); + } +} + +static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct common_chat_inputs & inputs, bool allow_python_tag_builtin_tools) { + auto builtin_tools = json::array(); + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + + auto handle_builtin_tool = [&](const std::string & name, const json & parameters) { + if (name == "wolfram_alpha") { + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py + expect_tool_parameters(name, parameters, {"query"}); + } else if (name == "web_search" || name == "brave_search") { + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py + expect_tool_parameters(name, parameters, {"query"}); + } else if (name == "python" || name == "code_interpreter") { + // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py + expect_tool_parameters(name, parameters, {"code"}); + } else { + return false; + } + + std::vector kvs; + for (const auto & [key, value] : parameters.at("properties").items()) { + kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value)); + } + + tool_rules.push_back( + builder.add_rule( + name + "-call", + "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\"")); + builtin_tools.push_back(name); + + return true; + }; + + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + std::string name = function["name"]; + auto parameters = function["parameters"]; + builder.resolve_refs(parameters); + + // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime + if (allow_python_tag_builtin_tools) { + handle_builtin_tool(name, parameters); + } + tool_rules.push_back( + builder.add_rule( + name + "-call", + "\"{\" space " + "( \"\\\"type\\\":\" space \"\\\"function\\\",\" space )? " + "\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " + + builder.add_schema(name + "-args", parameters) + + " \"}\"")); + data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true}); + }); + data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true}); + data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true}); + if (!builtin_tools.empty()) { + data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false}); + } + builder.add_rule("root", string_join(tool_rules, " | ")); + }, grammar_options); + data.additional_stops.push_back("<|eom_id|>"); + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, { + {"tools_in_user_message", false}, + {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools}, + }); + data.format = allow_python_tag_builtin_tools && !builtin_tools.empty() + ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS + : COMMON_CHAT_FORMAT_LLAMA_3_X; + return data; +} +static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) { + // TODO: tighten & simplify the parser, don't accept leading text context. + static std::regex function_regex("\\{[\\s\\n\\r]*(?:\"type\"[\\s\\n\\r]*:[\\s\\n\\r]*\"function\"[\\s\\n\\r]*,[\\s\\n\\r]*|[\\s\\n\\r]*)\"name\"[\\s\\n\\r]*:[\\s\\n\\r]*\"([^\"]+)\"[\\s\\n\\r]*,[\\s\\n\\r]*\"parameters\": "); + static std::regex close_regex("\\}"); + static std::regex builtin_call_regex("<\\|python_tag\\|>([^.(]+)\\.call\\((.*)\\)"); + + if (with_builtin_tools) { + std::smatch match; + if (std::regex_match(input, match, builtin_call_regex)) { + auto name = match[1].str(); + auto raw_args = match[2].str(); + + // TODO: if/when builtin tools start accepting more than 1 argument, use parse_json for real parsing. + auto it_eq = raw_args.find('='); + auto arg_name = raw_args.substr(0, it_eq); + auto arg_value_str = raw_args.substr(it_eq + 1); + auto arg_value = json::parse(arg_value_str); + + return { + /* .role = */ "assistant", + /* .content = */ match.prefix().str(), + /* .tool_calls = */ { + { + /* .name = */ match[1], + /* .arguments = */ (json { + {arg_name, arg_value}, + }).dump(), + /* .id = */ "", + }, + }, + }; + } + } + return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex); +} + +static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + common_chat_params data; + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + std::string name = function["name"]; + auto parameters = function["parameters"]; + auto args_rule = builder.add_schema(name + "-args", parameters); + tool_rules.push_back(builder.add_rule(name + "-call", + "\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\"")); + }); + data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false}); + data.preserved_tokens = { + "<｜tool▁sep｜>", + "<｜tool▁call▁end｜>", + }; + builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space"); + }, grammar_options); + auto prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.prompt = prompt; + data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1; + return data; +} +static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input) { + static std::regex trigger_regex("<｜tool▁calls▁begin｜>"); + static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n"); + static std::regex close_regex("```<｜tool▁call▁end｜>"); + return parse_json_tool_calls(input, trigger_regex, function_regex, close_regex); +} + +static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + fprintf(stderr, "%s\n", __func__); + common_chat_params data; + data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, { + {"datetime", "Jan 29 2025 13:00:00 GMT"}, + {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))}, + }); + if (!inputs.tools.is_null() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + auto schemas = json::array(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + schemas.push_back({ + {"type", "object"}, + {"properties", { + {"name", { + {"type", "string"}, + {"const", function["name"]}, + }}, + {"arguments", function["parameters"]}, + }}, + {"required", json::array({"name", "arguments", "id"})}, + }); + }); + auto schema = json { + {"type", "array"}, + {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}}, + {"minItems", 1}, + }; + if (!inputs.parallel_tool_calls) { + schema["maxItems"] = 1; + } + builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema)); + }, grammar_options); + data.grammar_triggers.push_back({" functools[", /* .at_start = */ false}); + data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2; + } else { + data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + } + return data; +} +static common_chat_msg common_chat_parse_firefunction_v2(const std::string & input) { + return parse_prefixed_json_tool_call_array(input, " functools[", /* rstrip_prefix= */ 1); +} + +static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}... + // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar + common_chat_params data; + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2; + if (!inputs.tools.is_null() && !inputs.tools.empty()) { + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector first_tool_rules; + std::vector subsequent_tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + std::string name = function["name"]; + auto parameters = function["parameters"]; + auto args_rule = builder.add_schema(name + "-args", parameters); + first_tool_rules.push_back(builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule)); + subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>" + name + "\\n\" " + args_rule)); + data.grammar_triggers.push_back({name, /* .at_start = */ true}); + data.grammar_triggers.push_back({">>>" + name, /* .at_start = */ false}); + }); + auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space"; + if (inputs.parallel_tool_calls) { + auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space"; + builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*"); + } else { + builder.add_rule("root", first_rule); + } + + }, grammar_options); + } + return data; +} + +static bool consume(std::string::const_iterator & it, const std::string::const_iterator & end, const std::string & expected) { + auto expected_it = expected.begin(); + auto tmp_it = it; + while (tmp_it != end && expected_it != expected.end() && *tmp_it == *expected_it) { + ++tmp_it; + ++expected_it; + } + if (expected_it == expected.end()) { + it = tmp_it; + return true; + } + return false; +} + +static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) { + static std::regex function_regex(R"((?:>>>)?(\w+)\n)"); + static std::regex close_regex(R"($|(?=>>>))"); + + std::string content; + auto it = input.begin(); + const auto end = input.end(); + + if (consume(it, end, "all\n")) { + std::smatch match; + if (std::regex_search(it, end, match, function_regex)) { + auto fun_it = match.prefix().second; + content = std::string(it, fun_it); + it = fun_it; + } else { + common_chat_msg res; + res.role = "assistant"; + res.content = std::string(it, end); + return res; + } + } + // TODO: tighten & simplify. + try { + auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex); + res.content = content + res.content; + return res; + } catch (const std::exception & e) { + LOG_ERR("Failed to parse functionary v3.2 input: %s\n", e.what()); + common_chat_msg res; + res.role = "assistant"; + res.content = input; + return res; + } +} + +static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt + common_chat_params data; + json tools = inputs.tools.is_null() ? inputs.tools : json::array(); + std::string python_code_argument_name; + auto has_raw_python = false; + + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + const auto & parameters = function["parameters"]; + std::string name = function["name"]; + if (name == "python" || name == "ipython") { + if (!parameters.contains("type")) { + throw std::runtime_error("Missing type in python tool"); + } + has_raw_python = true; + auto type = parameters.at("type"); + if (type == "object") { + auto properties = parameters.at("properties"); + for (auto it = properties.begin(); it != properties.end(); ++it) { + if (it.value().at("type") == "string") { + if (!python_code_argument_name.empty()) { + throw std::runtime_error("Multiple string arguments found in python tool"); + } + python_code_argument_name = it.key(); + } + } + if (python_code_argument_name.empty()) { + throw std::runtime_error("No string argument found in python tool"); + } + } else if (type != "string") { + throw std::runtime_error("Invalid type in python tool: " + type.dump()); + } + } + tool_rules.push_back(builder.add_rule(name + "-call", "\"\" " + builder.add_schema(name + "-args", parameters) + " \"\" space")); + }); + if (has_raw_python) { + tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*")); + data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false}); + } + auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space"; + builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call); + data.grammar_triggers.push_back({"([\s\S\n]*)$)"); + std::smatch match; + if (std::regex_search(input, match, python_tag_regex)) { + auto code = match[1].str(); + return { + /* .role = */ "assistant", + /* .content = */ match.prefix().str(), + /* .tool_calls = */ { + { + /* .name = */ "python", + /* .arguments = */ (json {{"code", code}}).dump(), + /* .id = */ "", + }, + } + }; + } + static std::regex function_regex(R"()"); + static std::regex close_regex(R"()"); + // TODO: tighten & simplify. + return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex); +} + +static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + common_chat_params data; + // (content)?({"name": "foo", "arguments": {"a": 1}})* + data.grammar_lazy = inputs.tool_choice != "required"; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + std::vector tool_rules; + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool["function"]; + std::string name = function["name"]; + auto parameters = function["parameters"]; + builder.resolve_refs(parameters); + tool_rules.push_back(builder.add_schema(name + "-call", { + {"type", "object"}, + {"properties", json { + {"name", json {{"const", name}}}, + {"arguments", parameters}, + }}, + {"required", json::array({"name", "arguments"})}, + })); + }); + auto tool_call = "\"\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"\" space"; + builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call); + data.grammar_triggers.push_back({"", /* .at_start = */ false}); + data.preserved_tokens = { "" }; + }, grammar_options); + + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO; + return data; +} +static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input) { + try { + std::regex start_pattern(R"([\n\s]*)"); + std::regex middle_pattern(R"([\n\s]*[\n\s]*)"); + std::regex end_pattern(R"([\n\s]*[\n\s]*$)"); + + auto end = input.end(); + std::sregex_iterator rend; + std::sregex_iterator rit(input.begin(), end, start_pattern); + if (rit == rend) { + return { + /* .role = */ "assistant", + /* .content = */ input, + /* .tool_calls = */ {}, + }; + } + + common_chat_msg result; + result.role = "assistant"; + result.content = rit->prefix(); + + auto it = rit->suffix().first; + while (it != end) { + json call; + if (!parse_json(it, end, call)) { + throw std::runtime_error("Failed to parse json tool call"); + } + const auto & arguments = call["arguments"]; + result.tool_calls.push_back({ + call["name"], + arguments.dump(), + // arguments.is_string() ? arguments.get() : arguments.dump(), + /* id= */ "", + }); + rit = {it, end, middle_pattern}; + if (rit != rend) { + it = rit->suffix().first; + } else { + rit = {it, end, end_pattern}; + if (rit == rend) { + throw std::runtime_error("Malformed input, missing "); + } + break; + } + } + return result; + } catch (const std::exception & e) { + return { + /* .role = */ "assistant", + /* .content = */ input, + /* .tool_calls = */ {}, + }; + } +} + +static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + common_chat_params data; + data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt); + data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + data.grammar_lazy = false; + if (!inputs.json_schema.is_null()) { + if (!inputs.grammar.empty()) { + throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both"); + } + data.grammar = json_schema_to_grammar(inputs.json_schema); + } else { + data.grammar = inputs.grammar.empty(); + } + return data; +} + +common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) { + auto has_tools = !inputs.tools.is_null() && inputs.tool_choice != "none"; + LOG_DBG("[%s] has_tools=%s\n", __func__, has_tools ? "true" : "false"); + + if (has_tools && !inputs.grammar.empty()) { + throw std::runtime_error("Cannot specify grammar with tools"); + } + + const auto & src = tmpl.source(); + if (src.find(">>>all") != std::string::npos) { + // Functionary prepends "all\n" to plain content outputs, so we use the parser no matter when + return common_chat_params_init_functionary_v3_2(tmpl, inputs); + } + if (src.find(" functools[") != std::string::npos) { + // Firefunction v2 requires datetime and functions in the context, even w/o tools. + return common_chat_params_init_firefunction_v2(tmpl, inputs); + } + + if (!has_tools) { + return common_chat_params_init_without_tools(tmpl, inputs); + } + + if (src.find("") != std::string::npos) { + return common_chat_params_init_hermes_2_pro(tmpl, inputs); + } + if (src.find("<|start_header_id|>") != std::string::npos + && src.find("ipython<|end_header_id|>") != std::string::npos) { + auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos; + return common_chat_params_init_llama_3_1_tool_calls(tmpl, inputs, allow_python_tag_builtin_tools); + } + if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos) { + return common_chat_params_init_deepseek_r1(tmpl, inputs); + } + if (src.find("[TOOL_CALLS]") != std::string::npos) { + return common_chat_params_init_mistral_nemo(tmpl, inputs); + } + if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos) { + return common_chat_params_init_command_r7b(tmpl, inputs); + } + return common_chat_params_init_generic(tmpl, inputs); +} + +static common_chat_msg common_chat_parse_content_only(const std::string & input) { + return { + /* .role = */ "assistant", + /* .content = */ input, + /* .tool_calls = */ {}, + }; +} + +common_chat_msg common_chat_parse(const std::string & input, common_chat_format format) { + switch (format) { + case COMMON_CHAT_FORMAT_CONTENT_ONLY: + return common_chat_parse_content_only(input); + case COMMON_CHAT_FORMAT_GENERIC: + return common_chat_parse_generic(input); + case COMMON_CHAT_FORMAT_MISTRAL_NEMO: + return common_chat_parse_mistral_nemo(input); + case COMMON_CHAT_FORMAT_LLAMA_3_X: + return common_chat_parse_llama_3_1(input); + case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: + return common_chat_parse_llama_3_1(input, /* with_builtin_tools= */ true); + case COMMON_CHAT_FORMAT_DEEPSEEK_R1: + return common_chat_parse_deepseek_r1(input); + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: + return common_chat_parse_functionary_v3_2(input); + case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: + return common_chat_parse_functionary_v3_1_llama_3_1(input); + case COMMON_CHAT_FORMAT_HERMES_2_PRO: + return common_chat_parse_hermes_2_pro(input); + case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: + return common_chat_parse_firefunction_v2(input); + case COMMON_CHAT_FORMAT_COMMAND_R7B: + return common_chat_parse_command_r7b(input); + default: + throw std::runtime_error("Unsupported format: " + common_chat_format_name(format)); + } +} diff --git a/common/chat.hpp b/common/chat.hpp new file mode 100644 index 000000000..33e64a430 --- /dev/null +++ b/common/chat.hpp @@ -0,0 +1,52 @@ +// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers. + +#pragma once + +#include "common.h" +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +struct common_chat_inputs { + json messages; + json tools; + json tool_choice; + json json_schema; + bool parallel_tool_calls; + bool stream; + std::string grammar; + bool add_generation_prompt = true; +}; + +enum common_chat_format { + COMMON_CHAT_FORMAT_CONTENT_ONLY, + COMMON_CHAT_FORMAT_GENERIC, + COMMON_CHAT_FORMAT_MISTRAL_NEMO, + COMMON_CHAT_FORMAT_LLAMA_3_X, + COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS, + COMMON_CHAT_FORMAT_DEEPSEEK_R1, + COMMON_CHAT_FORMAT_FIREFUNCTION_V2, + COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2, + COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1, + COMMON_CHAT_FORMAT_HERMES_2_PRO, + COMMON_CHAT_FORMAT_COMMAND_R7B, + + COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats +}; + +struct common_chat_params { + common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + json prompt; + std::string grammar; + bool grammar_lazy = false; + std::vector grammar_triggers; + std::vector preserved_tokens; + std::vector additional_stops; +}; + +struct common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & params); +std::string common_chat_format_name(common_chat_format format); +common_chat_msg common_chat_parse( const std::string & input, common_chat_format format); diff --git a/scripts/gen-build-info-cpp.cmake b/common/cmake/build-info-gen-cpp.cmake similarity index 86% rename from scripts/gen-build-info-cpp.cmake rename to common/cmake/build-info-gen-cpp.cmake index d89338920..fbc92b52c 100644 --- a/scripts/gen-build-info-cpp.cmake +++ b/common/cmake/build-info-gen-cpp.cmake @@ -1,7 +1,7 @@ -include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) +include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake) set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in") -set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp") +set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp") # Only write the build info if it changed if(EXISTS ${OUTPUT_FILE}) diff --git a/common/common.cpp b/common/common.cpp index 938c428cf..8661e164a 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1,21 +1,39 @@ +#if defined(_MSC_VER) +#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING +#endif + +#include "ggml.h" +#include "gguf.h" + #include "common.h" +#include "log.h" +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT +#include "json.hpp" +#include "json-schema-to-grammar.h" #include "llama.h" +#include "chat.hpp" +#include "chat-template.hpp" #include -#include +#include +#include #include +#include +#include #include #include +#include #include -#include #include +#include #include #include #include +#include #include #include #include -#include #if defined(__APPLE__) && defined(__MACH__) #include @@ -27,7 +45,6 @@ #ifndef NOMINMAX # define NOMINMAX #endif -#include #include #include #include @@ -37,25 +54,57 @@ #include #include #endif +#if defined(LLAMA_USE_CURL) +#include +#include +#include +#endif #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif -#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) -#define GGML_USE_CUBLAS_SYCL +#if defined(LLAMA_USE_CURL) +#ifdef __linux__ +#include +#elif defined(_WIN32) +# if !defined(PATH_MAX) +# define PATH_MAX MAX_PATH +# endif +#else +#include #endif +#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083 -#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN) -#define GGML_USE_CUBLAS_SYCL_VULKAN -#endif +// +// CURL utils +// -int32_t get_num_physical_cores() { +using curl_ptr = std::unique_ptr; + +// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one +struct curl_slist_ptr { + struct curl_slist * ptr = nullptr; + ~curl_slist_ptr() { + if (ptr) { + curl_slist_free_all(ptr); + } + } +}; +#endif // LLAMA_USE_CURL + +using json = nlohmann::ordered_json; + +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores std::unordered_set siblings; for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) { - std::ifstream thread_siblings("/sys/devices/system/cpu" + std::ifstream thread_siblings("/sys/devices/system/cpu/cpu" + std::to_string(cpu) + "/topology/thread_siblings"); if (!thread_siblings.is_open()) { break; // no more cpus @@ -79,14 +128,494 @@ int32_t get_num_physical_cores() { if (result == 0) { return num_physical_cores; } -#elif defined(_WIN32) - //TODO: Implement +#elif defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later + // TODO: windows + arm64 + mingw64 + unsigned int n_threads_win = std::thread::hardware_concurrency(); + unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4; + + DWORD buffer_size = 0; + if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) { + if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) { + return default_threads; + } + } + + std::vector buffer(buffer_size); + if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast(buffer.data()), &buffer_size)) { + return default_threads; + } + + int32_t num_physical_cores = 0; + PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast(buffer.data()); + while (buffer_size > 0) { + if (info->Relationship == RelationProcessorCore) { + num_physical_cores += info->Processor.GroupCount; + } + buffer_size -= info->Size; + info = reinterpret_cast(reinterpret_cast(info) + info->Size); + } + + return num_physical_cores > 0 ? num_physical_cores : default_threads; #endif unsigned int n_threads = std::thread::hardware_concurrency(); return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4; } -void process_escapes(std::string& input) { +#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) +#include + +static void cpuid(unsigned leaf, unsigned subleaf, + unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) { + __asm__("movq\t%%rbx,%%rsi\n\t" + "cpuid\n\t" + "xchgq\t%%rbx,%%rsi" + : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx) + : "0"(leaf), "2"(subleaf)); +} + +static int pin_cpu(int cpu) { + cpu_set_t mask; + CPU_ZERO(&mask); + CPU_SET(cpu, &mask); + return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask); +} + +static bool is_hybrid_cpu(void) { + unsigned eax, ebx, ecx, edx; + cpuid(7, 0, &eax, &ebx, &ecx, &edx); + return !!(edx & (1u << 15)); +} + +static bool is_running_on_efficiency_core(void) { + unsigned eax, ebx, ecx, edx; + cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx); + int intel_atom = 0x20; + int core_type = (eax & 0xff000000u) >> 24; + return core_type == intel_atom; +} + +static int cpu_count_math_cpus(int n_cpu) { + int result = 0; + for (int cpu = 0; cpu < n_cpu; ++cpu) { + if (pin_cpu(cpu)) { + return -1; + } + if (is_running_on_efficiency_core()) { + continue; // efficiency cores harm lockstep threading + } + ++cpu; // hyperthreading isn't useful for linear algebra + ++result; + } + return result; +} + +#endif // __x86_64__ && __linux__ + +/** + * Returns number of CPUs on system that are useful for math. + */ +int32_t cpu_get_num_math() { +#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) + int n_cpu = sysconf(_SC_NPROCESSORS_ONLN); + if (n_cpu < 1) { + return cpu_get_num_physical_cores(); + } + if (is_hybrid_cpu()) { + cpu_set_t affinity; + if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { + int result = cpu_count_math_cpus(n_cpu); + pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); + if (result > 0) { + return result; + } + } + } +#endif + return cpu_get_num_physical_cores(); +} + +// Helper for setting process priority + +#if defined(_WIN32) + +bool set_process_priority(enum ggml_sched_priority prio) { + if (prio == GGML_SCHED_PRIO_NORMAL) { + return true; + } + + DWORD p = NORMAL_PRIORITY_CLASS; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break; + case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break; + case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break; + case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break; + } + + if (!SetPriorityClass(GetCurrentProcess(), p)) { + LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError()); + return false; + } + + return true; +} + +#else // MacOS and POSIX +#include +#include + +bool set_process_priority(enum ggml_sched_priority prio) { + if (prio == GGML_SCHED_PRIO_NORMAL) { + return true; + } + + int p = 0; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: p = 0; break; + case GGML_SCHED_PRIO_MEDIUM: p = -5; break; + case GGML_SCHED_PRIO_HIGH: p = -10; break; + case GGML_SCHED_PRIO_REALTIME: p = -20; break; + } + + if (!setpriority(PRIO_PROCESS, 0, p)) { + LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno); + return false; + } + return true; +} + +#endif + +// +// CLI argument parsing +// + + +void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) { + int32_t n_set = 0; + + if (cpuparams.n_threads < 0) { + // Assuming everything about cpuparams is invalid + if (role_model != nullptr) { + cpuparams = *role_model; + } else { + cpuparams.n_threads = cpu_get_num_math(); + } + } + + for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { + if (cpuparams.cpumask[i]) { + n_set++; + } + } + + if (n_set && n_set < cpuparams.n_threads) { + // Not enough set bits, may experience performance issues. + LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads); + } +} + +bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) { + size_t dash_loc = range.find('-'); + if (dash_loc == std::string::npos) { + LOG_ERR("Format of CPU range is invalid! Expected []-[].\n"); + return false; + } + + size_t start_i; + size_t end_i; + + if (dash_loc == 0) { + start_i = 0; + } else { + start_i = std::stoull(range.substr(0, dash_loc)); + if (start_i >= GGML_MAX_N_THREADS) { + LOG_ERR("Start index out of bounds!\n"); + return false; + } + } + + if (dash_loc == range.length() - 1) { + end_i = GGML_MAX_N_THREADS - 1; + } else { + end_i = std::stoull(range.substr(dash_loc + 1)); + if (end_i >= GGML_MAX_N_THREADS) { + LOG_ERR("End index out of bounds!\n"); + return false; + } + } + + for (size_t i = start_i; i <= end_i; i++) { + boolmask[i] = true; + } + + return true; +} + +bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) { + // Discard potential 0x prefix + size_t start_i = 0; + if (mask.length() >= 2 && mask.substr(0, 2) == "0x") { + start_i = 2; + } + + size_t num_digits = mask.length() - start_i; + if (num_digits > 128) num_digits = 128; + + size_t end_i = num_digits + start_i; + + for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) { + char c = mask.at(i); + int8_t id = c; + + if ((c >= '0' && c <= '9')) { + id -= '0'; + } else if (c >= 'a' && c <= 'f') { + id -= 'a' - 10; + } else if (c >= 'A' && c <= 'F') { + id -= 'A' - 10; + } else { + LOG_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i)); + return false; + } + + boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0); + boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0); + boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0); + boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0); + } + + return true; +} + +void common_init() { + llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) { + if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) { + common_log_add(common_log_main(), level, "%s", text); + } + }, NULL); + +#ifdef NDEBUG + const char * build_type = ""; +#else + const char * build_type = " (debug)"; +#endif + + LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type); +} + +std::string common_params_get_system_info(const common_params & params) { + std::ostringstream os; + + os << "system_info: n_threads = " << params.cpuparams.n_threads; + if (params.cpuparams_batch.n_threads != -1) { + os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")"; + } +#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later + // TODO: windows + arm64 + mingw64 + DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS); + os << " / " << logicalProcessorCount << " | " << llama_print_system_info(); +#else + os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info(); +#endif + + return os.str(); +} + +// +// String utils +// + +std::string string_format(const char * fmt, ...) { + va_list ap; + va_list ap2; + va_start(ap, fmt); + va_copy(ap2, ap); + int size = vsnprintf(NULL, 0, fmt, ap); + GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT + std::vector buf(size + 1); + int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); + GGML_ASSERT(size2 == size); + va_end(ap2); + va_end(ap); + return std::string(buf.data(), size); +} + +std::string string_strip(const std::string & str) { + size_t start = 0; + size_t end = str.size(); + while (start < end && std::isspace(str[start])) { + start++; + } + while (end > start && std::isspace(str[end - 1])) { + end--; + } + return str.substr(start, end - start); +} + +std::string string_get_sortable_timestamp() { + using clock = std::chrono::system_clock; + + const clock::time_point current_time = clock::now(); + const time_t as_time_t = clock::to_time_t(current_time); + char timestamp_no_ns[100]; + std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); + + const int64_t ns = std::chrono::duration_cast( + current_time.time_since_epoch() % 1000000000).count(); + char timestamp_ns[11]; + snprintf(timestamp_ns, 11, "%09" PRId64, ns); + + return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); +} + +void string_replace_all(std::string & s, const std::string & search, const std::string & replace) { + if (search.empty()) { + return; + } + std::string builder; + builder.reserve(s.length()); + size_t pos = 0; + size_t last_pos = 0; + while ((pos = s.find(search, last_pos)) != std::string::npos) { + builder.append(s, last_pos, pos - last_pos); + builder.append(replace); + last_pos = pos + search.length(); + } + builder.append(s, last_pos, std::string::npos); + s = std::move(builder); +} + +std::string string_join(const std::vector & values, const std::string & separator) { + std::ostringstream result; + for (size_t i = 0; i < values.size(); ++i) { + if (i > 0) { + result << separator; + } + result << values[i]; + } + return result.str(); +} + +std::vector string_split(const std::string & str, const std::string & delimiter) { + std::vector parts; + size_t start = 0; + size_t end = str.find(delimiter); + + while (end != std::string::npos) { + parts.push_back(str.substr(start, end - start)); + start = end + delimiter.length(); + end = str.find(delimiter, start); + } + + parts.push_back(str.substr(start)); + + return parts; +} + +std::string string_repeat(const std::string & str, size_t n) { + if (n == 0) { + return ""; + } + + std::string result; + result.reserve(str.length() * n); + + for (size_t i = 0; i < n; ++i) { + result += str; + } + + return result; +} + +std::string string_from(bool value) { + return value ? "true" : "false"; +} + +std::string string_from(const std::vector & values) { + std::stringstream buf; + + buf << "[ "; + bool first = true; + for (auto e : values) { + if (first) { + first = false; + } else { + buf << ", "; + } + buf << std::to_string(e); + } + buf << " ]"; + + return buf.str(); +} + +std::string string_from(const struct llama_context * ctx, const std::vector & tokens) { + std::stringstream buf; + + buf << "[ "; + + bool first = true; + for (const auto & token : tokens) { + if (!first) { + buf << ", "; + } else { + first = false; + } + + auto detokenized = common_token_to_piece(ctx, token); + + detokenized.erase( + std::remove_if( + detokenized.begin(), + detokenized.end(), + [](const unsigned char c) { return !std::isprint(c); }), + detokenized.end()); + + buf << "'" << detokenized << "'" + << ":" << std::to_string(token); + } + + buf << " ]"; + + return buf.str(); +} + +std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch) { + std::stringstream buf; + + buf << "[ "; + + bool first = true; + for (int i = 0; i < batch.n_tokens; ++i) { + if (!first) { + buf << ", "; + } else { + first = false; + } + + auto detokenized = common_token_to_piece(ctx, batch.token[i]); + + detokenized.erase( + std::remove_if( + detokenized.begin(), + detokenized.end(), + [](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 << " ]"; + + return buf.str(); +} + +void string_process_escapes(std::string & input) { std::size_t input_len = input.length(); std::size_t output_idx = 0; @@ -123,1348 +652,139 @@ void process_escapes(std::string& input) { input.resize(output_idx); } -bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { - bool result = true; - try { - if (!gpt_params_parse_ex(argc, argv, params)) { - gpt_print_usage(argc, argv, gpt_params()); - exit(0); +bool string_parse_kv_override(const char * data, std::vector & overrides) { + const char * sep = strchr(data, '='); + if (sep == nullptr || sep - data >= 128) { + LOG_ERR("%s: malformed KV override '%s'\n", __func__, data); + return false; + } + llama_model_kv_override kvo; + std::strncpy(kvo.key, data, sep - data); + kvo.key[sep - data] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = std::atol(sep); + } else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; + kvo.val_f64 = std::atof(sep); + } else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.val_bool = true; + } else if (std::strcmp(sep, "false") == 0) { + kvo.val_bool = false; + } else { + LOG_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data); + return false; } + } else if (strncmp(sep, "str:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + if (strlen(sep) > 127) { + LOG_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); + return false; + } + strncpy(kvo.val_str, sep, 127); + kvo.val_str[127] = '\0'; + } else { + LOG_ERR("%s: invalid type for KV override '%s'\n", __func__, data); + return false; } - catch (const std::invalid_argument & ex) { - fprintf(stderr, "%s\n", ex.what()); - gpt_print_usage(argc, argv, gpt_params()); - exit(1); - } - return result; + overrides.emplace_back(std::move(kvo)); + return true; } -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { - bool invalid_param = false; - std::string arg; - const std::string arg_prefix = "--"; - llama_sampling_params & sparams = params.sparams; +// +// Filesystem utils +// - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } +// Validate if a filename is safe to use +// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function +bool fs_validate_filename(const std::string & filename) { + if (!filename.length()) { + // Empty filename invalid + return false; + } + if (filename.length() > 255) { + // Limit at common largest possible filename on Linux filesystems + // to avoid unnecessary further validation + // (On systems with smaller limits it will be caught by the OS) + return false; + } - if (arg == "-s" || arg == "--seed") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.seed = std::stoul(argv[i]); - } else if (arg == "-t" || arg == "--threads") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads = std::stoi(argv[i]); - if (params.n_threads <= 0) { - params.n_threads = std::thread::hardware_concurrency(); - } - } else if (arg == "-tb" || arg == "--threads-batch") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - if (params.n_threads_batch <= 0) { - params.n_threads_batch = std::thread::hardware_concurrency(); - } - } else if (arg == "-td" || arg == "--threads-draft") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_draft = std::stoi(argv[i]); - if (params.n_threads_draft <= 0) { - params.n_threads_draft = std::thread::hardware_concurrency(); - } - } else if (arg == "-tbd" || arg == "--threads-batch-draft") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_batch_draft = std::stoi(argv[i]); - if (params.n_threads_batch_draft <= 0) { - params.n_threads_batch_draft = std::thread::hardware_concurrency(); - } - } else if (arg == "-p" || arg == "--prompt") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.prompt = argv[i]; - } else if (arg == "-e" || arg == "--escape") { - params.escape = true; - } else if (arg == "--prompt-cache") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.path_prompt_cache = argv[i]; - } else if (arg == "--prompt-cache-all") { - params.prompt_cache_all = true; - } else if (arg == "--prompt-cache-ro") { - params.prompt_cache_ro = true; - } else if (arg == "-bf" || arg == "--binary-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i], std::ios::binary); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - // store the external file name in params - params.prompt_file = argv[i]; - std::ostringstream ss; - ss << file.rdbuf(); - params.prompt = ss.str(); - fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]); - } else if (arg == "-f" || arg == "--file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - // store the external file name in params - params.prompt_file = argv[i]; - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); - if (!params.prompt.empty() && params.prompt.back() == '\n') { - params.prompt.pop_back(); - } - } else if (arg == "-n" || arg == "--n-predict") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_predict = std::stoi(argv[i]); - } else if (arg == "--top-k") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.top_k = std::stoi(argv[i]); - } else if (arg == "-c" || arg == "--ctx-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_ctx = std::stoi(argv[i]); - } else if (arg == "--grp-attn-n" || arg == "-gan") { - if (++i >= argc) { - invalid_param = true; - break; - } + 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; - params.grp_attn_n = std::stoi(argv[i]); - } else if (arg == "--grp-attn-w" || arg == "-gaw") { - if (++i >= argc) { - invalid_param = true; - break; - } +#if defined(__clang__) +# pragma clang diagnostic pop +#endif - params.grp_attn_w = std::stoi(argv[i]); - } else if (arg == "--rope-freq-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_base = std::stof(argv[i]); - } else if (arg == "--rope-freq-scale") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_scale = std::stof(argv[i]); - } else if (arg == "--rope-scaling") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } - else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } - else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } - else { invalid_param = true; break; } - } else if (arg == "--rope-scale") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_scale = 1.0f/std::stof(argv[i]); - } else if (arg == "--yarn-orig-ctx") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_orig_ctx = std::stoi(argv[i]); - } else if (arg == "--yarn-ext-factor") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_ext_factor = std::stof(argv[i]); - } else if (arg == "--yarn-attn-factor") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_attn_factor = std::stof(argv[i]); - } else if (arg == "--yarn-beta-fast") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_fast = std::stof(argv[i]); - } else if (arg == "--yarn-beta-slow") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_slow = std::stof(argv[i]); - } else if (arg == "--defrag-thold" || arg == "-dt") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.defrag_thold = std::stof(argv[i]); - } else if (arg == "--samplers") { - if (++i >= argc) { - invalid_param = true; - break; - } - const auto sampler_names = string_split(argv[i], ';'); - sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); - } else if (arg == "--sampling-seq") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.samplers_sequence = sampler_types_from_chars(argv[i]); - } else if (arg == "--top-p") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.top_p = std::stof(argv[i]); - } else if (arg == "--min-p") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.min_p = std::stof(argv[i]); - } else if (arg == "--temp") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.temp = std::stof(argv[i]); - sparams.temp = std::max(sparams.temp, 0.0f); - } else if (arg == "--tfs") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.tfs_z = std::stof(argv[i]); - } else if (arg == "--typical") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.typical_p = std::stof(argv[i]); - } else if (arg == "--repeat-last-n") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_last_n = std::stoi(argv[i]); - sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n); - } else if (arg == "--repeat-penalty") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_repeat = std::stof(argv[i]); - } else if (arg == "--frequency-penalty") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_freq = std::stof(argv[i]); - } else if (arg == "--presence-penalty") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.penalty_present = std::stof(argv[i]); - } else if (arg == "--dynatemp-range") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.dynatemp_range = std::stof(argv[i]); - } else if (arg == "--dynatemp-exp") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.dynatemp_exponent = std::stof(argv[i]); - } else if (arg == "--mirostat") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.mirostat = std::stoi(argv[i]); - } else if (arg == "--mirostat-lr") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.mirostat_eta = std::stof(argv[i]); - } else if (arg == "--mirostat-ent") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.mirostat_tau = std::stof(argv[i]); - } else if (arg == "--cfg-negative-prompt") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.cfg_negative_prompt = argv[i]; - } else if (arg == "--cfg-negative-prompt-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(sparams.cfg_negative_prompt)); - if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') { - sparams.cfg_negative_prompt.pop_back(); - } - } else if (arg == "--cfg-scale") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.cfg_scale = std::stof(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_batch = std::stoi(argv[i]); - } else if (arg == "--keep") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_keep = std::stoi(argv[i]); - } else if (arg == "--draft") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_draft = std::stoi(argv[i]); - } else if (arg == "--chunks") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_chunks = std::stoi(argv[i]); - } else if (arg == "-np" || arg == "--parallel") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_parallel = std::stoi(argv[i]); - } else if (arg == "-ns" || arg == "--sequences") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_sequences = std::stoi(argv[i]); - } else if (arg == "--p-accept" || arg == "-pa") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.p_accept = std::stof(argv[i]); - } else if (arg == "--p-split" || arg == "-ps") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.p_split = std::stof(argv[i]); - } else if (arg == "-m" || arg == "--model") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model = argv[i]; - } else if (arg == "-md" || arg == "--model-draft") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_draft = argv[i]; - } else if (arg == "-a" || arg == "--alias") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_alias = argv[i]; - } else if (arg == "--lora") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(argv[i], 1.0f); - params.use_mmap = false; - } else if (arg == "--lora-scaled") { - if (++i >= argc) { - invalid_param = true; - break; - } - const char * lora_adapter = argv[i]; - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); - params.use_mmap = false; - } else if (arg == "--lora-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_base = argv[i]; - } else if (arg == "--mmproj") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.mmproj = argv[i]; - } else if (arg == "--image") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.image = argv[i]; - } else if (arg == "-i" || arg == "--interactive") { - params.interactive = true; - } else if (arg == "--embedding") { - params.embedding = true; - } else if (arg == "--interactive-first") { - params.interactive_first = true; - } else if (arg == "-ins" || arg == "--instruct") { - params.instruct = true; - } else if (arg == "-cml" || arg == "--chatml") { - params.chatml = true; - } else if (arg == "--infill") { - params.infill = true; - } else if (arg == "-dkvc" || arg == "--dump-kv-cache") { - params.dump_kv_cache = true; - } else if (arg == "-nkvo" || arg == "--no-kv-offload") { - params.no_kv_offload = true; - } else if (arg == "-ctk" || arg == "--cache-type-k") { - params.cache_type_k = argv[++i]; - } else if (arg == "-ctv" || arg == "--cache-type-v") { - params.cache_type_v = argv[++i]; - } else if (arg == "--multiline-input") { - params.multiline_input = true; - } else if (arg == "--simple-io") { - params.simple_io = true; - } else if (arg == "-cb" || arg == "--cont-batching") { - params.cont_batching = true; - } else if (arg == "--color") { - params.use_color = true; - } else if (arg == "--mlock") { - params.use_mlock = true; - } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_gpu_layers = std::stoi(argv[i]); - if (!llama_supports_gpu_offload()) { - 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 == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_gpu_layers_draft = std::stoi(argv[i]); - if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); - } - } else if (arg == "--main-gpu" || arg == "-mg") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.main_gpu = std::stoi(argv[i]); -#ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the main GPU has no effect.\n"); -#endif // GGML_USE_CUBLAS_SYCL - } else if (arg == "--split-mode" || arg == "-sm") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string arg_next = argv[i]; - if (arg_next == "none") { - params.split_mode = LLAMA_SPLIT_MODE_NONE; - } else if (arg_next == "layer") { - params.split_mode = LLAMA_SPLIT_MODE_LAYER; - } else if (arg_next == "row") { - params.split_mode = LLAMA_SPLIT_MODE_ROW; - } else { - invalid_param = true; - break; - } -#ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUBLAS_SYCL + filename_utf32 = converter.from_bytes(filename); - } else if (arg == "--tensor-split" || arg == "-ts") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string arg_next = argv[i]; - - // split string by , and / - const std::regex regex{R"([,/]+)"}; - std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; - std::vector split_arg{it, {}}; - if (split_arg.size() >= llama_max_devices()) { - invalid_param = true; - break; - } - for (size_t i = 0; i < llama_max_devices(); ++i) { - if (i < split_arg.size()) { - params.tensor_split[i] = std::stof(split_arg[i]); - } else { - params.tensor_split[i] = 0.0f; - } - } -#ifndef GGML_USE_CUBLAS_SYCL_VULKAN - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); -#endif // GGML_USE_CUBLAS_SYCL - } else if (arg == "--no-mmap") { - params.use_mmap = false; - } else if (arg == "--numa") { - if (++i >= argc) { - invalid_param = true; - break; - } - 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; } - } else if (arg == "--verbose-prompt") { - params.verbose_prompt = true; - } else if (arg == "--no-display-prompt") { - params.display_prompt = false; - } else if (arg == "-r" || arg == "--reverse-prompt") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.antiprompt.emplace_back(argv[i]); - } else if (arg == "-ld" || arg == "--logdir") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.logdir = argv[i]; - - if (params.logdir.back() != DIRECTORY_SEPARATOR) { - params.logdir += DIRECTORY_SEPARATOR; - } - } else if (arg == "--save-all-logits" || arg == "--kl-divergence-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.logits_file = argv[i]; - } else if (arg == "--perplexity" || arg == "--all-logits") { - params.logits_all = true; - } else if (arg == "--ppl-stride") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.ppl_stride = std::stoi(argv[i]); - } else if (arg == "-ptc" || arg == "--print-token-count") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_print = std::stoi(argv[i]); - } else if (arg == "--ppl-output-type") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.ppl_output_type = std::stoi(argv[i]); - } else if (arg == "--hellaswag") { - params.hellaswag = true; - } else if (arg == "--hellaswag-tasks") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.hellaswag_tasks = std::stoi(argv[i]); - } else if (arg == "--winogrande") { - params.winogrande = true; - } else if (arg == "--winogrande-tasks") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.winogrande_tasks = std::stoi(argv[i]); - } else if (arg == "--multiple-choice") { - params.multiple_choice = true; - } else if (arg == "--multiple-choice-tasks") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.multiple_choice_tasks = std::stoi(argv[i]); - } else if (arg == "--kl-divergence") { - params.kl_divergence = true; - } else if (arg == "--ignore-eos") { - params.ignore_eos = true; - } else if (arg == "--no-penalize-nl") { - sparams.penalize_nl = false; - } else if (arg == "-l" || arg == "--logit-bias") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::stringstream ss(argv[i]); - llama_token key; - char sign; - std::string value_str; - try { - if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) { - sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f); - } else { - throw std::exception(); - } - } catch (const std::exception&) { - invalid_param = true; - break; - } - } else if (arg == "-h" || arg == "--help") { + // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used, + // or invalid encodings were encountered. Reject such attempts + std::string filename_reencoded = converter.to_bytes(filename_utf32); + if (filename_reencoded != filename) { return false; - - } else if (arg == "--version") { - fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); - fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); - exit(0); - } else if (arg == "--random-prompt") { - params.random_prompt = true; - } else if (arg == "--in-prefix-bos") { - params.input_prefix_bos = true; - } else if (arg == "--in-prefix") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.input_prefix = argv[i]; - } else if (arg == "--in-suffix") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.input_suffix = argv[i]; - } else if (arg == "--grammar") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.grammar = argv[i]; - } else if (arg == "--grammar-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::copy( - std::istreambuf_iterator(file), - std::istreambuf_iterator(), - std::back_inserter(sparams.grammar) - ); - } else if (arg == "--override-kv") { - if (++i >= argc) { - invalid_param = true; - break; - } - char * sep = strchr(argv[i], '='); - if (sep == nullptr || sep - argv[i] >= 128) { - fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - struct llama_model_kv_override kvo; - std::strncpy(kvo.key, argv[i], sep - argv[i]); - kvo.key[sep - argv[i]] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.int_value = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.float_value = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.bool_value = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.bool_value = false; - } else { - fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - } else { - fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - params.kv_overrides.push_back(kvo); -#ifndef LOG_DISABLE_LOGS - // Parse args for logging parameters - } else if ( log_param_single_parse( argv[i] ) ) { - // Do nothing, log_param_single_parse automatically does it's thing - // and returns if a match was found and parsed. - } else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) { - // We have a matching known parameter requiring an argument, - // now we need to check if there is anything after this argv - // and flag invalid_param or parse it. - if (++i >= argc) { - invalid_param = true; - break; - } - if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) { - invalid_param = true; - break; - } - // End of Parse args for logging parameters -#endif // LOG_DISABLE_LOGS - } else { - throw std::invalid_argument("error: unknown argument: " + arg); } - } - if (invalid_param) { - throw std::invalid_argument("error: invalid parameter for argument: " + arg); - } - if (params.prompt_cache_all && - (params.interactive || params.interactive_first || - params.instruct)) { - - throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); + } catch (const std::exception &) { + return false; } - if (params.escape) { - process_escapes(params.prompt); - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); - process_escapes(sparams.cfg_negative_prompt); - for (auto & antiprompt : params.antiprompt) { - process_escapes(antiprompt); + // Check for forbidden codepoints: + // - Control characters + // - Unicode equivalents of illegal characters + // - UTF-16 surrogate pairs + // - UTF-8 replacement character + // - Byte order mark (BOM) + // - Illegal characters: / \ : * ? " < > | + for (char32_t c : filename_utf32) { + if (c <= 0x1F // Control characters (C0) + || c == 0x7F // Control characters (DEL) + || (c >= 0x80 && c <= 0x9F) // Control characters (C1) + || c == 0xFF0E // Fullwidth Full Stop (period equivalent) + || c == 0x2215 // Division Slash (forward slash equivalent) + || c == 0x2216 // Set Minus (backslash equivalent) + || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs + || c == 0xFFFD // Replacement Character (UTF-8) + || c == 0xFEFF // Byte Order Mark (BOM) + || c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters + || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') { + return false; } } - if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(); - params.kv_overrides.back().key[0] = 0; + // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename + // Unicode and other whitespace is not affected, only 0x20 space + if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') { + return false; + } + + // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead) + if (filename.find("..") != std::string::npos) { + return false; + } + + // Reject "." + if (filename == ".") { + return false; } return true; } -void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - const llama_sampling_params & sparams = params.sparams; - - std::string sampler_type_chars; - std::string sampler_type_names; - for (const auto sampler_type : sparams.samplers_sequence) { - sampler_type_chars += static_cast(sampler_type); - sampler_type_names += sampler_type_to_name_string(sampler_type) + ";"; - } - sampler_type_names.pop_back(); - - printf("\n"); - printf("usage: %s [options]\n", argv[0]); - printf("\n"); - printf("options:\n"); - printf(" -h, --help show this help message and exit\n"); - printf(" --version show version and build info\n"); - printf(" -i, --interactive run in interactive mode\n"); - printf(" --interactive-first run in interactive mode and wait for input right away\n"); - printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); - printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n"); - printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); - printf(" -r PROMPT, --reverse-prompt PROMPT\n"); - printf(" halt generation at PROMPT, return control in interactive mode\n"); - printf(" (can be specified more than once for multiple prompts).\n"); - printf(" --color colorise output to distinguish prompt and user input from generations\n"); - printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); - printf(" -t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N\n"); - printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" -td N, --threads-draft N"); - printf(" number of threads to use during generation (default: same as --threads)\n"); - printf(" -tbd N, --threads-batch-draft N\n"); - printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n"); - printf(" -p PROMPT, --prompt PROMPT\n"); - printf(" prompt to start generation with (default: empty)\n"); - printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); - printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); - printf(" not supported with --interactive or other interactive options\n"); - printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); - printf(" --random-prompt start with a randomized prompt.\n"); - printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); - printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n"); - printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); - printf(" -f FNAME, --file FNAME\n"); - printf(" prompt file to start generation.\n"); - printf(" -bf FNAME, --binary-file FNAME\n"); - printf(" binary file containing multiple choice tasks.\n"); - printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); - printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - printf(" --samplers samplers that will be used for generation in the order, separated by \';\'\n"); - printf(" (default: %s)\n", sampler_type_names.c_str()); - printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str()); - printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k); - printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p); - printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p); - printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z); - printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p); - printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n); - printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat); - printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present); - printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq); - printf(" --dynatemp-range N dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range); - printf(" --dynatemp-exp N dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent); - printf(" --mirostat N use Mirostat sampling.\n"); - printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); - printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat); - printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)sparams.mirostat_eta); - printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)sparams.mirostat_tau); - printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); - printf(" modifies the likelihood of token appearing in the completion,\n"); - printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); - printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); - printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); - printf(" --grammar-file FNAME file to read grammar from\n"); - printf(" --cfg-negative-prompt PROMPT\n"); - printf(" negative prompt to use for guidance. (default: empty)\n"); - printf(" --cfg-negative-prompt-file FNAME\n"); - printf(" negative prompt file to use for guidance. (default: empty)\n"); - printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale); - printf(" --rope-scaling {none,linear,yarn}\n"); - printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); - printf(" --rope-scale N RoPE context scaling factor, expands context by a factor of N\n"); - printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); - printf(" --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)\n"); - printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); - printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); - printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); - printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" -dt N, --defrag-thold N\n"); - printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); - printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); - printf(" --no-penalize-nl do not penalize newline token\n"); - printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); - printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n"); - printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); - printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); - printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n"); - printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks); - printf(" --multiple-choice compute multiple choice score over random tasks from datafile supplied with -f\n"); - printf(" --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks); - printf(" --kl-divergence computes KL-divergence to logits provided via --kl-divergence-base\n"); - printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); - printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); - printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); - printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); - printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); - printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept); - printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); - printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n"); - if (llama_supports_mlock()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); - } - if (llama_supports_mmap()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); - } - printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); - printf(" - distribute: spread execution evenly over all nodes\n"); - printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); - printf(" - numactl: use the CPU map provided by numactl\n"); - printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); - printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); - if (llama_supports_gpu_offload()) { - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -ngld N, --n-gpu-layers-draft N\n"); - printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); - } - printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); - printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); - printf(" -gan N, --grp-attn-n N\n"); - printf(" group-attention factor (default: %d)\n", params.grp_attn_n); - printf(" -gaw N, --grp-attn-w N\n"); - printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w); - printf(" -dkvc, --dump-kv-cache\n"); - printf(" verbose print of the KV cache\n"); - printf(" -nkvo, --no-kv-offload\n"); - printf(" disable KV offload\n"); - printf(" -ctk TYPE, --cache-type-k TYPE\n"); - printf(" KV cache data type for K (default: %s)\n", params.cache_type_k.c_str()); - printf(" -ctv TYPE, --cache-type-v TYPE\n"); - printf(" KV cache data type for V (default: %s)\n", params.cache_type_v.c_str()); - printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: %s)\n", params.model.c_str()); - printf(" -md FNAME, --model-draft FNAME\n"); - printf(" draft model for speculative decoding\n"); - printf(" -ld LOGDIR, --logdir LOGDIR\n"); - printf(" path under which to save YAML logs (no logging if unset)\n"); - printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -ptc N, --print-token-count N\n"); - printf(" print token count every N tokens (default: %d)\n", params.n_print); - printf("\n"); -#ifndef LOG_DISABLE_LOGS - log_print_usage(); -#endif // LOG_DISABLE_LOGS -} - -std::string get_system_info(const gpt_params & params) { - std::ostringstream os; - - os << "system_info: n_threads = " << params.n_threads; - if (params.n_threads_batch != -1) { - os << " (n_threads_batch = " << params.n_threads_batch << ")"; - } - os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info(); - - return os.str(); -} - -std::string gpt_random_prompt(std::mt19937 & rng) { - const int r = rng() % 10; - switch (r) { - case 0: return "So"; - case 1: return "Once upon a time"; - case 2: return "When"; - case 3: return "The"; - case 4: return "After"; - case 5: return "If"; - case 6: return "import"; - case 7: return "He"; - case 8: return "She"; - case 9: return "They"; - } - - GGML_UNREACHABLE(); -} - -// -// String utils -// - -std::vector string_split(std::string input, char separator) { - std::vector parts; - size_t separator_pos = input.find(separator); - while (separator_pos != std::string::npos) { - std::string part = input.substr(0, separator_pos); - parts.emplace_back(part); - input = input.substr(separator_pos + 1); - separator_pos = input.find(separator); - } - parts.emplace_back(input); - return parts; -} - -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names) { - std::unordered_map sampler_canonical_name_map { - {"top_k", llama_sampler_type::TOP_K}, - {"top_p", llama_sampler_type::TOP_P}, - {"typical_p", llama_sampler_type::TYPICAL_P}, - {"min_p", llama_sampler_type::MIN_P}, - {"tfs_z", llama_sampler_type::TFS_Z}, - {"temperature", llama_sampler_type::TEMPERATURE} - }; - - // since samplers names are written multiple ways - // make it ready for both system names and input names - std::unordered_map sampler_alt_name_map { - {"top-k", llama_sampler_type::TOP_K}, - {"top-p", llama_sampler_type::TOP_P}, - {"nucleus", llama_sampler_type::TOP_P}, - {"typical-p", llama_sampler_type::TYPICAL_P}, - {"typical", llama_sampler_type::TYPICAL_P}, - {"min-p", llama_sampler_type::MIN_P}, - {"tfs-z", llama_sampler_type::TFS_Z}, - {"tfs", llama_sampler_type::TFS_Z}, - {"temp", llama_sampler_type::TEMPERATURE} - }; - - std::vector sampler_types; - sampler_types.reserve(names.size()); - for (const auto & name : names) - { - auto sampler_item = sampler_canonical_name_map.find(name); - if (sampler_item != sampler_canonical_name_map.end()) - { - sampler_types.push_back(sampler_item->second); - } - else - { - if (allow_alt_names) - { - sampler_item = sampler_alt_name_map.find(name); - if (sampler_item != sampler_alt_name_map.end()) - { - sampler_types.push_back(sampler_item->second); - } - } - } - } - return sampler_types; -} - -std::vector sampler_types_from_chars(const std::string & names_string) { - std::unordered_map sampler_name_map { - {'k', llama_sampler_type::TOP_K}, - {'p', llama_sampler_type::TOP_P}, - {'y', llama_sampler_type::TYPICAL_P}, - {'m', llama_sampler_type::MIN_P}, - {'f', llama_sampler_type::TFS_Z}, - {'t', llama_sampler_type::TEMPERATURE} - }; - - std::vector sampler_types; - sampler_types.reserve(names_string.size()); - for (const auto & c : names_string) { - const auto sampler_item = sampler_name_map.find(c); - if (sampler_item != sampler_name_map.end()) { - sampler_types.push_back(sampler_item->second); - } - } - return sampler_types; -} - -std::string sampler_type_to_name_string(llama_sampler_type sampler_type) { - switch (sampler_type) { - case llama_sampler_type::TOP_K: return "top_k"; - case llama_sampler_type::TFS_Z: return "tfs_z"; - case llama_sampler_type::TYPICAL_P: return "typical_p"; - case llama_sampler_type::TOP_P: return "top_p"; - case llama_sampler_type::MIN_P: return "min_p"; - case llama_sampler_type::TEMPERATURE: return "temperature"; - default : return ""; - } -} - -// -// Model utils -// - -struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) { - auto mparams = llama_model_default_params(); - - if (params.n_gpu_layers != -1) { - mparams.n_gpu_layers = params.n_gpu_layers; - } - mparams.main_gpu = params.main_gpu; - mparams.split_mode = params.split_mode; - mparams.tensor_split = params.tensor_split; - mparams.use_mmap = params.use_mmap; - mparams.use_mlock = params.use_mlock; - if (params.kv_overrides.empty()) { - mparams.kv_overrides = NULL; - } else { - GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key"); - mparams.kv_overrides = params.kv_overrides.data(); - } - - return mparams; -} - -static ggml_type kv_cache_type_from_str(const std::string & s) { - if (s == "f32") { - return GGML_TYPE_F32; - } - if (s == "f16") { - return GGML_TYPE_F16; - } - if (s == "q8_0") { - return GGML_TYPE_Q8_0; - } - if (s == "q4_0") { - return GGML_TYPE_Q4_0; - } - if (s == "q4_1") { - return GGML_TYPE_Q4_1; - } - if (s == "q5_0") { - return GGML_TYPE_Q5_0; - } - if (s == "q5_1") { - return GGML_TYPE_Q5_1; - } - - throw std::runtime_error("Invalid cache type: " + s); -} - -struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { - auto cparams = llama_context_default_params(); - - cparams.n_ctx = params.n_ctx; - cparams.n_batch = params.n_batch; - cparams.n_threads = params.n_threads; - cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - cparams.seed = params.seed; - cparams.logits_all = params.logits_all; - cparams.embedding = params.embedding; - cparams.rope_scaling_type = params.rope_scaling_type; - cparams.rope_freq_base = params.rope_freq_base; - cparams.rope_freq_scale = params.rope_freq_scale; - cparams.yarn_ext_factor = params.yarn_ext_factor; - cparams.yarn_attn_factor = params.yarn_attn_factor; - cparams.yarn_beta_fast = params.yarn_beta_fast; - cparams.yarn_beta_slow = params.yarn_beta_slow; - cparams.yarn_orig_ctx = params.yarn_orig_ctx; - cparams.defrag_thold = params.defrag_thold; - cparams.offload_kqv = !params.no_kv_offload; - - cparams.type_k = kv_cache_type_from_str(params.cache_type_k); - cparams.type_v = kv_cache_type_from_str(params.cache_type_v); - - return cparams; -} - -void llama_batch_clear(struct llama_batch & batch) { - batch.n_tokens = 0; -} - -void llama_batch_add( - struct llama_batch & batch, - llama_token id, - llama_pos pos, - const std::vector & seq_ids, - bool logits) { - batch.token [batch.n_tokens] = id; - batch.pos [batch.n_tokens] = pos; - batch.n_seq_id[batch.n_tokens] = seq_ids.size(); - for (size_t i = 0; i < seq_ids.size(); ++i) { - batch.seq_id[batch.n_tokens][i] = seq_ids[i]; - } - batch.logits [batch.n_tokens] = logits; - - batch.n_tokens++; -} - -std::tuple llama_init_from_gpt_params(gpt_params & params) { - auto mparams = llama_model_params_from_gpt_params(params); - - llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); - if (model == NULL) { - fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); - return std::make_tuple(nullptr, nullptr); - } - - auto cparams = llama_context_params_from_gpt_params(params); - - llama_context * lctx = llama_new_context_with_model(model, cparams); - if (lctx == NULL) { - fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - - for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { - const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]); - float lora_scale = std::get<1>(params.lora_adapter[i]); - int err = llama_model_apply_lora_from_file(model, - lora_adapter.c_str(), - lora_scale, - ((i > 0) || params.lora_base.empty()) - ? NULL - : params.lora_base.c_str(), - params.n_threads); - if (err != 0) { - fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - } - - if (params.ignore_eos) { - params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; - } - - { - LOG("warming up the model with an empty run\n"); - - std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); - llama_kv_cache_clear(lctx); - llama_reset_timings(lctx); - } - - return std::make_tuple(model, lctx); -} - -// -// Vocab utils -// - -std::vector llama_tokenize( - const struct llama_context * ctx, - const std::string & text, - bool add_bos, - bool special) { - return llama_tokenize(llama_get_model(ctx), text, add_bos, special); -} - -std::vector llama_tokenize( - const struct llama_model * model, - const std::string & text, - bool add_bos, - bool special) { - // upper limit for the number of tokens - int n_tokens = text.length() + add_bos; - std::vector result(n_tokens); - n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special); - if (n_tokens < 0) { - result.resize(-n_tokens); - int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special); - GGML_ASSERT(check == -n_tokens); - } else { - result.resize(n_tokens); - } - return result; -} - -std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { - std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); - if (n_tokens < 0) { - result.resize(-n_tokens); - int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); - GGML_ASSERT(check == -n_tokens); - } else { - result.resize(n_tokens); - } - - return std::string(result.data(), result.size()); -} - -std::string llama_detokenize_spm(llama_context * ctx, const std::vector & tokens) { - const llama_token bos_id = llama_token_bos(llama_get_model(ctx)); - - std::string piece; - std::string result; - - for (size_t i = 0; i < tokens.size(); ++i) { - piece = llama_token_to_piece(ctx, tokens[i]); - - // remove the leading space of the first non-BOS token - if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') { - piece = piece.substr(1); - } - - result += piece; - } - - return result; -} - -std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & tokens) { - std::string piece; - std::string result; - - for (size_t i = 0; i < tokens.size(); ++i) { - piece = llama_token_to_piece(ctx, tokens[i]); - - result += piece; - } - - // NOTE: the original tokenizer decodes bytes after collecting the pieces. - return result; -} - -bool llama_should_add_bos_token(const llama_model * model) { - const int add_bos = llama_add_bos_token(model); - - return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); -} - -// -// YAML utils -// - // returns true if successful, false otherwise -bool create_directory_with_parents(const std::string & path) { +bool fs_create_directory_with_parents(const std::string & path) { #ifdef _WIN32 std::wstring_convert> converter; std::wstring wpath = converter.from_bytes(path); @@ -1535,257 +855,1106 @@ bool create_directory_with_parents(const std::string & path) { #endif // _WIN32 } -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; +std::string fs_get_cache_directory() { + std::string cache_directory = ""; + auto ensure_trailing_slash = [](std::string p) { + // Make sure to add trailing slash + if (p.back() != DIRECTORY_SEPARATOR) { + p += DIRECTORY_SEPARATOR; + } + return p; + }; + if (getenv("LLAMA_CACHE")) { + cache_directory = std::getenv("LLAMA_CACHE"); + } else { +#ifdef __linux__ + if (std::getenv("XDG_CACHE_HOME")) { + cache_directory = std::getenv("XDG_CACHE_HOME"); + } else { + cache_directory = std::getenv("HOME") + std::string("/.cache/"); + } +#elif defined(__APPLE__) + cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); +#elif defined(_WIN32) + cache_directory = std::getenv("LOCALAPPDATA"); +#endif // __linux__ + cache_directory = ensure_trailing_slash(cache_directory); + cache_directory += "llama.cpp"; } - - 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()); + return ensure_trailing_slash(cache_directory); } -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; +std::string fs_get_cache_file(const std::string & filename) { + GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos); + std::string cache_directory = fs_get_cache_directory(); + const bool success = fs_create_directory_with_parents(cache_directory); + if (!success) { + throw std::runtime_error("failed to create cache directory: " + cache_directory); } - - 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()); + return cache_directory + filename; } -void dump_string_yaml_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; +// +// Model utils +// +struct common_init_result common_init_from_params(common_params & params) { + common_init_result iparams; + auto mparams = common_model_params_to_llama(params); + + llama_model * model = nullptr; + + if (!params.hf_repo.empty() && !params.hf_file.empty()) { + 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, params.model, params.hf_token, mparams); + } else { + model = llama_model_load_from_file(params.model.c_str(), mparams); } - size_t pos_start = 0; - size_t pos_found = 0; - - if (!data_str.empty() && (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 (model == NULL) { + LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.c_str()); + return iparams; } - if (data_str.find('\n') == std::string::npos) { - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; + const llama_vocab * vocab = llama_model_get_vocab(model); + + if (params.reranking) { + bool ok = true; + + if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__); + ok = false; + } + + if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__); + ok = false; + } + + if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__); + ok = false; + } + + if (!ok) { + llama_model_free(model); + + return iparams; + } } - 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; + auto cparams = common_context_params_to_llama(params); + + llama_context * lctx = llama_init_from_model(model, cparams); + if (lctx == NULL) { + LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str()); + llama_model_free(model); + return iparams; + } + + if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) { + LOG_WRN("%s: KV cache shifting is not supported for this model, disabling KV cache shifting\n", __func__); + params.ctx_shift = false; + } + + 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_model_n_layer(model); + + const auto cvec = common_control_vector_load(params.control_vectors); + if (cvec.n_embd == -1) { + llama_free(lctx); + llama_model_free(model); + + return iparams; + } + + int err = llama_apply_adapter_cvec( + lctx, + cvec.data.data(), + cvec.data.size(), + cvec.n_embd, + params.control_vector_layer_start, + params.control_vector_layer_end); + if (err) { + llama_free(lctx); + llama_model_free(model); + + return iparams; + } + } + + // load and optionally apply lora adapters + for (auto & la : params.lora_adapters) { + llama_adapter_lora_ptr lora; + lora.reset(llama_adapter_lora_init(model, la.path.c_str())); + if (lora == nullptr) { + LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str()); + llama_free(lctx); + llama_model_free(model); + return iparams; + } + + la.ptr = lora.get(); + iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters + } + + if (!params.lora_init_without_apply) { + common_set_adapter_lora(lctx, params.lora_adapters); + } + + if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); + params.sampling.ignore_eos = false; + } + + if (params.sampling.ignore_eos) { + for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) { + if (llama_vocab_is_eog(vocab, i)) { + LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY); + params.sampling.logit_bias.push_back({i, -INFINITY}); + } + } + } + + if (params.sampling.penalty_last_n == -1) { + LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + params.sampling.penalty_last_n = llama_n_ctx(lctx); + } + + if (params.sampling.dry_penalty_last_n == -1) { + LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); + } + + if (params.warmup) { + LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__); + + std::vector tmp; + llama_token bos = llama_vocab_bos(vocab); + llama_token eos = llama_vocab_eos(vocab); + + // some models (e.g. T5) don't have a BOS token + if (bos != LLAMA_TOKEN_NULL) { + tmp.push_back(bos); + } + if (eos != LLAMA_TOKEN_NULL) { + tmp.push_back(eos); + } + if (tmp.empty()) { + tmp.push_back(0); + } + + if (llama_model_has_encoder(model)) { + llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size())); + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { + decoder_start_token_id = bos; + } + tmp.clear(); + tmp.push_back(decoder_start_token_id); + } + if (llama_model_has_decoder(model)) { + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch))); + } + llama_kv_cache_clear(lctx); + llama_synchronize(lctx); + llama_perf_context_reset(lctx); + } + + iparams.model.reset(model); + iparams.context.reset(lctx); + + return iparams; +} + +void common_set_adapter_lora(struct llama_context * ctx, std::vector & lora) { + llama_clear_adapter_lora(ctx); + for (auto & la : lora) { + if (la.scale != 0.0f) { + llama_set_adapter_lora(ctx, la.ptr, la.scale); + } } } -std::string get_sortable_timestamp() { - using clock = std::chrono::system_clock; +struct llama_model_params common_model_params_to_llama(common_params & params) { + auto mparams = llama_model_default_params(); - const clock::time_point current_time = clock::now(); - const time_t as_time_t = clock::to_time_t(current_time); - char timestamp_no_ns[100]; - std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); + if (!params.devices.empty()) { + mparams.devices = params.devices.data(); + } + if (params.n_gpu_layers != -1) { + mparams.n_gpu_layers = params.n_gpu_layers; + } + mparams.main_gpu = params.main_gpu; + mparams.split_mode = params.split_mode; + mparams.tensor_split = params.tensor_split; + mparams.use_mmap = params.use_mmap; + mparams.use_mlock = params.use_mlock; + mparams.check_tensors = params.check_tensors; + if (params.kv_overrides.empty()) { + mparams.kv_overrides = NULL; + } else { + GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key"); + mparams.kv_overrides = params.kv_overrides.data(); + } - const int64_t ns = std::chrono::duration_cast( - current_time.time_since_epoch() % 1000000000).count(); - char timestamp_ns[11]; - snprintf(timestamp_ns, 11, "%09" PRId64, ns); - - return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); + return mparams; } -void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { - const llama_sampling_params & sparams = params.sparams; +struct llama_context_params common_context_params_to_llama(const common_params & params) { + auto cparams = llama_context_default_params(); - 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_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false"); - fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); - fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "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_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_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"); + cparams.n_ctx = params.n_ctx; + cparams.n_seq_max = params.n_parallel; + cparams.n_batch = params.n_batch; + cparams.n_ubatch = params.n_ubatch; + cparams.n_threads = params.cpuparams.n_threads; + cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ? + params.cpuparams.n_threads : params.cpuparams_batch.n_threads; + cparams.logits_all = params.logits_all; + cparams.embeddings = params.embedding; + cparams.rope_scaling_type = params.rope_scaling_type; + cparams.rope_freq_base = params.rope_freq_base; + cparams.rope_freq_scale = params.rope_freq_scale; + cparams.yarn_ext_factor = params.yarn_ext_factor; + cparams.yarn_attn_factor = params.yarn_attn_factor; + cparams.yarn_beta_fast = params.yarn_beta_fast; + cparams.yarn_beta_slow = params.yarn_beta_slow; + cparams.yarn_orig_ctx = params.yarn_orig_ctx; + cparams.pooling_type = params.pooling_type; + cparams.attention_type = params.attention_type; + cparams.defrag_thold = params.defrag_thold; + cparams.cb_eval = params.cb_eval; + cparams.cb_eval_user_data = params.cb_eval_user_data; + cparams.offload_kqv = !params.no_kv_offload; + cparams.flash_attn = params.flash_attn; + cparams.no_perf = params.no_perf; + + if (params.reranking) { + cparams.embeddings = true; + cparams.pooling_type = LLAMA_POOLING_TYPE_RANK; + } + + cparams.type_k = params.cache_type_k; + cparams.type_v = params.cache_type_v; + + return cparams; +} + +struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) { + struct ggml_threadpool_params tpp; + + ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults + + if (params.mask_valid) { + std::memcpy(&tpp.cpumask, ¶ms.cpumask, GGML_MAX_N_THREADS); + } + + tpp.prio = params.priority; + tpp.poll = params.poll; + tpp.strict_cpu = params.strict_cpu; + + return tpp; +} + +#ifdef LLAMA_USE_CURL + +#define CURL_MAX_RETRY 3 +#define CURL_RETRY_DELAY_SECONDS 2 + +static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) { + int remaining_attempts = max_attempts; + + while (remaining_attempts > 0) { + LOG_INF("%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts); + + CURLcode res = curl_easy_perform(curl); + if (res == CURLE_OK) { + return true; + } + + int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000; + LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay); + + remaining_attempts--; + std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay)); + } + + LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts); + + return false; +} + +static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) { + // Initialize libcurl + curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_slist_ptr http_headers; + if (!curl) { + LOG_ERR("%s: error initializing libcurl\n", __func__); + return false; + } + + bool force_download = false; + + // Set the URL, allow to follow http redirection + curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); + + // Check if hf-token or bearer-token was specified + if (!hf_token.empty()) { + std::string auth_header = "Authorization: Bearer " + hf_token; + http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); + } + +#if defined(_WIN32) + // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of + // operating system. Currently implemented under MS-Windows. + curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); +#endif + + // Check if the file already exists locally + auto file_exists = std::filesystem::exists(path); + + // If the file exists, check its JSON metadata companion file. + std::string metadata_path = path + ".json"; + nlohmann::json metadata; + std::string etag; + std::string last_modified; + + if (file_exists) { + // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block). + std::ifstream metadata_in(metadata_path); + if (metadata_in.good()) { + try { + metadata_in >> metadata; + LOG_INF("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str()); + if (metadata.contains("url") && metadata.at("url").is_string()) { + auto previous_url = metadata.at("url").get(); + if (previous_url != url) { + LOG_ERR("%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str()); + return false; + } + } + if (metadata.contains("etag") && metadata.at("etag").is_string()) { + etag = metadata.at("etag"); + } + if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) { + last_modified = metadata.at("lastModified"); + } + } catch (const nlohmann::json::exception & e) { + LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what()); + return false; + } + } + } else { + LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str()); + } + + // Send a HEAD request to retrieve the etag and last-modified headers + struct common_load_model_from_url_headers { + std::string etag; + std::string last_modified; + }; + + common_load_model_from_url_headers headers; + + { + typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *); + auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { + common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata; + + static std::regex header_regex("([^:]+): (.*)\r\n"); + static std::regex etag_regex("ETag", std::regex_constants::icase); + static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase); + + std::string header(buffer, n_items); + std::smatch match; + if (std::regex_match(header, match, header_regex)) { + const std::string & key = match[1]; + const std::string & value = match[2]; + if (std::regex_match(key, match, etag_regex)) { + headers->etag = value; + } else if (std::regex_match(key, match, last_modified_regex)) { + headers->last_modified = value; + } + } + return n_items; + }; + + curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress + curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast(header_callback)); + curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers); + + bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS); + if (!was_perform_successful) { + return false; + } + + long http_code = 0; + curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code); + if (http_code != 200) { + // HEAD not supported, we don't know if the file has changed + // force trigger downloading + force_download = true; + LOG_ERR("%s: HEAD invalid http status code received: %ld\n", __func__, http_code); + } + } + + bool should_download = !file_exists || force_download; + if (!should_download) { + if (!etag.empty() && etag != headers.etag) { + LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str()); + should_download = true; + } else if (!last_modified.empty() && last_modified != headers.last_modified) { + LOG_WRN("%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str()); + should_download = true; + } + } + if (should_download) { + std::string path_temporary = path + ".downloadInProgress"; + if (file_exists) { + LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str()); + if (remove(path.c_str()) != 0) { + LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str()); + return false; + } + } + + // Set the output file + + struct FILE_deleter { + void operator()(FILE * f) const { + fclose(f); + } + }; + + std::unique_ptr outfile(fopen(path_temporary.c_str(), "wb")); + if (!outfile) { + LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path.c_str()); + return false; + } + + typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd); + auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t { + return fwrite(data, size, nmemb, (FILE *)fd); + }; + curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L); + curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get()); + + // display download progress + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L); + + // helper function to hide password in URL + auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string { + std::size_t protocol_pos = url.find("://"); + if (protocol_pos == std::string::npos) { + return url; // Malformed URL + } + + std::size_t at_pos = url.find('@', protocol_pos + 3); + if (at_pos == std::string::npos) { + return url; // No password in URL + } + + return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos); + }; + + // start the download + LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__, + llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str()); + bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS); + if (!was_perform_successful) { + return false; + } + + long http_code = 0; + curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code); + if (http_code < 200 || http_code >= 400) { + LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code); + return false; + } + + // Causes file to be closed explicitly here before we rename it. + outfile.reset(); + + // Write the updated JSON metadata file. + metadata.update({ + {"url", url}, + {"etag", headers.etag}, + {"lastModified", headers.last_modified} + }); + std::ofstream(metadata_path) << metadata.dump(4); + LOG_INF("%s: file metadata saved: %s\n", __func__, metadata_path.c_str()); + + if (rename(path_temporary.c_str(), path.c_str()) != 0) { + LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str()); + return false; + } + } + + return true; +} + +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) { + // Basic validation of the model_url + if (model_url.empty()) { + LOG_ERR("%s: invalid model_url\n", __func__); + return NULL; + } + + if (!common_download_file(model_url, local_path, hf_token)) { + return NULL; + } + + // check for additional GGUFs split to download + int n_split = 0; + { + struct gguf_init_params gguf_params = { + /*.no_alloc = */ true, + /*.ctx = */ NULL, + }; + auto * ctx_gguf = gguf_init_from_file(local_path.c_str(), gguf_params); + if (!ctx_gguf) { + LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, local_path.c_str()); + return NULL; + } + + auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT); + if (key_n_split >= 0) { + n_split = gguf_get_val_u16(ctx_gguf, key_n_split); + } + + gguf_free(ctx_gguf); + } + + if (n_split > 1) { + char split_prefix[PATH_MAX] = {0}; + char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0}; + + // Verify the first split file format + // and extract split URL and PATH prefixes + { + if (!llama_split_prefix(split_prefix, sizeof(split_prefix), local_path.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, local_path.c_str(), n_split); + return NULL; + } + + if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url.c_str(), n_split); + return NULL; + } + } + + // Prepare download in parallel + std::vector> futures_download; + for (int idx = 1; idx < n_split; idx++) { + futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool { + char split_path[PATH_MAX] = {0}; + llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split); + + char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0}; + llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split); + + return common_download_file(split_url, split_path, hf_token); + }, idx)); + } + + // Wait for all downloads to complete + for (auto & f : futures_download) { + if (!f.get()) { + return NULL; + } + } + } + + return llama_model_load_from_file(local_path.c_str(), 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) { + // construct hugging face model url: + // + // --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf + // https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf + // + // --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf + // https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf + // + + std::string model_url = "https://huggingface.co/"; + model_url += repo; + model_url += "/resolve/main/"; + model_url += remote_path; + + return common_load_model_from_url(model_url, local_path, hf_token, params); +} + +/** + * Allow getting the HF file from the HF repo with tag (like ollama), for example: + * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4 + * - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M + * - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s + * Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo) + * + * Return pair of (with "repo" already having tag removed) + * + * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files. + */ +std::pair common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & hf_token) { + auto parts = string_split(hf_repo_with_tag, ':'); + std::string tag = parts.size() > 1 ? parts.back() : "latest"; + std::string hf_repo = parts[0]; + if (string_split(hf_repo, '/').size() != 2) { + throw std::invalid_argument("error: invalid HF repo format, expected /[:quant]\n"); + } + + // fetch model info from Hugging Face Hub API + json model_info; + curl_ptr curl(curl_easy_init(), &curl_easy_cleanup); + curl_slist_ptr http_headers; + std::string res_str; + std::string url = "https://huggingface.co/v2/" + hf_repo + "/manifests/" + tag; + curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); + typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data); + auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t { + static_cast(data)->append((char * ) ptr, size * nmemb); + return size * nmemb; + }; + curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str); +#if defined(_WIN32) + curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); +#endif + if (!hf_token.empty()) { + std::string auth_header = "Authorization: Bearer " + hf_token; + http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str()); + } + // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response + http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp"); + http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json"); + curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr); + + CURLcode res = curl_easy_perform(curl.get()); + + if (res != CURLE_OK) { + throw std::runtime_error("error: cannot make GET request to HF API"); + } + + long res_code; + curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code); + if (res_code == 200) { + model_info = json::parse(res_str); + } else if (res_code == 401) { + throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token"); + } else { + throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str())); + } + + // check response + if (!model_info.contains("ggufFile")) { + throw std::runtime_error("error: model does not have ggufFile"); + } + json & gguf_file = model_info.at("ggufFile"); + if (!gguf_file.contains("rfilename")) { + throw std::runtime_error("error: ggufFile does not have rfilename"); + } + + return std::make_pair(hf_repo, gguf_file.at("rfilename")); +} -#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))); +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*/) { + LOG_WRN("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); + return nullptr; +} -#ifdef __OPTIMIZE__ - fprintf(stream, "optimize: true\n"); -#else - fprintf(stream, "optimize: false\n"); -#endif // __OPTIMIZE__ +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*/) { + LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); + return nullptr; +} - fprintf(stream, "time: %s\n", timestamp.c_str()); +std::pair common_get_hf_file(const std::string &, const std::string &) { + LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); + return std::make_pair("", ""); +} - fprintf(stream, "\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "# User Inputs #\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "\n"); +#endif // LLAMA_USE_CURL - fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); - fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); - dump_string_yaml_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str()); - fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale); - 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, "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); - dump_string_yaml_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); +// +// Batch utils +// - const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); - const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; - fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); +void common_batch_clear(struct llama_batch & batch) { + batch.n_tokens = 0; +} - dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str()); - fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); - dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str()); - fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); - 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()); +void common_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits) { + GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded"); - fprintf(stream, "logit_bias:\n"); - for (std::pair lb : sparams.logit_bias) { - if (ignore_eos && lb.first == logit_bias_eos->first) { - continue; - } - fprintf(stream, " %d: %f", lb.first, lb.second); + batch.token [batch.n_tokens] = id; + batch.pos [batch.n_tokens] = pos; + batch.n_seq_id[batch.n_tokens] = seq_ids.size(); + for (size_t i = 0; i < seq_ids.size(); ++i) { + batch.seq_id[batch.n_tokens][i] = seq_ids[i]; + } + batch.logits [batch.n_tokens] = logits; + + batch.n_tokens++; +} + +// +// Token utils +// + +size_t common_lcp(const llama_tokens & a, const llama_tokens & b) { + size_t i; + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} + + return i; +} + +size_t common_lcs(const llama_tokens & a, const llama_tokens & b) { + // check for empty sequences + if (a.empty() || b.empty()) { + return 0; } - fprintf(stream, "lora:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) != 1.0f) { - continue; - } - fprintf(stream, " - %s\n", std::get<0>(la).c_str()); - } - fprintf(stream, "lora_scaled:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) == 1.0f) { - continue; - } - fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la)); - } - fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); - 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: models/7B/ggml-model.bin\n", params.model.c_str()); - 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, "no_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); - dump_string_yaml_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"); - dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens); - fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); - fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); + // get the lengths of the input sequences + size_t a_len = a.size(); + size_t b_len = b.size(); - 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; + // initialize the maximum length of the longest common subsequence (LCS) + size_t max_length = 0; + + // use two rows instead of a 2D matrix to optimize space + 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; + } } - fprintf(stream, " - %s\n", ap.c_str()); + // update the previous row for the next iteration + prev_row = curr_row; } - 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, "seed: %u # default: -1 (random seed)\n", params.seed); - 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, "temp: %f # default: 0.8\n", sparams.temp); + // return the maximum length of the LCS + return max_length; +} - const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); - dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); +// +// Vocab utils +// - fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); - fprintf(stream, "threads: %d # default: %u\n", params.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, "typical_p: %f # default: 1.0\n", sparams.typical_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"); +std::vector common_tokenize( + const struct llama_context * ctx, + const std::string & text, + bool add_special, + bool parse_special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_tokenize(vocab, text, add_special, parse_special); +} + +std::vector common_tokenize( + const struct llama_vocab * vocab, + const std::string & text, + bool add_special, + bool parse_special) { + // upper limit for the number of tokens + int n_tokens = text.length() + 2 * add_special; + std::vector result(n_tokens); + n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); + if (n_tokens < 0) { + result.resize(-n_tokens); + int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); + GGML_ASSERT(check == -n_tokens); + } else { + result.resize(n_tokens); + } + return result; +} + +std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_token_to_piece(vocab, token, special); +} + +std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) { + std::string piece; + piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n' + const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); + if (n_chars < 0) { + piece.resize(-n_chars); + int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); + GGML_ASSERT(check == -n_chars); + } + else { + piece.resize(n_chars); + } + + return piece; +} + +std::string common_detokenize(const struct llama_context * ctx, const std::vector & tokens, bool special) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + return common_detokenize(vocab, tokens, special); +} + +std::string common_detokenize(const struct llama_vocab * vocab, const std::vector & tokens, bool special) { + std::string text; + text.resize(std::max(text.capacity(), tokens.size())); + int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + if (n_chars < 0) { + text.resize(-n_chars); + n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); + GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization + } + + text.resize(n_chars); + + // NOTE: the original tokenizer decodes bytes after collecting the pieces. + return text; +} + +// +// Chat template utils +// + +bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) { + if (use_jinja) { + try { + auto chat_template = common_chat_template(tmpl, "", ""); + common_chat_inputs inputs; + inputs.messages = json::array({{ + {"role", "user"}, + {"content", "test"}, + }}); + common_chat_params_init(chat_template, inputs); + return true; + } catch (const std::exception & e) { + LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what()); + return false; + } + } + llama_chat_message chat[] = {{"user", "test"}}; + const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0); + return res >= 0; +} + +std::string common_chat_apply_template( + const common_chat_template & tmpl, + const std::vector & msgs, + bool add_ass, + bool use_jinja) { + if (use_jinja) { + auto messages = json::array(); + for (const auto & msg : msgs) { + messages.push_back({{"role", msg.role}, {"content", msg.content}}); + } + common_chat_inputs inputs; + inputs.messages = messages; + inputs.add_generation_prompt = add_ass; + return common_chat_params_init(tmpl, inputs).prompt; + } + + int alloc_size = 0; + std::vector chat; + for (const auto & msg : msgs) { + chat.push_back({msg.role.c_str(), msg.content.c_str()}); + alloc_size += (msg.role.size() + msg.content.size()) * 1.25; + } + + std::vector buf(alloc_size); + + // run the first time to get the total output length + int32_t res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size()); + + // error: chat template is not supported + if (res < 0) { + // if the custom "tmpl" is not supported, we throw an error + // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template() + throw std::runtime_error("this custom template is not supported"); + } + + // if it turns out that our buffer is too small, we resize it + if ((size_t) res > buf.size()) { + buf.resize(res); + res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size()); + } + + std::string formatted_chat(buf.data(), res); + return formatted_chat; +} + +std::string common_chat_format_single( + const common_chat_template & tmpl, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja) { + std::ostringstream ss; + auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(tmpl, past_msg, false, use_jinja); + std::vector chat_new(past_msg); + // if the past_msg ends with a newline, we must preserve it in the formatted version + if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') { + ss << "\n"; + }; + // format chat with new_msg + chat_new.push_back(new_msg); + auto fmt_new_msg = common_chat_apply_template(tmpl, chat_new, add_ass, use_jinja); + // get the diff part + ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size()); + return ss.str(); +} + +std::string common_chat_format_example(const common_chat_template & tmpl, bool use_jinja) { + std::vector msgs = { + {"system", "You are a helpful assistant", {}}, + {"user", "Hello", {}}, + {"assistant", "Hi there", {}}, + {"user", "How are you?", {}}, + }; + return common_chat_apply_template(tmpl, msgs, true, use_jinja); +} + +#define CHATML_TEMPLATE_SRC \ + "{%- for message in messages -%}\n" \ + " {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \ + "{%- endfor -%}\n" \ + "{%- if add_generation_prompt -%}\n" \ + " {{- '<|im_start|>assistant\n' -}}\n" \ + "{%- endif -%}" + +common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override) +{ + std::string default_template_src; + std::string template_tool_use_src; + + bool has_explicit_template = !chat_template_override.empty(); + if (chat_template_override.empty()) { + auto str = llama_model_chat_template(model, /* name */ nullptr); + if (str) { + default_template_src = str; + has_explicit_template = true; + } + str = llama_model_chat_template(model, /* name */ "tool_use"); + if (str) { + template_tool_use_src = str; + has_explicit_template = true; + } + } else { + default_template_src = chat_template_override; + } + if (default_template_src.empty() || default_template_src == "chatml") { + if (!template_tool_use_src.empty()) { + default_template_src = template_tool_use_src; + } else { + default_template_src = CHATML_TEMPLATE_SRC; + } + } + auto vocab = llama_model_get_vocab(model); + const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) { + if (token == LLAMA_TOKEN_NULL) { + if (default_template_src.find(jinja_variable_name) != std::string::npos + || template_tool_use_src.find(jinja_variable_name) != std::string::npos) { + LOG_WRN("%s: warning: vocab does not have a %s token, jinja template won't work as intended.\n", __func__, name); + } + return std::string(); + } else { + return common_token_to_piece(vocab, token, true); + } + }; + auto token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token"); + auto token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token"); + try { + return { + has_explicit_template, + std::make_unique(default_template_src, token_bos, token_eos), + template_tool_use_src.empty() + ? nullptr + : std::make_unique(template_tool_use_src, token_bos, token_eos), + }; + } catch (const std::exception & e) { + LOG_ERR("%s: failed to parse chat template: %s\n", __func__, e.what()); + return { + has_explicit_template, + std::make_unique(CHATML_TEMPLATE_SRC, token_bos, token_eos), + nullptr, + }; + } } // // KV cache utils // -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { +void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d", - view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); + view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); llama_kv_cache_view_cell * c_curr = view.cells; llama_seq_id * cs_curr = view.cells_sequences; - for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) { if (i % row_size == 0) { printf("\n%5d: ", i); } int seq_count = 0; - for (int j = 0; j < view.n_max_seq; j++) { + for (int j = 0; j < view.n_seq_max; j++) { if (cs_curr[j] >= 0) { seq_count++; } } putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]); @@ -1794,18 +1963,18 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { +void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n", - view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); + view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx); std::unordered_map seqs; llama_kv_cache_view_cell * c_curr = view.cells; llama_seq_id * cs_curr = view.cells_sequences; - for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { - for (int j = 0; j < view.n_max_seq; j++) { + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) { + for (int j = 0; j < view.n_seq_max; j++) { if (cs_curr[j] < 0) { continue; } if (seqs.find(cs_curr[j]) == seqs.end()) { if (seqs.size() + 1 >= sizeof(slot_chars)) { break; } @@ -1824,11 +1993,11 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { c_curr = view.cells; cs_curr = view.cells_sequences; - for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) { + for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) { if (i % row_size == 0) { printf("\n%5d: ", i); } - for (int j = 0; j < view.n_max_seq; j++) { + for (int j = 0; j < view.n_seq_max; j++) { if (cs_curr[j] >= 0) { const auto & it = seqs.find(cs_curr[j]); putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+'); @@ -1841,3 +2010,189 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } + +// +// Embedding utils +// + +void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) { + double sum = 0.0; + + switch (embd_norm) { + case -1: // no normalisation + sum = 1.0; + break; + case 0: // max absolute + for (int i = 0; i < n; i++) { + if (sum < std::abs(inp[i])) { + sum = std::abs(inp[i]); + } + } + sum /= 32760.0; // make an int16 range + break; + case 2: // euclidean + for (int i = 0; i < n; i++) { + sum += inp[i] * inp[i]; + } + sum = std::sqrt(sum); + break; + default: // p-norm (euclidean is p-norm p=2) + for (int i = 0; i < n; i++) { + sum += std::pow(std::abs(inp[i]), embd_norm); + } + sum = std::pow(sum, 1.0 / embd_norm); + break; + } + + const float norm = sum > 0.0 ? 1.0 / sum : 0.0f; + + for (int i = 0; i < n; i++) { + out[i] = inp[i] * norm; + } +} + +float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){ + double sum = 0.0; + double sum1 = 0.0; + double sum2 = 0.0; + + for (int i = 0; i < n; i++) { + sum += embd1[i] * embd2[i]; + sum1 += embd1[i] * embd1[i]; + sum2 += embd2[i] * embd2[i]; + } + + // Handle the case where one or both vectors are zero vectors + if (sum1 == 0.0 || sum2 == 0.0) { + if (sum1 == 0.0 && sum2 == 0.0) { + return 1.0f; // two zero vectors are similar + } + return 0.0f; + } + + return sum / (sqrt(sum1) * sqrt(sum2)); +} + +// +// Control vector utils +// + +static common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) { + common_control_vector_data result = { -1, {} }; + + ggml_context * ctx = nullptr; + struct gguf_init_params meta_gguf_params = { + /* .no_alloc = */ false, + /* .ctx = */ &ctx, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params); + if (!ctx_gguf) { + LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str()); + return result; + } + + int32_t n_tensors = gguf_get_n_tensors(ctx_gguf); + if (n_tensors == 0) { + LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str()); + } + + for (int i = 0; i < n_tensors; i++) { + std::string name = gguf_get_tensor_name(ctx_gguf, i); + + int layer_idx = -1; + + // split on '.' + size_t dotpos = name.find('.'); + if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") { + try { + layer_idx = std::stoi(name.substr(dotpos + 1)); + } catch (...) { + layer_idx = -1; + } + } + if (layer_idx < 0) { + LOG_ERR("%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } else if (layer_idx == 0) { + LOG_ERR("%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + + struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str()); + if (tensor->type != GGML_TYPE_F32) { + LOG_ERR("%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + if (ggml_n_dims(tensor) != 1) { + LOG_ERR("%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + + if (result.n_embd == -1) { + result.n_embd = ggml_nelements(tensor); + } else if (ggml_nelements(tensor) != result.n_embd) { + LOG_ERR("%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str()); + result.n_embd = -1; + break; + } + + // extend if necessary - do not store data for layer 0 (it's not used) + result.data.resize(std::max(result.data.size(), static_cast(result.n_embd * layer_idx)), 0.0f); + + const float * src = (const float *) tensor->data; + float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0] + for (int j = 0; j < result.n_embd; j++) { + dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file + } + + } + + if (result.n_embd == -1) { + LOG_WRN("%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str()); + result.data.clear(); + } + + gguf_free(ctx_gguf); + ggml_free(ctx); + + return result; +} + +common_control_vector_data common_control_vector_load(const std::vector & load_infos) { + common_control_vector_data result = { -1, {} }; + + for (const auto & info : load_infos) { + auto cur = common_control_vector_load_one(info); + + if (cur.n_embd == -1) { + result.n_embd = -1; + break; + } + if (result.n_embd != -1 && result.n_embd != cur.n_embd) { + LOG_ERR("%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str()); + result.n_embd = -1; + break; + } + + if (result.n_embd == -1) { + result = std::move(cur); + } else { + result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary + for (size_t i = 0; i < cur.data.size(); i++) { + result.data[i] += cur.data[i]; + } + } + } + + if (result.n_embd == -1) { + LOG_ERR("%s: no valid control vector files passed\n", __func__); + result.data.clear(); + } + + return result; +} + diff --git a/common/common.h b/common/common.h index ab62bdb82..b208d0c7e 100644 --- a/common/common.h +++ b/common/common.h @@ -2,20 +2,12 @@ #pragma once -#include "llama.h" +#include "llama-cpp.h" -#include "sampling.h" - -#define LOG_NO_FILE_LINE_FUNCTION -#include "log.h" - -#include +#include #include #include -#include -#include -#include -#include +#include #ifdef _WIN32 #define DIRECTORY_SEPARATOR '\\' @@ -27,234 +19,699 @@ #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) #define print_build_info() do { \ - fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ + fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) +#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf" + +struct common_adapter_lora_info { + std::string path; + float scale; + + struct llama_adapter_lora * ptr; +}; + +using llama_tokens = std::vector; + // build info extern int LLAMA_BUILD_NUMBER; -extern char const *LLAMA_COMMIT; -extern char const *LLAMA_COMPILER; -extern char const *LLAMA_BUILD_TARGET; +extern const char * LLAMA_COMMIT; +extern const char * LLAMA_COMPILER; +extern const char * LLAMA_BUILD_TARGET; + +struct common_control_vector_load_info; // -// CLI argument parsing +// CPU utils // -int32_t get_num_physical_cores(); -struct gpt_params { - uint32_t seed = -1; // RNG seed +struct cpu_params { + int n_threads = -1; + bool cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask. + bool mask_valid = false; // Default: any CPU + enum ggml_sched_priority priority = GGML_SCHED_PRIO_NORMAL; // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime) + bool strict_cpu = false; // Use strict CPU placement + uint32_t poll = 50; // Polling (busywait) level (0 - no polling, 100 - mostly polling) +}; - int32_t n_threads = get_num_physical_cores(); - int32_t n_threads_draft = -1; - int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) - int32_t n_threads_batch_draft = -1; - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 512; // 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 = 8; // 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_accept = 0.5f; // speculative decoding accept probability - 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) - llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs - 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 n_beams = 0; // if non-zero then use beam search of given width. - 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) - float rope_freq_base = 0.0f; // RoPE base frequency - float rope_freq_scale = 0.0f; // RoPE frequency scaling factor +int32_t cpu_get_num_physical_cores(); +int32_t cpu_get_num_math(); + +// +// Common params +// + +enum llama_example { + LLAMA_EXAMPLE_COMMON, + LLAMA_EXAMPLE_SPECULATIVE, + LLAMA_EXAMPLE_MAIN, + LLAMA_EXAMPLE_INFILL, + LLAMA_EXAMPLE_EMBEDDING, + LLAMA_EXAMPLE_PERPLEXITY, + LLAMA_EXAMPLE_RETRIEVAL, + LLAMA_EXAMPLE_PASSKEY, + LLAMA_EXAMPLE_IMATRIX, + LLAMA_EXAMPLE_BENCH, + LLAMA_EXAMPLE_SERVER, + LLAMA_EXAMPLE_CVECTOR_GENERATOR, + LLAMA_EXAMPLE_EXPORT_LORA, + LLAMA_EXAMPLE_LLAVA, + LLAMA_EXAMPLE_LOOKUP, + LLAMA_EXAMPLE_PARALLEL, + LLAMA_EXAMPLE_TTS, + + LLAMA_EXAMPLE_COUNT, +}; + +enum common_sampler_type { + COMMON_SAMPLER_TYPE_NONE = 0, + COMMON_SAMPLER_TYPE_DRY = 1, + COMMON_SAMPLER_TYPE_TOP_K = 2, + COMMON_SAMPLER_TYPE_TOP_P = 3, + COMMON_SAMPLER_TYPE_MIN_P = 4, + //COMMON_SAMPLER_TYPE_TFS_Z = 5, + COMMON_SAMPLER_TYPE_TYPICAL_P = 6, + COMMON_SAMPLER_TYPE_TEMPERATURE = 7, + COMMON_SAMPLER_TYPE_XTC = 8, + COMMON_SAMPLER_TYPE_INFILL = 9, + COMMON_SAMPLER_TYPE_PENALTIES = 10, +}; + +// dimensionality reduction methods, used by cvector-generator +enum dimre_method { + DIMRE_METHOD_PCA, + DIMRE_METHOD_MEAN, +}; + +enum common_conversation_mode { + COMMON_CONVERSATION_MODE_DISABLED = 0, + COMMON_CONVERSATION_MODE_ENABLED = 1, + COMMON_CONVERSATION_MODE_AUTO = 2, +}; + +struct common_grammar_trigger { + std::string word; + bool at_start; +}; + +// 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 + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens + int32_t top_k = 40; // <= 0 to use vocab size + float top_p = 0.95f; // 1.0 = disabled + float min_p = 0.05f; // 0.0 = disabled + float xtc_probability = 0.00f; // 0.0 = disabled + float xtc_threshold = 0.10f; // > 0.5 disables XTC + float typ_p = 1.00f; // typical_p, 1.0 = disabled + float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities + float dynatemp_range = 0.00f; // 0.0 = disabled + float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler + int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float penalty_repeat = 1.00f; // 1.0 = disabled + float penalty_freq = 0.00f; // 0.0 = disabled + float penalty_present = 0.00f; // 0.0 = disabled + float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition: + float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length) + int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty + int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size) + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate + 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 + + + std::vector samplers = { + COMMON_SAMPLER_TYPE_PENALTIES, + COMMON_SAMPLER_TYPE_DRY, + COMMON_SAMPLER_TYPE_TOP_K, + COMMON_SAMPLER_TYPE_TYPICAL_P, + COMMON_SAMPLER_TYPE_TOP_P, + COMMON_SAMPLER_TYPE_MIN_P, + COMMON_SAMPLER_TYPE_XTC, + COMMON_SAMPLER_TYPE_TEMPERATURE, + }; + + std::string grammar; // optional BNF-like grammar to constrain sampling + bool grammar_lazy = false; + std::vector grammar_trigger_words; // optional trigger words to trigger lazy grammar + std::vector grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens. + std::set preserved_tokens; + + std::vector logit_bias; // logit biases to apply + + // print the parameters into a string + 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 hf_repo = ""; // HF repo // NOLINT + std::string hf_file = ""; // HF file // NOLINT + + std::string model = ""; // draft model for speculative decoding // NOLINT + std::string model_url = ""; // model url to download // NOLINT +}; + +struct common_params_vocoder { + std::string hf_repo = ""; // HF repo // NOLINT + std::string hf_file = ""; // HF file // NOLINT + + std::string model = ""; // model path // NOLINT + std::string model_url = ""; // model url to download // NOLINT + + bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy // NOLINT +}; + +struct common_params { + int32_t n_predict = -1; // new tokens to predict + 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_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 + 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) + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor - float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor 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 - int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; - ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length + float defrag_thold = 0.1f; // KV cache defragmentation threshold - // // sampling parameters - struct llama_sampling_params sparams; + // offload params + std::vector devices; // devices to use for offloading - std::string model = "models/7B/ggml-model-f16.gguf"; // model path - std::string model_draft = ""; // draft model for speculative decoding - std::string model_alias = "unknown"; // model alias - std::string prompt = ""; - std::string prompt_file = ""; // store the external prompt file name - std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state - std::string input_prefix = ""; // string to prefix user inputs with - std::string input_suffix = ""; // string to suffix user inputs with - std::vector antiprompt; // string upon seeing which more user input is prompted - std::string logdir = ""; // directory in which to save YAML log files - std::string logits_file = ""; // file for saving *all* logits + 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; + + ggml_backend_sched_eval_callback cb_eval = nullptr; + void * cb_eval_user_data = nullptr; + + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + + 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_params_sampling sampling; + struct common_params_speculative speculative; + struct common_params_vocoder vocoder; + + std::string model = ""; // model path // NOLINT + std::string model_alias = ""; // model alias // NOLINT + std::string model_url = ""; // model url to download // NOLINT + std::string hf_token = ""; // HF token // NOLINT + std::string hf_repo = ""; // HF repo // NOLINT + std::string hf_file = ""; // HF file // NOLINT + std::string prompt = ""; // NOLINT + std::string prompt_file = ""; // store the external prompt file name // NOLINT + 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 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 + + std::vector in_files; // all input files + std::vector antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) std::vector kv_overrides; - // TODO: avoid tuple, use struct - std::vector> lora_adapter; // lora adapter path with user defined scale - std::string lora_base = ""; // base model path for the lora adapter + bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply) + std::vector lora_adapters; // lora adapter path with user defined scale - int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. - int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line - // (which is more convenient to use for plotting) - // - bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt - size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score + std::vector control_vectors; // control vector with user defined scale - bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt - size_t winogrande_tasks= 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed + int32_t verbosity = 0; + int32_t control_vector_layer_start = -1; // layer range for control vector + int32_t control_vector_layer_end = -1; // layer range for control vector - bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt - size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed + int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. + int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line + // (which is more convenient to use for plotting) + // + bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt + size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score - bool kl_divergence = false; // compute KL-divergence + bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt + size_t winogrande_tasks = 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed - bool random_prompt = false; // do not randomize prompt if none provided + bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt + size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed + + bool kl_divergence = false; // compute KL divergence + + bool usage = false; // print usage bool use_color = false; // use color to distinguish generations and inputs + bool special = false; // enable special token output bool interactive = false; // interactive mode - bool chatml = false; // chatml mode (used for models trained on chatml syntax) + bool interactive_first = false; // wait for user input immediately bool prompt_cache_all = false; // save user input and generations to prompt cache bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it - bool embedding = false; // get only sentence embedding - bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\" - bool interactive_first = false; // wait for user input immediately + bool escape = true; // escape "\n", "\r", "\t", "\'", "\"", and "\\" bool multiline_input = false; // reverse the usage of `\` bool simple_io = false; // improves compatibility with subprocesses and limited consoles - bool cont_batching = false; // insert new sequences for decoding on-the-fly + bool cont_batching = true; // insert new sequences for decoding on-the-fly + bool flash_attn = false; // flash attention + bool no_perf = false; // disable performance metrics + bool ctx_shift = true; // context shift on inifinite text generation bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix - bool ignore_eos = false; // ignore generated EOS tokens - bool instruct = false; // instruction mode (used for Alpaca models) bool logits_all = false; // return logits for all tokens in the batch bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory bool verbose_prompt = false; // print prompt tokens before generation bool display_prompt = true; // print prompt before generation - bool infill = false; // use infill mode bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes bool no_kv_offload = false; // disable KV offloading + bool warmup = true; // warmup run + bool check_tensors = false; // validate tensor data - std::string cache_type_k = "f16"; // KV cache data type for the K - std::string cache_type_v = "f16"; // KV cache data type for the V + ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K + ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V + + common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO; // multimodal models (see examples/llava) - std::string mmproj = ""; // path to multimodal projector - std::string image = ""; // path to an image file + std::string mmproj = ""; // path to multimodal projector // NOLINT + std::vector image; // path to image file(s) + + // embedding + bool embedding = false; // get only sentence embedding + int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) + std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix + std::string embd_sep = "\n"; // separator of embeddings + bool reranking = false; // enable reranking support on server + + // server params + int32_t port = 8080; // server listens on this network port + int32_t timeout_read = 600; // http read timeout in seconds + int32_t timeout_write = timeout_read; // http write timeout in seconds + int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool) + int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting + + std::string hostname = "127.0.0.1"; + std::string public_path = ""; // NOLINT + std::string chat_template = ""; // NOLINT + bool use_jinja = false; // NOLINT + bool enable_chat_template = true; + + std::vector api_keys; + + std::string ssl_file_key = ""; // NOLINT + std::string ssl_file_cert = ""; // NOLINT + + // "advanced" endpoints are disabled by default for better security + bool webui = true; + bool endpoint_slots = false; + bool endpoint_props = false; // only control POST requests, not GET + bool endpoint_metrics = false; + + bool log_json = false; + + std::string slot_save_path; + + float slot_prompt_similarity = 0.5f; + + // batched-bench params + bool is_pp_shared = false; + + std::vector n_pp; + std::vector n_tg; + std::vector n_pl; + + // retrieval params + std::vector context_files; // context files to embed + + int32_t chunk_size = 64; // chunk size for context embedding + + std::string chunk_separator = "\n"; // chunk separator for context embedding + + // passkey params + int32_t n_junk = 250; // number of times to repeat the junk text + int32_t i_pos = -1; // position of the passkey in the junk text + + // imatrix params + std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file + + int32_t n_out_freq = 10; // output the imatrix every n_out_freq iterations + int32_t n_save_freq = 0; // save the imatrix every n_save_freq iterations + int32_t i_chunk = 0; // start processing from this chunk + + bool process_output = false; // collect data for the output tensor + bool compute_ppl = true; // whether to compute perplexity + + // cvector-generator params + int n_pca_batch = 100; + int n_pca_iterations = 1000; + dimre_method cvector_dimre_method = DIMRE_METHOD_PCA; + std::string cvector_outfile = "control_vector.gguf"; + std::string cvector_positive_file = "examples/cvector-generator/positive.txt"; + std::string cvector_negative_file = "examples/cvector-generator/negative.txt"; + + bool spm_infill = false; // suffix/prefix/middle pattern for infill + + std::string lora_outfile = "ggml-lora-merged-f16.gguf"; + + // batched-bench params + bool batched_bench_output_jsonl = false; }; -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); +// call once at the start of a program if it uses libcommon +// initializes the logging system and prints info about the build +void common_init(); -bool gpt_params_parse(int argc, char ** argv, gpt_params & params); +std::string common_params_get_system_info(const common_params & params); -void gpt_print_usage(int argc, char ** argv, const gpt_params & params); - -std::string get_system_info(const gpt_params & params); - -std::string gpt_random_prompt(std::mt19937 & rng); - -void process_escapes(std::string& input); +bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]); +bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]); +void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr); +bool set_process_priority(enum ggml_sched_priority prio); // // String utils // -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names); -std::vector sampler_types_from_chars(const std::string & names_string); -std::vector string_split(std::string input, char separator); -std::string sampler_type_to_name_string(llama_sampler_type sampler_type); +#ifdef __GNUC__ +#ifdef __MINGW32__ +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) +#else +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) +#endif +#else +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) +#endif + +LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2) +std::string string_format(const char * fmt, ...); + +std::string string_strip(const std::string & str); +std::string string_get_sortable_timestamp(); + +std::string string_join(const std::vector & values, const std::string & separator); +std::vector string_split(const std::string & str, const std::string & delimiter); +std::string string_repeat(const std::string & str, size_t n); + +void string_replace_all(std::string & s, const std::string & search, const std::string & replace); + +template +static std::vector string_split(const std::string & str, char delim) { + static_assert(!std::is_same::value, "Please use the specialized version for std::string"); + std::vector values; + std::istringstream str_stream(str); + std::string token; + while (std::getline(str_stream, token, delim)) { + T value; + std::istringstream token_stream(token); + token_stream >> value; + values.push_back(value); + } + return values; +} + +template<> +std::vector string_split(const std::string & input, char separator) +{ + std::vector parts; + size_t begin_pos = 0; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(begin_pos, separator_pos - begin_pos); + parts.emplace_back(part); + begin_pos = separator_pos + 1; + separator_pos = input.find(separator, begin_pos); + } + parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos)); + return parts; +} + +static bool string_starts_with(const std::string & str, + const std::string & prefix) { // While we wait for C++20's std::string::starts_with... + return str.rfind(prefix, 0) == 0; +} + +static bool string_ends_with(const std::string & str, + const std::string & suffix) { // While we wait for C++20's std::string::ends_with... + return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0; +} + +bool string_parse_kv_override(const char * data, std::vector & overrides); +void string_process_escapes(std::string & input); + +std::string string_from(bool value); +std::string string_from(const std::vector & values); +std::string string_from(const struct llama_context * ctx, const std::vector & tokens); +std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch); + +// +// Filesystem utils +// + +bool fs_validate_filename(const std::string & filename); +bool fs_create_directory_with_parents(const std::string & path); + +std::string fs_get_cache_directory(); +std::string fs_get_cache_file(const std::string & filename); // // Model utils // -// TODO: avoid tuplue, use struct -std::tuple llama_init_from_gpt_params(gpt_params & params); +// note: defines object's lifetime +struct common_init_result { + llama_model_ptr model; + llama_context_ptr context; -struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params); -struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params); + std::vector lora; +}; +struct common_init_result common_init_from_params(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 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); + +std::pair common_get_hf_file( + const std::string & hf_repo_with_tag, + const std::string & hf_token); + +// clear LoRA adapters from context, then apply new list of adapters +void common_set_adapter_lora(struct llama_context * ctx, std::vector & lora); + +// // Batch utils +// -void llama_batch_clear(struct llama_batch & batch); +void common_batch_clear(struct llama_batch & batch); -void llama_batch_add( +void common_batch_add( struct llama_batch & batch, llama_token id, llama_pos pos, 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 // // tokenizes a string into a vector of tokens // should work similar to Python's `tokenizer.encode` -std::vector llama_tokenize( +std::vector common_tokenize( const struct llama_context * ctx, const std::string & text, - bool add_bos, - bool special = false); + bool add_special, + bool parse_special = false); -std::vector llama_tokenize( - const struct llama_model * model, +std::vector common_tokenize( + const struct llama_vocab * vocab, const std::string & text, - bool add_bos, - bool special = false); + bool add_special, + bool parse_special = false); -// tokenizes a token into a piece +// tokenizes a token into a piece, optionally renders special/control tokens // should work similar to Python's `tokenizer.id_to_piece` -std::string llama_token_to_piece( +std::string common_token_to_piece( const struct llama_context * ctx, - llama_token token); + llama_token token, + bool special = true); -// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function -// that takes into account the tokenizer type and decides how to handle the leading space -// -// detokenizes a vector of tokens into a string -// should work similar to Python's `tokenizer.decode` -// removes the leading space from the first non-BOS token -std::string llama_detokenize_spm( - llama_context * ctx, - const std::vector & tokens); +std::string common_token_to_piece( + const struct llama_vocab * vocab, + llama_token token, + bool special = true); // detokenizes a vector of tokens into a string // should work similar to Python's `tokenizer.decode` -std::string llama_detokenize_bpe( - llama_context * ctx, - const std::vector & tokens); +// optionally renders special/control tokens +std::string common_detokenize( + const struct llama_context * ctx, + const std::vector & tokens, + bool special = true); -// Uses the value from the model metadata if possible, otherwise -// defaults to true when model type is SPM, otherwise false. -bool llama_should_add_bos_token(const llama_model * model); +std::string common_detokenize( + const struct llama_vocab * vocab, + const std::vector & tokens, + bool special = true); // -// YAML utils +// Chat template utils // -bool create_directory_with_parents(const std::string & path); -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data); -std::string get_sortable_timestamp(); +struct common_tool_call { + std::string name; + std::string arguments; + std::string id; +}; -void dump_non_result_info_yaml( - FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); +// same with llama_chat_message, but uses std::string +struct common_chat_msg { + std::string role; + std::string content; + std::vector tool_calls; + std::string tool_plan = ""; +}; + +// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid +bool common_chat_verify_template(const std::string & tmpl, bool use_jinja); + +namespace minja { + class chat_template; +} + +typedef minja::chat_template common_chat_template; + +struct common_chat_templates { + bool has_explicit_template; // Model had builtin template or template overridde was specified. + std::unique_ptr template_default; // always set (defaults to chatml) + std::unique_ptr template_tool_use; +}; + +// CPP wrapper for llama_chat_apply_template +// If the built-in template is not supported, we default to chatml +// If the custom "tmpl" is not supported, we throw an error +std::string common_chat_apply_template( + const common_chat_template & tmpl, + const std::vector & chat, + bool add_ass, + bool use_jinja); + +// Format single message, while taking into account the position of that message in chat history +std::string common_chat_format_single( + const common_chat_template & tmpl, + const std::vector & past_msg, + const common_chat_msg & new_msg, + bool add_ass, + bool use_jinja); + +// Returns an example of formatted chat +std::string common_chat_format_example( + const common_chat_template & tmpl, bool use_jinja); + +common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override); // // KV cache utils // // Dump the KV cache view with the number of sequences per cell. -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); +void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); +void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40); + +// +// Embedding utils +// + +// TODO: repace embd_norm with an enum +void common_embd_normalize(const float * inp, float * out, int n, int embd_norm); + +float common_embd_similarity_cos(const float * embd1, const float * embd2, int n); + +// +// Control vector utils +// + +struct common_control_vector_data { + int n_embd; + + // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd + std::vector data; +}; + +struct common_control_vector_load_info { + float strength; + + std::string fname; +}; + +// Load control vectors, scale each by strength, and add them together. +// On error, returns {-1, empty} +common_control_vector_data common_control_vector_load(const std::vector & load_infos); + +// +// Split utils +// + +namespace { + +const char * const LLM_KV_SPLIT_NO = "split.no"; +const char * const LLM_KV_SPLIT_COUNT = "split.count"; +const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; + +} diff --git a/common/console.cpp b/common/console.cpp index f65cbc6ed..078a8d678 100644 --- a/common/console.cpp +++ b/common/console.cpp @@ -94,6 +94,9 @@ namespace console { simple_io = true; } } + if (simple_io) { + _setmode(_fileno(stdin), _O_U8TEXT); + } #else // POSIX-specific console initialization if (!simple_io) { diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp deleted file mode 100644 index bf89a96f3..000000000 --- a/common/grammar-parser.cpp +++ /dev/null @@ -1,424 +0,0 @@ -#include "grammar-parser.h" -#include -#include -#include -#include -#include -#include - -namespace grammar_parser { - // NOTE: assumes valid utf8 (but checks for overrun) - // copied from llama.cpp - static std::pair decode_utf8(const char * src) { - static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; - uint8_t first_byte = static_cast(*src); - uint8_t highbits = first_byte >> 4; - int len = lookup[highbits]; - uint8_t mask = (1 << (8 - len)) - 1; - uint32_t value = first_byte & mask; - const char * end = src + len; // may overrun! - const char * pos = src + 1; - for ( ; pos < end && *pos; pos++) { - value = (value << 6) + (static_cast(*pos) & 0x3F); - } - return std::make_pair(value, pos); - } - - static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { - uint32_t next_id = static_cast(state.symbol_ids.size()); - auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id)); - return result.first->second; - } - - static uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) { - uint32_t next_id = static_cast(state.symbol_ids.size()); - state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id; - return next_id; - } - - static void add_rule( - parse_state & state, - uint32_t rule_id, - const std::vector & rule) { - if (state.rules.size() <= rule_id) { - state.rules.resize(rule_id + 1); - } - state.rules[rule_id] = rule; - } - - static bool is_word_char(char c) { - return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9'); - } - - static std::pair parse_hex(const char * src, int size) { - const char * pos = src; - const char * end = src + size; - uint32_t value = 0; - for ( ; pos < end && *pos; pos++) { - value <<= 4; - char c = *pos; - if ('a' <= c && c <= 'f') { - value += c - 'a' + 10; - } else if ('A' <= c && c <= 'F') { - value += c - 'A' + 10; - } else if ('0' <= c && c <= '9') { - value += c - '0'; - } else { - break; - } - } - if (pos != end) { - throw std::runtime_error("expecting " + std::to_string(size) + " hex chars at " + src); - } - return std::make_pair(value, pos); - } - - static const char * parse_space(const char * src, bool newline_ok) { - const char * pos = src; - while (*pos == ' ' || *pos == '\t' || *pos == '#' || - (newline_ok && (*pos == '\r' || *pos == '\n'))) { - if (*pos == '#') { - while (*pos && *pos != '\r' && *pos != '\n') { - pos++; - } - } else { - pos++; - } - } - return pos; - } - - static const char * parse_name(const char * src) { - const char * pos = src; - while (is_word_char(*pos)) { - pos++; - } - if (pos == src) { - throw std::runtime_error(std::string("expecting name at ") + src); - } - return pos; - } - - static std::pair parse_char(const char * src) { - if (*src == '\\') { - switch (src[1]) { - case 'x': return parse_hex(src + 2, 2); - case 'u': return parse_hex(src + 2, 4); - case 'U': return parse_hex(src + 2, 8); - case 't': return std::make_pair('\t', src + 2); - case 'r': return std::make_pair('\r', src + 2); - case 'n': return std::make_pair('\n', src + 2); - case '\\': - case '"': - case '[': - case ']': - return std::make_pair(src[1], src + 2); - default: - throw std::runtime_error(std::string("unknown escape at ") + src); - } - } else if (*src) { - return decode_utf8(src); - } - throw std::runtime_error("unexpected end of input"); - } - - const char * parse_alternates( - parse_state & state, - const char * src, - const std::string & rule_name, - uint32_t rule_id, - bool is_nested); - - static const char * parse_sequence( - parse_state & state, - const char * src, - const std::string & rule_name, - std::vector & out_elements, - bool is_nested) { - size_t last_sym_start = out_elements.size(); - const char * pos = src; - while (*pos) { - if (*pos == '"') { // literal string - pos++; - last_sym_start = out_elements.size(); - while (*pos != '"') { - auto char_pair = parse_char(pos); - pos = char_pair.second; - out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first}); - } - pos = parse_space(pos + 1, is_nested); - } else if (*pos == '[') { // char range(s) - pos++; - enum llama_gretype start_type = LLAMA_GRETYPE_CHAR; - if (*pos == '^') { - pos++; - start_type = LLAMA_GRETYPE_CHAR_NOT; - } - last_sym_start = out_elements.size(); - while (*pos != ']') { - auto char_pair = parse_char(pos); - pos = char_pair.second; - enum llama_gretype type = last_sym_start < out_elements.size() - ? LLAMA_GRETYPE_CHAR_ALT - : start_type; - - out_elements.push_back({type, char_pair.first}); - if (pos[0] == '-' && pos[1] != ']') { - auto endchar_pair = parse_char(pos + 1); - pos = endchar_pair.second; - out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first}); - } - } - pos = parse_space(pos + 1, is_nested); - } else if (is_word_char(*pos)) { // rule reference - const char * name_end = parse_name(pos); - uint32_t ref_rule_id = get_symbol_id(state, pos, name_end - pos); - pos = parse_space(name_end, is_nested); - last_sym_start = out_elements.size(); - out_elements.push_back({LLAMA_GRETYPE_RULE_REF, ref_rule_id}); - } else if (*pos == '(') { // grouping - // parse nested alternates into synthesized rule - pos = parse_space(pos + 1, true); - uint32_t sub_rule_id = generate_symbol_id(state, rule_name); - pos = parse_alternates(state, pos, rule_name, sub_rule_id, true); - last_sym_start = out_elements.size(); - // output reference to synthesized rule - out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); - if (*pos != ')') { - throw std::runtime_error(std::string("expecting ')' at ") + pos); - } - pos = parse_space(pos + 1, is_nested); - } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator - if (last_sym_start == out_elements.size()) { - throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos); - } - - // apply transformation to previous symbol (last_sym_start to end) according to - // rewrite rules: - // S* --> S' ::= S S' | - // S+ --> S' ::= S S' | S - // S? --> S' ::= S | - uint32_t sub_rule_id = generate_symbol_id(state, rule_name); - std::vector sub_rule; - // add preceding symbol to generated rule - sub_rule.insert( - sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); - if (*pos == '*' || *pos == '+') { - // cause generated rule to recurse - sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); - } - // mark start of alternate def - sub_rule.push_back({LLAMA_GRETYPE_ALT, 0}); - if (*pos == '+') { - // add preceding symbol as alternate only for '+' (otherwise empty) - sub_rule.insert( - sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); - } - sub_rule.push_back({LLAMA_GRETYPE_END, 0}); - add_rule(state, sub_rule_id, sub_rule); - - // in original rule, replace previous symbol with reference to generated rule - out_elements.resize(last_sym_start); - out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); - - pos = parse_space(pos + 1, is_nested); - } else { - break; - } - } - return pos; - } - - const char * parse_alternates( - parse_state & state, - const char * src, - const std::string & rule_name, - uint32_t rule_id, - bool is_nested) { - std::vector rule; - const char * pos = parse_sequence(state, src, rule_name, rule, is_nested); - while (*pos == '|') { - rule.push_back({LLAMA_GRETYPE_ALT, 0}); - pos = parse_space(pos + 1, true); - pos = parse_sequence(state, pos, rule_name, rule, is_nested); - } - rule.push_back({LLAMA_GRETYPE_END, 0}); - add_rule(state, rule_id, rule); - return pos; - } - - static const char * parse_rule(parse_state & state, const char * src) { - const char * name_end = parse_name(src); - const char * pos = parse_space(name_end, false); - size_t name_len = name_end - src; - uint32_t rule_id = get_symbol_id(state, src, name_len); - const std::string name(src, name_len); - - if (!(pos[0] == ':' && pos[1] == ':' && pos[2] == '=')) { - throw std::runtime_error(std::string("expecting ::= at ") + pos); - } - pos = parse_space(pos + 3, true); - - pos = parse_alternates(state, pos, name, rule_id, false); - - if (*pos == '\r') { - pos += pos[1] == '\n' ? 2 : 1; - } else if (*pos == '\n') { - pos++; - } else if (*pos) { - throw std::runtime_error(std::string("expecting newline or end at ") + pos); - } - return parse_space(pos, true); - } - - parse_state parse(const char * src) { - try { - parse_state state; - const char * pos = parse_space(src, true); - while (*pos) { - pos = parse_rule(state, pos); - } - return state; - } catch (const std::exception & err) { - fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what()); - return parse_state(); - } - } - - static void print_grammar_char(FILE * file, uint32_t c) { - if (0x20 <= c && c <= 0x7f) { - fprintf(file, "%c", static_cast(c)); - } else { - // cop out of encoding UTF-8 - fprintf(file, "", c); - } - } - - static bool is_char_element(llama_grammar_element elem) { - switch (elem.type) { - case LLAMA_GRETYPE_CHAR: return true; - case LLAMA_GRETYPE_CHAR_NOT: return true; - case LLAMA_GRETYPE_CHAR_ALT: return true; - case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true; - default: return false; - } - } - - static void print_rule_binary(FILE * file, const std::vector & rule) { - for (auto elem : rule) { - switch (elem.type) { - case LLAMA_GRETYPE_END: fprintf(file, "END"); break; - case LLAMA_GRETYPE_ALT: fprintf(file, "ALT"); break; - case LLAMA_GRETYPE_RULE_REF: fprintf(file, "RULE_REF"); break; - case LLAMA_GRETYPE_CHAR: fprintf(file, "CHAR"); break; - case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, "CHAR_NOT"); break; - case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break; - case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break; - } - switch (elem.type) { - case LLAMA_GRETYPE_END: - case LLAMA_GRETYPE_ALT: - case LLAMA_GRETYPE_RULE_REF: - fprintf(file, "(%u) ", elem.value); - break; - case LLAMA_GRETYPE_CHAR: - case LLAMA_GRETYPE_CHAR_NOT: - case LLAMA_GRETYPE_CHAR_RNG_UPPER: - case LLAMA_GRETYPE_CHAR_ALT: - fprintf(file, "(\""); - print_grammar_char(file, elem.value); - fprintf(file, "\") "); - break; - } - } - fprintf(file, "\n"); - } - - static void print_rule( - FILE * file, - uint32_t rule_id, - const std::vector & rule, - const std::map & symbol_id_names) { - if (rule.empty() || rule.back().type != LLAMA_GRETYPE_END) { - throw std::runtime_error( - "malformed rule, does not end with LLAMA_GRETYPE_END: " + std::to_string(rule_id)); - } - fprintf(file, "%s ::= ", symbol_id_names.at(rule_id).c_str()); - for (size_t i = 0, end = rule.size() - 1; i < end; i++) { - llama_grammar_element elem = rule[i]; - switch (elem.type) { - case LLAMA_GRETYPE_END: - throw std::runtime_error( - "unexpected end of rule: " + std::to_string(rule_id) + "," + - std::to_string(i)); - case LLAMA_GRETYPE_ALT: - fprintf(file, "| "); - break; - case LLAMA_GRETYPE_RULE_REF: - fprintf(file, "%s ", symbol_id_names.at(elem.value).c_str()); - break; - case LLAMA_GRETYPE_CHAR: - fprintf(file, "["); - print_grammar_char(file, elem.value); - break; - case LLAMA_GRETYPE_CHAR_NOT: - fprintf(file, "[^"); - print_grammar_char(file, elem.value); - break; - case LLAMA_GRETYPE_CHAR_RNG_UPPER: - if (i == 0 || !is_char_element(rule[i - 1])) { - throw std::runtime_error( - "LLAMA_GRETYPE_CHAR_RNG_UPPER without preceding char: " + - std::to_string(rule_id) + "," + std::to_string(i)); - } - fprintf(file, "-"); - print_grammar_char(file, elem.value); - break; - case LLAMA_GRETYPE_CHAR_ALT: - if (i == 0 || !is_char_element(rule[i - 1])) { - throw std::runtime_error( - "LLAMA_GRETYPE_CHAR_ALT without preceding char: " + - std::to_string(rule_id) + "," + std::to_string(i)); - } - print_grammar_char(file, elem.value); - break; - } - if (is_char_element(elem)) { - switch (rule[i + 1].type) { - case LLAMA_GRETYPE_CHAR_ALT: - case LLAMA_GRETYPE_CHAR_RNG_UPPER: - break; - default: - fprintf(file, "] "); - } - } - } - fprintf(file, "\n"); - } - - void print_grammar(FILE * file, const parse_state & state) { - try { - std::map symbol_id_names; - for (const auto & kv : state.symbol_ids) { - symbol_id_names[kv.second] = kv.first; - } - for (size_t i = 0, end = state.rules.size(); i < end; i++) { - // fprintf(file, "%zu: ", i); - // print_rule_binary(file, state.rules[i]); - print_rule(file, uint32_t(i), state.rules[i], symbol_id_names); - // fprintf(file, "\n"); - } - } catch (const std::exception & err) { - fprintf(stderr, "\n%s: error printing grammar: %s\n", __func__, err.what()); - } - } - - std::vector parse_state::c_rules() { - std::vector ret; - ret.reserve(rules.size()); - for (const auto & rule : rules) { - ret.push_back(rule.data()); - } - return ret; - } -} diff --git a/common/grammar-parser.h b/common/grammar-parser.h deleted file mode 100644 index 9037d7272..000000000 --- a/common/grammar-parser.h +++ /dev/null @@ -1,29 +0,0 @@ -// Implements a parser for an extended Backus-Naur form (BNF), producing the -// binary context-free grammar format specified by llama.h. Supports character -// ranges, grouping, and repetition operators. As an example, a grammar for -// arithmetic might look like: -// -// root ::= expr -// expr ::= term ([-+*/] term)* -// term ::= num | "(" space expr ")" space -// num ::= [0-9]+ space -// space ::= [ \t\n]* - -#pragma once -#include "llama.h" -#include -#include -#include -#include - -namespace grammar_parser { - struct parse_state { - std::map symbol_ids; - std::vector> rules; - - std::vector c_rules(); - }; - - parse_state parse(const char * src); - void print_grammar(FILE * file, const parse_state & state); -} diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp new file mode 100644 index 000000000..3ebcc3d9f --- /dev/null +++ b/common/json-schema-to-grammar.cpp @@ -0,0 +1,1025 @@ +#include "json-schema-to-grammar.h" +#include "common.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") { + auto has_max = max_items != std::numeric_limits::max(); + + if (min_items == 0 && max_items == 1) { + return item_rule + "?"; + } + + if (separator_rule.empty()) { + if (min_items == 1 && !has_max) { + return item_rule + "+"; + } else if (min_items == 0 && !has_max) { + return item_rule + "*"; + } else { + return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}"; + } + } + + auto result = item_rule + " " + build_repetition("(" + separator_rule + " " + item_rule + ")", min_items == 0 ? 0 : min_items - 1, has_max ? max_items - 1 : max_items); + if (min_items == 0) { + result = "(" + result + ")?"; + } + return result; +} + +/* Minimalistic replacement for std::string_view, which is only available from C++17 onwards */ +class string_view { + const std::string & _str; + const size_t _start; + const size_t _end; +public: + string_view(const std::string & str, size_t start = 0, size_t end = std::string::npos) : _str(str), _start(start), _end(end == std::string::npos ? str.length() : end) {} + + size_t size() const { + return _end - _start; + } + + size_t length() const { + return size(); + } + + operator std::string() const { + return str(); + } + + std::string str() const { + return _str.substr(_start, _end - _start); + } + + string_view substr(size_t pos, size_t len = std::string::npos) const { + return string_view(_str, _start + pos, len == std::string::npos ? _end : _start + pos + len); + } + + char operator[](size_t pos) const { + auto index = _start + pos; + if (index >= _end) { + throw std::out_of_range("string_view index out of range"); + } + return _str[_start + pos]; + } + + bool operator==(const string_view & other) const { + std::string this_str = *this; + std::string other_str = other; + return this_str == other_str; + } +}; + +static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) { + auto has_min = min_value != std::numeric_limits::min(); + auto has_max = max_value != std::numeric_limits::max(); + + auto digit_range = [&](char from, char to) { + out << "["; + if (from == to) { + out << from; + } else { + out << from << "-" << to; + } + out << "]"; + }; + auto more_digits = [&](int min_digits, int max_digits) { + out << "[0-9]"; + if (min_digits == max_digits && min_digits == 1) { + return; + } + out << "{"; + out << min_digits; + if (max_digits != min_digits) { + out << ","; + if (max_digits != std::numeric_limits::max()) { + out << max_digits; + } + } + out << "}"; + }; + std::function uniform_range = + [&](const string_view & from, const string_view & to) { + size_t i = 0; + while (i < from.length() && i < to.length() && from[i] == to[i]) { + i++; + } + if (i > 0) { + out << "\"" << from.substr(0, i).str() << "\""; + } + if (i < from.length() && i < to.length()) { + if (i > 0) { + out << " "; + } + auto sub_len = from.length() - i - 1; + if (sub_len > 0) { + auto from_sub = from.substr(i + 1); + auto to_sub = to.substr(i + 1); + auto sub_zeros = string_repeat("0", sub_len); + auto sub_nines = string_repeat("9", sub_len); + + auto to_reached = false; + out << "("; + if (from_sub == sub_zeros) { + digit_range(from[i], to[i] - 1); + out << " "; + more_digits(sub_len, sub_len); + } else { + out << "[" << from[i] << "] "; + out << "("; + uniform_range(from_sub, sub_nines); + out << ")"; + if (from[i] < to[i] - 1) { + out << " | "; + if (to_sub == sub_nines) { + digit_range(from[i] + 1, to[i]); + to_reached = true; + } else { + digit_range(from[i] + 1, to[i] - 1); + } + out << " "; + more_digits(sub_len, sub_len); + } + } + if (!to_reached) { + out << " | "; + digit_range(to[i], to[i]); + out << " "; + uniform_range(sub_zeros, to_sub); + } + out << ")"; + } else { + out << "[" << from[i] << "-" << to[i] << "]"; + } + } + }; + + if (has_min && has_max) { + if (min_value < 0 && max_value < 0) { + out << "\"-\" ("; + _build_min_max_int(-max_value, -min_value, out, decimals_left, /* top_level= */ true); + out << ")"; + return; + } + + if (min_value < 0) { + out << "\"-\" ("; + _build_min_max_int(0, -min_value, out, decimals_left, /* top_level= */ true); + out << ") | "; + min_value = 0; + } + + auto min_s = std::to_string(min_value); + auto max_s = std::to_string(max_value); + auto min_digits = min_s.length(); + auto max_digits = max_s.length(); + + for (auto digits = min_digits; digits < max_digits; digits++) { + uniform_range(min_s, string_repeat("9", digits)); + min_s = "1" + string_repeat("0", digits); + out << " | "; + } + uniform_range(min_s, max_s); + return; + } + + auto less_decimals = std::max(decimals_left - 1, 1); + + if (has_min) { + if (min_value < 0) { + out << "\"-\" ("; + _build_min_max_int(std::numeric_limits::min(), -min_value, out, decimals_left, /* top_level= */ false); + out << ") | [0] | [1-9] "; + more_digits(0, decimals_left - 1); + } else if (min_value == 0) { + if (top_level) { + out << "[0] | [1-9] "; + more_digits(0, less_decimals); + } else { + more_digits(1, decimals_left); + } + } else if (min_value <= 9) { + char c = '0' + min_value; + auto range_start = top_level ? '1' : '0'; + if (c > range_start) { + digit_range(range_start, c - 1); + out << " "; + more_digits(1, less_decimals); + out << " | "; + } + digit_range(c, '9'); + out << " "; + more_digits(0, less_decimals); + } else { + auto min_s = std::to_string(min_value); + auto len = min_s.length(); + auto c = min_s[0]; + + if (c > '1') { + digit_range(top_level ? '1' : '0', c - 1); + out << " "; + more_digits(len, less_decimals); + out << " | "; + } + digit_range(c, c); + out << " ("; + _build_min_max_int(std::stoi(min_s.substr(1)), std::numeric_limits::max(), out, less_decimals, /* top_level= */ false); + out << ")"; + if (c < '9') { + out << " | "; + digit_range(c + 1, '9'); + out << " "; + more_digits(len - 1, less_decimals); + } + } + return; + } + + if (has_max) { + if (max_value >= 0) { + if (top_level) { + out << "\"-\" [1-9] "; + more_digits(0, less_decimals); + out << " | "; + } + _build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true); + } else { + out << "\"-\" ("; + _build_min_max_int(-max_value, std::numeric_limits::max(), out, decimals_left, /* top_level= */ false); + out << ")"; + } + return; + } + + throw std::runtime_error("At least one of min_value or max_value must be set"); +} + +const std::string SPACE_RULE = "| \" \" | \"\\n\" [ \\t]{0,20}"; + +struct BuiltinRule { + std::string content; + std::vector deps; +}; + +std::unordered_map PRIMITIVE_RULES = { + {"boolean", {"(\"true\" | \"false\") space", {}}}, + {"decimal-part", {"[0-9]{1,16}", {}}}, + {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}}, + {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}}, + {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}}, + {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}}, + {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}}, + {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}}, + {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}}, + {"char", {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}}, + {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}}, + {"null", {"\"null\" space", {}}}, +}; + +std::unordered_map STRING_FORMAT_RULES = { + {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}}, + {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}}, + {"date-time", {"date \"T\" time", {"date", "time"}}}, + {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}}, + {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}}, + {"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}} +}; + +static bool is_reserved_name(const std::string & name) { + static std::unordered_set RESERVED_NAMES; + if (RESERVED_NAMES.empty()) { + RESERVED_NAMES.insert("root"); + for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first); + for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first); + } + return RESERVED_NAMES.find(name) != RESERVED_NAMES.end(); +} + +std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+"); +std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]"); +std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]"); +std::unordered_map GRAMMAR_LITERAL_ESCAPES = { + {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"} +}; + +std::unordered_set NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'}; +std::unordered_set ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'}; + +static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function & replacement) { + std::smatch match; + std::string result; + + std::string::const_iterator searchStart(input.cbegin()); + std::string::const_iterator searchEnd(input.cend()); + + while (std::regex_search(searchStart, searchEnd, match, regex)) { + result.append(searchStart, searchStart + match.position()); + result.append(replacement(match)); + searchStart = match.suffix().first; + } + + result.append(searchStart, searchEnd); + + return result; +} + +static std::string format_literal(const std::string & literal) { + std::string escaped = replacePattern(literal, GRAMMAR_LITERAL_ESCAPE_RE, [&](const std::smatch & match) { + char c = match.str()[0]; + return GRAMMAR_LITERAL_ESCAPES.at(c); + }); + return "\"" + escaped + "\""; +} + +class SchemaConverter { +private: + friend std::string build_grammar(const std::function & cb, const common_grammar_options & options); + std::function _fetch_json; + bool _dotall; + std::map _rules; + std::unordered_map _refs; + std::unordered_set _refs_being_resolved; + std::vector _errors; + std::vector _warnings; + + std::string _add_rule(const std::string & name, const std::string & rule) { + std::string esc_name = regex_replace(name, INVALID_RULE_CHARS_RE, "-"); + if (_rules.find(esc_name) == _rules.end() || _rules[esc_name] == rule) { + _rules[esc_name] = rule; + return esc_name; + } else { + int i = 0; + while (_rules.find(esc_name + std::to_string(i)) != _rules.end() && _rules[esc_name + std::to_string(i)] != rule) { + i++; + } + std::string key = esc_name + std::to_string(i); + _rules[key] = rule; + return key; + } + } + + std::string _generate_union_rule(const std::string & name, const std::vector & alt_schemas) { + std::vector rules; + for (size_t i = 0; i < alt_schemas.size(); i++) { + rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i))); + } + return string_join(rules, " | "); + } + + std::string _visit_pattern(const std::string & pattern, const std::string & name) { + if (!(pattern.front() == '^' && pattern.back() == '$')) { + _errors.push_back("Pattern must start with '^' and end with '$'"); + return ""; + } + std::string sub_pattern = pattern.substr(1, pattern.length() - 2); + std::unordered_map sub_rule_ids; + + size_t i = 0; + size_t length = sub_pattern.length(); + + using literal_or_rule = std::pair; + auto to_rule = [&](const literal_or_rule & ls) { + auto is_literal = ls.second; + auto s = ls.first; + return is_literal ? "\"" + s + "\"" : s; + }; + std::function transform = [&]() -> literal_or_rule { + size_t start = i; + std::vector seq; + + auto get_dot = [&]() { + std::string rule; + if (_dotall) { + rule = "[\\U00000000-\\U0010FFFF]"; + } else { + rule = "[^\\x0A\\x0D]"; + } + return _add_rule("dot", rule); + }; + + // Joins the sequence, merging consecutive literals together. + auto join_seq = [&]() { + std::vector ret; + + std::string literal; + auto flush_literal = [&]() { + if (literal.empty()) { + return false; + } + ret.emplace_back(literal, true); + literal.clear(); + return true; + }; + + for (const auto & item : seq) { + auto is_literal = item.second; + if (is_literal) { + literal += item.first; + } else { + flush_literal(); + ret.push_back(item); + } + } + flush_literal(); + + std::vector results; + for (const auto & item : ret) { + results.push_back(to_rule(item)); + } + return std::make_pair(string_join(results, " "), false); + }; + + while (i < length) { + char c = sub_pattern[i]; + if (c == '.') { + seq.emplace_back(get_dot(), false); + i++; + } else if (c == '(') { + i++; + if (i < length) { + if (sub_pattern[i] == '?') { + _warnings.push_back("Unsupported pattern syntax"); + } + } + seq.emplace_back("(" + to_rule(transform()) + ")", false); + } else if (c == ')') { + i++; + if (start > 0 && sub_pattern[start - 1] != '(') { + _errors.push_back("Unbalanced parentheses"); + } + return join_seq(); + } else if (c == '[') { + std::string square_brackets = std::string(1, c); + i++; + while (i < length && sub_pattern[i] != ']') { + if (sub_pattern[i] == '\\') { + square_brackets += sub_pattern.substr(i, 2); + i += 2; + } else { + square_brackets += sub_pattern[i]; + i++; + } + } + if (i >= length) { + _errors.push_back("Unbalanced square brackets"); + } + square_brackets += ']'; + i++; + seq.emplace_back(square_brackets, false); + } else if (c == '|') { + seq.emplace_back("|", false); + i++; + } else if (c == '*' || c == '+' || c == '?') { + seq.back() = std::make_pair(to_rule(seq.back()) + c, false); + i++; + } else if (c == '{') { + std::string curly_brackets = std::string(1, c); + i++; + while (i < length && sub_pattern[i] != '}') { + curly_brackets += sub_pattern[i]; + i++; + } + if (i >= length) { + _errors.push_back("Unbalanced curly brackets"); + } + curly_brackets += '}'; + i++; + auto nums = string_split(curly_brackets.substr(1, curly_brackets.length() - 2), ","); + int min_times = 0; + int max_times = std::numeric_limits::max(); + try { + if (nums.size() == 1) { + min_times = max_times = std::stoi(nums[0]); + } else if (nums.size() != 2) { + _errors.push_back("Wrong number of values in curly brackets"); + } else { + if (!nums[0].empty()) { + min_times = std::stoi(nums[0]); + } + if (!nums[1].empty()) { + max_times = std::stoi(nums[1]); + } + } + } catch (const std::invalid_argument & e) { + _errors.push_back("Invalid number in curly brackets"); + return std::make_pair("", false); + } + auto &last = seq.back(); + auto &sub = last.first; + auto sub_is_literal = last.second; + + if (!sub_is_literal) { + std::string & sub_id = sub_rule_ids[sub]; + if (sub_id.empty()) { + sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub); + } + sub = sub_id; + } + seq.back().first = build_repetition( + sub_is_literal ? "\"" + sub + "\"" : sub, + min_times, + max_times, + "" + ); + seq.back().second = false; + } else { + std::string literal; + auto is_non_literal = [&](char c) { + return NON_LITERAL_SET.find(c) != NON_LITERAL_SET.end(); + }; + while (i < length) { + if (sub_pattern[i] == '\\' && i < length - 1) { + char next = sub_pattern[i + 1]; + if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.find(next) != ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.end()) { + i++; + literal += sub_pattern[i]; + i++; + } else { + literal += sub_pattern.substr(i, 2); + i += 2; + } + } else if (sub_pattern[i] == '"') { + literal += "\\\""; + i++; + } else if (!is_non_literal(sub_pattern[i]) && + (i == length - 1 || literal.empty() || sub_pattern[i + 1] == '.' || !is_non_literal(sub_pattern[i + 1]))) { + literal += sub_pattern[i]; + i++; + } else { + break; + } + } + if (!literal.empty()) { + seq.emplace_back(literal, true); + } + } + } + return join_seq(); + }; + return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space"); + } + + /* + Returns a rule that matches a JSON string that is none of the provided strings + + not_strings({"a"}) + -> ["] ( [a] char+ | [^"a] char* )? ["] space + not_strings({"and", "also"}) + -> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space + */ + std::string _not_strings(const std::vector & strings) { + + struct TrieNode { + std::map children; + bool is_end_of_string; + + TrieNode() : is_end_of_string(false) {} + + void insert(const std::string & string) { + auto node = this; + for (char c : string) { + node = &node->children[c]; + } + node->is_end_of_string = true; + } + }; + + TrieNode trie; + for (const auto & s : strings) { + trie.insert(s); + } + + std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char")); + std::ostringstream out; + out << "[\"] ( "; + std::function visit = [&](const TrieNode & node) { + std::ostringstream rejects; + auto first = true; + for (const auto & kv : node.children) { + rejects << kv.first; + if (first) { + first = false; + } else { + out << " | "; + } + out << "[" << kv.first << "]"; + if (!kv.second.children.empty()) { + out << " ("; + visit(kv.second); + out << ")"; + } else if (kv.second.is_end_of_string) { + out << " " << char_rule << "+"; + } + } + if (!node.children.empty()) { + if (!first) { + out << " | "; + } + out << "[^\"" << rejects.str() << "] " << char_rule << "*"; + } + }; + visit(trie); + + out << " )"; + if (!trie.is_end_of_string) { + out << "?"; + } + out << " [\"] space"; + return out.str(); + } + + std::string _resolve_ref(const std::string & ref) { + std::string ref_name = ref.substr(ref.find_last_of('/') + 1); + if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) { + _refs_being_resolved.insert(ref); + json resolved = _refs[ref]; + ref_name = visit(resolved, ref_name); + _refs_being_resolved.erase(ref); + } + return ref_name; + } + + std::string _build_object_rule( + const std::vector> & properties, + const std::unordered_set & required, + const std::string & name, + const json & additional_properties) + { + std::vector required_props; + std::vector optional_props; + std::unordered_map prop_kv_rule_names; + std::vector prop_names; + for (const auto & kv : properties) { + const auto &prop_name = kv.first; + const auto &prop_schema = kv.second; + + std::string prop_rule_name = visit(prop_schema, name + (name.empty() ? "" : "-") + prop_name); + prop_kv_rule_names[prop_name] = _add_rule( + name + (name.empty() ? "" : "-") + prop_name + "-kv", + format_literal(json(prop_name).dump()) + " space \":\" space " + prop_rule_name + ); + if (required.find(prop_name) != required.end()) { + required_props.push_back(prop_name); + } else { + optional_props.push_back(prop_name); + } + prop_names.push_back(prop_name); + } + if ((additional_properties.is_boolean() && additional_properties.get()) || additional_properties.is_object()) { + std::string sub_name = name + (name.empty() ? "" : "-") + "additional"; + std::string value_rule = + additional_properties.is_object() ? visit(additional_properties, sub_name + "-value") + : _add_primitive("value", PRIMITIVE_RULES.at("value")); + + auto key_rule = + prop_names.empty() ? _add_primitive("string", PRIMITIVE_RULES.at("string")) + : _add_rule(sub_name + "-k", _not_strings(prop_names)); + std::string kv_rule = _add_rule(sub_name + "-kv", key_rule + " \":\" space " + value_rule); + prop_kv_rule_names["*"] = kv_rule; + optional_props.push_back("*"); + } + + std::string rule = "\"{\" space "; + for (size_t i = 0; i < required_props.size(); i++) { + if (i > 0) { + rule += " \",\" space "; + } + rule += prop_kv_rule_names[required_props[i]]; + } + + if (!optional_props.empty()) { + rule += " ("; + if (!required_props.empty()) { + rule += " \",\" space ( "; + } + + std::function &, bool)> get_recursive_refs = [&](const std::vector & ks, bool first_is_optional) { + std::string res; + if (ks.empty()) { + return res; + } + std::string k = ks[0]; + std::string kv_rule_name = prop_kv_rule_names[k]; + std::string comma_ref = "( \",\" space " + kv_rule_name + " )"; + if (first_is_optional) { + res = comma_ref + (k == "*" ? "*" : "?"); + } else { + res = kv_rule_name + (k == "*" ? " " + comma_ref + "*" : ""); + } + if (ks.size() > 1) { + res += " " + _add_rule( + name + (name.empty() ? "" : "-") + k + "-rest", + get_recursive_refs(std::vector(ks.begin() + 1, ks.end()), true) + ); + } + return res; + }; + + for (size_t i = 0; i < optional_props.size(); i++) { + if (i > 0) { + rule += " | "; + } + rule += get_recursive_refs(std::vector(optional_props.begin() + i, optional_props.end()), false); + } + if (!required_props.empty()) { + rule += " )"; + } + rule += " )?"; + } + + rule += " \"}\" space"; + + return rule; + } + + std::string _add_primitive(const std::string & name, const BuiltinRule & rule) { + auto n = _add_rule(name, rule.content); + for (const auto & dep : rule.deps) { + BuiltinRule dep_rule; + auto it = PRIMITIVE_RULES.find(dep); + if (it == PRIMITIVE_RULES.end()) { + it = STRING_FORMAT_RULES.find(dep); + if (it == STRING_FORMAT_RULES.end()) { + _errors.push_back("Rule " + dep + " not known"); + continue; + } + } + if (_rules.find(dep) == _rules.end()) { + _add_primitive(dep, it->second); + } + } + return n; + } + +public: + SchemaConverter( + const std::function & fetch_json, + bool dotall, + bool compact_spaces) + : _fetch_json(fetch_json), _dotall(dotall) + { + _rules["space"] = compact_spaces ? "\" \"?" : SPACE_RULE; + } + + void resolve_refs(json & schema, const std::string & url) { + /* + * Resolves all $ref fields in the given schema, fetching any remote schemas, + * replacing each $ref with absolute reference URL and populates _refs with the + * respective referenced (sub)schema dictionaries. + */ + std::function visit_refs = [&](json & n) { + if (n.is_array()) { + for (auto & x : n) { + visit_refs(x); + } + } else if (n.is_object()) { + if (n.contains("$ref")) { + std::string ref = n["$ref"]; + if (_refs.find(ref) == _refs.end()) { + json target; + if (ref.find("https://") == 0) { + std::string base_url = ref.substr(0, ref.find('#')); + auto it = _refs.find(base_url); + if (it != _refs.end()) { + target = it->second; + } else { + // Fetch the referenced schema and resolve its refs + auto referenced = _fetch_json(ref); + resolve_refs(referenced, base_url); + _refs[base_url] = referenced; + } + if (ref.find('#') == std::string::npos || ref.substr(ref.find('#') + 1).empty()) { + return; + } + } else if (ref.find("#/") == 0) { + target = schema; + n["$ref"] = url + ref; + ref = url + ref; + } else { + _errors.push_back("Unsupported ref: " + ref); + return; + } + std::string pointer = ref.substr(ref.find('#') + 1); + std::vector tokens = string_split(pointer, "/"); + for (size_t i = 1; i < tokens.size(); ++i) { + std::string sel = tokens[i]; + if (target.is_null() || !target.contains(sel)) { + _errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump()); + return; + } + target = target[sel]; + } + _refs[ref] = target; + } + } else { + for (auto & kv : n.items()) { + visit_refs(kv.value()); + } + } + } + }; + + visit_refs(schema); + } + + std::string _generate_constant_rule(const json & value) { + return format_literal(value.dump()); + } + + std::string visit(const json & schema, const std::string & name) { + json schema_type = schema.contains("type") ? schema["type"] : json(); + std::string schema_format = schema.contains("format") ? schema["format"].get() : ""; + std::string rule_name = is_reserved_name(name) ? name + "-" : name.empty() ? "root" : name; + + if (schema.contains("$ref")) { + return _add_rule(rule_name, _resolve_ref(schema["$ref"])); + } else if (schema.contains("oneOf") || schema.contains("anyOf")) { + std::vector alt_schemas = schema.contains("oneOf") ? schema["oneOf"].get>() : schema["anyOf"].get>(); + return _add_rule(rule_name, _generate_union_rule(name, alt_schemas)); + } else if (schema_type.is_array()) { + std::vector schema_types; + for (const auto & t : schema_type) { + json schema_copy(schema); + schema_copy["type"] = t; + schema_types.push_back(schema_copy); + } + return _add_rule(rule_name, _generate_union_rule(name, schema_types)); + } else if (schema.contains("const")) { + return _add_rule(rule_name, _generate_constant_rule(schema["const"]) + " space"); + } else if (schema.contains("enum")) { + std::vector enum_values; + for (const auto & v : schema["enum"]) { + enum_values.push_back(_generate_constant_rule(v)); + } + return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space"); + } else if ((schema_type.is_null() || schema_type == "object") + && (schema.contains("properties") || + (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) { + std::unordered_set required; + if (schema.contains("required") && schema["required"].is_array()) { + for (const auto & item : schema["required"]) { + if (item.is_string()) { + required.insert(item.get()); + } + } + } + std::vector> properties; + if (schema.contains("properties")) { + for (const auto & prop : schema["properties"].items()) { + properties.emplace_back(prop.key(), prop.value()); + } + } + return _add_rule(rule_name, + _build_object_rule( + properties, required, name, + schema.contains("additionalProperties") ? schema["additionalProperties"] : json())); + } else if ((schema_type.is_null() || schema_type == "object") && schema.contains("allOf")) { + std::unordered_set required; + std::vector> properties; + std::string hybrid_name = name; + std::function add_component = [&](const json & comp_schema, bool is_required) { + if (comp_schema.contains("$ref")) { + add_component(_refs[comp_schema["$ref"]], is_required); + } else if (comp_schema.contains("properties")) { + for (const auto & prop : comp_schema["properties"].items()) { + properties.emplace_back(prop.key(), prop.value()); + if (is_required) { + required.insert(prop.key()); + } + } + } else { + // todo warning + } + }; + for (auto & t : schema["allOf"]) { + if (t.contains("anyOf")) { + for (auto & tt : t["anyOf"]) { + add_component(tt, false); + } + } else { + add_component(t, true); + } + } + return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json())); + } else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) { + json items = schema.contains("items") ? schema["items"] : schema["prefixItems"]; + if (items.is_array()) { + std::string rule = "\"[\" space "; + for (size_t i = 0; i < items.size(); i++) { + if (i > 0) { + rule += " \",\" space "; + } + rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i)); + } + rule += " \"]\" space"; + return _add_rule(rule_name, rule); + } else { + std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item"); + int min_items = schema.contains("minItems") ? schema["minItems"].get() : 0; + json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json(); + int max_items = max_items_json.is_number_integer() ? max_items_json.get() : std::numeric_limits::max(); + + return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space"); + } + } else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) { + return _visit_pattern(schema["pattern"], rule_name); + } else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) { + return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid")); + } else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) { + auto prim_name = schema_format + "-string"; + return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name))); + } else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) { + std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char")); + int min_len = schema.contains("minLength") ? schema["minLength"].get() : 0; + int max_len = schema.contains("maxLength") ? schema["maxLength"].get() : std::numeric_limits::max(); + return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space"); + } else if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) { + int min_value = std::numeric_limits::min(); + int max_value = std::numeric_limits::max(); + if (schema.contains("minimum")) { + min_value = schema["minimum"].get(); + } else if (schema.contains("exclusiveMinimum")) { + min_value = schema["exclusiveMinimum"].get() + 1; + } + if (schema.contains("maximum")) { + max_value = schema["maximum"].get(); + } else if (schema.contains("exclusiveMaximum")) { + max_value = schema["exclusiveMaximum"].get() - 1; + } + std::stringstream out; + out << "("; + _build_min_max_int(min_value, max_value, out); + out << ") space"; + return _add_rule(rule_name, out.str()); + } else if (schema.empty() || schema_type == "object") { + return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object"))); + } else { + if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get()) == PRIMITIVE_RULES.end()) { + _errors.push_back("Unrecognized schema: " + schema.dump()); + return ""; + } + // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return _add_primitive(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); + } + } + + void check_errors() { + if (!_errors.empty()) { + throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n")); + } + if (!_warnings.empty()) { + fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str()); + } + } + + std::string format_grammar() { + std::stringstream ss; + for (const auto & kv : _rules) { + ss << kv.first << " ::= " << kv.second << std::endl; + } + return ss.str(); + } +}; + +std::string json_schema_to_grammar(const json & schema, bool force_gbnf) { +#ifdef LLAMA_USE_LLGUIDANCE + if (!force_gbnf) { + return "%llguidance {}\nstart: %json " + schema.dump(); + } +#else + (void)force_gbnf; +#endif // LLAMA_USE_LLGUIDANCE + return build_grammar([&](const common_grammar_builder & callbacks) { + auto copy = schema; + callbacks.resolve_refs(copy); + callbacks.add_schema("", copy); + }); +} + +std::string build_grammar(const std::function & cb, const common_grammar_options & options) { + SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall, options.compact_spaces); + common_grammar_builder builder { + /* .add_rule = */ [&](const std::string & name, const std::string & rule) { + return converter._add_rule(name, rule); + }, + /* .add_schema = */ [&](const std::string & name, const nlohmann::ordered_json & schema) { + return converter.visit(schema, name == "root" ? "" : name); + }, + /* .resolve_refs = */ [&](nlohmann::ordered_json & schema) { + converter.resolve_refs(schema, ""); + } + }; + cb(builder); + converter.check_errors(); + return converter.format_grammar(); +} diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h new file mode 100644 index 000000000..62a3b0a44 --- /dev/null +++ b/common/json-schema-to-grammar.h @@ -0,0 +1,22 @@ +#pragma once + +#include "ggml.h" +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT +#include "json.hpp" + +std::string json_schema_to_grammar(const nlohmann::ordered_json & schema, + bool force_gbnf = false); + +struct common_grammar_builder { + std::function add_rule; + std::function add_schema; + std::function resolve_refs; +}; + +struct common_grammar_options { + bool dotall = false; + bool compact_spaces = false; +}; + +std::string build_grammar(const std::function & cb, const common_grammar_options & options = {}); diff --git a/examples/server/json.hpp b/common/json.hpp similarity index 93% rename from examples/server/json.hpp rename to common/json.hpp index ea945f346..a858728c4 100644 --- a/examples/server/json.hpp +++ b/common/json.hpp @@ -1,9 +1,9 @@ // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT /****************************************************************************\ @@ -27,7 +27,6 @@ #endif // JSON_NO_IO #include // random_access_iterator_tag #include // unique_ptr -#include // accumulate #include // string, stoi, to_string #include // declval, forward, move, pair, swap #include // vector @@ -35,10 +34,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -48,10 +47,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -60,7 +59,7 @@ #ifndef JSON_SKIP_LIBRARY_VERSION_CHECK #if defined(NLOHMANN_JSON_VERSION_MAJOR) && defined(NLOHMANN_JSON_VERSION_MINOR) && defined(NLOHMANN_JSON_VERSION_PATCH) - #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 2 + #if NLOHMANN_JSON_VERSION_MAJOR != 3 || NLOHMANN_JSON_VERSION_MINOR != 11 || NLOHMANN_JSON_VERSION_PATCH != 3 #warning "Already included a different version of the library!" #endif #endif @@ -68,7 +67,7 @@ #define NLOHMANN_JSON_VERSION_MAJOR 3 // NOLINT(modernize-macro-to-enum) #define NLOHMANN_JSON_VERSION_MINOR 11 // NOLINT(modernize-macro-to-enum) -#define NLOHMANN_JSON_VERSION_PATCH 2 // NOLINT(modernize-macro-to-enum) +#define NLOHMANN_JSON_VERSION_PATCH 3 // NOLINT(modernize-macro-to-enum) #ifndef JSON_DIAGNOSTICS #define JSON_DIAGNOSTICS 0 @@ -150,10 +149,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -173,16 +172,19 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT #include // nullptr_t #include // exception +#if JSON_DIAGNOSTICS + #include // accumulate +#endif #include // runtime_error #include // to_string #include // vector @@ -190,10 +192,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -206,10 +208,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -218,10 +220,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -231,10 +233,10 @@ // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -318,10 +320,10 @@ NLOHMANN_JSON_NAMESPACE_END // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-FileCopyrightText: 2016-2021 Evan Nemerson // SPDX-License-Identifier: MIT @@ -2483,6 +2485,14 @@ JSON_HEDLEY_DIAGNOSTIC_POP #endif #endif +#ifndef JSON_HAS_STATIC_RTTI + #if !defined(_HAS_STATIC_RTTI) || _HAS_STATIC_RTTI != 0 + #define JSON_HAS_STATIC_RTTI 1 + #else + #define JSON_HAS_STATIC_RTTI 0 + #endif +#endif + #ifdef JSON_HAS_CPP_17 #define JSON_INLINE_VARIABLE inline #else @@ -2590,12 +2600,13 @@ JSON_HEDLEY_DIAGNOSTIC_POP class NumberUnsignedType, class NumberFloatType, \ template class AllocatorType, \ template class JSONSerializer, \ - class BinaryType> + class BinaryType, \ + class CustomBaseClass> #define NLOHMANN_BASIC_JSON_TPL \ basic_json + AllocatorType, JSONSerializer, BinaryType, CustomBaseClass> // Macros to simplify conversion from/to types @@ -2745,7 +2756,10 @@ JSON_HEDLEY_DIAGNOSTIC_POP #define NLOHMANN_DEFINE_TYPE_INTRUSIVE_WITH_DEFAULT(Type, ...) \ friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ - friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } + friend void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } + +#define NLOHMANN_DEFINE_TYPE_INTRUSIVE_ONLY_SERIALIZE(Type, ...) \ + friend void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } /*! @brief macro @@ -2756,10 +2770,12 @@ JSON_HEDLEY_DIAGNOSTIC_POP inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM, __VA_ARGS__)) } +#define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_ONLY_SERIALIZE(Type, ...) \ + inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } + #define NLOHMANN_DEFINE_TYPE_NON_INTRUSIVE_WITH_DEFAULT(Type, ...) \ inline void to_json(nlohmann::json& nlohmann_json_j, const Type& nlohmann_json_t) { NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_TO, __VA_ARGS__)) } \ - inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { Type nlohmann_json_default_obj; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } - + inline void from_json(const nlohmann::json& nlohmann_json_j, Type& nlohmann_json_t) { const Type nlohmann_json_default_obj{}; NLOHMANN_JSON_EXPAND(NLOHMANN_JSON_PASTE(NLOHMANN_JSON_FROM_WITH_DEFAULT, __VA_ARGS__)) } // inspired from https://stackoverflow.com/a/26745591 // allows to call any std function as if (e.g. with begin): @@ -2923,10 +2939,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -2998,10 +3014,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3040,10 +3056,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-FileCopyrightText: 2018 The Abseil Authors // SPDX-License-Identifier: MIT @@ -3214,10 +3230,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3226,14 +3242,15 @@ NLOHMANN_JSON_NAMESPACE_END #include // false_type, is_constructible, is_integral, is_same, true_type #include // declval #include // tuple +#include // char_traits // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3298,10 +3315,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3318,10 +3335,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -3342,10 +3359,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT #ifndef INCLUDE_NLOHMANN_JSON_FWD_HPP_ @@ -3389,7 +3406,8 @@ NLOHMANN_JSON_NAMESPACE_END template class AllocatorType = std::allocator, template class JSONSerializer = adl_serializer, - class BinaryType = std::vector> + class BinaryType = std::vector, // cppcheck-suppress syntaxError + class CustomBaseClass = void> class basic_json; /// @brief JSON Pointer defines a string syntax for identifying a specific value within a JSON document @@ -3577,6 +3595,63 @@ struct actual_object_comparator template using actual_object_comparator_t = typename actual_object_comparator::type; +///////////////// +// char_traits // +///////////////// + +// Primary template of char_traits calls std char_traits +template +struct char_traits : std::char_traits +{}; + +// Explicitly define char traits for unsigned char since it is not standard +template<> +struct char_traits : std::char_traits +{ + using char_type = unsigned char; + using int_type = uint64_t; + + // Redefine to_int_type function + static int_type to_int_type(char_type c) noexcept + { + return static_cast(c); + } + + static char_type to_char_type(int_type i) noexcept + { + return static_cast(i); + } + + static constexpr int_type eof() noexcept + { + return static_cast(EOF); + } +}; + +// Explicitly define char traits for signed char since it is not standard +template<> +struct char_traits : std::char_traits +{ + using char_type = signed char; + using int_type = uint64_t; + + // Redefine to_int_type function + static int_type to_int_type(char_type c) noexcept + { + return static_cast(c); + } + + static char_type to_char_type(int_type i) noexcept + { + return static_cast(i); + } + + static constexpr int_type eof() noexcept + { + return static_cast(EOF); + } +}; + /////////////////// // is_ functions // /////////////////// @@ -3613,7 +3688,6 @@ template struct is_default_constructible> : conjunction...> {}; - template struct is_constructible : std::is_constructible {}; @@ -3629,7 +3703,6 @@ struct is_constructible> : is_default_constructible struct is_constructible> : is_default_constructible> {}; - template struct is_iterator_traits : std::false_type {}; @@ -4039,7 +4112,6 @@ struct value_in_range_of_impl2 } }; - template struct value_in_range_of_impl2 { @@ -4138,10 +4210,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -4165,28 +4237,28 @@ inline std::size_t concat_length() } template -inline std::size_t concat_length(const char* cstr, Args&& ... rest); +inline std::size_t concat_length(const char* cstr, const Args& ... rest); template -inline std::size_t concat_length(const StringType& str, Args&& ... rest); +inline std::size_t concat_length(const StringType& str, const Args& ... rest); template -inline std::size_t concat_length(const char /*c*/, Args&& ... rest) +inline std::size_t concat_length(const char /*c*/, const Args& ... rest) { - return 1 + concat_length(std::forward(rest)...); + return 1 + concat_length(rest...); } template -inline std::size_t concat_length(const char* cstr, Args&& ... rest) +inline std::size_t concat_length(const char* cstr, const Args& ... rest) { // cppcheck-suppress ignoredReturnValue - return ::strlen(cstr) + concat_length(std::forward(rest)...); + return ::strlen(cstr) + concat_length(rest...); } template -inline std::size_t concat_length(const StringType& str, Args&& ... rest) +inline std::size_t concat_length(const StringType& str, const Args& ... rest) { - return str.size() + concat_length(std::forward(rest)...); + return str.size() + concat_length(rest...); } template @@ -4277,7 +4349,7 @@ template inline OutStringType concat(Args && ... args) { OutStringType str; - str.reserve(concat_length(std::forward(args)...)); + str.reserve(concat_length(args...)); concat_into(str, std::forward(args)...); return str; } @@ -4286,7 +4358,6 @@ inline OutStringType concat(Args && ... args) NLOHMANN_JSON_NAMESPACE_END - NLOHMANN_JSON_NAMESPACE_BEGIN namespace detail { @@ -4334,9 +4405,9 @@ class exception : public std::exception { case value_t::array: { - for (std::size_t i = 0; i < current->m_parent->m_value.array->size(); ++i) + for (std::size_t i = 0; i < current->m_parent->m_data.m_value.array->size(); ++i) { - if (¤t->m_parent->m_value.array->operator[](i) == current) + if (¤t->m_parent->m_data.m_value.array->operator[](i) == current) { tokens.emplace_back(std::to_string(i)); break; @@ -4347,7 +4418,7 @@ class exception : public std::exception case value_t::object: { - for (const auto& element : *current->m_parent->m_value.object) + for (const auto& element : *current->m_parent->m_data.m_value.object) { if (&element.second == current) { @@ -4410,17 +4481,17 @@ class parse_error : public exception template::value, int> = 0> static parse_error create(int id_, const position_t& pos, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("parse_error", id_), "parse error", - position_string(pos), ": ", exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("parse_error", id_), "parse error", + position_string(pos), ": ", exception::diagnostics(context), what_arg); return {id_, pos.chars_read_total, w.c_str()}; } template::value, int> = 0> static parse_error create(int id_, std::size_t byte_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("parse_error", id_), "parse error", - (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""), - ": ", exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("parse_error", id_), "parse error", + (byte_ != 0 ? (concat(" at byte ", std::to_string(byte_))) : ""), + ": ", exception::diagnostics(context), what_arg); return {id_, byte_, w.c_str()}; } @@ -4454,7 +4525,7 @@ class invalid_iterator : public exception template::value, int> = 0> static invalid_iterator create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("invalid_iterator", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4472,7 +4543,7 @@ class type_error : public exception template::value, int> = 0> static type_error create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("type_error", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4489,7 +4560,7 @@ class out_of_range : public exception template::value, int> = 0> static out_of_range create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("out_of_range", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4506,7 +4577,7 @@ class other_error : public exception template::value, int> = 0> static other_error create(int id_, const std::string& what_arg, BasicJsonContext context) { - std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg); + const std::string w = concat(exception::name("other_error", id_), exception::diagnostics(context), what_arg); return {id_, w.c_str()}; } @@ -4525,10 +4596,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -4549,10 +4620,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5055,10 +5126,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5075,10 +5146,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5147,10 +5218,10 @@ template class iteration_proxy_value // older GCCs are a bit fussy and require explicit noexcept specifiers on defaulted functions iteration_proxy_value(iteration_proxy_value&&) noexcept(std::is_nothrow_move_constructible::value - && std::is_nothrow_move_constructible::value) = default; + && std::is_nothrow_move_constructible::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations) iteration_proxy_value& operator=(iteration_proxy_value&&) noexcept(std::is_nothrow_move_assignable::value - && std::is_nothrow_move_assignable::value) = default; + && std::is_nothrow_move_assignable::value) = default; // NOLINT(hicpp-noexcept-move,performance-noexcept-move-constructor,cppcoreguidelines-noexcept-move-operations) ~iteration_proxy_value() = default; /// dereference operator (needed for range-based for) @@ -5297,11 +5368,11 @@ namespace std #pragma clang diagnostic ignored "-Wmismatched-tags" #endif template -class tuple_size<::nlohmann::detail::iteration_proxy_value> +class tuple_size<::nlohmann::detail::iteration_proxy_value> // NOLINT(cert-dcl58-cpp) : public std::integral_constant {}; template -class tuple_element> +class tuple_element> // NOLINT(cert-dcl58-cpp) { public: using type = decltype( @@ -5340,7 +5411,7 @@ namespace detail /* * Note all external_constructor<>::construct functions need to call - * j.m_value.destroy(j.m_type) to avoid a memory leak in case j contains an + * j.m_data.m_value.destroy(j.m_data.m_type) to avoid a memory leak in case j contains an * allocated value (e.g., a string). See bug issue * https://github.com/nlohmann/json/issues/2865 for more information. */ @@ -5353,9 +5424,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::boolean_t b) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::boolean; - j.m_value = b; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::boolean; + j.m_data.m_value = b; j.assert_invariant(); } }; @@ -5366,18 +5437,18 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::string_t& s) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::string; - j.m_value = s; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::string; + j.m_data.m_value = s; j.assert_invariant(); } template static void construct(BasicJsonType& j, typename BasicJsonType::string_t&& s) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::string; - j.m_value = std::move(s); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::string; + j.m_data.m_value = std::move(s); j.assert_invariant(); } @@ -5386,9 +5457,9 @@ struct external_constructor int > = 0 > static void construct(BasicJsonType& j, const CompatibleStringType& str) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::string; - j.m_value.string = j.template create(str); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::string; + j.m_data.m_value.string = j.template create(str); j.assert_invariant(); } }; @@ -5399,18 +5470,18 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::binary_t& b) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::binary; - j.m_value = typename BasicJsonType::binary_t(b); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::binary; + j.m_data.m_value = typename BasicJsonType::binary_t(b); j.assert_invariant(); } template static void construct(BasicJsonType& j, typename BasicJsonType::binary_t&& b) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::binary; - j.m_value = typename BasicJsonType::binary_t(std::move(b)); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::binary; + j.m_data.m_value = typename BasicJsonType::binary_t(std::move(b)); j.assert_invariant(); } }; @@ -5421,9 +5492,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::number_float_t val) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::number_float; - j.m_value = val; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::number_float; + j.m_data.m_value = val; j.assert_invariant(); } }; @@ -5434,9 +5505,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::number_unsigned_t val) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::number_unsigned; - j.m_value = val; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::number_unsigned; + j.m_data.m_value = val; j.assert_invariant(); } }; @@ -5447,9 +5518,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::number_integer_t val) noexcept { - j.m_value.destroy(j.m_type); - j.m_type = value_t::number_integer; - j.m_value = val; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::number_integer; + j.m_data.m_value = val; j.assert_invariant(); } }; @@ -5460,9 +5531,9 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::array_t& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = arr; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = arr; j.set_parents(); j.assert_invariant(); } @@ -5470,9 +5541,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::array_t&& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = std::move(arr); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = std::move(arr); j.set_parents(); j.assert_invariant(); } @@ -5485,9 +5556,9 @@ struct external_constructor using std::begin; using std::end; - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value.array = j.template create(begin(arr), end(arr)); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value.array = j.template create(begin(arr), end(arr)); j.set_parents(); j.assert_invariant(); } @@ -5495,14 +5566,14 @@ struct external_constructor template static void construct(BasicJsonType& j, const std::vector& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = value_t::array; - j.m_value.array->reserve(arr.size()); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = value_t::array; + j.m_data.m_value.array->reserve(arr.size()); for (const bool x : arr) { - j.m_value.array->push_back(x); - j.set_parent(j.m_value.array->back()); + j.m_data.m_value.array->push_back(x); + j.set_parent(j.m_data.m_value.array->back()); } j.assert_invariant(); } @@ -5511,13 +5582,13 @@ struct external_constructor enable_if_t::value, int> = 0> static void construct(BasicJsonType& j, const std::valarray& arr) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::array; - j.m_value = value_t::array; - j.m_value.array->resize(arr.size()); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::array; + j.m_data.m_value = value_t::array; + j.m_data.m_value.array->resize(arr.size()); if (arr.size() > 0) { - std::copy(std::begin(arr), std::end(arr), j.m_value.array->begin()); + std::copy(std::begin(arr), std::end(arr), j.m_data.m_value.array->begin()); } j.set_parents(); j.assert_invariant(); @@ -5530,9 +5601,9 @@ struct external_constructor template static void construct(BasicJsonType& j, const typename BasicJsonType::object_t& obj) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::object; - j.m_value = obj; + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::object; + j.m_data.m_value = obj; j.set_parents(); j.assert_invariant(); } @@ -5540,9 +5611,9 @@ struct external_constructor template static void construct(BasicJsonType& j, typename BasicJsonType::object_t&& obj) { - j.m_value.destroy(j.m_type); - j.m_type = value_t::object; - j.m_value = std::move(obj); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::object; + j.m_data.m_value = std::move(obj); j.set_parents(); j.assert_invariant(); } @@ -5554,9 +5625,9 @@ struct external_constructor using std::begin; using std::end; - j.m_value.destroy(j.m_type); - j.m_type = value_t::object; - j.m_value.object = j.template create(begin(obj), end(obj)); + j.m_data.m_value.destroy(j.m_data.m_type); + j.m_data.m_type = value_t::object; + j.m_data.m_value.object = j.template create(begin(obj), end(obj)); j.set_parents(); j.assert_invariant(); } @@ -5626,7 +5697,8 @@ template::type; - external_constructor::construct(j, static_cast(e)); + static constexpr value_t integral_value_t = std::is_unsigned::value ? value_t::number_unsigned : value_t::number_integer; + external_constructor::construct(j, static_cast(e)); } #endif // JSON_DISABLE_ENUM_SERIALIZATION @@ -5796,10 +5868,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -5908,10 +5980,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6041,10 +6113,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6067,10 +6139,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6094,6 +6166,8 @@ NLOHMANN_JSON_NAMESPACE_END // #include +// #include + NLOHMANN_JSON_NAMESPACE_BEGIN namespace detail @@ -6140,7 +6214,6 @@ class file_input_adapter std::FILE* m_file; }; - /*! Input adapter for a (caching) istream. Ignores a UFT Byte Order Mark at beginning of input. Does not support changing the underlying std::streambuf @@ -6214,16 +6287,16 @@ class iterator_input_adapter : current(std::move(first)), end(std::move(last)) {} - typename std::char_traits::int_type get_character() + typename char_traits::int_type get_character() { if (JSON_HEDLEY_LIKELY(current != end)) { - auto result = std::char_traits::to_int_type(*current); + auto result = char_traits::to_int_type(*current); std::advance(current, 1); return result; } - return std::char_traits::eof(); + return char_traits::eof(); } private: @@ -6239,7 +6312,6 @@ class iterator_input_adapter } }; - template struct wide_string_input_helper; @@ -6363,7 +6435,7 @@ struct wide_string_input_helper } }; -// Wraps another input apdater to convert wide character types into individual bytes. +// Wraps another input adapter to convert wide character types into individual bytes. template class wide_string_input_adapter { @@ -6408,7 +6480,6 @@ class wide_string_input_adapter std::size_t utf8_bytes_filled = 0; }; - template struct iterator_input_adapter_factory { @@ -6565,10 +6636,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -6710,7 +6781,6 @@ struct json_sax virtual ~json_sax() = default; }; - namespace detail { /*! @@ -6812,7 +6882,7 @@ class json_sax_dom_parser JSON_ASSERT(ref_stack.back()->is_object()); // add null at given key and store the reference for later - object_element = &(ref_stack.back()->m_value.object->operator[](val)); + object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val)); return true; } @@ -6887,8 +6957,8 @@ class json_sax_dom_parser if (ref_stack.back()->is_array()) { - ref_stack.back()->m_value.array->emplace_back(std::forward(v)); - return &(ref_stack.back()->m_value.array->back()); + ref_stack.back()->m_data.m_value.array->emplace_back(std::forward(v)); + return &(ref_stack.back()->m_data.m_value.array->back()); } JSON_ASSERT(ref_stack.back()->is_object()); @@ -7007,7 +7077,7 @@ class json_sax_dom_callback_parser // add discarded value at given key and store the reference for later if (keep && ref_stack.back()) { - object_element = &(ref_stack.back()->m_value.object->operator[](val) = discarded); + object_element = &(ref_stack.back()->m_data.m_value.object->operator[](val) = discarded); } return true; @@ -7092,7 +7162,7 @@ class json_sax_dom_callback_parser // remove discarded value if (!keep && !ref_stack.empty() && ref_stack.back()->is_array()) { - ref_stack.back()->m_value.array->pop_back(); + ref_stack.back()->m_data.m_value.array->pop_back(); } return true; @@ -7159,7 +7229,7 @@ class json_sax_dom_callback_parser if (ref_stack.empty()) { root = std::move(value); - return {true, &root}; + return {true, & root}; } // skip this value if we already decided to skip the parent @@ -7175,8 +7245,8 @@ class json_sax_dom_callback_parser // array if (ref_stack.back()->is_array()) { - ref_stack.back()->m_value.array->emplace_back(std::move(value)); - return {true, &(ref_stack.back()->m_value.array->back())}; + ref_stack.back()->m_data.m_value.array->emplace_back(std::move(value)); + return {true, & (ref_stack.back()->m_data.m_value.array->back())}; } // object @@ -7201,9 +7271,9 @@ class json_sax_dom_callback_parser /// stack to model hierarchy of values std::vector ref_stack {}; /// stack to manage which values to keep - std::vector keep_stack {}; + std::vector keep_stack {}; // NOLINT(readability-redundant-member-init) /// stack to manage which object keys to keep - std::vector key_keep_stack {}; + std::vector key_keep_stack {}; // NOLINT(readability-redundant-member-init) /// helper to hold the reference for the next object element BasicJsonType* object_element = nullptr; /// whether a syntax error occurred @@ -7298,10 +7368,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -7322,6 +7392,8 @@ NLOHMANN_JSON_NAMESPACE_END // #include +// #include + NLOHMANN_JSON_NAMESPACE_BEGIN namespace detail @@ -7416,7 +7488,7 @@ class lexer : public lexer_base using number_float_t = typename BasicJsonType::number_float_t; using string_t = typename BasicJsonType::string_t; using char_type = typename InputAdapterType::char_type; - using char_int_type = typename std::char_traits::int_type; + using char_int_type = typename char_traits::int_type; public: using token_type = typename lexer_base::token_type; @@ -7523,7 +7595,7 @@ class lexer : public lexer_base for (auto range = ranges.begin(); range != ranges.end(); ++range) { get(); - if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) + if (JSON_HEDLEY_LIKELY(*range <= current && current <= *(++range))) // NOLINT(bugprone-inc-dec-in-conditions) { add(current); } @@ -7566,7 +7638,7 @@ class lexer : public lexer_base switch (get()) { // end of file while parsing string - case std::char_traits::eof(): + case char_traits::eof(): { error_message = "invalid string: missing closing quote"; return token_type::parse_error; @@ -8155,7 +8227,7 @@ class lexer : public lexer_base { case '\n': case '\r': - case std::char_traits::eof(): + case char_traits::eof(): case '\0': return true; @@ -8172,7 +8244,7 @@ class lexer : public lexer_base { switch (get()) { - case std::char_traits::eof(): + case char_traits::eof(): case '\0': { error_message = "invalid comment; missing closing '*/'"; @@ -8601,10 +8673,10 @@ scan_number_done: token_type scan_literal(const char_type* literal_text, const std::size_t length, token_type return_type) { - JSON_ASSERT(std::char_traits::to_char_type(current) == literal_text[0]); + JSON_ASSERT(char_traits::to_char_type(current) == literal_text[0]); for (std::size_t i = 1; i < length; ++i) { - if (JSON_HEDLEY_UNLIKELY(std::char_traits::to_char_type(get()) != literal_text[i])) + if (JSON_HEDLEY_UNLIKELY(char_traits::to_char_type(get()) != literal_text[i])) { error_message = "invalid literal"; return token_type::parse_error; @@ -8622,7 +8694,7 @@ scan_number_done: { token_buffer.clear(); token_string.clear(); - token_string.push_back(std::char_traits::to_char_type(current)); + token_string.push_back(char_traits::to_char_type(current)); } /* @@ -8630,7 +8702,7 @@ scan_number_done: This function provides the interface to the used input adapter. It does not throw in case the input reached EOF, but returns a - `std::char_traits::eof()` in that case. Stores the scanned characters + `char_traits::eof()` in that case. Stores the scanned characters for use in error messages. @return character read from the input @@ -8650,9 +8722,9 @@ scan_number_done: current = ia.get_character(); } - if (JSON_HEDLEY_LIKELY(current != std::char_traits::eof())) + if (JSON_HEDLEY_LIKELY(current != char_traits::eof())) { - token_string.push_back(std::char_traits::to_char_type(current)); + token_string.push_back(char_traits::to_char_type(current)); } if (current == '\n') @@ -8691,7 +8763,7 @@ scan_number_done: --position.chars_read_current_line; } - if (JSON_HEDLEY_LIKELY(current != std::char_traits::eof())) + if (JSON_HEDLEY_LIKELY(current != char_traits::eof())) { JSON_ASSERT(!token_string.empty()); token_string.pop_back(); @@ -8885,7 +8957,7 @@ scan_number_done: // end of input (the null byte is needed when parsing from // string literals) case '\0': - case std::char_traits::eof(): + case char_traits::eof(): return token_type::end_of_input; // error @@ -8903,7 +8975,7 @@ scan_number_done: const bool ignore_comments = false; /// the current character - char_int_type current = std::char_traits::eof(); + char_int_type current = char_traits::eof(); /// whether the next get() call should just return current bool next_unget = false; @@ -8937,10 +9009,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -9129,7 +9201,6 @@ static inline bool little_endianness(int num = 1) noexcept return *reinterpret_cast(&num) == 1; } - /////////////////// // binary reader // /////////////////// @@ -9147,7 +9218,7 @@ class binary_reader using binary_t = typename BasicJsonType::binary_t; using json_sax_t = SAX; using char_type = typename InputAdapterType::char_type; - using char_int_type = typename std::char_traits::int_type; + using char_int_type = typename char_traits::int_type; public: /*! @@ -9220,7 +9291,7 @@ class binary_reader get(); } - if (JSON_HEDLEY_UNLIKELY(current != std::char_traits::eof())) + if (JSON_HEDLEY_UNLIKELY(current != char_traits::eof())) { return sax->parse_error(chars_read, get_token_string(), parse_error::create(110, chars_read, exception_message(input_format, concat("expected end of input; last byte: 0x", get_token_string()), "value"), nullptr)); @@ -9303,7 +9374,7 @@ class binary_reader exception_message(input_format_t::bson, concat("string length must be at least 1, is ", std::to_string(len)), "string"), nullptr)); } - return get_string(input_format_t::bson, len - static_cast(1), result) && get() != std::char_traits::eof(); + return get_string(input_format_t::bson, len - static_cast(1), result) && get() != char_traits::eof(); } /*! @@ -9404,7 +9475,7 @@ class binary_reader { std::array cr{{}}; static_cast((std::snprintf)(cr.data(), cr.size(), "%.2hhX", static_cast(element_type))); // NOLINT(cppcoreguidelines-pro-type-vararg,hicpp-vararg) - std::string cr_str{cr.data()}; + const std::string cr_str{cr.data()}; return sax->parse_error(element_type_parse_position, cr_str, parse_error::create(114, element_type_parse_position, concat("Unsupported BSON record type 0x", cr_str), nullptr)); } @@ -9497,7 +9568,7 @@ class binary_reader switch (get_char ? get() : current) { // EOF - case std::char_traits::eof(): + case char_traits::eof(): return unexpect_eof(input_format_t::cbor, "value"); // Integer 0x00..0x17 (0..23) @@ -10272,7 +10343,7 @@ class binary_reader switch (get()) { // EOF - case std::char_traits::eof(): + case char_traits::eof(): return unexpect_eof(input_format_t::msgpack, "value"); // positive fixint @@ -11339,7 +11410,7 @@ class binary_reader exception_message(input_format, concat("expected '#' after type information; last byte: 0x", last_token), "size"), nullptr)); } - bool is_error = get_ubjson_size_value(result.first, is_ndarray); + const bool is_error = get_ubjson_size_value(result.first, is_ndarray); if (input_format == input_format_t::bjdata && is_ndarray) { if (inside_ndarray) @@ -11354,7 +11425,7 @@ class binary_reader if (current == '#') { - bool is_error = get_ubjson_size_value(result.first, is_ndarray); + const bool is_error = get_ubjson_size_value(result.first, is_ndarray); if (input_format == input_format_t::bjdata && is_ndarray) { return sax->parse_error(chars_read, get_token_string(), parse_error::create(112, chars_read, @@ -11374,7 +11445,7 @@ class binary_reader { switch (prefix) { - case std::char_traits::eof(): // EOF + case char_traits::eof(): // EOF return unexpect_eof(input_format, "value"); case 'T': // true @@ -11819,7 +11890,7 @@ class binary_reader This function provides the interface to the used input adapter. It does not throw in case the input reached EOF, but returns a -'ve valued - `std::char_traits::eof()` in that case. + `char_traits::eof()` in that case. @return character read from the input */ @@ -11961,7 +12032,7 @@ class binary_reader JSON_HEDLEY_NON_NULL(3) bool unexpect_eof(const input_format_t format, const char* context) const { - if (JSON_HEDLEY_UNLIKELY(current == std::char_traits::eof())) + if (JSON_HEDLEY_UNLIKELY(current == char_traits::eof())) { return sax->parse_error(chars_read, "", parse_error::create(110, chars_read, exception_message(format, "unexpected end of input", context), nullptr)); @@ -12028,7 +12099,7 @@ class binary_reader InputAdapterType ia; /// the current character - char_int_type current = std::char_traits::eof(); + char_int_type current = char_traits::eof(); /// the number of characters read std::size_t chars_read = 0; @@ -12090,10 +12161,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12439,13 +12510,25 @@ class parser m_lexer.get_token_string(), parse_error::create(101, m_lexer.get_position(), exception_message(token_type::uninitialized, "value"), nullptr)); } + case token_type::end_of_input: + { + if (JSON_HEDLEY_UNLIKELY(m_lexer.get_position().chars_read_total == 1)) + { + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), + "attempting to parse an empty input; check that your input string or stream contains the expected JSON", nullptr)); + } + return sax->parse_error(m_lexer.get_position(), + m_lexer.get_token_string(), + parse_error::create(101, m_lexer.get_position(), exception_message(token_type::literal_or_value, "value"), nullptr)); + } case token_type::uninitialized: case token_type::end_array: case token_type::end_object: case token_type::name_separator: case token_type::value_separator: - case token_type::end_of_input: case token_type::literal_or_value: default: // the last token was unexpected { @@ -12607,10 +12690,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12620,10 +12703,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12779,10 +12862,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -12887,7 +12970,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { @@ -12984,17 +13067,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - m_it.object_iterator = m_object->m_value.object->begin(); + m_it.object_iterator = m_object->m_data.m_value.object->begin(); break; } case value_t::array: { - m_it.array_iterator = m_object->m_value.array->begin(); + m_it.array_iterator = m_object->m_data.m_value.array->begin(); break; } @@ -13028,17 +13111,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - m_it.object_iterator = m_object->m_value.object->end(); + m_it.object_iterator = m_object->m_data.m_value.object->end(); break; } case value_t::array: { - m_it.array_iterator = m_object->m_value.array->end(); + m_it.array_iterator = m_object->m_data.m_value.array->end(); break; } @@ -13067,17 +13150,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end()); + JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end()); return m_it.object_iterator->second; } case value_t::array: { - JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end()); + JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end()); return *m_it.array_iterator; } @@ -13111,17 +13194,17 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { - JSON_ASSERT(m_it.object_iterator != m_object->m_value.object->end()); + JSON_ASSERT(m_it.object_iterator != m_object->m_data.m_value.object->end()); return &(m_it.object_iterator->second); } case value_t::array: { - JSON_ASSERT(m_it.array_iterator != m_object->m_value.array->end()); + JSON_ASSERT(m_it.array_iterator != m_object->m_data.m_value.array->end()); return &*m_it.array_iterator; } @@ -13164,7 +13247,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { @@ -13215,7 +13298,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: { @@ -13262,7 +13345,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: return (m_it.object_iterator == other.m_it.object_iterator); @@ -13307,7 +13390,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(213, "cannot compare order of object iterators", m_object)); @@ -13363,7 +13446,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object)); @@ -13442,7 +13525,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(209, "cannot use offsets with object iterators", m_object)); @@ -13471,7 +13554,7 @@ class iter_impl // NOLINT(cppcoreguidelines-special-member-functions,hicpp-speci { JSON_ASSERT(m_object != nullptr); - switch (m_object->m_type) + switch (m_object->m_data.m_type) { case value_t::object: JSON_THROW(invalid_iterator::create(208, "cannot use operator[] for object iterators", m_object)); @@ -13541,10 +13624,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -13673,13 +13756,55 @@ NLOHMANN_JSON_NAMESPACE_END // #include +// #include +// __ _____ _____ _____ +// __| | __| | | | JSON for Modern C++ +// | | |__ | | | | | | version 3.11.3 +// |_____|_____|_____|_|___| https://github.com/nlohmann/json +// +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann +// SPDX-License-Identifier: MIT + + + +#include // conditional, is_same + +// #include + + +NLOHMANN_JSON_NAMESPACE_BEGIN +namespace detail +{ + +/*! +@brief Default base class of the @ref basic_json class. + +So that the correct implementations of the copy / move ctors / assign operators +of @ref basic_json do not require complex case distinctions +(no base class / custom base class used as customization point), +@ref basic_json always has a base class. +By default, this class is used because it is empty and thus has no effect +on the behavior of @ref basic_json. +*/ +struct json_default_base {}; + +template +using json_base_class = typename std::conditional < + std::is_same::value, + json_default_base, + T + >::type; + +} // namespace detail +NLOHMANN_JSON_NAMESPACE_END + // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -13911,7 +14036,7 @@ class json_pointer const char* p = s.c_str(); char* p_end = nullptr; errno = 0; // strtoull doesn't reset errno - unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int) + const unsigned long long res = std::strtoull(p, &p_end, 10); // NOLINT(runtime/int) if (p == p_end // invalid input or empty string || errno == ERANGE // out of range || JSON_HEDLEY_UNLIKELY(static_cast(p_end - p) != s.size())) // incomplete read @@ -14067,7 +14192,7 @@ class json_pointer if (reference_token == "-") { // explicitly treat "-" as index beyond the end - ptr = &ptr->operator[](ptr->m_value.array->size()); + ptr = &ptr->operator[](ptr->m_data.m_value.array->size()); } else { @@ -14119,7 +14244,7 @@ class json_pointer { // "-" always fails the range check JSON_THROW(detail::out_of_range::create(402, detail::concat( - "array index '-' (", std::to_string(ptr->m_value.array->size()), + "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr)); } @@ -14176,7 +14301,7 @@ class json_pointer if (JSON_HEDLEY_UNLIKELY(reference_token == "-")) { // "-" cannot be used for const access - JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_value.array->size()), ") is out of range"), ptr)); + JSON_THROW(detail::out_of_range::create(402, detail::concat("array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr)); } // use unchecked array access @@ -14226,7 +14351,7 @@ class json_pointer { // "-" always fails the range check JSON_THROW(detail::out_of_range::create(402, detail::concat( - "array index '-' (", std::to_string(ptr->m_value.array->size()), + "array index '-' (", std::to_string(ptr->m_data.m_value.array->size()), ") is out of range"), ptr)); } @@ -14421,7 +14546,7 @@ class json_pointer { case detail::value_t::array: { - if (value.m_value.array->empty()) + if (value.m_data.m_value.array->empty()) { // flatten empty array as null result[reference_string] = nullptr; @@ -14429,10 +14554,10 @@ class json_pointer else { // iterate array and use index as reference string - for (std::size_t i = 0; i < value.m_value.array->size(); ++i) + for (std::size_t i = 0; i < value.m_data.m_value.array->size(); ++i) { flatten(detail::concat(reference_string, '/', std::to_string(i)), - value.m_value.array->operator[](i), result); + value.m_data.m_value.array->operator[](i), result); } } break; @@ -14440,7 +14565,7 @@ class json_pointer case detail::value_t::object: { - if (value.m_value.object->empty()) + if (value.m_data.m_value.object->empty()) { // flatten empty object as null result[reference_string] = nullptr; @@ -14448,7 +14573,7 @@ class json_pointer else { // iterate object and use keys as reference string - for (const auto& element : *value.m_value.object) + for (const auto& element : *value.m_data.m_value.object) { flatten(detail::concat(reference_string, '/', detail::escape(element.first)), element.second, result); } @@ -14495,7 +14620,7 @@ class json_pointer BasicJsonType result; // iterate the JSON object values - for (const auto& element : *value.m_value.object) + for (const auto& element : *value.m_data.m_value.object) { if (JSON_HEDLEY_UNLIKELY(!element.second.is_primitive())) { @@ -14671,10 +14796,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -14763,10 +14888,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -14789,10 +14914,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -14978,7 +15103,7 @@ class binary_writer { case value_t::object: { - write_bson_object(*j.m_value.object); + write_bson_object(*j.m_data.m_value.object); break; } @@ -15013,7 +15138,7 @@ class binary_writer case value_t::boolean: { - oa->write_character(j.m_value.boolean + oa->write_character(j.m_data.m_value.boolean ? to_char_type(0xF5) : to_char_type(0xF4)); break; @@ -15021,42 +15146,42 @@ class binary_writer case value_t::number_integer: { - if (j.m_value.number_integer >= 0) + if (j.m_data.m_value.number_integer >= 0) { // CBOR does not differentiate between positive signed // integers and unsigned integers. Therefore, we used the // code from the value_t::number_unsigned case here. - if (j.m_value.number_integer <= 0x17) + if (j.m_data.m_value.number_integer <= 0x17) { - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x18)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x19)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x1A)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } else { oa->write_character(to_char_type(0x1B)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } } else { // The conversions below encode the sign in the first // byte, and the value is converted to a positive number. - const auto positive_number = -1 - j.m_value.number_integer; - if (j.m_value.number_integer >= -24) + const auto positive_number = -1 - j.m_data.m_value.number_integer; + if (j.m_data.m_value.number_integer >= -24) { write_number(static_cast(0x20 + positive_number)); } @@ -15086,52 +15211,52 @@ class binary_writer case value_t::number_unsigned: { - if (j.m_value.number_unsigned <= 0x17) + if (j.m_data.m_value.number_unsigned <= 0x17) { - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x18)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x19)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { oa->write_character(to_char_type(0x1A)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } else { oa->write_character(to_char_type(0x1B)); - write_number(static_cast(j.m_value.number_unsigned)); + write_number(static_cast(j.m_data.m_value.number_unsigned)); } break; } case value_t::number_float: { - if (std::isnan(j.m_value.number_float)) + if (std::isnan(j.m_data.m_value.number_float)) { // NaN is 0xf97e00 in CBOR oa->write_character(to_char_type(0xF9)); oa->write_character(to_char_type(0x7E)); oa->write_character(to_char_type(0x00)); } - else if (std::isinf(j.m_value.number_float)) + else if (std::isinf(j.m_data.m_value.number_float)) { // Infinity is 0xf97c00, -Infinity is 0xf9fc00 oa->write_character(to_char_type(0xf9)); - oa->write_character(j.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC)); + oa->write_character(j.m_data.m_value.number_float > 0 ? to_char_type(0x7C) : to_char_type(0xFC)); oa->write_character(to_char_type(0x00)); } else { - write_compact_float(j.m_value.number_float, detail::input_format_t::cbor); + write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::cbor); } break; } @@ -15139,7 +15264,7 @@ class binary_writer case value_t::string: { // step 1: write control byte and the string length - const auto N = j.m_value.string->size(); + const auto N = j.m_data.m_value.string->size(); if (N <= 0x17) { write_number(static_cast(0x60 + N)); @@ -15169,15 +15294,15 @@ class binary_writer // step 2: write the string oa->write_characters( - reinterpret_cast(j.m_value.string->c_str()), - j.m_value.string->size()); + reinterpret_cast(j.m_data.m_value.string->c_str()), + j.m_data.m_value.string->size()); break; } case value_t::array: { // step 1: write control byte and the array size - const auto N = j.m_value.array->size(); + const auto N = j.m_data.m_value.array->size(); if (N <= 0x17) { write_number(static_cast(0x80 + N)); @@ -15206,7 +15331,7 @@ class binary_writer // LCOV_EXCL_STOP // step 2: write each element - for (const auto& el : *j.m_value.array) + for (const auto& el : *j.m_data.m_value.array) { write_cbor(el); } @@ -15215,32 +15340,32 @@ class binary_writer case value_t::binary: { - if (j.m_value.binary->has_subtype()) + if (j.m_data.m_value.binary->has_subtype()) { - if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xd8)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } - else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xd9)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } - else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xda)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } - else if (j.m_value.binary->subtype() <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.binary->subtype() <= (std::numeric_limits::max)()) { write_number(static_cast(0xdb)); - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } } // step 1: write control byte and the binary array size - const auto N = j.m_value.binary->size(); + const auto N = j.m_data.m_value.binary->size(); if (N <= 0x17) { write_number(static_cast(0x40 + N)); @@ -15270,7 +15395,7 @@ class binary_writer // step 2: write each element oa->write_characters( - reinterpret_cast(j.m_value.binary->data()), + reinterpret_cast(j.m_data.m_value.binary->data()), N); break; @@ -15279,7 +15404,7 @@ class binary_writer case value_t::object: { // step 1: write control byte and the object size - const auto N = j.m_value.object->size(); + const auto N = j.m_data.m_value.object->size(); if (N <= 0x17) { write_number(static_cast(0xA0 + N)); @@ -15308,7 +15433,7 @@ class binary_writer // LCOV_EXCL_STOP // step 2: write each element - for (const auto& el : *j.m_value.object) + for (const auto& el : *j.m_data.m_value.object) { write_cbor(el.first); write_cbor(el.second); @@ -15337,7 +15462,7 @@ class binary_writer case value_t::boolean: // true and false { - oa->write_character(j.m_value.boolean + oa->write_character(j.m_data.m_value.boolean ? to_char_type(0xC3) : to_char_type(0xC2)); break; @@ -15345,75 +15470,75 @@ class binary_writer case value_t::number_integer: { - if (j.m_value.number_integer >= 0) + if (j.m_data.m_value.number_integer >= 0) { // MessagePack does not differentiate between positive // signed integers and unsigned integers. Therefore, we used // the code from the value_t::number_unsigned case here. - if (j.m_value.number_unsigned < 128) + if (j.m_data.m_value.number_unsigned < 128) { // positive fixnum - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 8 oa->write_character(to_char_type(0xCC)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 16 oa->write_character(to_char_type(0xCD)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 32 oa->write_character(to_char_type(0xCE)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 64 oa->write_character(to_char_type(0xCF)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } } else { - if (j.m_value.number_integer >= -32) + if (j.m_data.m_value.number_integer >= -32) { // negative fixnum - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 8 oa->write_character(to_char_type(0xD0)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 16 oa->write_character(to_char_type(0xD1)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 32 oa->write_character(to_char_type(0xD2)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_integer >= (std::numeric_limits::min)() && - j.m_value.number_integer <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_integer >= (std::numeric_limits::min)() && + j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { // int 64 oa->write_character(to_char_type(0xD3)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } } break; @@ -15421,48 +15546,48 @@ class binary_writer case value_t::number_unsigned: { - if (j.m_value.number_unsigned < 128) + if (j.m_data.m_value.number_unsigned < 128) { // positive fixnum - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 8 oa->write_character(to_char_type(0xCC)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 16 oa->write_character(to_char_type(0xCD)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 32 oa->write_character(to_char_type(0xCE)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } - else if (j.m_value.number_unsigned <= (std::numeric_limits::max)()) + else if (j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { // uint 64 oa->write_character(to_char_type(0xCF)); - write_number(static_cast(j.m_value.number_integer)); + write_number(static_cast(j.m_data.m_value.number_integer)); } break; } case value_t::number_float: { - write_compact_float(j.m_value.number_float, detail::input_format_t::msgpack); + write_compact_float(j.m_data.m_value.number_float, detail::input_format_t::msgpack); break; } case value_t::string: { // step 1: write control byte and the string length - const auto N = j.m_value.string->size(); + const auto N = j.m_data.m_value.string->size(); if (N <= 31) { // fixstr @@ -15489,15 +15614,15 @@ class binary_writer // step 2: write the string oa->write_characters( - reinterpret_cast(j.m_value.string->c_str()), - j.m_value.string->size()); + reinterpret_cast(j.m_data.m_value.string->c_str()), + j.m_data.m_value.string->size()); break; } case value_t::array: { // step 1: write control byte and the array size - const auto N = j.m_value.array->size(); + const auto N = j.m_data.m_value.array->size(); if (N <= 15) { // fixarray @@ -15517,7 +15642,7 @@ class binary_writer } // step 2: write each element - for (const auto& el : *j.m_value.array) + for (const auto& el : *j.m_data.m_value.array) { write_msgpack(el); } @@ -15528,10 +15653,10 @@ class binary_writer { // step 0: determine if the binary type has a set subtype to // determine whether or not to use the ext or fixext types - const bool use_ext = j.m_value.binary->has_subtype(); + const bool use_ext = j.m_data.m_value.binary->has_subtype(); // step 1: write control byte and the byte string length - const auto N = j.m_value.binary->size(); + const auto N = j.m_data.m_value.binary->size(); if (N <= (std::numeric_limits::max)()) { std::uint8_t output_type{}; @@ -15576,18 +15701,18 @@ class binary_writer } else if (N <= (std::numeric_limits::max)()) { - std::uint8_t output_type = use_ext - ? 0xC8 // ext 16 - : 0xC5; // bin 16 + const std::uint8_t output_type = use_ext + ? 0xC8 // ext 16 + : 0xC5; // bin 16 oa->write_character(to_char_type(output_type)); write_number(static_cast(N)); } else if (N <= (std::numeric_limits::max)()) { - std::uint8_t output_type = use_ext - ? 0xC9 // ext 32 - : 0xC6; // bin 32 + const std::uint8_t output_type = use_ext + ? 0xC9 // ext 32 + : 0xC6; // bin 32 oa->write_character(to_char_type(output_type)); write_number(static_cast(N)); @@ -15596,12 +15721,12 @@ class binary_writer // step 1.5: if this is an ext type, write the subtype if (use_ext) { - write_number(static_cast(j.m_value.binary->subtype())); + write_number(static_cast(j.m_data.m_value.binary->subtype())); } // step 2: write the byte string oa->write_characters( - reinterpret_cast(j.m_value.binary->data()), + reinterpret_cast(j.m_data.m_value.binary->data()), N); break; @@ -15610,7 +15735,7 @@ class binary_writer case value_t::object: { // step 1: write control byte and the object size - const auto N = j.m_value.object->size(); + const auto N = j.m_data.m_value.object->size(); if (N <= 15) { // fixmap @@ -15630,7 +15755,7 @@ class binary_writer } // step 2: write each element - for (const auto& el : *j.m_value.object) + for (const auto& el : *j.m_data.m_value.object) { write_msgpack(el.first); write_msgpack(el.second); @@ -15670,7 +15795,7 @@ class binary_writer { if (add_prefix) { - oa->write_character(j.m_value.boolean + oa->write_character(j.m_data.m_value.boolean ? to_char_type('T') : to_char_type('F')); } @@ -15679,19 +15804,19 @@ class binary_writer case value_t::number_integer: { - write_number_with_ubjson_prefix(j.m_value.number_integer, add_prefix, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.number_integer, add_prefix, use_bjdata); break; } case value_t::number_unsigned: { - write_number_with_ubjson_prefix(j.m_value.number_unsigned, add_prefix, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.number_unsigned, add_prefix, use_bjdata); break; } case value_t::number_float: { - write_number_with_ubjson_prefix(j.m_value.number_float, add_prefix, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.number_float, add_prefix, use_bjdata); break; } @@ -15701,10 +15826,10 @@ class binary_writer { oa->write_character(to_char_type('S')); } - write_number_with_ubjson_prefix(j.m_value.string->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.string->size(), true, use_bjdata); oa->write_characters( - reinterpret_cast(j.m_value.string->c_str()), - j.m_value.string->size()); + reinterpret_cast(j.m_data.m_value.string->c_str()), + j.m_data.m_value.string->size()); break; } @@ -15716,7 +15841,7 @@ class binary_writer } bool prefix_required = true; - if (use_type && !j.m_value.array->empty()) + if (use_type && !j.m_data.m_value.array->empty()) { JSON_ASSERT(use_count); const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata); @@ -15739,10 +15864,10 @@ class binary_writer if (use_count) { oa->write_character(to_char_type('#')); - write_number_with_ubjson_prefix(j.m_value.array->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.array->size(), true, use_bjdata); } - for (const auto& el : *j.m_value.array) + for (const auto& el : *j.m_data.m_value.array) { write_ubjson(el, use_count, use_type, prefix_required, use_bjdata); } @@ -15762,7 +15887,7 @@ class binary_writer oa->write_character(to_char_type('[')); } - if (use_type && !j.m_value.binary->empty()) + if (use_type && !j.m_data.m_value.binary->empty()) { JSON_ASSERT(use_count); oa->write_character(to_char_type('$')); @@ -15772,21 +15897,21 @@ class binary_writer if (use_count) { oa->write_character(to_char_type('#')); - write_number_with_ubjson_prefix(j.m_value.binary->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.binary->size(), true, use_bjdata); } if (use_type) { oa->write_characters( - reinterpret_cast(j.m_value.binary->data()), - j.m_value.binary->size()); + reinterpret_cast(j.m_data.m_value.binary->data()), + j.m_data.m_value.binary->size()); } else { - for (size_t i = 0; i < j.m_value.binary->size(); ++i) + for (size_t i = 0; i < j.m_data.m_value.binary->size(); ++i) { oa->write_character(to_char_type('U')); - oa->write_character(j.m_value.binary->data()[i]); + oa->write_character(j.m_data.m_value.binary->data()[i]); } } @@ -15800,9 +15925,9 @@ class binary_writer case value_t::object: { - if (use_bjdata && j.m_value.object->size() == 3 && j.m_value.object->find("_ArrayType_") != j.m_value.object->end() && j.m_value.object->find("_ArraySize_") != j.m_value.object->end() && j.m_value.object->find("_ArrayData_") != j.m_value.object->end()) + if (use_bjdata && j.m_data.m_value.object->size() == 3 && j.m_data.m_value.object->find("_ArrayType_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArraySize_") != j.m_data.m_value.object->end() && j.m_data.m_value.object->find("_ArrayData_") != j.m_data.m_value.object->end()) { - if (!write_bjdata_ndarray(*j.m_value.object, use_count, use_type)) // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata) + if (!write_bjdata_ndarray(*j.m_data.m_value.object, use_count, use_type)) // decode bjdata ndarray in the JData format (https://github.com/NeuroJSON/jdata) { break; } @@ -15814,7 +15939,7 @@ class binary_writer } bool prefix_required = true; - if (use_type && !j.m_value.object->empty()) + if (use_type && !j.m_data.m_value.object->empty()) { JSON_ASSERT(use_count); const CharType first_prefix = ubjson_prefix(j.front(), use_bjdata); @@ -15837,10 +15962,10 @@ class binary_writer if (use_count) { oa->write_character(to_char_type('#')); - write_number_with_ubjson_prefix(j.m_value.object->size(), true, use_bjdata); + write_number_with_ubjson_prefix(j.m_data.m_value.object->size(), true, use_bjdata); } - for (const auto& el : *j.m_value.object) + for (const auto& el : *j.m_data.m_value.object) { write_number_with_ubjson_prefix(el.first.size(), true, use_bjdata); oa->write_characters( @@ -15990,19 +16115,19 @@ class binary_writer void write_bson_unsigned(const string_t& name, const BasicJsonType& j) { - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { write_bson_entry_header(name, 0x10 /* int32 */); - write_number(static_cast(j.m_value.number_unsigned), true); + write_number(static_cast(j.m_data.m_value.number_unsigned), true); } - else if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + else if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { write_bson_entry_header(name, 0x12 /* int64 */); - write_number(static_cast(j.m_value.number_unsigned), true); + write_number(static_cast(j.m_data.m_value.number_unsigned), true); } else { - JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j)); + JSON_THROW(out_of_range::create(407, concat("integer number ", std::to_string(j.m_data.m_value.number_unsigned), " cannot be represented by BSON as it does not fit int64"), &j)); } } @@ -16083,13 +16208,13 @@ class binary_writer switch (j.type()) { case value_t::object: - return header_size + calc_bson_object_size(*j.m_value.object); + return header_size + calc_bson_object_size(*j.m_data.m_value.object); case value_t::array: - return header_size + calc_bson_array_size(*j.m_value.array); + return header_size + calc_bson_array_size(*j.m_data.m_value.array); case value_t::binary: - return header_size + calc_bson_binary_size(*j.m_value.binary); + return header_size + calc_bson_binary_size(*j.m_data.m_value.binary); case value_t::boolean: return header_size + 1ul; @@ -16098,13 +16223,13 @@ class binary_writer return header_size + 8ul; case value_t::number_integer: - return header_size + calc_bson_integer_size(j.m_value.number_integer); + return header_size + calc_bson_integer_size(j.m_data.m_value.number_integer); case value_t::number_unsigned: - return header_size + calc_bson_unsigned_size(j.m_value.number_unsigned); + return header_size + calc_bson_unsigned_size(j.m_data.m_value.number_unsigned); case value_t::string: - return header_size + calc_bson_string_size(*j.m_value.string); + return header_size + calc_bson_string_size(*j.m_data.m_value.string); case value_t::null: return header_size + 0ul; @@ -16130,28 +16255,28 @@ class binary_writer switch (j.type()) { case value_t::object: - return write_bson_object_entry(name, *j.m_value.object); + return write_bson_object_entry(name, *j.m_data.m_value.object); case value_t::array: - return write_bson_array(name, *j.m_value.array); + return write_bson_array(name, *j.m_data.m_value.array); case value_t::binary: - return write_bson_binary(name, *j.m_value.binary); + return write_bson_binary(name, *j.m_data.m_value.binary); case value_t::boolean: - return write_bson_boolean(name, j.m_value.boolean); + return write_bson_boolean(name, j.m_data.m_value.boolean); case value_t::number_float: - return write_bson_double(name, j.m_value.number_float); + return write_bson_double(name, j.m_data.m_value.number_float); case value_t::number_integer: - return write_bson_integer(name, j.m_value.number_integer); + return write_bson_integer(name, j.m_data.m_value.number_integer); case value_t::number_unsigned: return write_bson_unsigned(name, j); case value_t::string: - return write_bson_string(name, *j.m_value.string); + return write_bson_string(name, *j.m_data.m_value.string); case value_t::null: return write_bson_null(name); @@ -16173,8 +16298,8 @@ class binary_writer */ static std::size_t calc_bson_object_size(const typename BasicJsonType::object_t& value) { - std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast(0), - [](size_t result, const typename BasicJsonType::object_t::value_type & el) + const std::size_t document_size = std::accumulate(value.begin(), value.end(), static_cast(0), + [](size_t result, const typename BasicJsonType::object_t::value_type & el) { return result += calc_bson_element_size(el.first, el.second); }); @@ -16424,35 +16549,35 @@ class binary_writer return 'Z'; case value_t::boolean: - return j.m_value.boolean ? 'T' : 'F'; + return j.m_data.m_value.boolean ? 'T' : 'F'; case value_t::number_integer: { - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'i'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'U'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'I'; } - if (use_bjdata && ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)())) + if (use_bjdata && ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)())) { return 'u'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'l'; } - if (use_bjdata && ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)())) + if (use_bjdata && ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)())) { return 'm'; } - if ((std::numeric_limits::min)() <= j.m_value.number_integer && j.m_value.number_integer <= (std::numeric_limits::max)()) + if ((std::numeric_limits::min)() <= j.m_data.m_value.number_integer && j.m_data.m_value.number_integer <= (std::numeric_limits::max)()) { return 'L'; } @@ -16462,35 +16587,35 @@ class binary_writer case value_t::number_unsigned: { - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'i'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'U'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'I'; } - if (use_bjdata && j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'u'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'l'; } - if (use_bjdata && j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (use_bjdata && j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'm'; } - if (j.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) + if (j.m_data.m_value.number_unsigned <= static_cast((std::numeric_limits::max)())) { return 'L'; } - if (use_bjdata && j.m_value.number_unsigned <= (std::numeric_limits::max)()) + if (use_bjdata && j.m_data.m_value.number_unsigned <= (std::numeric_limits::max)()) { return 'M'; } @@ -16499,7 +16624,7 @@ class binary_writer } case value_t::number_float: - return get_ubjson_float_prefix(j.m_value.number_float); + return get_ubjson_float_prefix(j.m_data.m_value.number_float); case value_t::string: return 'S'; @@ -16548,7 +16673,7 @@ class binary_writer std::size_t len = (value.at(key).empty() ? 0 : 1); for (const auto& el : value.at(key)) { - len *= static_cast(el.m_value.number_unsigned); + len *= static_cast(el.m_data.m_value.number_unsigned); } key = "_ArrayData_"; @@ -16570,70 +16695,70 @@ class binary_writer { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'i') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'u') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'I') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'm') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'l') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'M') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_unsigned), true); + write_number(static_cast(el.m_data.m_value.number_unsigned), true); } } else if (dtype == 'L') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_integer), true); + write_number(static_cast(el.m_data.m_value.number_integer), true); } } else if (dtype == 'd') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_float), true); + write_number(static_cast(el.m_data.m_value.number_float), true); } } else if (dtype == 'D') { for (const auto& el : value.at(key)) { - write_number(static_cast(el.m_value.number_float), true); + write_number(static_cast(el.m_data.m_value.number_float), true); } } return false; @@ -16757,11 +16882,11 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // // SPDX-FileCopyrightText: 2008-2009 Björn Hoehrmann -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -16782,11 +16907,11 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // // SPDX-FileCopyrightText: 2009 Florian Loitsch -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -17692,7 +17817,7 @@ void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value) // NB: If the neighbors are computed for single-precision numbers, there is a single float // (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision // value is off by 1 ulp. -#if 0 +#if 0 // NOLINT(readability-avoid-unconditional-preprocessor-if) const boundaries w = compute_boundaries(static_cast(value)); #else const boundaries w = compute_boundaries(value); @@ -17994,11 +18119,11 @@ class serializer const unsigned int indent_step, const unsigned int current_indent = 0) { - switch (val.m_type) + switch (val.m_data.m_type) { case value_t::object: { - if (val.m_value.object->empty()) + if (val.m_data.m_value.object->empty()) { o->write_characters("{}", 2); return; @@ -18016,8 +18141,8 @@ class serializer } // first n-1 elements - auto i = val.m_value.object->cbegin(); - for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i) + auto i = val.m_data.m_value.object->cbegin(); + for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i) { o->write_characters(indent_string.c_str(), new_indent); o->write_character('\"'); @@ -18028,8 +18153,8 @@ class serializer } // last element - JSON_ASSERT(i != val.m_value.object->cend()); - JSON_ASSERT(std::next(i) == val.m_value.object->cend()); + JSON_ASSERT(i != val.m_data.m_value.object->cend()); + JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend()); o->write_characters(indent_string.c_str(), new_indent); o->write_character('\"'); dump_escaped(i->first, ensure_ascii); @@ -18045,8 +18170,8 @@ class serializer o->write_character('{'); // first n-1 elements - auto i = val.m_value.object->cbegin(); - for (std::size_t cnt = 0; cnt < val.m_value.object->size() - 1; ++cnt, ++i) + auto i = val.m_data.m_value.object->cbegin(); + for (std::size_t cnt = 0; cnt < val.m_data.m_value.object->size() - 1; ++cnt, ++i) { o->write_character('\"'); dump_escaped(i->first, ensure_ascii); @@ -18056,8 +18181,8 @@ class serializer } // last element - JSON_ASSERT(i != val.m_value.object->cend()); - JSON_ASSERT(std::next(i) == val.m_value.object->cend()); + JSON_ASSERT(i != val.m_data.m_value.object->cend()); + JSON_ASSERT(std::next(i) == val.m_data.m_value.object->cend()); o->write_character('\"'); dump_escaped(i->first, ensure_ascii); o->write_characters("\":", 2); @@ -18071,7 +18196,7 @@ class serializer case value_t::array: { - if (val.m_value.array->empty()) + if (val.m_data.m_value.array->empty()) { o->write_characters("[]", 2); return; @@ -18089,8 +18214,8 @@ class serializer } // first n-1 elements - for (auto i = val.m_value.array->cbegin(); - i != val.m_value.array->cend() - 1; ++i) + for (auto i = val.m_data.m_value.array->cbegin(); + i != val.m_data.m_value.array->cend() - 1; ++i) { o->write_characters(indent_string.c_str(), new_indent); dump(*i, true, ensure_ascii, indent_step, new_indent); @@ -18098,9 +18223,9 @@ class serializer } // last element - JSON_ASSERT(!val.m_value.array->empty()); + JSON_ASSERT(!val.m_data.m_value.array->empty()); o->write_characters(indent_string.c_str(), new_indent); - dump(val.m_value.array->back(), true, ensure_ascii, indent_step, new_indent); + dump(val.m_data.m_value.array->back(), true, ensure_ascii, indent_step, new_indent); o->write_character('\n'); o->write_characters(indent_string.c_str(), current_indent); @@ -18111,16 +18236,16 @@ class serializer o->write_character('['); // first n-1 elements - for (auto i = val.m_value.array->cbegin(); - i != val.m_value.array->cend() - 1; ++i) + for (auto i = val.m_data.m_value.array->cbegin(); + i != val.m_data.m_value.array->cend() - 1; ++i) { dump(*i, false, ensure_ascii, indent_step, current_indent); o->write_character(','); } // last element - JSON_ASSERT(!val.m_value.array->empty()); - dump(val.m_value.array->back(), false, ensure_ascii, indent_step, current_indent); + JSON_ASSERT(!val.m_data.m_value.array->empty()); + dump(val.m_data.m_value.array->back(), false, ensure_ascii, indent_step, current_indent); o->write_character(']'); } @@ -18131,7 +18256,7 @@ class serializer case value_t::string: { o->write_character('\"'); - dump_escaped(*val.m_value.string, ensure_ascii); + dump_escaped(*val.m_data.m_value.string, ensure_ascii); o->write_character('\"'); return; } @@ -18153,24 +18278,24 @@ class serializer o->write_characters("\"bytes\": [", 10); - if (!val.m_value.binary->empty()) + if (!val.m_data.m_value.binary->empty()) { - for (auto i = val.m_value.binary->cbegin(); - i != val.m_value.binary->cend() - 1; ++i) + for (auto i = val.m_data.m_value.binary->cbegin(); + i != val.m_data.m_value.binary->cend() - 1; ++i) { dump_integer(*i); o->write_characters(", ", 2); } - dump_integer(val.m_value.binary->back()); + dump_integer(val.m_data.m_value.binary->back()); } o->write_characters("],\n", 3); o->write_characters(indent_string.c_str(), new_indent); o->write_characters("\"subtype\": ", 11); - if (val.m_value.binary->has_subtype()) + if (val.m_data.m_value.binary->has_subtype()) { - dump_integer(val.m_value.binary->subtype()); + dump_integer(val.m_data.m_value.binary->subtype()); } else { @@ -18184,21 +18309,21 @@ class serializer { o->write_characters("{\"bytes\":[", 10); - if (!val.m_value.binary->empty()) + if (!val.m_data.m_value.binary->empty()) { - for (auto i = val.m_value.binary->cbegin(); - i != val.m_value.binary->cend() - 1; ++i) + for (auto i = val.m_data.m_value.binary->cbegin(); + i != val.m_data.m_value.binary->cend() - 1; ++i) { dump_integer(*i); o->write_character(','); } - dump_integer(val.m_value.binary->back()); + dump_integer(val.m_data.m_value.binary->back()); } o->write_characters("],\"subtype\":", 12); - if (val.m_value.binary->has_subtype()) + if (val.m_data.m_value.binary->has_subtype()) { - dump_integer(val.m_value.binary->subtype()); + dump_integer(val.m_data.m_value.binary->subtype()); o->write_character('}'); } else @@ -18211,7 +18336,7 @@ class serializer case value_t::boolean: { - if (val.m_value.boolean) + if (val.m_data.m_value.boolean) { o->write_characters("true", 4); } @@ -18224,19 +18349,19 @@ class serializer case value_t::number_integer: { - dump_integer(val.m_value.number_integer); + dump_integer(val.m_data.m_value.number_integer); return; } case value_t::number_unsigned: { - dump_integer(val.m_value.number_unsigned); + dump_integer(val.m_data.m_value.number_unsigned); return; } case value_t::number_float: { - dump_float(val.m_value.number_float); + dump_float(val.m_data.m_value.number_float); return; } @@ -18810,8 +18935,8 @@ class serializer ? (byte & 0x3fu) | (codep << 6u) : (0xFFu >> type) & (byte); - std::size_t index = 256u + static_cast(state) * 16u + static_cast(type); - JSON_ASSERT(index < 400); + const std::size_t index = 256u + static_cast(state) * 16u + static_cast(type); + JSON_ASSERT(index < utf8d.size()); state = utf8d[index]; return state; } @@ -18878,10 +19003,10 @@ NLOHMANN_JSON_NAMESPACE_END // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -18998,7 +19123,7 @@ template , template::value, int> = 0> - T & at(KeyType && key) + T & at(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19026,7 +19151,7 @@ template , template::value, int> = 0> - const T & at(KeyType && key) const + const T & at(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19060,7 +19185,7 @@ template , template::value, int> = 0> - size_type erase(KeyType && key) + size_type erase(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19151,7 +19276,7 @@ template , template::value, int> = 0> - size_type count(KeyType && key) const + size_type count(KeyType && key) const // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19177,7 +19302,7 @@ template , template::value, int> = 0> - iterator find(KeyType && key) + iterator find(KeyType && key) // NOLINT(cppcoreguidelines-missing-std-forward) { for (auto it = this->begin(); it != this->end(); ++it) { @@ -19240,7 +19365,9 @@ NLOHMANN_JSON_NAMESPACE_END #if defined(JSON_HAS_CPP_17) - #include + #if JSON_HAS_STATIC_RTTI + #include + #endif #include #endif @@ -19271,6 +19398,7 @@ The invariants are checked by member function assert_invariant(). */ NLOHMANN_BASIC_JSON_TPL_DECLARATION class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-special-member-functions) + : public ::nlohmann::detail::json_base_class { private: template friend struct detail::external_constructor; @@ -19297,6 +19425,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// workaround type for MSVC using basic_json_t = NLOHMANN_BASIC_JSON_TPL; + using json_base_class_t = ::nlohmann::detail::json_base_class; JSON_PRIVATE_UNLESS_TESTED: // convenience aliases for types residing in namespace detail; @@ -19368,7 +19497,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - ///////////////////// // container types // ///////////////////// @@ -19410,7 +19538,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - /// @brief returns the allocator associated with the container /// @sa https://json.nlohmann.me/api/basic_json/get_allocator/ static allocator_type get_allocator() @@ -19425,7 +19552,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { basic_json result; - result["copyright"] = "(C) 2013-2022 Niels Lohmann"; + result["copyright"] = "(C) 2013-2023 Niels Lohmann"; result["name"] = "JSON for Modern C++"; result["url"] = "https://github.com/nlohmann/json"; result["version"]["string"] = @@ -19473,7 +19600,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec result["compiler"] = {{"family", "unknown"}, {"version", "unknown"}}; #endif - #if defined(_MSVC_LANG) result["compiler"]["c++"] = std::to_string(_MSVC_LANG); #elif defined(__cplusplus) @@ -19484,7 +19610,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec return result; } - /////////////////////////// // JSON value data types // /////////////////////////// @@ -19692,7 +19817,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec object = nullptr; // silence warning, see #821 if (JSON_HEDLEY_UNLIKELY(t == value_t::null)) { - JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.2", nullptr)); // LCOV_EXCL_LINE + JSON_THROW(other_error::create(500, "961c151d2e87f2686a955a9be24d316f1362bf21 3.11.3", nullptr)); // LCOV_EXCL_LINE } break; } @@ -19731,6 +19856,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec void destroy(value_t t) { + if ( + (t == value_t::object && object == nullptr) || + (t == value_t::array && array == nullptr) || + (t == value_t::string && string == nullptr) || + (t == value_t::binary && binary == nullptr) + ) + { + //not initialized (e.g. due to exception in the ctor) + return; + } if (t == value_t::array || t == value_t::object) { // flatten the current json_value to a heap-allocated stack @@ -19761,18 +19896,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // its children to the stack to be processed later if (current_item.is_array()) { - std::move(current_item.m_value.array->begin(), current_item.m_value.array->end(), std::back_inserter(stack)); + std::move(current_item.m_data.m_value.array->begin(), current_item.m_data.m_value.array->end(), std::back_inserter(stack)); - current_item.m_value.array->clear(); + current_item.m_data.m_value.array->clear(); } else if (current_item.is_object()) { - for (auto&& it : *current_item.m_value.object) + for (auto&& it : *current_item.m_data.m_value.object) { stack.push_back(std::move(it.second)); } - current_item.m_value.object->clear(); + current_item.m_data.m_value.object->clear(); } // it's now safe that current_item get destructed @@ -19849,10 +19984,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec */ void assert_invariant(bool check_parents = true) const noexcept { - JSON_ASSERT(m_type != value_t::object || m_value.object != nullptr); - JSON_ASSERT(m_type != value_t::array || m_value.array != nullptr); - JSON_ASSERT(m_type != value_t::string || m_value.string != nullptr); - JSON_ASSERT(m_type != value_t::binary || m_value.binary != nullptr); + JSON_ASSERT(m_data.m_type != value_t::object || m_data.m_value.object != nullptr); + JSON_ASSERT(m_data.m_type != value_t::array || m_data.m_value.array != nullptr); + JSON_ASSERT(m_data.m_type != value_t::string || m_data.m_value.string != nullptr); + JSON_ASSERT(m_data.m_type != value_t::binary || m_data.m_value.binary != nullptr); #if JSON_DIAGNOSTICS JSON_TRY @@ -19871,11 +20006,11 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec void set_parents() { #if JSON_DIAGNOSTICS - switch (m_type) + switch (m_data.m_type) { case value_t::array: { - for (auto& element : *m_value.array) + for (auto& element : *m_data.m_value.array) { element.m_parent = this; } @@ -19884,7 +20019,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec case value_t::object: { - for (auto& element : *m_value.object) + for (auto& element : *m_data.m_value.object) { element.second.m_parent = this; } @@ -19925,7 +20060,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { // see https://github.com/nlohmann/json/issues/2838 JSON_ASSERT(type() == value_t::array); - if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity)) + if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity)) { // capacity has changed: update all parents set_parents(); @@ -19981,7 +20116,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief create an empty value with a given type /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ basic_json(const value_t v) - : m_type(v), m_value(v) + : m_data(v) { assert_invariant(); } @@ -20055,12 +20190,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec *this = nullptr; break; case value_t::discarded: - m_type = value_t::discarded; + m_data.m_type = value_t::discarded; break; default: // LCOV_EXCL_LINE JSON_ASSERT(false); // NOLINT(cert-dcl03-c,hicpp-static-assert,misc-static-assert) LCOV_EXCL_LINE } - JSON_ASSERT(m_type == val.type()); + JSON_ASSERT(m_data.m_type == val.type()); set_parents(); assert_invariant(); } @@ -20076,7 +20211,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec bool is_an_object = std::all_of(init.begin(), init.end(), [](const detail::json_ref& element_ref) { - return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[0].is_string(); + // The cast is to ensure op[size_type] is called, bearing in mind size_type may not be int; + // (many string types can be constructed from 0 via its null-pointer guise, so we get a + // broken call to op[key_type], the wrong semantics and a 4804 warning on Windows) + return element_ref->is_array() && element_ref->size() == 2 && (*element_ref)[static_cast(0)].is_string(); }); // adjust type if type deduction is not wanted @@ -20098,22 +20236,22 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_an_object) { // the initializer list is a list of pairs -> create object - m_type = value_t::object; - m_value = value_t::object; + m_data.m_type = value_t::object; + m_data.m_value = value_t::object; for (auto& element_ref : init) { auto element = element_ref.moved_or_copied(); - m_value.object->emplace( - std::move(*((*element.m_value.array)[0].m_value.string)), - std::move((*element.m_value.array)[1])); + m_data.m_value.object->emplace( + std::move(*((*element.m_data.m_value.array)[0].m_data.m_value.string)), + std::move((*element.m_data.m_value.array)[1])); } } else { // the initializer list describes an array -> create array - m_type = value_t::array; - m_value.array = create(init.begin(), init.end()); + m_data.m_type = value_t::array; + m_data.m_value.array = create(init.begin(), init.end()); } set_parents(); @@ -20126,8 +20264,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(const typename binary_t::container_type& init) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = init; + res.m_data.m_type = value_t::binary; + res.m_data.m_value = init; return res; } @@ -20137,8 +20275,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(const typename binary_t::container_type& init, typename binary_t::subtype_type subtype) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = binary_t(init, subtype); + res.m_data.m_type = value_t::binary; + res.m_data.m_value = binary_t(init, subtype); return res; } @@ -20148,8 +20286,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(typename binary_t::container_type&& init) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = std::move(init); + res.m_data.m_type = value_t::binary; + res.m_data.m_value = std::move(init); return res; } @@ -20159,8 +20297,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec static basic_json binary(typename binary_t::container_type&& init, typename binary_t::subtype_type subtype) { auto res = basic_json(); - res.m_type = value_t::binary; - res.m_value = binary_t(std::move(init), subtype); + res.m_data.m_type = value_t::binary; + res.m_data.m_value = binary_t(std::move(init), subtype); return res; } @@ -20182,10 +20320,9 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief construct an array with count copies of given value /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ - basic_json(size_type cnt, const basic_json& val) - : m_type(value_t::array) + basic_json(size_type cnt, const basic_json& val): + m_data{cnt, val} { - m_value.array = create(cnt, val); set_parents(); assert_invariant(); } @@ -20207,10 +20344,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } // copy type from first iterator - m_type = first.m_object->m_type; + m_data.m_type = first.m_object->m_data.m_type; // check if iterator range is complete for primitive values - switch (m_type) + switch (m_data.m_type) { case value_t::boolean: case value_t::number_float: @@ -20235,55 +20372,55 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec break; } - switch (m_type) + switch (m_data.m_type) { case value_t::number_integer: { - m_value.number_integer = first.m_object->m_value.number_integer; + m_data.m_value.number_integer = first.m_object->m_data.m_value.number_integer; break; } case value_t::number_unsigned: { - m_value.number_unsigned = first.m_object->m_value.number_unsigned; + m_data.m_value.number_unsigned = first.m_object->m_data.m_value.number_unsigned; break; } case value_t::number_float: { - m_value.number_float = first.m_object->m_value.number_float; + m_data.m_value.number_float = first.m_object->m_data.m_value.number_float; break; } case value_t::boolean: { - m_value.boolean = first.m_object->m_value.boolean; + m_data.m_value.boolean = first.m_object->m_data.m_value.boolean; break; } case value_t::string: { - m_value = *first.m_object->m_value.string; + m_data.m_value = *first.m_object->m_data.m_value.string; break; } case value_t::object: { - m_value.object = create(first.m_it.object_iterator, - last.m_it.object_iterator); + m_data.m_value.object = create(first.m_it.object_iterator, + last.m_it.object_iterator); break; } case value_t::array: { - m_value.array = create(first.m_it.array_iterator, - last.m_it.array_iterator); + m_data.m_value.array = create(first.m_it.array_iterator, + last.m_it.array_iterator); break; } case value_t::binary: { - m_value = *first.m_object->m_value.binary; + m_data.m_value = *first.m_object->m_data.m_value.binary; break; } @@ -20297,7 +20434,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec assert_invariant(); } - /////////////////////////////////////// // other constructors and destructor // /////////////////////////////////////// @@ -20310,58 +20446,59 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief copy constructor /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ basic_json(const basic_json& other) - : m_type(other.m_type) + : json_base_class_t(other) { + m_data.m_type = other.m_data.m_type; // check of passed value is valid other.assert_invariant(); - switch (m_type) + switch (m_data.m_type) { case value_t::object: { - m_value = *other.m_value.object; + m_data.m_value = *other.m_data.m_value.object; break; } case value_t::array: { - m_value = *other.m_value.array; + m_data.m_value = *other.m_data.m_value.array; break; } case value_t::string: { - m_value = *other.m_value.string; + m_data.m_value = *other.m_data.m_value.string; break; } case value_t::boolean: { - m_value = other.m_value.boolean; + m_data.m_value = other.m_data.m_value.boolean; break; } case value_t::number_integer: { - m_value = other.m_value.number_integer; + m_data.m_value = other.m_data.m_value.number_integer; break; } case value_t::number_unsigned: { - m_value = other.m_value.number_unsigned; + m_data.m_value = other.m_data.m_value.number_unsigned; break; } case value_t::number_float: { - m_value = other.m_value.number_float; + m_data.m_value = other.m_data.m_value.number_float; break; } case value_t::binary: { - m_value = *other.m_value.binary; + m_data.m_value = *other.m_data.m_value.binary; break; } @@ -20378,15 +20515,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief move constructor /// @sa https://json.nlohmann.me/api/basic_json/basic_json/ basic_json(basic_json&& other) noexcept - : m_type(std::move(other.m_type)), - m_value(std::move(other.m_value)) + : json_base_class_t(std::forward(other)), + m_data(std::move(other.m_data)) { // check that passed value is valid other.assert_invariant(false); // invalidate payload - other.m_type = value_t::null; - other.m_value = {}; + other.m_data.m_type = value_t::null; + other.m_data.m_value = {}; set_parents(); assert_invariant(); @@ -20398,15 +20535,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec std::is_nothrow_move_constructible::value&& std::is_nothrow_move_assignable::value&& std::is_nothrow_move_constructible::value&& - std::is_nothrow_move_assignable::value + std::is_nothrow_move_assignable::value&& + std::is_nothrow_move_assignable::value ) { // check that passed value is valid other.assert_invariant(); using std::swap; - swap(m_type, other.m_type); - swap(m_value, other.m_value); + swap(m_data.m_type, other.m_data.m_type); + swap(m_data.m_value, other.m_data.m_value); + json_base_class_t::operator=(std::move(other)); set_parents(); assert_invariant(); @@ -20418,7 +20557,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec ~basic_json() noexcept { assert_invariant(false); - m_value.destroy(m_type); } /// @} @@ -20458,7 +20596,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/type/ constexpr value_t type() const noexcept { - return m_type; + return m_data.m_type; } /// @brief return whether type is primitive @@ -20479,14 +20617,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/is_null/ constexpr bool is_null() const noexcept { - return m_type == value_t::null; + return m_data.m_type == value_t::null; } /// @brief return whether value is a boolean /// @sa https://json.nlohmann.me/api/basic_json/is_boolean/ constexpr bool is_boolean() const noexcept { - return m_type == value_t::boolean; + return m_data.m_type == value_t::boolean; } /// @brief return whether value is a number @@ -20500,63 +20638,63 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/is_number_integer/ constexpr bool is_number_integer() const noexcept { - return m_type == value_t::number_integer || m_type == value_t::number_unsigned; + return m_data.m_type == value_t::number_integer || m_data.m_type == value_t::number_unsigned; } /// @brief return whether value is an unsigned integer number /// @sa https://json.nlohmann.me/api/basic_json/is_number_unsigned/ constexpr bool is_number_unsigned() const noexcept { - return m_type == value_t::number_unsigned; + return m_data.m_type == value_t::number_unsigned; } /// @brief return whether value is a floating-point number /// @sa https://json.nlohmann.me/api/basic_json/is_number_float/ constexpr bool is_number_float() const noexcept { - return m_type == value_t::number_float; + return m_data.m_type == value_t::number_float; } /// @brief return whether value is an object /// @sa https://json.nlohmann.me/api/basic_json/is_object/ constexpr bool is_object() const noexcept { - return m_type == value_t::object; + return m_data.m_type == value_t::object; } /// @brief return whether value is an array /// @sa https://json.nlohmann.me/api/basic_json/is_array/ constexpr bool is_array() const noexcept { - return m_type == value_t::array; + return m_data.m_type == value_t::array; } /// @brief return whether value is a string /// @sa https://json.nlohmann.me/api/basic_json/is_string/ constexpr bool is_string() const noexcept { - return m_type == value_t::string; + return m_data.m_type == value_t::string; } /// @brief return whether value is a binary array /// @sa https://json.nlohmann.me/api/basic_json/is_binary/ constexpr bool is_binary() const noexcept { - return m_type == value_t::binary; + return m_data.m_type == value_t::binary; } /// @brief return whether value is discarded /// @sa https://json.nlohmann.me/api/basic_json/is_discarded/ constexpr bool is_discarded() const noexcept { - return m_type == value_t::discarded; + return m_data.m_type == value_t::discarded; } /// @brief return the type of the JSON value (implicit) /// @sa https://json.nlohmann.me/api/basic_json/operator_value_t/ constexpr operator value_t() const noexcept { - return m_type; + return m_data.m_type; } /// @} @@ -20571,7 +20709,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { if (JSON_HEDLEY_LIKELY(is_boolean())) { - return m_value.boolean; + return m_data.m_value.boolean; } JSON_THROW(type_error::create(302, detail::concat("type must be boolean, but is ", type_name()), this)); @@ -20580,97 +20718,97 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// get a pointer to the value (object) object_t* get_impl_ptr(object_t* /*unused*/) noexcept { - return is_object() ? m_value.object : nullptr; + return is_object() ? m_data.m_value.object : nullptr; } /// get a pointer to the value (object) constexpr const object_t* get_impl_ptr(const object_t* /*unused*/) const noexcept { - return is_object() ? m_value.object : nullptr; + return is_object() ? m_data.m_value.object : nullptr; } /// get a pointer to the value (array) array_t* get_impl_ptr(array_t* /*unused*/) noexcept { - return is_array() ? m_value.array : nullptr; + return is_array() ? m_data.m_value.array : nullptr; } /// get a pointer to the value (array) constexpr const array_t* get_impl_ptr(const array_t* /*unused*/) const noexcept { - return is_array() ? m_value.array : nullptr; + return is_array() ? m_data.m_value.array : nullptr; } /// get a pointer to the value (string) string_t* get_impl_ptr(string_t* /*unused*/) noexcept { - return is_string() ? m_value.string : nullptr; + return is_string() ? m_data.m_value.string : nullptr; } /// get a pointer to the value (string) constexpr const string_t* get_impl_ptr(const string_t* /*unused*/) const noexcept { - return is_string() ? m_value.string : nullptr; + return is_string() ? m_data.m_value.string : nullptr; } /// get a pointer to the value (boolean) boolean_t* get_impl_ptr(boolean_t* /*unused*/) noexcept { - return is_boolean() ? &m_value.boolean : nullptr; + return is_boolean() ? &m_data.m_value.boolean : nullptr; } /// get a pointer to the value (boolean) constexpr const boolean_t* get_impl_ptr(const boolean_t* /*unused*/) const noexcept { - return is_boolean() ? &m_value.boolean : nullptr; + return is_boolean() ? &m_data.m_value.boolean : nullptr; } /// get a pointer to the value (integer number) number_integer_t* get_impl_ptr(number_integer_t* /*unused*/) noexcept { - return is_number_integer() ? &m_value.number_integer : nullptr; + return is_number_integer() ? &m_data.m_value.number_integer : nullptr; } /// get a pointer to the value (integer number) constexpr const number_integer_t* get_impl_ptr(const number_integer_t* /*unused*/) const noexcept { - return is_number_integer() ? &m_value.number_integer : nullptr; + return is_number_integer() ? &m_data.m_value.number_integer : nullptr; } /// get a pointer to the value (unsigned number) number_unsigned_t* get_impl_ptr(number_unsigned_t* /*unused*/) noexcept { - return is_number_unsigned() ? &m_value.number_unsigned : nullptr; + return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr; } /// get a pointer to the value (unsigned number) constexpr const number_unsigned_t* get_impl_ptr(const number_unsigned_t* /*unused*/) const noexcept { - return is_number_unsigned() ? &m_value.number_unsigned : nullptr; + return is_number_unsigned() ? &m_data.m_value.number_unsigned : nullptr; } /// get a pointer to the value (floating-point number) number_float_t* get_impl_ptr(number_float_t* /*unused*/) noexcept { - return is_number_float() ? &m_value.number_float : nullptr; + return is_number_float() ? &m_data.m_value.number_float : nullptr; } /// get a pointer to the value (floating-point number) constexpr const number_float_t* get_impl_ptr(const number_float_t* /*unused*/) const noexcept { - return is_number_float() ? &m_value.number_float : nullptr; + return is_number_float() ? &m_data.m_value.number_float : nullptr; } /// get a pointer to the value (binary) binary_t* get_impl_ptr(binary_t* /*unused*/) noexcept { - return is_binary() ? m_value.binary : nullptr; + return is_binary() ? m_data.m_value.binary : nullptr; } /// get a pointer to the value (binary) constexpr const binary_t* get_impl_ptr(const binary_t* /*unused*/) const noexcept { - return is_binary() ? m_value.binary : nullptr; + return is_binary() ? m_data.m_value.binary : nullptr; } /*! @@ -21053,7 +21191,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec #if defined(JSON_HAS_CPP_17) && (defined(__GNUC__) || (defined(_MSC_VER) && _MSC_VER >= 1910 && _MSC_VER <= 1914)) detail::negation>, #endif -#if defined(JSON_HAS_CPP_17) +#if defined(JSON_HAS_CPP_17) && JSON_HAS_STATIC_RTTI detail::negation>, #endif detail::is_detected_lazy @@ -21090,7 +21228,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - //////////////////// // element access // //////////////////// @@ -21108,7 +21245,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { JSON_TRY { - return set_parent(m_value.array->at(idx)); + return set_parent(m_data.m_value.array->at(idx)); } JSON_CATCH (std::out_of_range&) { @@ -21131,7 +21268,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { JSON_TRY { - return m_value.array->at(idx); + return m_data.m_value.array->at(idx); } JSON_CATCH (std::out_of_range&) { @@ -21155,8 +21292,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(key); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(key); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this)); } @@ -21175,8 +21312,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(std::forward(key)); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(std::forward(key)); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward(key)), "' not found"), this)); } @@ -21193,8 +21330,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(key); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(key); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", key, "' not found"), this)); } @@ -21213,8 +21350,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(304, detail::concat("cannot use at() with ", type_name()), this)); } - auto it = m_value.object->find(std::forward(key)); - if (it == m_value.object->end()) + auto it = m_data.m_value.object->find(std::forward(key)); + if (it == m_data.m_value.object->end()) { JSON_THROW(out_of_range::create(403, detail::concat("key '", string_t(std::forward(key)), "' not found"), this)); } @@ -21228,8 +21365,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty array if (is_null()) { - m_type = value_t::array; - m_value.array = create(); + m_data.m_type = value_t::array; + m_data.m_value.array = create(); assert_invariant(); } @@ -21237,17 +21374,17 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (JSON_HEDLEY_LIKELY(is_array())) { // fill up array with null values if given idx is outside range - if (idx >= m_value.array->size()) + if (idx >= m_data.m_value.array->size()) { #if JSON_DIAGNOSTICS // remember array size & capacity before resizing - const auto old_size = m_value.array->size(); - const auto old_capacity = m_value.array->capacity(); + const auto old_size = m_data.m_value.array->size(); + const auto old_capacity = m_data.m_value.array->capacity(); #endif - m_value.array->resize(idx + 1); + m_data.m_value.array->resize(idx + 1); #if JSON_DIAGNOSTICS - if (JSON_HEDLEY_UNLIKELY(m_value.array->capacity() != old_capacity)) + if (JSON_HEDLEY_UNLIKELY(m_data.m_value.array->capacity() != old_capacity)) { // capacity has changed: update all parents set_parents(); @@ -21261,7 +21398,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec assert_invariant(); } - return m_value.array->operator[](idx); + return m_data.m_value.array->operator[](idx); } JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this)); @@ -21274,7 +21411,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // const operator[] only works for arrays if (JSON_HEDLEY_LIKELY(is_array())) { - return m_value.array->operator[](idx); + return m_data.m_value.array->operator[](idx); } JSON_THROW(type_error::create(305, detail::concat("cannot use operator[] with a numeric argument with ", type_name()), this)); @@ -21287,15 +21424,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty object if (is_null()) { - m_type = value_t::object; - m_value.object = create(); + m_data.m_type = value_t::object; + m_data.m_value.object = create(); assert_invariant(); } // operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto result = m_value.object->emplace(std::move(key), nullptr); + auto result = m_data.m_value.object->emplace(std::move(key), nullptr); return set_parent(result.first->second); } @@ -21309,8 +21446,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // const operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto it = m_value.object->find(key); - JSON_ASSERT(it != m_value.object->end()); + auto it = m_data.m_value.object->find(key); + JSON_ASSERT(it != m_data.m_value.object->end()); return it->second; } @@ -21340,15 +21477,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty object if (is_null()) { - m_type = value_t::object; - m_value.object = create(); + m_data.m_type = value_t::object; + m_data.m_value.object = create(); assert_invariant(); } // operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto result = m_value.object->emplace(std::forward(key), nullptr); + auto result = m_data.m_value.object->emplace(std::forward(key), nullptr); return set_parent(result.first->second); } @@ -21364,8 +21501,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // const operator[] only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { - auto it = m_value.object->find(std::forward(key)); - JSON_ASSERT(it != m_value.object->end()); + auto it = m_data.m_value.object->find(std::forward(key)); + JSON_ASSERT(it != m_data.m_value.object->end()); return it->second; } @@ -21602,7 +21739,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec IteratorType result = end(); - switch (m_type) + switch (m_data.m_type) { case value_t::boolean: case value_t::number_float: @@ -21619,32 +21756,32 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_string()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.string); - std::allocator_traits::deallocate(alloc, m_value.string, 1); - m_value.string = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.string); + std::allocator_traits::deallocate(alloc, m_data.m_value.string, 1); + m_data.m_value.string = nullptr; } else if (is_binary()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.binary); - std::allocator_traits::deallocate(alloc, m_value.binary, 1); - m_value.binary = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.binary); + std::allocator_traits::deallocate(alloc, m_data.m_value.binary, 1); + m_data.m_value.binary = nullptr; } - m_type = value_t::null; + m_data.m_type = value_t::null; assert_invariant(); break; } case value_t::object: { - result.m_it.object_iterator = m_value.object->erase(pos.m_it.object_iterator); + result.m_it.object_iterator = m_data.m_value.object->erase(pos.m_it.object_iterator); break; } case value_t::array: { - result.m_it.array_iterator = m_value.array->erase(pos.m_it.array_iterator); + result.m_it.array_iterator = m_data.m_value.array->erase(pos.m_it.array_iterator); break; } @@ -21672,7 +21809,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec IteratorType result = end(); - switch (m_type) + switch (m_data.m_type) { case value_t::boolean: case value_t::number_float: @@ -21690,33 +21827,33 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_string()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.string); - std::allocator_traits::deallocate(alloc, m_value.string, 1); - m_value.string = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.string); + std::allocator_traits::deallocate(alloc, m_data.m_value.string, 1); + m_data.m_value.string = nullptr; } else if (is_binary()) { AllocatorType alloc; - std::allocator_traits::destroy(alloc, m_value.binary); - std::allocator_traits::deallocate(alloc, m_value.binary, 1); - m_value.binary = nullptr; + std::allocator_traits::destroy(alloc, m_data.m_value.binary); + std::allocator_traits::deallocate(alloc, m_data.m_value.binary, 1); + m_data.m_value.binary = nullptr; } - m_type = value_t::null; + m_data.m_type = value_t::null; assert_invariant(); break; } case value_t::object: { - result.m_it.object_iterator = m_value.object->erase(first.m_it.object_iterator, + result.m_it.object_iterator = m_data.m_value.object->erase(first.m_it.object_iterator, last.m_it.object_iterator); break; } case value_t::array: { - result.m_it.array_iterator = m_value.array->erase(first.m_it.array_iterator, + result.m_it.array_iterator = m_data.m_value.array->erase(first.m_it.array_iterator, last.m_it.array_iterator); break; } @@ -21741,7 +21878,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this)); } - return m_value.object->erase(std::forward(key)); + return m_data.m_value.object->erase(std::forward(key)); } template < typename KeyType, detail::enable_if_t < @@ -21754,10 +21891,10 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(type_error::create(307, detail::concat("cannot use erase() with ", type_name()), this)); } - const auto it = m_value.object->find(std::forward(key)); - if (it != m_value.object->end()) + const auto it = m_data.m_value.object->find(std::forward(key)); + if (it != m_data.m_value.object->end()) { - m_value.object->erase(it); + m_data.m_value.object->erase(it); return 1; } return 0; @@ -21795,7 +21932,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(out_of_range::create(401, detail::concat("array index ", std::to_string(idx), " is out of range"), this)); } - m_value.array->erase(m_value.array->begin() + static_cast(idx)); + m_data.m_value.array->erase(m_data.m_value.array->begin() + static_cast(idx)); } else { @@ -21805,7 +21942,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - //////////// // lookup // //////////// @@ -21821,7 +21957,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(key); + result.m_it.object_iterator = m_data.m_value.object->find(key); } return result; @@ -21835,7 +21971,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(key); + result.m_it.object_iterator = m_data.m_value.object->find(key); } return result; @@ -21851,7 +21987,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(std::forward(key)); + result.m_it.object_iterator = m_data.m_value.object->find(std::forward(key)); } return result; @@ -21867,7 +22003,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (is_object()) { - result.m_it.object_iterator = m_value.object->find(std::forward(key)); + result.m_it.object_iterator = m_data.m_value.object->find(std::forward(key)); } return result; @@ -21878,7 +22014,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec size_type count(const typename object_t::key_type& key) const { // return 0 for all nonobject types - return is_object() ? m_value.object->count(key) : 0; + return is_object() ? m_data.m_value.object->count(key) : 0; } /// @brief returns the number of occurrences of a key in a JSON object @@ -21888,14 +22024,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec size_type count(KeyType && key) const { // return 0 for all nonobject types - return is_object() ? m_value.object->count(std::forward(key)) : 0; + return is_object() ? m_data.m_value.object->count(std::forward(key)) : 0; } /// @brief check the existence of an element in a JSON object /// @sa https://json.nlohmann.me/api/basic_json/contains/ bool contains(const typename object_t::key_type& key) const { - return is_object() && m_value.object->find(key) != m_value.object->end(); + return is_object() && m_data.m_value.object->find(key) != m_data.m_value.object->end(); } /// @brief check the existence of an element in a JSON object @@ -21904,7 +22040,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec detail::is_usable_as_basic_json_key_type::value, int> = 0> bool contains(KeyType && key) const { - return is_object() && m_value.object->find(std::forward(key)) != m_value.object->end(); + return is_object() && m_data.m_value.object->find(std::forward(key)) != m_data.m_value.object->end(); } /// @brief check the existence of an element in a JSON object given a JSON pointer @@ -21923,7 +22059,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - /////////////// // iterators // /////////////// @@ -22062,7 +22197,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - ////////////// // capacity // ////////////// @@ -22074,7 +22208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/empty/ bool empty() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::null: { @@ -22085,13 +22219,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec case value_t::array: { // delegate call to array_t::empty() - return m_value.array->empty(); + return m_data.m_value.array->empty(); } case value_t::object: { // delegate call to object_t::empty() - return m_value.object->empty(); + return m_data.m_value.object->empty(); } case value_t::string: @@ -22113,7 +22247,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/size/ size_type size() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::null: { @@ -22124,13 +22258,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec case value_t::array: { // delegate call to array_t::size() - return m_value.array->size(); + return m_data.m_value.array->size(); } case value_t::object: { // delegate call to object_t::size() - return m_value.object->size(); + return m_data.m_value.object->size(); } case value_t::string: @@ -22152,18 +22286,18 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/max_size/ size_type max_size() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::array: { // delegate call to array_t::max_size() - return m_value.array->max_size(); + return m_data.m_value.array->max_size(); } case value_t::object: { // delegate call to object_t::max_size() - return m_value.object->max_size(); + return m_data.m_value.object->max_size(); } case value_t::null: @@ -22184,7 +22318,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @} - /////////////// // modifiers // /////////////// @@ -22196,53 +22329,53 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @sa https://json.nlohmann.me/api/basic_json/clear/ void clear() noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::number_integer: { - m_value.number_integer = 0; + m_data.m_value.number_integer = 0; break; } case value_t::number_unsigned: { - m_value.number_unsigned = 0; + m_data.m_value.number_unsigned = 0; break; } case value_t::number_float: { - m_value.number_float = 0.0; + m_data.m_value.number_float = 0.0; break; } case value_t::boolean: { - m_value.boolean = false; + m_data.m_value.boolean = false; break; } case value_t::string: { - m_value.string->clear(); + m_data.m_value.string->clear(); break; } case value_t::binary: { - m_value.binary->clear(); + m_data.m_value.binary->clear(); break; } case value_t::array: { - m_value.array->clear(); + m_data.m_value.array->clear(); break; } case value_t::object: { - m_value.object->clear(); + m_data.m_value.object->clear(); break; } @@ -22266,15 +22399,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an array if (is_null()) { - m_type = value_t::array; - m_value = value_t::array; + m_data.m_type = value_t::array; + m_data.m_value = value_t::array; assert_invariant(); } // add element to array (move semantics) - const auto old_capacity = m_value.array->capacity(); - m_value.array->push_back(std::move(val)); - set_parent(m_value.array->back(), old_capacity); + const auto old_capacity = m_data.m_value.array->capacity(); + m_data.m_value.array->push_back(std::move(val)); + set_parent(m_data.m_value.array->back(), old_capacity); // if val is moved from, basic_json move constructor marks it null, so we do not call the destructor } @@ -22299,15 +22432,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an array if (is_null()) { - m_type = value_t::array; - m_value = value_t::array; + m_data.m_type = value_t::array; + m_data.m_value = value_t::array; assert_invariant(); } // add element to array - const auto old_capacity = m_value.array->capacity(); - m_value.array->push_back(val); - set_parent(m_value.array->back(), old_capacity); + const auto old_capacity = m_data.m_value.array->capacity(); + m_data.m_value.array->push_back(val); + set_parent(m_data.m_value.array->back(), old_capacity); } /// @brief add an object to an array @@ -22331,13 +22464,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an object if (is_null()) { - m_type = value_t::object; - m_value = value_t::object; + m_data.m_type = value_t::object; + m_data.m_value = value_t::object; assert_invariant(); } // add element to object - auto res = m_value.object->insert(val); + auto res = m_data.m_value.object->insert(val); set_parent(res.first->second); } @@ -22387,15 +22520,15 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an array if (is_null()) { - m_type = value_t::array; - m_value = value_t::array; + m_data.m_type = value_t::array; + m_data.m_value = value_t::array; assert_invariant(); } // add element to array (perfect forwarding) - const auto old_capacity = m_value.array->capacity(); - m_value.array->emplace_back(std::forward(args)...); - return set_parent(m_value.array->back(), old_capacity); + const auto old_capacity = m_data.m_value.array->capacity(); + m_data.m_value.array->emplace_back(std::forward(args)...); + return set_parent(m_data.m_value.array->back(), old_capacity); } /// @brief add an object to an object if key does not exist @@ -22412,13 +22545,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // transform null object into an object if (is_null()) { - m_type = value_t::object; - m_value = value_t::object; + m_data.m_type = value_t::object; + m_data.m_value = value_t::object; assert_invariant(); } // add element to array (perfect forwarding) - auto res = m_value.object->emplace(std::forward(args)...); + auto res = m_data.m_value.object->emplace(std::forward(args)...); set_parent(res.first->second); // create result iterator and set iterator to the result of emplace @@ -22436,14 +22569,14 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec iterator insert_iterator(const_iterator pos, Args&& ... args) { iterator result(this); - JSON_ASSERT(m_value.array != nullptr); + JSON_ASSERT(m_data.m_value.array != nullptr); - auto insert_pos = std::distance(m_value.array->begin(), pos.m_it.array_iterator); - m_value.array->insert(pos.m_it.array_iterator, std::forward(args)...); - result.m_it.array_iterator = m_value.array->begin() + insert_pos; + auto insert_pos = std::distance(m_data.m_value.array->begin(), pos.m_it.array_iterator); + m_data.m_value.array->insert(pos.m_it.array_iterator, std::forward(args)...); + result.m_it.array_iterator = m_data.m_value.array->begin() + insert_pos; // This could have been written as: - // result.m_it.array_iterator = m_value.array->insert(pos.m_it.array_iterator, cnt, val); + // result.m_it.array_iterator = m_data.m_value.array->insert(pos.m_it.array_iterator, cnt, val); // but the return value of insert is missing in GCC 4.8, so it is written this way instead. set_parents(); @@ -22570,7 +22703,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_THROW(invalid_iterator::create(202, "iterators first and last must point to objects", this)); } - m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator); + m_data.m_value.object->insert(first.m_it.object_iterator, last.m_it.object_iterator); } /// @brief updates a JSON object from another object, overwriting existing keys @@ -22587,8 +22720,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // implicitly convert null value to an empty object if (is_null()) { - m_type = value_t::object; - m_value.object = create(); + m_data.m_type = value_t::object; + m_data.m_value.object = create(); assert_invariant(); } @@ -22613,16 +22746,16 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec { if (merge_objects && it.value().is_object()) { - auto it2 = m_value.object->find(it.key()); - if (it2 != m_value.object->end()) + auto it2 = m_data.m_value.object->find(it.key()); + if (it2 != m_data.m_value.object->end()) { it2->second.update(it.value(), true); continue; } } - m_value.object->operator[](it.key()) = it.value(); + m_data.m_value.object->operator[](it.key()) = it.value(); #if JSON_DIAGNOSTICS - m_value.object->operator[](it.key()).m_parent = this; + m_data.m_value.object->operator[](it.key()).m_parent = this; #endif } } @@ -22632,12 +22765,12 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec void swap(reference other) noexcept ( std::is_nothrow_move_constructible::value&& std::is_nothrow_move_assignable::value&& - std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_constructible::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap) std::is_nothrow_move_assignable::value ) { - std::swap(m_type, other.m_type); - std::swap(m_value, other.m_value); + std::swap(m_data.m_type, other.m_data.m_type); + std::swap(m_data.m_value, other.m_data.m_value); set_parents(); other.set_parents(); @@ -22649,7 +22782,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec friend void swap(reference left, reference right) noexcept ( std::is_nothrow_move_constructible::value&& std::is_nothrow_move_assignable::value&& - std::is_nothrow_move_constructible::value&& + std::is_nothrow_move_constructible::value&& // NOLINT(cppcoreguidelines-noexcept-swap,performance-noexcept-swap) std::is_nothrow_move_assignable::value ) { @@ -22658,13 +22791,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(array_t& other) // NOLINT(bugprone-exception-escape) + void swap(array_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for arrays if (JSON_HEDLEY_LIKELY(is_array())) { using std::swap; - swap(*(m_value.array), other); + swap(*(m_data.m_value.array), other); } else { @@ -22674,13 +22807,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(object_t& other) // NOLINT(bugprone-exception-escape) + void swap(object_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for objects if (JSON_HEDLEY_LIKELY(is_object())) { using std::swap; - swap(*(m_value.object), other); + swap(*(m_data.m_value.object), other); } else { @@ -22690,13 +22823,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(string_t& other) // NOLINT(bugprone-exception-escape) + void swap(string_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for strings if (JSON_HEDLEY_LIKELY(is_string())) { using std::swap; - swap(*(m_value.string), other); + swap(*(m_data.m_value.string), other); } else { @@ -22706,13 +22839,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec /// @brief exchanges the values /// @sa https://json.nlohmann.me/api/basic_json/swap/ - void swap(binary_t& other) // NOLINT(bugprone-exception-escape) + void swap(binary_t& other) // NOLINT(bugprone-exception-escape,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) { // swap only works for strings if (JSON_HEDLEY_LIKELY(is_binary())) { using std::swap; - swap(*(m_value.binary), other); + swap(*(m_data.m_value.binary), other); } else { @@ -22728,7 +22861,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec if (JSON_HEDLEY_LIKELY(is_binary())) { using std::swap; - swap(*(m_value.binary), other); + swap(*(m_data.m_value.binary), other); } else { @@ -22756,31 +22889,31 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec switch (lhs_type) \ { \ case value_t::array: \ - return (*lhs.m_value.array) op (*rhs.m_value.array); \ + return (*lhs.m_data.m_value.array) op (*rhs.m_data.m_value.array); \ \ case value_t::object: \ - return (*lhs.m_value.object) op (*rhs.m_value.object); \ + return (*lhs.m_data.m_value.object) op (*rhs.m_data.m_value.object); \ \ case value_t::null: \ return (null_result); \ \ case value_t::string: \ - return (*lhs.m_value.string) op (*rhs.m_value.string); \ + return (*lhs.m_data.m_value.string) op (*rhs.m_data.m_value.string); \ \ case value_t::boolean: \ - return (lhs.m_value.boolean) op (rhs.m_value.boolean); \ + return (lhs.m_data.m_value.boolean) op (rhs.m_data.m_value.boolean); \ \ case value_t::number_integer: \ - return (lhs.m_value.number_integer) op (rhs.m_value.number_integer); \ + return (lhs.m_data.m_value.number_integer) op (rhs.m_data.m_value.number_integer); \ \ case value_t::number_unsigned: \ - return (lhs.m_value.number_unsigned) op (rhs.m_value.number_unsigned); \ + return (lhs.m_data.m_value.number_unsigned) op (rhs.m_data.m_value.number_unsigned); \ \ case value_t::number_float: \ - return (lhs.m_value.number_float) op (rhs.m_value.number_float); \ + return (lhs.m_data.m_value.number_float) op (rhs.m_data.m_value.number_float); \ \ case value_t::binary: \ - return (*lhs.m_value.binary) op (*rhs.m_value.binary); \ + return (*lhs.m_data.m_value.binary) op (*rhs.m_data.m_value.binary); \ \ case value_t::discarded: \ default: \ @@ -22789,27 +22922,27 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } \ else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_float) \ { \ - return static_cast(lhs.m_value.number_integer) op rhs.m_value.number_float; \ + return static_cast(lhs.m_data.m_value.number_integer) op rhs.m_data.m_value.number_float; \ } \ else if (lhs_type == value_t::number_float && rhs_type == value_t::number_integer) \ { \ - return lhs.m_value.number_float op static_cast(rhs.m_value.number_integer); \ + return lhs.m_data.m_value.number_float op static_cast(rhs.m_data.m_value.number_integer); \ } \ else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_float) \ { \ - return static_cast(lhs.m_value.number_unsigned) op rhs.m_value.number_float; \ + return static_cast(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_float; \ } \ else if (lhs_type == value_t::number_float && rhs_type == value_t::number_unsigned) \ { \ - return lhs.m_value.number_float op static_cast(rhs.m_value.number_unsigned); \ + return lhs.m_data.m_value.number_float op static_cast(rhs.m_data.m_value.number_unsigned); \ } \ else if (lhs_type == value_t::number_unsigned && rhs_type == value_t::number_integer) \ { \ - return static_cast(lhs.m_value.number_unsigned) op rhs.m_value.number_integer; \ + return static_cast(lhs.m_data.m_value.number_unsigned) op rhs.m_data.m_value.number_integer; \ } \ else if (lhs_type == value_t::number_integer && rhs_type == value_t::number_unsigned) \ { \ - return lhs.m_value.number_integer op static_cast(rhs.m_value.number_unsigned); \ + return lhs.m_data.m_value.number_integer op static_cast(rhs.m_data.m_value.number_unsigned); \ } \ else if(compares_unordered(lhs, rhs))\ {\ @@ -22826,8 +22959,8 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // an operation is computed as an odd number of inverses of others static bool compares_unordered(const_reference lhs, const_reference rhs, bool inverse = false) noexcept { - if ((lhs.is_number_float() && std::isnan(lhs.m_value.number_float) && rhs.is_number()) - || (rhs.is_number_float() && std::isnan(rhs.m_value.number_float) && lhs.is_number())) + if ((lhs.is_number_float() && std::isnan(lhs.m_data.m_value.number_float) && rhs.is_number()) + || (rhs.is_number_float() && std::isnan(rhs.m_data.m_value.number_float) && lhs.is_number())) { return true; } @@ -23171,7 +23304,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec #endif // JSON_NO_IO /// @} - ///////////////////// // deserialization // ///////////////////// @@ -23328,7 +23460,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec JSON_HEDLEY_RETURNS_NON_NULL const char* type_name() const noexcept { - switch (m_type) + switch (m_data.m_type) { case value_t::null: return "null"; @@ -23352,17 +23484,43 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } } - JSON_PRIVATE_UNLESS_TESTED: ////////////////////// // member variables // ////////////////////// - /// the type of the current element - value_t m_type = value_t::null; + struct data + { + /// the type of the current element + value_t m_type = value_t::null; - /// the value of the current element - json_value m_value = {}; + /// the value of the current element + json_value m_value = {}; + + data(const value_t v) + : m_type(v), m_value(v) + { + } + + data(size_type cnt, const basic_json& val) + : m_type(value_t::array) + { + m_value.array = create(cnt, val); + } + + data() noexcept = default; + data(data&&) noexcept = default; + data(const data&) noexcept = delete; + data& operator=(data&&) noexcept = delete; + data& operator=(const data&) noexcept = delete; + + ~data() noexcept + { + m_value.destroy(m_type); + } + }; + + data m_data = {}; #if JSON_DIAGNOSTICS /// a pointer to a parent value (for debugging purposes) @@ -23543,7 +23701,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec return from_cbor(ptr, ptr + len, strict, allow_exceptions, tag_handler); } - JSON_HEDLEY_WARN_UNUSED_RESULT JSON_HEDLEY_DEPRECATED_FOR(3.8.0, from_cbor(ptr, ptr + len)) static basic_json from_cbor(detail::span_input_adapter&& i, @@ -23667,7 +23824,6 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec return res ? result : basic_json(value_t::discarded); } - /// @brief create a JSON value from an input in BJData format /// @sa https://json.nlohmann.me/api/basic_json/from_bjdata/ template @@ -23890,7 +24046,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec } // make sure the top element of the pointer exists - json_pointer top_pointer = ptr.top(); + json_pointer const top_pointer = ptr.top(); if (top_pointer != ptr) { result.at(top_pointer); @@ -23902,7 +24058,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec // parent must exist when performing patch add per RFC6902 specs basic_json& parent = result.at(ptr); - switch (parent.m_type) + switch (parent.m_data.m_type) { case value_t::null: case value_t::object: @@ -23948,7 +24104,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec }; // wrapper for "remove" operation; remove value at ptr - const auto operation_remove = [this, &result](json_pointer & ptr) + const auto operation_remove = [this, & result](json_pointer & ptr) { // get reference to parent of JSON pointer ptr const auto last_path = ptr.back(); @@ -23991,13 +24147,13 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec bool string_type) -> basic_json & { // find value - auto it = val.m_value.object->find(member); + auto it = val.m_data.m_value.object->find(member); // context-sensitive error message - const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\''); + const auto error_msg = (op == "op") ? "operation" : detail::concat("operation '", op, '\''); // NOLINT(bugprone-unused-local-non-trivial-variable) // check if desired value is present - if (JSON_HEDLEY_UNLIKELY(it == val.m_value.object->end())) + if (JSON_HEDLEY_UNLIKELY(it == val.m_data.m_value.object->end())) { // NOLINTNEXTLINE(performance-inefficient-string-concatenation) JSON_THROW(parse_error::create(105, 0, detail::concat(error_msg, " must have member '", member, "'"), &val)); @@ -24052,7 +24208,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec json_pointer from_ptr(from_path); // the "from" location must exist - use at() - basic_json v = result.at(from_ptr); + basic_json const v = result.at(from_ptr); // The move operation is functionally identical to a // "remove" operation on the "from" location, followed @@ -24069,7 +24225,7 @@ class basic_json // NOLINT(cppcoreguidelines-special-member-functions,hicpp-spec const json_pointer from_ptr(from_path); // the "from" location must exist - use at() - basic_json v = result.at(from_ptr); + basic_json const v = result.at(from_ptr); // The copy is functionally identical to an "add" // operation at the target location using the value @@ -24311,7 +24467,11 @@ inline namespace json_literals /// @brief user-defined string literal for JSON values /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json/ JSON_HEDLEY_NON_NULL(1) -inline nlohmann::json operator "" _json(const char* s, std::size_t n) +#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) + inline nlohmann::json operator ""_json(const char* s, std::size_t n) +#else + inline nlohmann::json operator "" _json(const char* s, std::size_t n) +#endif { return nlohmann::json::parse(s, s + n); } @@ -24319,7 +24479,11 @@ inline nlohmann::json operator "" _json(const char* s, std::size_t n) /// @brief user-defined string literal for JSON pointer /// @sa https://json.nlohmann.me/api/basic_json/operator_literal_json_pointer/ JSON_HEDLEY_NON_NULL(1) -inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n) +#if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) + inline nlohmann::json::json_pointer operator ""_json_pointer(const char* s, std::size_t n) +#else + inline nlohmann::json::json_pointer operator "" _json_pointer(const char* s, std::size_t n) +#endif { return nlohmann::json::json_pointer(std::string(s, n)); } @@ -24338,7 +24502,7 @@ namespace std // NOLINT(cert-dcl58-cpp) /// @brief hash value for JSON objects /// @sa https://json.nlohmann.me/api/basic_json/std_hash/ NLOHMANN_BASIC_JSON_TPL_DECLARATION -struct hash +struct hash // NOLINT(cert-dcl58-cpp) { std::size_t operator()(const nlohmann::NLOHMANN_BASIC_JSON_TPL& j) const { @@ -24371,8 +24535,8 @@ struct less< ::nlohmann::detail::value_t> // do not remove the space after '<', /// @brief exchanges the values of two JSON objects /// @sa https://json.nlohmann.me/api/basic_json/std_swap/ NLOHMANN_BASIC_JSON_TPL_DECLARATION -inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept( // NOLINT(readability-inconsistent-declaration-parameter-name) - is_nothrow_move_constructible::value&& // NOLINT(misc-redundant-expression) +inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC_JSON_TPL& j2) noexcept( // NOLINT(readability-inconsistent-declaration-parameter-name, cert-dcl58-cpp) + is_nothrow_move_constructible::value&& // NOLINT(misc-redundant-expression,cppcoreguidelines-noexcept-swap,performance-noexcept-swap) is_nothrow_move_assignable::value) { j1.swap(j2); @@ -24383,17 +24547,22 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC } // namespace std #if JSON_USE_GLOBAL_UDLS - using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers) - using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers) + #if !defined(JSON_HEDLEY_GCC_VERSION) || JSON_HEDLEY_GCC_VERSION_CHECK(4,9,0) + using nlohmann::literals::json_literals::operator ""_json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers) + using nlohmann::literals::json_literals::operator ""_json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers) + #else + using nlohmann::literals::json_literals::operator "" _json; // NOLINT(misc-unused-using-decls,google-global-names-in-headers) + using nlohmann::literals::json_literals::operator "" _json_pointer; //NOLINT(misc-unused-using-decls,google-global-names-in-headers) + #endif #endif // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT @@ -24428,16 +24597,17 @@ inline void swap(nlohmann::NLOHMANN_BASIC_JSON_TPL& j1, nlohmann::NLOHMANN_BASIC #undef JSON_HAS_EXPERIMENTAL_FILESYSTEM #undef JSON_HAS_THREE_WAY_COMPARISON #undef JSON_HAS_RANGES + #undef JSON_HAS_STATIC_RTTI #undef JSON_USE_LEGACY_DISCARDED_VALUE_COMPARISON #endif // #include // __ _____ _____ _____ // __| | __| | | | JSON for Modern C++ -// | | |__ | | | | | | version 3.11.2 +// | | |__ | | | | | | version 3.11.3 // |_____|_____|_____|_|___| https://github.com/nlohmann/json // -// SPDX-FileCopyrightText: 2013-2022 Niels Lohmann +// SPDX-FileCopyrightText: 2013-2023 Niels Lohmann // SPDX-License-Identifier: MIT diff --git a/common/llguidance.cpp b/common/llguidance.cpp new file mode 100644 index 000000000..2feeb93c8 --- /dev/null +++ b/common/llguidance.cpp @@ -0,0 +1,270 @@ +#include "sampling.h" +#include "log.h" + +#ifdef LLAMA_USE_LLGUIDANCE + +# include "llguidance.h" +# include + +struct llama_sampler_llg { + const llama_vocab * vocab; + std::string grammar_kind; + std::string grammar_data; + LlgTokenizer * tokenizer; + LlgConstraint * grammar; + LlgMaskResult llg_res; + bool has_llg_res; +}; + +static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind, + const char * grammar_data) { + LlgConstraintInit cinit; + llg_constraint_init_set_defaults(&cinit, tokenizer); + const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL"); + if (log_level && *log_level) { + cinit.log_stderr_level = atoi(log_level); + } + auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data); + if (llg_get_error(c)) { + LOG_ERR("llg error: %s\n", llg_get_error(c)); + llg_free_constraint(c); + return nullptr; + } + return c; +} + +static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) { + return "llguidance"; +} + +static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (ctx->grammar) { + LlgCommitResult res; + llg_commit_token(ctx->grammar, token, &res); + ctx->has_llg_res = false; + } +} + +static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (ctx->grammar) { + if (!ctx->has_llg_res) { + if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) { + ctx->has_llg_res = true; + } else { + LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar)); + llg_free_constraint(ctx->grammar); + ctx->grammar = nullptr; + } + } + if (ctx->has_llg_res) { + if (ctx->llg_res.is_stop) { + for (size_t i = 0; i < cur_p->size; ++i) { + if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) { + cur_p->data[i].logit = -INFINITY; + } + } + } else { + const uint32_t * mask = ctx->llg_res.sample_mask; + for (size_t i = 0; i < cur_p->size; ++i) { + auto token = cur_p->data[i].id; + if ((mask[token / 32] & (1 << (token % 32))) == 0) { + cur_p->data[i].logit = -INFINITY; + } + } + } + } + } +} + +static void llama_sampler_llg_reset(llama_sampler * smpl) { + auto * ctx = (llama_sampler_llg *) smpl->ctx; + if (!ctx->grammar) { + return; + } + + auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str()); + llg_free_constraint(ctx->grammar); + ctx->grammar = grammar_new; + ctx->has_llg_res = false; +} + +static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) { + const auto * ctx = (const llama_sampler_llg *) smpl->ctx; + + auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr); + + // copy the state + { + auto * result_ctx = (llama_sampler_llg *) result->ctx; + + if (ctx->grammar) { + result_ctx->grammar_kind = ctx->grammar_kind; + result_ctx->grammar_data = ctx->grammar_data; + result_ctx->grammar = llg_clone_constraint(ctx->grammar); + result_ctx->tokenizer = llg_clone_tokenizer(ctx->tokenizer); + } + } + + return result; +} + +static void llama_sampler_llg_free(llama_sampler * smpl) { + const auto * ctx = (llama_sampler_llg *) smpl->ctx; + + if (ctx->grammar) { + llg_free_constraint(ctx->grammar); + llg_free_tokenizer(ctx->tokenizer); + } + + delete ctx; +} + +static llama_sampler_i llama_sampler_llg_i = { + /* .name = */ llama_sampler_llg_name, + /* .accept = */ llama_sampler_llg_accept_impl, + /* .apply = */ llama_sampler_llg_apply, + /* .reset = */ llama_sampler_llg_reset, + /* .clone = */ llama_sampler_llg_clone, + /* .free = */ llama_sampler_llg_free, +}; + +static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len, + uint32_t * output_tokens, size_t output_tokens_len) { + const llama_vocab * vocab = (const llama_vocab *) user_data; + int r = 0; + try { + r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false, + true); + } catch (const std::exception & e) { + GGML_ABORT("llama_tokenize failed: %s\n", e.what()); + } + if (r < 0) { + return -r; + } + return r; +} + +static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) { + // TODO store the tokenizer in the vocab somehow + static const llama_vocab * vocab_cache; + static LlgTokenizer * tokenizer_cache; + + if (vocab_cache == vocab) { + return llg_clone_tokenizer(tokenizer_cache); + } + + auto tok_eos = llama_vocab_eot(vocab); + if (tok_eos == LLAMA_TOKEN_NULL) { + tok_eos = llama_vocab_eos(vocab); + } + + size_t vocab_size = llama_vocab_n_tokens(vocab); + + auto token_lens = new uint32_t[vocab_size]; + // we typically have ~7 bytes per token; let's go on the safe side here + auto token_bytes_size = vocab_size * 16 + 1024 * 1024; + auto token_bytes = new uint8_t[token_bytes_size]; + + size_t offset = 0; + for (size_t i = 0; i < vocab_size; i++) { + size_t max_token = 1024; + if (token_bytes_size - offset < max_token) { + GGML_ABORT("token_bytes buffer too small\n"); + } + + llama_token token = i; + auto dp = (char *) token_bytes + offset; + auto size = llama_detokenize(vocab, &token, 1, dp, max_token, false, false); + if (size < 0) { + GGML_ABORT("llama_detokenize failed\n"); + } + if (size == 0) { + size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true); + if (size < 0) { + GGML_ABORT("llama_detokenize failed\n"); + } + if (size != 0) { + *dp = '\xff'; // special token prefix marker + size += 1; + } + } + + token_lens[i] = size; + offset += size; + } + + LlgTokenizerInit tinit = { + /* .vocab_size = */ (uint32_t) vocab_size, + /* .tok_eos = */ (uint32_t) tok_eos, + /* .token_lens = */ token_lens, + /* .token_bytes = */ token_bytes, + /* .tokenizer_json = */ nullptr, + /* .tokenize_assumes_string = */ true, + /* .tokenize_fn = */ llama_sampler_llg_tokenize_fn, + /* .use_approximate_greedy_tokenize_fn = */ false, + /* .tokenize_user_data = */ vocab, + }; + + char error_buffer[1024]; + LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer)); + + delete[] token_bytes; + delete[] token_lens; + + if (tokenizer == nullptr) { + LOG_ERR("llg tokenizer error: %s\n", error_buffer); + return tokenizer; + } + + if (tokenizer_cache) { + llg_free_tokenizer(tokenizer_cache); + } + vocab_cache = vocab; + tokenizer_cache = tokenizer; + + return llg_clone_tokenizer(tokenizer_cache); +} + +llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind, + const char * grammar_data) { + auto * ctx = new llama_sampler_llg; + + if (grammar_kind != nullptr && grammar_kind[0] != '\0') { + auto tokenizer = llama_sampler_llg_new_tokenizer(vocab); + *ctx = { + /* .vocab = */ vocab, + /* .grammar_kind = */ grammar_kind, + /* .grammar_data = */ grammar_data, + /* .tokenizer = */ tokenizer, + /* .grammar = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data), + /* .llg_res = */ {}, + /* .has_llg_res = */ false, + }; + } else { + *ctx = { + /* .vocab = */ vocab, + /* .grammar_kind = */ {}, + /* .grammar_data = */ {}, + /* .tokenizer = */ nullptr, + /* .grammar = */ nullptr, + /* .llg_res = */ {}, + /* .has_llg_res = */ false, + }; + } + + return llama_sampler_init( + /* .iface = */ &llama_sampler_llg_i, + /* .ctx = */ ctx + ); +} + +#else + +llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) { + LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); + return nullptr; +} + +#endif // LLAMA_USE_LLGUIDANCE diff --git a/common/log.cpp b/common/log.cpp new file mode 100644 index 000000000..4bfbecf15 --- /dev/null +++ b/common/log.cpp @@ -0,0 +1,392 @@ +#include "log.h" + +#include +#include +#include +#include +#include +#include +#include + +int common_log_verbosity_thold = LOG_DEFAULT_LLAMA; + +void common_log_set_verbosity_thold(int verbosity) { + common_log_verbosity_thold = verbosity; +} + +static int64_t t_us() { + return std::chrono::duration_cast(std::chrono::system_clock::now().time_since_epoch()).count(); +} + +// colors +enum common_log_col : int { + COMMON_LOG_COL_DEFAULT = 0, + COMMON_LOG_COL_BOLD, + COMMON_LOG_COL_RED, + COMMON_LOG_COL_GREEN, + COMMON_LOG_COL_YELLOW, + COMMON_LOG_COL_BLUE, + COMMON_LOG_COL_MAGENTA, + COMMON_LOG_COL_CYAN, + COMMON_LOG_COL_WHITE, +}; + +// disable colors by default +static std::vector g_col = { + "", + "", + "", + "", + "", + "", + "", + "", + "", +}; + +struct common_log_entry { + enum ggml_log_level level; + + bool prefix; + + int64_t timestamp; + + std::vector msg; + + // signals the worker thread to stop + bool is_end; + + void print(FILE * file = nullptr) const { + FILE * fcur = file; + if (!fcur) { + // stderr displays DBG messages only when their verbosity level is not higher than the threshold + // these messages will still be logged to a file + if (level == GGML_LOG_LEVEL_DEBUG && common_log_verbosity_thold < LOG_DEFAULT_DEBUG) { + return; + } + + fcur = stdout; + + if (level != GGML_LOG_LEVEL_NONE) { + fcur = stderr; + } + } + + if (level != GGML_LOG_LEVEL_NONE && level != GGML_LOG_LEVEL_CONT && prefix) { + if (timestamp) { + // [M.s.ms.us] + fprintf(fcur, "%s%d.%02d.%03d.%03d%s ", + g_col[COMMON_LOG_COL_BLUE], + (int) (timestamp / 1000000 / 60), + (int) (timestamp / 1000000 % 60), + (int) (timestamp / 1000 % 1000), + (int) (timestamp % 1000), + g_col[COMMON_LOG_COL_DEFAULT]); + } + + switch (level) { + case GGML_LOG_LEVEL_INFO: fprintf(fcur, "%sI %s", g_col[COMMON_LOG_COL_GREEN], g_col[COMMON_LOG_COL_DEFAULT]); break; + case GGML_LOG_LEVEL_WARN: fprintf(fcur, "%sW %s", g_col[COMMON_LOG_COL_MAGENTA], "" ); break; + case GGML_LOG_LEVEL_ERROR: fprintf(fcur, "%sE %s", g_col[COMMON_LOG_COL_RED], "" ); break; + case GGML_LOG_LEVEL_DEBUG: fprintf(fcur, "%sD %s", g_col[COMMON_LOG_COL_YELLOW], "" ); break; + default: + break; + } + } + + fprintf(fcur, "%s", msg.data()); + + if (level == GGML_LOG_LEVEL_WARN || level == GGML_LOG_LEVEL_ERROR || level == GGML_LOG_LEVEL_DEBUG) { + fprintf(fcur, "%s", g_col[COMMON_LOG_COL_DEFAULT]); + } + + fflush(fcur); + } +}; + +struct common_log { + // default capacity - will be expanded if needed + common_log() : common_log(256) {} + + common_log(size_t capacity) { + file = nullptr; + prefix = false; + timestamps = false; + running = false; + t_start = t_us(); + + // initial message size - will be expanded if longer messages arrive + entries.resize(capacity); + for (auto & entry : entries) { + entry.msg.resize(256); + } + + head = 0; + tail = 0; + + resume(); + } + + ~common_log() { + pause(); + if (file) { + fclose(file); + } + } + +private: + std::mutex mtx; + std::thread thrd; + std::condition_variable cv; + + FILE * file; + + bool prefix; + bool timestamps; + bool running; + + int64_t t_start; + + // ring buffer of entries + std::vector entries; + size_t head; + size_t tail; + + // worker thread copies into this + common_log_entry cur; + +public: + void add(enum ggml_log_level level, const char * fmt, va_list args) { + std::lock_guard lock(mtx); + + if (!running) { + // discard messages while the worker thread is paused + return; + } + + auto & entry = entries[tail]; + + { + // cannot use args twice, so make a copy in case we need to expand the buffer + va_list args_copy; + va_copy(args_copy, args); + +#if 1 + const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args); + if (n >= entry.msg.size()) { + entry.msg.resize(n + 1); + vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args_copy); + } +#else + // hack for bolding arguments + + std::stringstream ss; + for (int i = 0; fmt[i] != 0; i++) { + if (fmt[i] == '%') { + ss << LOG_COL_BOLD; + while (fmt[i] != ' ' && fmt[i] != ')' && fmt[i] != ']' && fmt[i] != 0) ss << fmt[i++]; + ss << LOG_COL_DEFAULT; + if (fmt[i] == 0) break; + } + ss << fmt[i]; + } + const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args); + if (n >= entry.msg.size()) { + entry.msg.resize(n + 1); + vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy); + } +#endif + va_end(args_copy); + } + + entry.level = level; + entry.prefix = prefix; + entry.timestamp = 0; + if (timestamps) { + entry.timestamp = t_us() - t_start; + } + entry.is_end = false; + + tail = (tail + 1) % entries.size(); + if (tail == head) { + // expand the buffer + std::vector new_entries(2*entries.size()); + + size_t new_tail = 0; + + do { + new_entries[new_tail] = std::move(entries[head]); + + head = (head + 1) % entries.size(); + new_tail = (new_tail + 1); + } while (head != tail); + + head = 0; + tail = new_tail; + + for (size_t i = tail; i < new_entries.size(); i++) { + new_entries[i].msg.resize(256); + } + + entries = std::move(new_entries); + } + + cv.notify_one(); + } + + void resume() { + std::lock_guard lock(mtx); + + if (running) { + return; + } + + running = true; + + thrd = std::thread([this]() { + while (true) { + { + std::unique_lock lock(mtx); + cv.wait(lock, [this]() { return head != tail; }); + + cur = entries[head]; + + head = (head + 1) % entries.size(); + } + + if (cur.is_end) { + break; + } + + cur.print(); // stdout and stderr + + if (file) { + cur.print(file); + } + } + }); + } + + void pause() { + { + std::lock_guard lock(mtx); + + if (!running) { + return; + } + + running = false; + + // push an entry to signal the worker thread to stop + { + auto & entry = entries[tail]; + entry.is_end = true; + + tail = (tail + 1) % entries.size(); + } + + cv.notify_one(); + } + + thrd.join(); + } + + void set_file(const char * path) { + pause(); + + if (file) { + fclose(file); + } + + if (path) { + file = fopen(path, "w"); + } else { + file = nullptr; + } + + resume(); + } + + void set_colors(bool colors) { + pause(); + + if (colors) { + g_col[COMMON_LOG_COL_DEFAULT] = LOG_COL_DEFAULT; + g_col[COMMON_LOG_COL_BOLD] = LOG_COL_BOLD; + g_col[COMMON_LOG_COL_RED] = LOG_COL_RED; + g_col[COMMON_LOG_COL_GREEN] = LOG_COL_GREEN; + g_col[COMMON_LOG_COL_YELLOW] = LOG_COL_YELLOW; + g_col[COMMON_LOG_COL_BLUE] = LOG_COL_BLUE; + g_col[COMMON_LOG_COL_MAGENTA] = LOG_COL_MAGENTA; + g_col[COMMON_LOG_COL_CYAN] = LOG_COL_CYAN; + g_col[COMMON_LOG_COL_WHITE] = LOG_COL_WHITE; + } else { + for (size_t i = 0; i < g_col.size(); i++) { + g_col[i] = ""; + } + } + + resume(); + } + + void set_prefix(bool prefix) { + std::lock_guard lock(mtx); + + this->prefix = prefix; + } + + void set_timestamps(bool timestamps) { + std::lock_guard lock(mtx); + + this->timestamps = timestamps; + } +}; + +// +// public API +// + +struct common_log * common_log_init() { + return new common_log; +} + +struct common_log * common_log_main() { + static struct common_log log; + + return &log; +} + +void common_log_pause(struct common_log * log) { + log->pause(); +} + +void common_log_resume(struct common_log * log) { + log->resume(); +} + +void common_log_free(struct common_log * log) { + delete log; +} + +void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...) { + va_list args; + va_start(args, fmt); + log->add(level, fmt, args); + va_end(args); +} + +void common_log_set_file(struct common_log * log, const char * file) { + log->set_file(file); +} + +void common_log_set_colors(struct common_log * log, bool colors) { + log->set_colors(colors); +} + +void common_log_set_prefix(struct common_log * log, bool prefix) { + log->set_prefix(prefix); +} + +void common_log_set_timestamps(struct common_log * log, bool timestamps) { + log->set_timestamps(timestamps); +} diff --git a/common/log.h b/common/log.h index e4e1b9f4f..4ebc6314b 100644 --- a/common/log.h +++ b/common/log.h @@ -1,723 +1,103 @@ #pragma once -#include -#include -#include -#include -#include -#include -#include -#include +#include "ggml.h" // for ggml_log_level -// -------------------------------- -// -// Basic usage: -// -// -------- -// -// The LOG() and LOG_TEE() macros are ready to go by default -// they do not require any initialization. -// -// LOGLN() and LOG_TEELN() are variants which automatically -// include \n character at the end of the log string. -// -// LOG() behaves exactly like printf, by default writing to a logfile. -// LOG_TEE() additionally, prints to the screen too ( mimics Unix tee command ). -// -// Default logfile is named -// "llama..log" -// Default LOG_TEE() secondary output target is -// stderr -// -// Logs can be dynamically disabled or enabled using functions: -// log_disable() -// and -// log_enable() -// -// A log target can be changed with: -// log_set_target( string ) -// creating and opening, or re-opening a file by string filename -// or -// log_set_target( FILE* ) -// allowing to point at stderr, stdout, or any valid FILE* file handler. -// -// -------- -// -// End of Basic usage. -// -// -------------------------------- +#define LOG_CLR_TO_EOL "\033[K\r" +#define LOG_COL_DEFAULT "\033[0m" +#define LOG_COL_BOLD "\033[1m" +#define LOG_COL_RED "\033[31m" +#define LOG_COL_GREEN "\033[32m" +#define LOG_COL_YELLOW "\033[33m" +#define LOG_COL_BLUE "\033[34m" +#define LOG_COL_MAGENTA "\033[35m" +#define LOG_COL_CYAN "\033[36m" +#define LOG_COL_WHITE "\033[37m" -// Specifies a log target. -// default uses log_handler() with "llama.log" log file -// this can be changed, by defining LOG_TARGET -// like so: -// -// #define LOG_TARGET (a valid FILE*) -// #include "log.h" -// -// or it can be simply redirected to stdout or stderr -// like so: -// -// #define LOG_TARGET stderr -// #include "log.h" -// -// The log target can also be redirected to a different function -// like so: -// -// #define LOG_TARGET log_handler_different() -// #include "log.h" -// -// FILE* log_handler_different() -// { -// return stderr; -// } -// -// or: -// -// #define LOG_TARGET log_handler_another_one("somelog.log") -// #include "log.h" -// -// FILE* log_handler_another_one(char*filename) -// { -// static FILE* logfile = nullptr; -// (...) -// if( !logfile ) -// { -// fopen(...) -// } -// (...) -// return logfile -// } -// -#ifndef LOG_TARGET - #define LOG_TARGET log_handler() -#endif - -#ifndef LOG_TEE_TARGET - #define LOG_TEE_TARGET stderr -#endif - -// Utility for synchronizing log configuration state -// since std::optional was introduced only in c++17 -enum LogTriState -{ - LogTriStateSame, - LogTriStateFalse, - LogTriStateTrue -}; - -// Utility to obtain "pid" like unique process id and use it when creating log files. -inline std::string log_get_pid() -{ - static std::string pid; - if (pid.empty()) - { - // std::this_thread::get_id() is the most portable way of obtaining a "process id" - // it's not the same as "pid" but is unique enough to solve multiple instances - // trying to write to the same log. - std::stringstream ss; - ss << std::this_thread::get_id(); - pid = ss.str(); - } - - return pid; -} - -// Utility function for generating log file names with unique id based on thread id. -// invocation with log_filename_generator( "llama", "log" ) creates a string "llama..log" -// where the number is a runtime id of the current thread. - -#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(LogTriStateSame, log_file_basename, log_file_extension) - -// INTERNAL, DO NOT USE -inline std::string log_filename_generator_impl(LogTriState multilog, const std::string & log_file_basename, const std::string & log_file_extension) -{ - static bool _multilog = false; - - if (multilog != LogTriStateSame) - { - _multilog = multilog == LogTriStateTrue; - } - - std::stringstream buf; - - buf << log_file_basename; - if (_multilog) - { - buf << "."; - buf << log_get_pid(); - } - buf << "."; - buf << log_file_extension; - - return buf.str(); -} - -#ifndef LOG_DEFAULT_FILE_NAME - #define LOG_DEFAULT_FILE_NAME log_filename_generator("llama", "log") -#endif - -// Utility for turning #define values into string literals -// so we can have a define for stderr and -// we can print "stderr" instead of literal stderr, etc. -#define LOG_STRINGIZE1(s) #s -#define LOG_STRINGIZE(s) LOG_STRINGIZE1(s) - -#define LOG_TEE_TARGET_STRING LOG_STRINGIZE(LOG_TEE_TARGET) - -// Allows disabling timestamps. -// in order to disable, define LOG_NO_TIMESTAMPS -// like so: -// -// #define LOG_NO_TIMESTAMPS -// #include "log.h" -// -#ifndef LOG_NO_TIMESTAMPS - #ifndef _MSC_VER - #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] " - #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() - #else - #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] " - #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() - #endif +#ifndef __GNUC__ +# define LOG_ATTRIBUTE_FORMAT(...) +#elif defined(__MINGW32__) +# define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) #else - #define LOG_TIMESTAMP_FMT "%s" - #define LOG_TIMESTAMP_VAL ,"" +# define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) #endif -#ifdef LOG_TEE_TIMESTAMPS - #ifndef _MSC_VER - #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] " - #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() - #else - #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] " - #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() - #endif -#else - #define LOG_TEE_TIMESTAMP_FMT "%s" - #define LOG_TEE_TIMESTAMP_VAL ,"" -#endif +#define LOG_DEFAULT_DEBUG 1 +#define LOG_DEFAULT_LLAMA 0 -// Allows disabling file/line/function prefix -// in order to disable, define LOG_NO_FILE_LINE_FUNCTION -// like so: +// needed by the LOG_TMPL macro to avoid computing log arguments if the verbosity lower +// set via common_log_set_verbosity() +extern int common_log_verbosity_thold; + +void common_log_set_verbosity_thold(int verbosity); // not thread-safe + +// the common_log uses an internal worker thread to print/write log messages +// when the worker thread is paused, incoming log messages are discarded +struct common_log; + +struct common_log * common_log_init(); +struct common_log * common_log_main(); // singleton, automatically destroys itself on exit +void common_log_pause (struct common_log * log); // pause the worker thread, not thread-safe +void common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe +void common_log_free (struct common_log * log); + +LOG_ATTRIBUTE_FORMAT(3, 4) +void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...); + +// defaults: file = NULL, colors = false, prefix = false, timestamps = false // -// #define LOG_NO_FILE_LINE_FUNCTION -// #include "log.h" +// regular log output: // -#ifndef LOG_NO_FILE_LINE_FUNCTION - #ifndef _MSC_VER - #define LOG_FLF_FMT "[%24s:%5d][%24s] " - #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ - #else - #define LOG_FLF_FMT "[%24s:%5ld][%24s] " - #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ - #endif -#else - #define LOG_FLF_FMT "%s" - #define LOG_FLF_VAL ,"" -#endif - -#ifdef LOG_TEE_FILE_LINE_FUNCTION - #ifndef _MSC_VER - #define LOG_TEE_FLF_FMT "[%24s:%5d][%24s] " - #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ - #else - #define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] " - #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ - #endif -#else - #define LOG_TEE_FLF_FMT "%s" - #define LOG_TEE_FLF_VAL ,"" -#endif - -// INTERNAL, DO NOT USE -// USE LOG() INSTEAD +// ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34) +// llm_load_tensors: ggml ctx size = 0.27 MiB +// llm_load_tensors: offloading 32 repeating layers to GPU +// llm_load_tensors: offloading non-repeating layers to GPU // -#ifndef _MSC_VER - #define LOG_IMPL(str, ...) \ - do { \ - if (LOG_TARGET != nullptr) \ - { \ - fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \ - fflush(LOG_TARGET); \ - } \ +// with prefix = true, timestamps = true, the log output will look like this: +// +// 0.00.035.060 D ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34) +// 0.00.035.064 I llm_load_tensors: ggml ctx size = 0.27 MiB +// 0.00.090.578 I llm_load_tensors: offloading 32 repeating layers to GPU +// 0.00.090.579 I llm_load_tensors: offloading non-repeating layers to GPU +// +// I - info (stdout, V = 0) +// W - warning (stderr, V = 0) +// E - error (stderr, V = 0) +// D - debug (stderr, V = LOG_DEFAULT_DEBUG) +// + +void common_log_set_file (struct common_log * log, const char * file); // not thread-safe +void common_log_set_colors (struct common_log * log, bool colors); // not thread-safe +void common_log_set_prefix (struct common_log * log, bool prefix); // whether to output prefix to each log +void common_log_set_timestamps(struct common_log * log, bool timestamps); // whether to output timestamps in the prefix + +// helper macros for logging +// use these to avoid computing log arguments if the verbosity of the log is higher than the threshold +// +// for example: +// +// LOG_DBG("this is a debug message: %d\n", expensive_function()); +// +// this will avoid calling expensive_function() if LOG_DEFAULT_DEBUG > common_log_verbosity_thold +// + +#define LOG_TMPL(level, verbosity, ...) \ + do { \ + if ((verbosity) <= common_log_verbosity_thold) { \ + common_log_add(common_log_main(), (level), __VA_ARGS__); \ + } \ } while (0) -#else - #define LOG_IMPL(str, ...) \ - do { \ - if (LOG_TARGET != nullptr) \ - { \ - fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \ - fflush(LOG_TARGET); \ - } \ - } while (0) -#endif -// INTERNAL, DO NOT USE -// USE LOG_TEE() INSTEAD -// -#ifndef _MSC_VER - #define LOG_TEE_IMPL(str, ...) \ - do { \ - if (LOG_TARGET != nullptr) \ - { \ - fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \ - fflush(LOG_TARGET); \ - } \ - if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \ - { \ - fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL, __VA_ARGS__); \ - fflush(LOG_TEE_TARGET); \ - } \ - } while (0) -#else - #define LOG_TEE_IMPL(str, ...) \ - do { \ - if (LOG_TARGET != nullptr) \ - { \ - fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \ - fflush(LOG_TARGET); \ - } \ - if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \ - { \ - fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL "", ##__VA_ARGS__); \ - fflush(LOG_TEE_TARGET); \ - } \ - } while (0) -#endif +#define LOG(...) LOG_TMPL(GGML_LOG_LEVEL_NONE, 0, __VA_ARGS__) +#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__) -// The '\0' as a last argument, is a trick to bypass the silly -// "warning: ISO C++11 requires at least one argument for the "..." in a variadic macro" -// so we can have a single macro which can be called just like printf. +#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO, 0, __VA_ARGS__) +#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN, 0, __VA_ARGS__) +#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, 0, __VA_ARGS__) +#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_DEFAULT_DEBUG, __VA_ARGS__) +#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT, 0, __VA_ARGS__) -// Main LOG macro. -// behaves like printf, and supports arguments the exact same way. -// -#ifndef _MSC_VER - #define LOG(...) LOG_IMPL(__VA_ARGS__, "") -#else - #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "") -#endif - -// Main TEE macro. -// does the same as LOG -// and -// simultaneously writes stderr. -// -// Secondary target can be changed just like LOG_TARGET -// by defining LOG_TEE_TARGET -// -#ifndef _MSC_VER - #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") -#else - #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "") -#endif - -// LOG macro variants with auto endline. -#ifndef _MSC_VER - #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") - #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") -#else - #define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n") - #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n") -#endif - -// INTERNAL, DO NOT USE -inline FILE *log_handler1_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr) -{ - static bool _initialized = false; - static bool _append = false; - static bool _disabled = filename.empty() && target == nullptr; - static std::string log_current_filename{filename}; - static FILE *log_current_target{target}; - static FILE *logfile = nullptr; - - if (change) - { - if (append != LogTriStateSame) - { - _append = append == LogTriStateTrue; - return logfile; - } - - if (disable == LogTriStateTrue) - { - // Disable primary target - _disabled = true; - } - // If previously disabled, only enable, and keep previous target - else if (disable == LogTriStateFalse) - { - _disabled = false; - } - // Otherwise, process the arguments - else if (log_current_filename != filename || log_current_target != target) - { - _initialized = false; - } - } - - if (_disabled) - { - // Log is disabled - return nullptr; - } - - if (_initialized) - { - // with fallback in case something went wrong - return logfile ? logfile : stderr; - } - - // do the (re)initialization - if (target != nullptr) - { - if (logfile != nullptr && logfile != stdout && logfile != stderr) - { - fclose(logfile); - } - - log_current_filename = LOG_DEFAULT_FILE_NAME; - log_current_target = target; - - logfile = target; - } - else - { - if (log_current_filename != filename) - { - if (logfile != nullptr && logfile != stdout && logfile != stderr) - { - fclose(logfile); - } - } - - logfile = fopen(filename.c_str(), _append ? "a" : "w"); - } - - if (!logfile) - { - // Verify whether the file was opened, otherwise fallback to stderr - logfile = stderr; - - fprintf(stderr, "Failed to open logfile '%s' with error '%s'\n", filename.c_str(), std::strerror(errno)); - fflush(stderr); - - // At this point we let the init flag be to true below, and let the target fallback to stderr - // otherwise we would repeatedly fopen() which was already unsuccessful - } - - _initialized = true; - - return logfile ? logfile : stderr; -} - -// INTERNAL, DO NOT USE -inline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriStateSame, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME) -{ - return log_handler1_impl(change, append, disable, filename, target); -} - -// Disables logs entirely at runtime. -// Makes LOG() and LOG_TEE() produce no output, -// until enabled back. -#define log_disable() log_disable_impl() - -// INTERNAL, DO NOT USE -inline FILE *log_disable_impl() -{ - return log_handler1_impl(true, LogTriStateSame, LogTriStateTrue); -} - -// Enables logs at runtime. -#define log_enable() log_enable_impl() - -// INTERNAL, DO NOT USE -inline FILE *log_enable_impl() -{ - return log_handler1_impl(true, LogTriStateSame, LogTriStateFalse); -} - -// Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*) -#define log_set_target(target) log_set_target_impl(target) - -// INTERNAL, DO NOT USE -inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, LogTriStateSame, filename); } -inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, LogTriStateSame, target); } - -// INTERNAL, DO NOT USE -inline FILE *log_handler() { return log_handler1_impl(); } - -// Enable or disable creating separate log files for each run. -// can ONLY be invoked BEFORE first log use. -#define log_multilog(enable) log_filename_generator_impl((enable) ? LogTriStateTrue : LogTriStateFalse, "", "") -// Enable or disable append mode for log file. -// can ONLY be invoked BEFORE first log use. -#define log_append(enable) log_append_impl(enable) -// INTERNAL, DO NOT USE -inline FILE *log_append_impl(bool enable) -{ - return log_handler1_impl(true, enable ? LogTriStateTrue : LogTriStateFalse, LogTriStateSame); -} - -inline void log_test() -{ - log_disable(); - LOG("01 Hello World to nobody, because logs are disabled!\n"); - log_enable(); - LOG("02 Hello World to default output, which is \"%s\" ( Yaaay, arguments! )!\n", LOG_STRINGIZE(LOG_TARGET)); - LOG_TEE("03 Hello World to **both** default output and " LOG_TEE_TARGET_STRING "!\n"); - log_set_target(stderr); - LOG("04 Hello World to stderr!\n"); - LOG_TEE("05 Hello World TEE with double printing to stderr prevented!\n"); - log_set_target(LOG_DEFAULT_FILE_NAME); - LOG("06 Hello World to default log file!\n"); - log_set_target(stdout); - LOG("07 Hello World to stdout!\n"); - log_set_target(LOG_DEFAULT_FILE_NAME); - LOG("08 Hello World to default log file again!\n"); - log_disable(); - LOG("09 Hello World _1_ into the void!\n"); - log_enable(); - LOG("10 Hello World back from the void ( you should not see _1_ in the log or the output )!\n"); - log_disable(); - log_set_target("llama.anotherlog.log"); - LOG("11 Hello World _2_ to nobody, new target was selected but logs are still disabled!\n"); - log_enable(); - LOG("12 Hello World this time in a new file ( you should not see _2_ in the log or the output )?\n"); - log_set_target("llama.yetanotherlog.log"); - LOG("13 Hello World this time in yet new file?\n"); - log_set_target(log_filename_generator("llama_autonamed", "log")); - LOG("14 Hello World in log with generated filename!\n"); -#ifdef _MSC_VER - LOG_TEE("15 Hello msvc TEE without arguments\n"); - LOG_TEE("16 Hello msvc TEE with (%d)(%s) arguments\n", 1, "test"); - LOG_TEELN("17 Hello msvc TEELN without arguments\n"); - LOG_TEELN("18 Hello msvc TEELN with (%d)(%s) arguments\n", 1, "test"); - LOG("19 Hello msvc LOG without arguments\n"); - LOG("20 Hello msvc LOG with (%d)(%s) arguments\n", 1, "test"); - LOGLN("21 Hello msvc LOGLN without arguments\n"); - LOGLN("22 Hello msvc LOGLN with (%d)(%s) arguments\n", 1, "test"); -#endif -} - -inline bool log_param_single_parse(const std::string & param) -{ - if ( param == "--log-test") - { - log_test(); - return true; - } - - if ( param == "--log-disable") - { - log_disable(); - return true; - } - - if ( param == "--log-enable") - { - log_enable(); - return true; - } - - if (param == "--log-new") - { - log_multilog(true); - return true; - } - - if (param == "--log-append") - { - log_append(true); - return true; - } - - return false; -} - -inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string & param, const std::string & next = std::string()) -{ - if ( param == "--log-file") - { - if (!check_but_dont_parse) - { - log_set_target(log_filename_generator(next.empty() ? "unnamed" : next, "log")); - } - - return true; - } - - return false; -} - -inline void log_print_usage() -{ - printf("log options:\n"); - /* format - printf(" -h, --help show this help message and exit\n");*/ - /* spacing - printf("__-param----------------Description\n");*/ - printf(" --log-test Run simple logging test\n"); - printf(" --log-disable Disable trace logs\n"); - printf(" --log-enable Enable trace logs\n"); - printf(" --log-file Specify a log filename (without extension)\n"); - printf(" --log-new Create a separate new log file on start. " - "Each log file will have unique name: \"..log\"\n"); - printf(" --log-append Don't truncate the old log file.\n"); -} - -#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv) - -// INTERNAL, DO NOT USE -inline void log_dump_cmdline_impl(int argc, char **argv) -{ - std::stringstream buf; - for (int i = 0; i < argc; ++i) - { - if (std::string(argv[i]).find(' ') != std::string::npos) - { - buf << " \"" << argv[i] <<"\""; - } - else - { - buf << " " << argv[i]; - } - } - LOGLN("Cmd:%s", buf.str().c_str()); -} - -#define log_tostr(var) log_var_to_string_impl(var).c_str() - -inline std::string log_var_to_string_impl(bool var) -{ - return var ? "true" : "false"; -} - -inline std::string log_var_to_string_impl(std::string var) -{ - return var; -} - -inline std::string log_var_to_string_impl(const std::vector & var) -{ - std::stringstream buf; - buf << "[ "; - bool first = true; - for (auto e : var) - { - if (first) - { - first = false; - } - else - { - buf << ", "; - } - buf << std::to_string(e); - } - buf << " ]"; - - return buf.str(); -} - -template -inline std::string LOG_TOKENS_TOSTR_PRETTY(const C & ctx, const T & tokens) -{ - std::stringstream buf; - buf << "[ "; - - bool first = true; - for (const auto &token : tokens) - { - if (!first) { - buf << ", "; - } else { - first = false; - } - - auto detokenized = llama_token_to_piece(ctx, token); - - detokenized.erase( - std::remove_if( - detokenized.begin(), - detokenized.end(), - [](const unsigned char c) { return !std::isprint(c); }), - detokenized.end()); - - buf - << "'" << detokenized << "'" - << ":" << std::to_string(token); - } - buf << " ]"; - - return buf.str(); -} - -template -inline std::string LOG_BATCH_TOSTR_PRETTY(const C & ctx, const B & batch) -{ - std::stringstream buf; - buf << "[ "; - - bool first = true; - for (int i = 0; i < batch.n_tokens; ++i) - { - if (!first) { - buf << ", "; - } else { - first = false; - } - - auto detokenized = llama_token_to_piece(ctx, batch.token[i]); - - detokenized.erase( - std::remove_if( - detokenized.begin(), - detokenized.end(), - [](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 << " ]"; - - return buf.str(); -} - -#ifdef LOG_DISABLE_LOGS - -#undef LOG -#define LOG(...) // dummy stub -#undef LOGLN -#define LOGLN(...) // dummy stub - -#undef LOG_TEE -#define LOG_TEE(...) fprintf(stderr, __VA_ARGS__) // convert to normal fprintf - -#undef LOG_TEELN -#define LOG_TEELN(...) fprintf(stderr, __VA_ARGS__) // convert to normal fprintf - -#undef LOG_DISABLE -#define LOG_DISABLE() // dummy stub - -#undef LOG_ENABLE -#define LOG_ENABLE() // dummy stub - -#undef LOG_ENABLE -#define LOG_ENABLE() // dummy stub - -#undef LOG_SET_TARGET -#define LOG_SET_TARGET(...) // dummy stub - -#undef LOG_DUMP_CMDLINE -#define LOG_DUMP_CMDLINE(...) // dummy stub - -#endif // LOG_DISABLE_LOGS +#define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO, verbosity, __VA_ARGS__) +#define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN, verbosity, __VA_ARGS__) +#define LOG_ERRV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, verbosity, __VA_ARGS__) +#define LOG_DBGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, verbosity, __VA_ARGS__) +#define LOG_CNTV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_CONT, verbosity, __VA_ARGS__) diff --git a/common/minja.hpp b/common/minja.hpp new file mode 100644 index 000000000..c58dd66e0 --- /dev/null +++ b/common/minja.hpp @@ -0,0 +1,2883 @@ +/* + Copyright 2024 Google LLC + + Use of this source code is governed by an MIT-style + license that can be found in the LICENSE file or at + https://opensource.org/licenses/MIT. +*/ +// SPDX-License-Identifier: MIT +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +using json = nlohmann::ordered_json; + +namespace minja { + +class Context; + +struct Options { + bool trim_blocks; // removes the first newline after a block + bool lstrip_blocks; // removes leading whitespace on the line of the block + bool keep_trailing_newline; // don't remove last newline +}; + +struct ArgumentsValue; + +inline std::string normalize_newlines(const std::string & s) { +#ifdef _WIN32 + static const std::regex nl_regex("\r\n"); + return std::regex_replace(s, nl_regex, "\n"); +#else + return s; +#endif +} + +/* Values that behave roughly like in Python. */ +class Value : public std::enable_shared_from_this { +public: + using CallableType = std::function &, ArgumentsValue &)>; + using FilterType = std::function &, ArgumentsValue &)>; + +private: + using ObjectType = nlohmann::ordered_map; // Only contains primitive keys + using ArrayType = std::vector; + + std::shared_ptr array_; + std::shared_ptr object_; + std::shared_ptr callable_; + json primitive_; + + Value(const std::shared_ptr & array) : array_(array) {} + Value(const std::shared_ptr & object) : object_(object) {} + Value(const std::shared_ptr & callable) : object_(std::make_shared()), callable_(callable) {} + + /* Python-style string repr */ + static void dump_string(const json & primitive, std::ostringstream & out, char string_quote = '\'') { + if (!primitive.is_string()) throw std::runtime_error("Value is not a string: " + primitive.dump()); + auto s = primitive.dump(); + if (string_quote == '"' || s.find('\'') != std::string::npos) { + out << s; + return; + } + // Reuse json dump, just changing string quotes + out << string_quote; + for (size_t i = 1, n = s.size() - 1; i < n; ++i) { + if (s[i] == '\\' && s[i + 1] == '"') { + out << '"'; + i++; + } else if (s[i] == string_quote) { + out << '\\' << string_quote; + } else { + out << s[i]; + } + } + out << string_quote; + } + void dump(std::ostringstream & out, int indent = -1, int level = 0, bool to_json = false) const { + auto print_indent = [&](int level) { + if (indent > 0) { + out << "\n"; + for (int i = 0, n = level * indent; i < n; ++i) out << ' '; + } + }; + auto print_sub_sep = [&]() { + out << ','; + if (indent < 0) out << ' '; + else print_indent(level + 1); + }; + + auto string_quote = to_json ? '"' : '\''; + + if (is_null()) out << "null"; + else if (array_) { + out << "["; + print_indent(level + 1); + for (size_t i = 0; i < array_->size(); ++i) { + if (i) print_sub_sep(); + (*array_)[i].dump(out, indent, level + 1, to_json); + } + print_indent(level); + out << "]"; + } else if (object_) { + out << "{"; + print_indent(level + 1); + for (auto begin = object_->begin(), it = begin; it != object_->end(); ++it) { + if (it != begin) print_sub_sep(); + if (it->first.is_string()) { + dump_string(it->first, out, string_quote); + } else { + out << string_quote << it->first.dump() << string_quote; + } + out << ": "; + it->second.dump(out, indent, level + 1, to_json); + } + print_indent(level); + out << "}"; + } else if (callable_) { + throw std::runtime_error("Cannot dump callable to JSON"); + } else if (is_boolean() && !to_json) { + out << (this->to_bool() ? "True" : "False"); + } else if (is_string() && !to_json) { + dump_string(primitive_, out, string_quote); + } else { + out << primitive_.dump(); + } + } + +public: + Value() {} + Value(const bool& v) : primitive_(v) {} + Value(const int64_t & v) : primitive_(v) {} + Value(const double& v) : primitive_(v) {} + Value(const std::nullptr_t &) {} + Value(const std::string & v) : primitive_(v) {} + Value(const char * v) : primitive_(std::string(v)) {} + + Value(const json & v) { + if (v.is_object()) { + auto object = std::make_shared(); + for (auto it = v.begin(); it != v.end(); ++it) { + (*object)[it.key()] = it.value(); + } + object_ = std::move(object); + } else if (v.is_array()) { + auto array = std::make_shared(); + for (const auto& item : v) { + array->push_back(Value(item)); + } + array_ = array; + } else { + primitive_ = v; + } + } + + std::vector keys() { + if (!object_) throw std::runtime_error("Value is not an object: " + dump()); + std::vector res; + for (const auto& item : *object_) { + res.push_back(item.first); + } + return res; + } + + size_t size() const { + if (is_object()) return object_->size(); + if (is_array()) return array_->size(); + if (is_string()) return primitive_.get().length(); + throw std::runtime_error("Value is not an array or object: " + dump()); + } + + static Value array(const std::vector values = {}) { + auto array = std::make_shared(); + for (const auto& item : values) { + array->push_back(item); + } + return Value(array); + } + static Value object(const std::shared_ptr object = std::make_shared()) { + return Value(object); + } + static Value callable(const CallableType & callable) { + return Value(std::make_shared(callable)); + } + + void insert(size_t index, const Value& v) { + if (!array_) + throw std::runtime_error("Value is not an array: " + dump()); + array_->insert(array_->begin() + index, v); + } + void push_back(const Value& v) { + if (!array_) + throw std::runtime_error("Value is not an array: " + dump()); + array_->push_back(v); + } + Value pop(const Value& index) { + if (is_array()) { + if (array_->empty()) + throw std::runtime_error("pop from empty list"); + if (index.is_null()) { + auto ret = array_->back(); + array_->pop_back(); + return ret; + } else if (!index.is_number_integer()) { + throw std::runtime_error("pop index must be an integer: " + index.dump()); + } else { + auto i = index.get(); + if (i < 0 || i >= static_cast(array_->size())) + throw std::runtime_error("pop index out of range: " + index.dump()); + auto it = array_->begin() + (i < 0 ? array_->size() + i : i); + auto ret = *it; + array_->erase(it); + return ret; + } + } else if (is_object()) { + if (!index.is_hashable()) + throw std::runtime_error("Unashable type: " + index.dump()); + auto it = object_->find(index.primitive_); + if (it == object_->end()) + throw std::runtime_error("Key not found: " + index.dump()); + auto ret = it->second; + object_->erase(it); + return ret; + } else { + throw std::runtime_error("Value is not an array or object: " + dump()); + } + } + Value get(const Value& key) { + if (array_) { + if (!key.is_number_integer()) { + return Value(); + } + auto index = key.get(); + return array_->at(index < 0 ? array_->size() + index : index); + } else if (object_) { + if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); + auto it = object_->find(key.primitive_); + if (it == object_->end()) return Value(); + return it->second; + } + return Value(); + } + void set(const Value& key, const Value& value) { + if (!object_) throw std::runtime_error("Value is not an object: " + dump()); + if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); + (*object_)[key.primitive_] = value; + } + Value call(const std::shared_ptr & context, ArgumentsValue & args) const { + if (!callable_) throw std::runtime_error("Value is not callable: " + dump()); + return (*callable_)(context, args); + } + + bool is_object() const { return !!object_; } + bool is_array() const { return !!array_; } + bool is_callable() const { return !!callable_; } + bool is_null() const { return !object_ && !array_ && primitive_.is_null() && !callable_; } + bool is_boolean() const { return primitive_.is_boolean(); } + bool is_number_integer() const { return primitive_.is_number_integer(); } + bool is_number_float() const { return primitive_.is_number_float(); } + bool is_number() const { return primitive_.is_number(); } + bool is_string() const { return primitive_.is_string(); } + bool is_iterable() const { return is_array() || is_object() || is_string(); } + + bool is_primitive() const { return !array_ && !object_ && !callable_; } + bool is_hashable() const { return is_primitive(); } + + bool empty() const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_string()) return primitive_.empty(); + if (is_array()) return array_->empty(); + if (is_object()) return object_->empty(); + return false; + } + + void for_each(const std::function & callback) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (array_) { + for (auto& item : *array_) { + callback(item); + } + } else if (object_) { + for (auto & item : *object_) { + Value key(item.first); + callback(key); + } + } else if (is_string()) { + for (char c : primitive_.get()) { + auto val = Value(std::string(1, c)); + callback(val); + } + } else { + throw std::runtime_error("Value is not iterable: " + dump()); + } + } + + bool to_bool() const { + if (is_null()) return false; + if (is_boolean()) return get(); + if (is_number()) return get() != 0; + if (is_string()) return !get().empty(); + if (is_array()) return !empty(); + return true; + } + + int64_t to_int() const { + if (is_null()) return 0; + if (is_boolean()) return get() ? 1 : 0; + if (is_number()) return static_cast(get()); + if (is_string()) { + try { + return std::stol(get()); + } catch (const std::exception &) { + return 0; + } + } + return 0; + } + + bool operator<(const Value & other) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_number() && other.is_number()) return get() < other.get(); + if (is_string() && other.is_string()) return get() < other.get(); + throw std::runtime_error("Cannot compare values: " + dump() + " < " + other.dump()); + } + bool operator>=(const Value & other) const { return !(*this < other); } + + bool operator>(const Value & other) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_number() && other.is_number()) return get() > other.get(); + if (is_string() && other.is_string()) return get() > other.get(); + throw std::runtime_error("Cannot compare values: " + dump() + " > " + other.dump()); + } + bool operator<=(const Value & other) const { return !(*this > other); } + + bool operator==(const Value & other) const { + if (callable_ || other.callable_) { + if (callable_.get() != other.callable_.get()) return false; + } + if (array_) { + if (!other.array_) return false; + if (array_->size() != other.array_->size()) return false; + for (size_t i = 0; i < array_->size(); ++i) { + if (!(*array_)[i].to_bool() || !(*other.array_)[i].to_bool() || (*array_)[i] != (*other.array_)[i]) return false; + } + return true; + } else if (object_) { + if (!other.object_) return false; + if (object_->size() != other.object_->size()) return false; + for (const auto& item : *object_) { + if (!item.second.to_bool() || !other.object_->count(item.first) || item.second != other.object_->at(item.first)) return false; + } + return true; + } else { + return primitive_ == other.primitive_; + } + } + bool operator!=(const Value & other) const { return !(*this == other); } + + bool contains(const char * key) const { return contains(std::string(key)); } + bool contains(const std::string & key) const { + if (array_) { + return false; + } else if (object_) { + return object_->find(key) != object_->end(); + } else { + throw std::runtime_error("contains can only be called on arrays and objects: " + dump()); + } + } + bool contains(const Value & value) const { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (array_) { + for (const auto& item : *array_) { + if (item.to_bool() && item == value) return true; + } + return false; + } else if (object_) { + if (!value.is_hashable()) throw std::runtime_error("Unashable type: " + value.dump()); + return object_->find(value.primitive_) != object_->end(); + } else { + throw std::runtime_error("contains can only be called on arrays and objects: " + dump()); + } + } + void erase(size_t index) { + if (!array_) throw std::runtime_error("Value is not an array: " + dump()); + array_->erase(array_->begin() + index); + } + void erase(const std::string & key) { + if (!object_) throw std::runtime_error("Value is not an object: " + dump()); + object_->erase(key); + } + const Value& at(const Value & index) const { + return const_cast(this)->at(index); + } + Value& at(const Value & index) { + if (!index.is_hashable()) throw std::runtime_error("Unashable type: " + dump()); + if (is_array()) return array_->at(index.get()); + if (is_object()) return object_->at(index.primitive_); + throw std::runtime_error("Value is not an array or object: " + dump()); + } + const Value& at(size_t index) const { + return const_cast(this)->at(index); + } + Value& at(size_t index) { + if (is_null()) + throw std::runtime_error("Undefined value or reference"); + if (is_array()) return array_->at(index); + if (is_object()) return object_->at(index); + throw std::runtime_error("Value is not an array or object: " + dump()); + } + + template + T get(const std::string & key, T default_value) const { + if (!contains(key)) return default_value; + return at(key).get(); + } + + template + T get() const { + if (is_primitive()) return primitive_.get(); + throw std::runtime_error("get not defined for this value type: " + dump()); + } + + std::string dump(int indent=-1, bool to_json=false) const { + std::ostringstream out; + dump(out, indent, 0, to_json); + return out.str(); + } + + Value operator-() const { + if (is_number_integer()) + return -get(); + else + return -get(); + } + std::string to_str() const { + if (is_string()) return get(); + if (is_number_integer()) return std::to_string(get()); + if (is_number_float()) return std::to_string(get()); + if (is_boolean()) return get() ? "True" : "False"; + if (is_null()) return "None"; + return dump(); + } + Value operator+(const Value& rhs) const { + if (is_string() || rhs.is_string()) { + return to_str() + rhs.to_str(); + } else if (is_number_integer() && rhs.is_number_integer()) { + return get() + rhs.get(); + } else if (is_array() && rhs.is_array()) { + auto res = Value::array(); + for (const auto& item : *array_) res.push_back(item); + for (const auto& item : *rhs.array_) res.push_back(item); + return res; + } else { + return get() + rhs.get(); + } + } + Value operator-(const Value& rhs) const { + if (is_number_integer() && rhs.is_number_integer()) + return get() - rhs.get(); + else + return get() - rhs.get(); + } + Value operator*(const Value& rhs) const { + if (is_string() && rhs.is_number_integer()) { + std::ostringstream out; + for (int64_t i = 0, n = rhs.get(); i < n; ++i) { + out << to_str(); + } + return out.str(); + } + else if (is_number_integer() && rhs.is_number_integer()) + return get() * rhs.get(); + else + return get() * rhs.get(); + } + Value operator/(const Value& rhs) const { + if (is_number_integer() && rhs.is_number_integer()) + return get() / rhs.get(); + else + return get() / rhs.get(); + } + Value operator%(const Value& rhs) const { + return get() % rhs.get(); + } +}; + +struct ArgumentsValue { + std::vector args; + std::vector> kwargs; + + bool has_named(const std::string & name) { + for (const auto & p : kwargs) { + if (p.first == name) return true; + } + return false; + } + + Value get_named(const std::string & name) { + for (const auto & [key, value] : kwargs) { + if (key == name) return value; + } + return Value(); + } + + bool empty() { + return args.empty() && kwargs.empty(); + } + + void expectArgs(const std::string & method_name, const std::pair & pos_count, const std::pair & kw_count) { + if (args.size() < pos_count.first || args.size() > pos_count.second || kwargs.size() < kw_count.first || kwargs.size() > kw_count.second) { + std::ostringstream out; + out << method_name << " must have between " << pos_count.first << " and " << pos_count.second << " positional arguments and between " << kw_count.first << " and " << kw_count.second << " keyword arguments"; + throw std::runtime_error(out.str()); + } + } +}; + +template <> +inline json Value::get() const { + if (is_primitive()) return primitive_; + if (is_null()) return json(); + if (array_) { + std::vector res; + for (const auto& item : *array_) { + res.push_back(item.get()); + } + return res; + } + if (object_) { + json res = json::object(); + for (const auto& [key, value] : *object_) { + if (key.is_string()) { + res[key.get()] = value.get(); + } else if (key.is_primitive()) { + res[key.dump()] = value.get(); + } else { + throw std::runtime_error("Invalid key type for conversion to JSON: " + key.dump()); + } + } + if (is_callable()) { + res["__callable__"] = true; + } + return res; + } + throw std::runtime_error("get not defined for this value type: " + dump()); +} + +} // namespace minja + +namespace std { + template <> + struct hash { + size_t operator()(const minja::Value & v) const { + if (!v.is_hashable()) + throw std::runtime_error("Unsupported type for hashing: " + v.dump()); + return std::hash()(v.get()); + } + }; +} // namespace std + +namespace minja { + +static std::string error_location_suffix(const std::string & source, size_t pos) { + auto get_line = [&](size_t line) { + auto start = source.begin(); + for (size_t i = 1; i < line; ++i) { + start = std::find(start, source.end(), '\n') + 1; + } + auto end = std::find(start, source.end(), '\n'); + return std::string(start, end); + }; + auto start = source.begin(); + auto end = source.end(); + auto it = start + pos; + auto line = std::count(start, it, '\n') + 1; + auto max_line = std::count(start, end, '\n') + 1; + auto col = pos - std::string(start, it).rfind('\n'); + std::ostringstream out; + out << " at row " << line << ", column " << col << ":\n"; + if (line > 1) out << get_line(line - 1) << "\n"; + out << get_line(line) << "\n"; + out << std::string(col - 1, ' ') << "^\n"; + if (line < max_line) out << get_line(line + 1) << "\n"; + + return out.str(); +} + +class Context : public std::enable_shared_from_this { + protected: + Value values_; + std::shared_ptr parent_; + public: + Context(Value && values, const std::shared_ptr & parent = nullptr) : values_(std::move(values)), parent_(parent) { + if (!values_.is_object()) throw std::runtime_error("Context values must be an object: " + values_.dump()); + } + virtual ~Context() {} + + static std::shared_ptr builtins(); + static std::shared_ptr make(Value && values, const std::shared_ptr & parent = builtins()); + + std::vector keys() { + return values_.keys(); + } + virtual Value get(const Value & key) { + if (values_.contains(key)) return values_.at(key); + if (parent_) return parent_->get(key); + return Value(); + } + virtual Value & at(const Value & key) { + if (values_.contains(key)) return values_.at(key); + if (parent_) return parent_->at(key); + throw std::runtime_error("Undefined variable: " + key.dump()); + } + virtual bool contains(const Value & key) { + if (values_.contains(key)) return true; + if (parent_) return parent_->contains(key); + return false; + } + virtual void set(const Value & key, const Value & value) { + values_.set(key, value); + } +}; + +struct Location { + std::shared_ptr source; + size_t pos; +}; + +class Expression { +protected: + virtual Value do_evaluate(const std::shared_ptr & context) const = 0; +public: + using Parameters = std::vector>>; + + Location location; + + Expression(const Location & location) : location(location) {} + virtual ~Expression() = default; + + Value evaluate(const std::shared_ptr & context) const { + try { + return do_evaluate(context); + } catch (const std::exception & e) { + std::ostringstream out; + out << e.what(); + if (location.source) out << error_location_suffix(*location.source, location.pos); + throw std::runtime_error(out.str()); + } + } +}; + +class VariableExpr : public Expression { + std::string name; +public: + VariableExpr(const Location & location, const std::string& n) + : Expression(location), name(n) {} + std::string get_name() const { return name; } + Value do_evaluate(const std::shared_ptr & context) const override { + if (!context->contains(name)) { + return Value(); + } + return context->at(name); + } +}; + +static void destructuring_assign(const std::vector & var_names, const std::shared_ptr & context, Value& item) { + if (var_names.size() == 1) { + Value name(var_names[0]); + context->set(name, item); + } else { + if (!item.is_array() || item.size() != var_names.size()) { + throw std::runtime_error("Mismatched number of variables and items in destructuring assignment"); + } + for (size_t i = 0; i < var_names.size(); ++i) { + context->set(var_names[i], item.at(i)); + } + } +} + +enum SpaceHandling { Keep, Strip, StripSpaces, StripNewline }; + +class TemplateToken { +public: + enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Generation, EndGeneration, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter, Break, Continue }; + + static std::string typeToString(Type t) { + switch (t) { + case Type::Text: return "text"; + case Type::Expression: return "expression"; + case Type::If: return "if"; + case Type::Else: return "else"; + case Type::Elif: return "elif"; + case Type::EndIf: return "endif"; + case Type::For: return "for"; + case Type::EndFor: return "endfor"; + case Type::Set: return "set"; + case Type::EndSet: return "endset"; + case Type::Comment: return "comment"; + case Type::Macro: return "macro"; + case Type::EndMacro: return "endmacro"; + case Type::Filter: return "filter"; + case Type::EndFilter: return "endfilter"; + case Type::Generation: return "generation"; + case Type::EndGeneration: return "endgeneration"; + case Type::Break: return "break"; + case Type::Continue: return "continue"; + } + return "Unknown"; + } + + TemplateToken(Type type, const Location & location, SpaceHandling pre, SpaceHandling post) : type(type), location(location), pre_space(pre), post_space(post) {} + virtual ~TemplateToken() = default; + + Type type; + Location location; + SpaceHandling pre_space = SpaceHandling::Keep; + SpaceHandling post_space = SpaceHandling::Keep; +}; + +struct TextTemplateToken : public TemplateToken { + std::string text; + TextTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Text, location, pre, post), text(t) {} +}; + +struct ExpressionTemplateToken : public TemplateToken { + std::shared_ptr expr; + ExpressionTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && e) : TemplateToken(Type::Expression, location, pre, post), expr(std::move(e)) {} +}; + +struct IfTemplateToken : public TemplateToken { + std::shared_ptr condition; + IfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::If, location, pre, post), condition(std::move(c)) {} +}; + +struct ElifTemplateToken : public TemplateToken { + std::shared_ptr condition; + ElifTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && c) : TemplateToken(Type::Elif, location, pre, post), condition(std::move(c)) {} +}; + +struct ElseTemplateToken : public TemplateToken { + ElseTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Else, location, pre, post) {} +}; + +struct EndIfTemplateToken : public TemplateToken { + EndIfTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndIf, location, pre, post) {} +}; + +struct MacroTemplateToken : public TemplateToken { + std::shared_ptr name; + Expression::Parameters params; + MacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && n, Expression::Parameters && p) + : TemplateToken(Type::Macro, location, pre, post), name(std::move(n)), params(std::move(p)) {} +}; + +struct EndMacroTemplateToken : public TemplateToken { + EndMacroTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndMacro, location, pre, post) {} +}; + +struct FilterTemplateToken : public TemplateToken { + std::shared_ptr filter; + FilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, std::shared_ptr && filter) + : TemplateToken(Type::Filter, location, pre, post), filter(std::move(filter)) {} +}; + +struct EndFilterTemplateToken : public TemplateToken { + EndFilterTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFilter, location, pre, post) {} +}; + +struct ForTemplateToken : public TemplateToken { + std::vector var_names; + std::shared_ptr iterable; + std::shared_ptr condition; + bool recursive; + ForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::vector & vns, std::shared_ptr && iter, + std::shared_ptr && c, bool r) + : TemplateToken(Type::For, location, pre, post), var_names(vns), iterable(std::move(iter)), condition(std::move(c)), recursive(r) {} +}; + +struct EndForTemplateToken : public TemplateToken { + EndForTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndFor, location, pre, post) {} +}; + +struct GenerationTemplateToken : public TemplateToken { + GenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::Generation, location, pre, post) {} +}; + +struct EndGenerationTemplateToken : public TemplateToken { + EndGenerationTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndGeneration, location, pre, post) {} +}; + +struct SetTemplateToken : public TemplateToken { + std::string ns; + std::vector var_names; + std::shared_ptr value; + SetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string & ns, const std::vector & vns, std::shared_ptr && v) + : TemplateToken(Type::Set, location, pre, post), ns(ns), var_names(vns), value(std::move(v)) {} +}; + +struct EndSetTemplateToken : public TemplateToken { + EndSetTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post) : TemplateToken(Type::EndSet, location, pre, post) {} +}; + +struct CommentTemplateToken : public TemplateToken { + std::string text; + CommentTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Comment, location, pre, post), text(t) {} +}; + +enum class LoopControlType { Break, Continue }; + +class LoopControlException : public std::runtime_error { +public: + LoopControlType control_type; + LoopControlException(const std::string & message, LoopControlType control_type) : std::runtime_error(message), control_type(control_type) {} + LoopControlException(LoopControlType control_type) + : std::runtime_error((control_type == LoopControlType::Continue ? "continue" : "break") + std::string(" outside of a loop")), + control_type(control_type) {} +}; + +struct LoopControlTemplateToken : public TemplateToken { + LoopControlType control_type; + LoopControlTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, LoopControlType control_type) : TemplateToken(Type::Break, location, pre, post), control_type(control_type) {} +}; + +class TemplateNode { + Location location_; +protected: + virtual void do_render(std::ostringstream & out, const std::shared_ptr & context) const = 0; + +public: + TemplateNode(const Location & location) : location_(location) {} + void render(std::ostringstream & out, const std::shared_ptr & context) const { + try { + do_render(out, context); + } catch (const LoopControlException & e) { + // TODO: make stack creation lazy. Only needed if it was thrown outside of a loop. + std::ostringstream err; + err << e.what(); + if (location_.source) err << error_location_suffix(*location_.source, location_.pos); + throw LoopControlException(err.str(), e.control_type); + } catch (const std::exception & e) { + std::ostringstream err; + err << e.what(); + if (location_.source) err << error_location_suffix(*location_.source, location_.pos); + throw std::runtime_error(err.str()); + } + } + const Location & location() const { return location_; } + virtual ~TemplateNode() = default; + std::string render(const std::shared_ptr & context) const { + std::ostringstream out; + render(out, context); + return out.str(); + } +}; + +class SequenceNode : public TemplateNode { + std::vector> children; +public: + SequenceNode(const Location & location, std::vector> && c) + : TemplateNode(location), children(std::move(c)) {} + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + for (const auto& child : children) child->render(out, context); + } +}; + +class TextNode : public TemplateNode { + std::string text; +public: + TextNode(const Location & location, const std::string& t) : TemplateNode(location), text(t) {} + void do_render(std::ostringstream & out, const std::shared_ptr &) const override { + out << text; + } +}; + +class ExpressionNode : public TemplateNode { + std::shared_ptr expr; +public: + ExpressionNode(const Location & location, std::shared_ptr && e) : TemplateNode(location), expr(std::move(e)) {} + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + if (!expr) throw std::runtime_error("ExpressionNode.expr is null"); + auto result = expr->evaluate(context); + if (result.is_string()) { + out << result.get(); + } else if (result.is_boolean()) { + out << (result.get() ? "True" : "False"); + } else if (!result.is_null()) { + out << result.dump(); + } + } +}; + +class IfNode : public TemplateNode { + std::vector, std::shared_ptr>> cascade; +public: + IfNode(const Location & location, std::vector, std::shared_ptr>> && c) + : TemplateNode(location), cascade(std::move(c)) {} + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + for (const auto& branch : cascade) { + auto enter_branch = true; + if (branch.first) { + enter_branch = branch.first->evaluate(context).to_bool(); + } + if (enter_branch) { + if (!branch.second) throw std::runtime_error("IfNode.cascade.second is null"); + branch.second->render(out, context); + return; + } + } + } +}; + +class LoopControlNode : public TemplateNode { + LoopControlType control_type_; + public: + LoopControlNode(const Location & location, LoopControlType control_type) : TemplateNode(location), control_type_(control_type) {} + void do_render(std::ostringstream &, const std::shared_ptr &) const override { + throw LoopControlException(control_type_); + } +}; + +class ForNode : public TemplateNode { + std::vector var_names; + std::shared_ptr iterable; + std::shared_ptr condition; + std::shared_ptr body; + bool recursive; + std::shared_ptr else_body; +public: + ForNode(const Location & location, std::vector && var_names, std::shared_ptr && iterable, + std::shared_ptr && condition, std::shared_ptr && body, bool recursive, std::shared_ptr && else_body) + : TemplateNode(location), var_names(var_names), iterable(std::move(iterable)), condition(std::move(condition)), body(std::move(body)), recursive(recursive), else_body(std::move(else_body)) {} + + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + // https://jinja.palletsprojects.com/en/3.0.x/templates/#for + if (!iterable) throw std::runtime_error("ForNode.iterable is null"); + if (!body) throw std::runtime_error("ForNode.body is null"); + + auto iterable_value = iterable->evaluate(context); + Value::CallableType loop_function; + + std::function visit = [&](Value& iter) { + auto filtered_items = Value::array(); + if (!iter.is_null()) { + if (!iterable_value.is_iterable()) { + throw std::runtime_error("For loop iterable must be iterable: " + iterable_value.dump()); + } + iterable_value.for_each([&](Value & item) { + destructuring_assign(var_names, context, item); + if (!condition || condition->evaluate(context).to_bool()) { + filtered_items.push_back(item); + } + }); + } + if (filtered_items.empty()) { + if (else_body) { + else_body->render(out, context); + } + } else { + auto loop = recursive ? Value::callable(loop_function) : Value::object(); + loop.set("length", (int64_t) filtered_items.size()); + + size_t cycle_index = 0; + loop.set("cycle", Value::callable([&](const std::shared_ptr &, ArgumentsValue & args) { + if (args.args.empty() || !args.kwargs.empty()) { + throw std::runtime_error("cycle() expects at least 1 positional argument and no named arg"); + } + auto item = args.args[cycle_index]; + cycle_index = (cycle_index + 1) % args.args.size(); + return item; + })); + auto loop_context = Context::make(Value::object(), context); + loop_context->set("loop", loop); + for (size_t i = 0, n = filtered_items.size(); i < n; ++i) { + auto & item = filtered_items.at(i); + destructuring_assign(var_names, loop_context, item); + loop.set("index", (int64_t) i + 1); + loop.set("index0", (int64_t) i); + loop.set("revindex", (int64_t) (n - i)); + loop.set("revindex0", (int64_t) (n - i - 1)); + loop.set("length", (int64_t) n); + loop.set("first", i == 0); + loop.set("last", i == (n - 1)); + loop.set("previtem", i > 0 ? filtered_items.at(i - 1) : Value()); + loop.set("nextitem", i < n - 1 ? filtered_items.at(i + 1) : Value()); + try { + body->render(out, loop_context); + } catch (const LoopControlException & e) { + if (e.control_type == LoopControlType::Break) break; + if (e.control_type == LoopControlType::Continue) continue; + } + } + } + }; + + if (recursive) { + loop_function = [&](const std::shared_ptr &, ArgumentsValue & args) { + if (args.args.size() != 1 || !args.kwargs.empty() || !args.args[0].is_array()) { + throw std::runtime_error("loop() expects exactly 1 positional iterable argument"); + } + auto & items = args.args[0]; + visit(items); + return Value(); + }; + } + + visit(iterable_value); + } +}; + +class MacroNode : public TemplateNode { + std::shared_ptr name; + Expression::Parameters params; + std::shared_ptr body; + std::unordered_map named_param_positions; +public: + MacroNode(const Location & location, std::shared_ptr && n, Expression::Parameters && p, std::shared_ptr && b) + : TemplateNode(location), name(std::move(n)), params(std::move(p)), body(std::move(b)) { + for (size_t i = 0; i < params.size(); ++i) { + const auto & name = params[i].first; + if (!name.empty()) { + named_param_positions[name] = i; + } + } + } + void do_render(std::ostringstream &, const std::shared_ptr & macro_context) const override { + if (!name) throw std::runtime_error("MacroNode.name is null"); + if (!body) throw std::runtime_error("MacroNode.body is null"); + auto callable = Value::callable([&](const std::shared_ptr & context, ArgumentsValue & args) { + auto call_context = macro_context; + std::vector param_set(params.size(), false); + for (size_t i = 0, n = args.args.size(); i < n; i++) { + auto & arg = args.args[i]; + if (i >= params.size()) throw std::runtime_error("Too many positional arguments for macro " + name->get_name()); + param_set[i] = true; + auto & param_name = params[i].first; + call_context->set(param_name, arg); + } + for (auto & [arg_name, value] : args.kwargs) { + auto it = named_param_positions.find(arg_name); + if (it == named_param_positions.end()) throw std::runtime_error("Unknown parameter name for macro " + name->get_name() + ": " + arg_name); + + call_context->set(arg_name, value); + param_set[it->second] = true; + } + // Set default values for parameters that were not passed + for (size_t i = 0, n = params.size(); i < n; i++) { + if (!param_set[i] && params[i].second != nullptr) { + auto val = params[i].second->evaluate(context); + call_context->set(params[i].first, val); + } + } + return body->render(call_context); + }); + macro_context->set(name->get_name(), callable); + } +}; + +class FilterNode : public TemplateNode { + std::shared_ptr filter; + std::shared_ptr body; + +public: + FilterNode(const Location & location, std::shared_ptr && f, std::shared_ptr && b) + : TemplateNode(location), filter(std::move(f)), body(std::move(b)) {} + + void do_render(std::ostringstream & out, const std::shared_ptr & context) const override { + if (!filter) throw std::runtime_error("FilterNode.filter is null"); + if (!body) throw std::runtime_error("FilterNode.body is null"); + auto filter_value = filter->evaluate(context); + if (!filter_value.is_callable()) { + throw std::runtime_error("Filter must be a callable: " + filter_value.dump()); + } + std::string rendered_body = body->render(context); + + ArgumentsValue filter_args = {{Value(rendered_body)}, {}}; + auto result = filter_value.call(context, filter_args); + out << result.to_str(); + } +}; + +class SetNode : public TemplateNode { + std::string ns; + std::vector var_names; + std::shared_ptr value; +public: + SetNode(const Location & location, const std::string & ns, const std::vector & vns, std::shared_ptr && v) + : TemplateNode(location), ns(ns), var_names(vns), value(std::move(v)) {} + void do_render(std::ostringstream &, const std::shared_ptr & context) const override { + if (!value) throw std::runtime_error("SetNode.value is null"); + if (!ns.empty()) { + if (var_names.size() != 1) { + throw std::runtime_error("Namespaced set only supports a single variable name"); + } + auto & name = var_names[0]; + auto ns_value = context->get(ns); + if (!ns_value.is_object()) throw std::runtime_error("Namespace '" + ns + "' is not an object"); + ns_value.set(name, this->value->evaluate(context)); + } else { + auto val = value->evaluate(context); + destructuring_assign(var_names, context, val); + } + } +}; + +class SetTemplateNode : public TemplateNode { + std::string name; + std::shared_ptr template_value; +public: + SetTemplateNode(const Location & location, const std::string & name, std::shared_ptr && tv) + : TemplateNode(location), name(name), template_value(std::move(tv)) {} + void do_render(std::ostringstream &, const std::shared_ptr & context) const override { + if (!template_value) throw std::runtime_error("SetTemplateNode.template_value is null"); + Value value { template_value->render(context) }; + context->set(name, value); + } +}; + +class IfExpr : public Expression { + std::shared_ptr condition; + std::shared_ptr then_expr; + std::shared_ptr else_expr; +public: + IfExpr(const Location & location, std::shared_ptr && c, std::shared_ptr && t, std::shared_ptr && e) + : Expression(location), condition(std::move(c)), then_expr(std::move(t)), else_expr(std::move(e)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!condition) throw std::runtime_error("IfExpr.condition is null"); + if (!then_expr) throw std::runtime_error("IfExpr.then_expr is null"); + if (condition->evaluate(context).to_bool()) { + return then_expr->evaluate(context); + } + if (else_expr) { + return else_expr->evaluate(context); + } + return nullptr; + } +}; + +class LiteralExpr : public Expression { + Value value; +public: + LiteralExpr(const Location & location, const Value& v) + : Expression(location), value(v) {} + Value do_evaluate(const std::shared_ptr &) const override { return value; } +}; + +class ArrayExpr : public Expression { + std::vector> elements; +public: + ArrayExpr(const Location & location, std::vector> && e) + : Expression(location), elements(std::move(e)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + auto result = Value::array(); + for (const auto& e : elements) { + if (!e) throw std::runtime_error("Array element is null"); + result.push_back(e->evaluate(context)); + } + return result; + } +}; + +class DictExpr : public Expression { + std::vector, std::shared_ptr>> elements; +public: + DictExpr(const Location & location, std::vector, std::shared_ptr>> && e) + : Expression(location), elements(std::move(e)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + auto result = Value::object(); + for (const auto& [key, value] : elements) { + if (!key) throw std::runtime_error("Dict key is null"); + if (!value) throw std::runtime_error("Dict value is null"); + result.set(key->evaluate(context), value->evaluate(context)); + } + return result; + } +}; + +class SliceExpr : public Expression { +public: + std::shared_ptr start, end; + SliceExpr(const Location & location, std::shared_ptr && s, std::shared_ptr && e) + : Expression(location), start(std::move(s)), end(std::move(e)) {} + Value do_evaluate(const std::shared_ptr &) const override { + throw std::runtime_error("SliceExpr not implemented"); + } +}; + +class SubscriptExpr : public Expression { + std::shared_ptr base; + std::shared_ptr index; +public: + SubscriptExpr(const Location & location, std::shared_ptr && b, std::shared_ptr && i) + : Expression(location), base(std::move(b)), index(std::move(i)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!base) throw std::runtime_error("SubscriptExpr.base is null"); + if (!index) throw std::runtime_error("SubscriptExpr.index is null"); + auto target_value = base->evaluate(context); + if (auto slice = dynamic_cast(index.get())) { + auto start = slice->start ? slice->start->evaluate(context).get() : 0; + auto end = slice->end ? slice->end->evaluate(context).get() : (int64_t) target_value.size(); + if (target_value.is_string()) { + std::string s = target_value.get(); + if (start < 0) start = s.size() + start; + if (end < 0) end = s.size() + end; + return s.substr(start, end - start); + } else if (target_value.is_array()) { + if (start < 0) start = target_value.size() + start; + if (end < 0) end = target_value.size() + end; + auto result = Value::array(); + for (auto i = start; i < end; ++i) { + result.push_back(target_value.at(i)); + } + return result; + } else { + throw std::runtime_error(target_value.is_null() ? "Cannot subscript null" : "Subscripting only supported on arrays and strings"); + } + } else { + auto index_value = index->evaluate(context); + if (target_value.is_null()) { + if (auto t = dynamic_cast(base.get())) { + throw std::runtime_error("'" + t->get_name() + "' is " + (context->contains(t->get_name()) ? "null" : "not defined")); + } + throw std::runtime_error("Trying to access property '" + index_value.dump() + "' on null!"); + } + return target_value.get(index_value); + } + } +}; + +class UnaryOpExpr : public Expression { +public: + enum class Op { Plus, Minus, LogicalNot, Expansion, ExpansionDict }; + std::shared_ptr expr; + Op op; + UnaryOpExpr(const Location & location, std::shared_ptr && e, Op o) + : Expression(location), expr(std::move(e)), op(o) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!expr) throw std::runtime_error("UnaryOpExpr.expr is null"); + auto e = expr->evaluate(context); + switch (op) { + case Op::Plus: return e; + case Op::Minus: return -e; + case Op::LogicalNot: return !e.to_bool(); + case Op::Expansion: + case Op::ExpansionDict: + throw std::runtime_error("Expansion operator is only supported in function calls and collections"); + + } + throw std::runtime_error("Unknown unary operator"); + } +}; + +class BinaryOpExpr : public Expression { +public: + enum class Op { StrConcat, Add, Sub, Mul, MulMul, Div, DivDiv, Mod, Eq, Ne, Lt, Gt, Le, Ge, And, Or, In, NotIn, Is, IsNot }; +private: + std::shared_ptr left; + std::shared_ptr right; + Op op; +public: + BinaryOpExpr(const Location & location, std::shared_ptr && l, std::shared_ptr && r, Op o) + : Expression(location), left(std::move(l)), right(std::move(r)), op(o) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!left) throw std::runtime_error("BinaryOpExpr.left is null"); + if (!right) throw std::runtime_error("BinaryOpExpr.right is null"); + auto l = left->evaluate(context); + + auto do_eval = [&](const Value & l) -> Value { + if (op == Op::Is || op == Op::IsNot) { + auto t = dynamic_cast(right.get()); + if (!t) throw std::runtime_error("Right side of 'is' operator must be a variable"); + + auto eval = [&]() { + const auto & name = t->get_name(); + if (name == "none") return l.is_null(); + if (name == "boolean") return l.is_boolean(); + if (name == "integer") return l.is_number_integer(); + if (name == "float") return l.is_number_float(); + if (name == "number") return l.is_number(); + if (name == "string") return l.is_string(); + if (name == "mapping") return l.is_object(); + if (name == "iterable") return l.is_iterable(); + if (name == "sequence") return l.is_array(); + if (name == "defined") return !l.is_null(); + throw std::runtime_error("Unknown type for 'is' operator: " + name); + }; + auto value = eval(); + return Value(op == Op::Is ? value : !value); + } + + if (op == Op::And) { + if (!l.to_bool()) return Value(false); + return right->evaluate(context).to_bool(); + } else if (op == Op::Or) { + if (l.to_bool()) return l; + return right->evaluate(context); + } + + auto r = right->evaluate(context); + switch (op) { + case Op::StrConcat: return l.to_str() + r.to_str(); + case Op::Add: return l + r; + case Op::Sub: return l - r; + case Op::Mul: return l * r; + case Op::Div: return l / r; + case Op::MulMul: return std::pow(l.get(), r.get()); + case Op::DivDiv: return l.get() / r.get(); + case Op::Mod: return l.get() % r.get(); + case Op::Eq: return l == r; + case Op::Ne: return l != r; + case Op::Lt: return l < r; + case Op::Gt: return l > r; + case Op::Le: return l <= r; + case Op::Ge: return l >= r; + case Op::In: return (r.is_array() || r.is_object()) && r.contains(l); + case Op::NotIn: return !(r.is_array() && r.contains(l)); + default: break; + } + throw std::runtime_error("Unknown binary operator"); + }; + + if (l.is_callable()) { + return Value::callable([l, do_eval](const std::shared_ptr & context, ArgumentsValue & args) { + auto ll = l.call(context, args); + return do_eval(ll); //args[0].second); + }); + } else { + return do_eval(l); + } + } +}; + +struct ArgumentsExpression { + std::vector> args; + std::vector>> kwargs; + + ArgumentsValue evaluate(const std::shared_ptr & context) const { + ArgumentsValue vargs; + for (const auto& arg : this->args) { + if (auto un_expr = std::dynamic_pointer_cast(arg)) { + if (un_expr->op == UnaryOpExpr::Op::Expansion) { + auto array = un_expr->expr->evaluate(context); + if (!array.is_array()) { + throw std::runtime_error("Expansion operator only supported on arrays"); + } + array.for_each([&](Value & value) { + vargs.args.push_back(value); + }); + continue; + } else if (un_expr->op == UnaryOpExpr::Op::ExpansionDict) { + auto dict = un_expr->expr->evaluate(context); + if (!dict.is_object()) { + throw std::runtime_error("ExpansionDict operator only supported on objects"); + } + dict.for_each([&](const Value & key) { + vargs.kwargs.push_back({key.get(), dict.at(key)}); + }); + continue; + } + } + vargs.args.push_back(arg->evaluate(context)); + } + for (const auto& [name, value] : this->kwargs) { + vargs.kwargs.push_back({name, value->evaluate(context)}); + } + return vargs; + } +}; + +static std::string strip(const std::string & s) { + auto start = s.find_first_not_of(" \t\n\r"); + if (start == std::string::npos) return ""; + auto end = s.find_last_not_of(" \t\n\r"); + return s.substr(start, end - start + 1); +} + +static std::string capitalize(const std::string & s) { + if (s.empty()) return s; + auto result = s; + result[0] = std::toupper(result[0]); + return result; +} + +static std::string html_escape(const std::string & s) { + std::string result; + result.reserve(s.size()); + for (const auto & c : s) { + switch (c) { + case '&': result += "&"; break; + case '<': result += "<"; break; + case '>': result += ">"; break; + case '"': result += """; break; + case '\'': result += "'"; break; + default: result += c; break; + } + } + return result; +} + +class MethodCallExpr : public Expression { + std::shared_ptr object; + std::shared_ptr method; + ArgumentsExpression args; +public: + MethodCallExpr(const Location & location, std::shared_ptr && obj, std::shared_ptr && m, ArgumentsExpression && a) + : Expression(location), object(std::move(obj)), method(std::move(m)), args(std::move(a)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!object) throw std::runtime_error("MethodCallExpr.object is null"); + if (!method) throw std::runtime_error("MethodCallExpr.method is null"); + auto obj = object->evaluate(context); + auto vargs = args.evaluate(context); + if (obj.is_null()) { + throw std::runtime_error("Trying to call method '" + method->get_name() + "' on null"); + } + if (obj.is_array()) { + if (method->get_name() == "append") { + vargs.expectArgs("append method", {1, 1}, {0, 0}); + obj.push_back(vargs.args[0]); + return Value(); + } else if (method->get_name() == "pop") { + vargs.expectArgs("pop method", {0, 1}, {0, 0}); + return obj.pop(vargs.args.empty() ? Value() : vargs.args[0]); + } else if (method->get_name() == "insert") { + vargs.expectArgs("insert method", {2, 2}, {0, 0}); + auto index = vargs.args[0].get(); + if (index < 0 || index > (int64_t) obj.size()) throw std::runtime_error("Index out of range for insert method"); + obj.insert(index, vargs.args[1]); + return Value(); + } + } else if (obj.is_object()) { + if (method->get_name() == "items") { + vargs.expectArgs("items method", {0, 0}, {0, 0}); + auto result = Value::array(); + for (const auto& key : obj.keys()) { + result.push_back(Value::array({key, obj.at(key)})); + } + return result; + } else if (method->get_name() == "pop") { + vargs.expectArgs("pop method", {1, 1}, {0, 0}); + return obj.pop(vargs.args[0]); + } else if (method->get_name() == "get") { + vargs.expectArgs("get method", {1, 2}, {0, 0}); + auto key = vargs.args[0]; + if (vargs.args.size() == 1) { + return obj.contains(key) ? obj.at(key) : Value(); + } else { + return obj.contains(key) ? obj.at(key) : vargs.args[1]; + } + } else if (obj.contains(method->get_name())) { + auto callable = obj.at(method->get_name()); + if (!callable.is_callable()) { + throw std::runtime_error("Property '" + method->get_name() + "' is not callable"); + } + return callable.call(context, vargs); + } + } else if (obj.is_string()) { + auto str = obj.get(); + if (method->get_name() == "strip") { + vargs.expectArgs("strip method", {0, 0}, {0, 0}); + return Value(strip(str)); + } else if (method->get_name() == "capitalize") { + vargs.expectArgs("capitalize method", {0, 0}, {0, 0}); + return Value(capitalize(str)); + } else if (method->get_name() == "endswith") { + vargs.expectArgs("endswith method", {1, 1}, {0, 0}); + auto suffix = vargs.args[0].get(); + return suffix.length() <= str.length() && std::equal(suffix.rbegin(), suffix.rend(), str.rbegin()); + } else if (method->get_name() == "title") { + vargs.expectArgs("title method", {0, 0}, {0, 0}); + auto res = str; + for (size_t i = 0, n = res.size(); i < n; ++i) { + if (i == 0 || std::isspace(res[i - 1])) res[i] = std::toupper(res[i]); + else res[i] = std::tolower(res[i]); + } + return res; + } + } + throw std::runtime_error("Unknown method: " + method->get_name()); + } +}; + +class CallExpr : public Expression { +public: + std::shared_ptr object; + ArgumentsExpression args; + CallExpr(const Location & location, std::shared_ptr && obj, ArgumentsExpression && a) + : Expression(location), object(std::move(obj)), args(std::move(a)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + if (!object) throw std::runtime_error("CallExpr.object is null"); + auto obj = object->evaluate(context); + if (!obj.is_callable()) { + throw std::runtime_error("Object is not callable: " + obj.dump(2)); + } + auto vargs = args.evaluate(context); + return obj.call(context, vargs); + } +}; + +class FilterExpr : public Expression { + std::vector> parts; +public: + FilterExpr(const Location & location, std::vector> && p) + : Expression(location), parts(std::move(p)) {} + Value do_evaluate(const std::shared_ptr & context) const override { + Value result; + bool first = true; + for (const auto& part : parts) { + if (!part) throw std::runtime_error("FilterExpr.part is null"); + if (first) { + first = false; + result = part->evaluate(context); + } else { + if (auto ce = dynamic_cast(part.get())) { + auto target = ce->object->evaluate(context); + ArgumentsValue args = ce->args.evaluate(context); + args.args.insert(args.args.begin(), result); + result = target.call(context, args); + } else { + auto callable = part->evaluate(context); + ArgumentsValue args; + args.args.insert(args.args.begin(), result); + result = callable.call(context, args); + } + } + } + return result; + } + + void prepend(std::shared_ptr && e) { + parts.insert(parts.begin(), std::move(e)); + } +}; + +class Parser { +private: + using CharIterator = std::string::const_iterator; + + std::shared_ptr template_str; + CharIterator start, end, it; + Options options; + + Parser(const std::shared_ptr& template_str, const Options & options) : template_str(template_str), options(options) { + if (!template_str) throw std::runtime_error("Template string is null"); + start = it = this->template_str->begin(); + end = this->template_str->end(); + } + + bool consumeSpaces(SpaceHandling space_handling = SpaceHandling::Strip) { + if (space_handling == SpaceHandling::Strip) { + while (it != end && std::isspace(*it)) ++it; + } + return true; + } + + std::unique_ptr parseString() { + auto doParse = [&](char quote) -> std::unique_ptr { + if (it == end || *it != quote) return nullptr; + std::string result; + bool escape = false; + for (++it; it != end; ++it) { + if (escape) { + escape = false; + switch (*it) { + case 'n': result += '\n'; break; + case 'r': result += '\r'; break; + case 't': result += '\t'; break; + case 'b': result += '\b'; break; + case 'f': result += '\f'; break; + case '\\': result += '\\'; break; + default: + if (*it == quote) { + result += quote; + } else { + result += *it; + } + break; + } + } else if (*it == '\\') { + escape = true; + } else if (*it == quote) { + ++it; + return std::make_unique(std::move(result)); + } else { + result += *it; + } + } + return nullptr; + }; + + consumeSpaces(); + if (it == end) return nullptr; + if (*it == '"') return doParse('"'); + if (*it == '\'') return doParse('\''); + return nullptr; + } + + json parseNumber(CharIterator& it, const CharIterator& end) { + auto before = it; + consumeSpaces(); + auto start = it; + bool hasDecimal = false; + bool hasExponent = false; + + if (it != end && (*it == '-' || *it == '+')) ++it; + + while (it != end) { + if (std::isdigit(*it)) { + ++it; + } else if (*it == '.') { + if (hasDecimal) throw std::runtime_error("Multiple decimal points"); + hasDecimal = true; + ++it; + } else if (it != start && (*it == 'e' || *it == 'E')) { + if (hasExponent) throw std::runtime_error("Multiple exponents"); + hasExponent = true; + ++it; + } else { + break; + } + } + if (start == it) { + it = before; + return json(); // No valid characters found + } + + std::string str(start, it); + try { + return json::parse(str); + } catch (json::parse_error& e) { + throw std::runtime_error("Failed to parse number: '" + str + "' (" + std::string(e.what()) + ")"); + return json(); + } + } + + /** integer, float, bool, string */ + std::shared_ptr parseConstant() { + auto start = it; + consumeSpaces(); + if (it == end) return nullptr; + if (*it == '"' || *it == '\'') { + auto str = parseString(); + if (str) return std::make_shared(*str); + } + static std::regex prim_tok(R"(true\b|True\b|false\b|False\b|None\b)"); + auto token = consumeToken(prim_tok); + if (!token.empty()) { + if (token == "true" || token == "True") return std::make_shared(true); + if (token == "false" || token == "False") return std::make_shared(false); + if (token == "None") return std::make_shared(nullptr); + throw std::runtime_error("Unknown constant token: " + token); + } + + auto number = parseNumber(it, end); + if (!number.is_null()) return std::make_shared(number); + + it = start; + return nullptr; + } + + class expression_parsing_error : public std::runtime_error { + const CharIterator it; + public: + expression_parsing_error(const std::string & message, const CharIterator & it) + : std::runtime_error(message), it(it) {} + size_t get_pos(const CharIterator & begin) const { + return std::distance(begin, it); + } + }; + + bool peekSymbols(const std::vector & symbols) const { + for (const auto & symbol : symbols) { + if (std::distance(it, end) >= (int64_t) symbol.size() && std::string(it, it + symbol.size()) == symbol) { + return true; + } + } + return false; + } + + std::vector consumeTokenGroups(const std::regex & regex, SpaceHandling space_handling = SpaceHandling::Strip) { + auto start = it; + consumeSpaces(space_handling); + std::smatch match; + if (std::regex_search(it, end, match, regex) && match.position() == 0) { + it += match[0].length(); + std::vector ret; + for (size_t i = 0, n = match.size(); i < n; ++i) { + ret.push_back(match[i].str()); + } + return ret; + } + it = start; + return {}; + } + std::string consumeToken(const std::regex & regex, SpaceHandling space_handling = SpaceHandling::Strip) { + auto start = it; + consumeSpaces(space_handling); + std::smatch match; + if (std::regex_search(it, end, match, regex) && match.position() == 0) { + it += match[0].length(); + return match[0].str(); + } + it = start; + return ""; + } + + std::string consumeToken(const std::string & token, SpaceHandling space_handling = SpaceHandling::Strip) { + auto start = it; + consumeSpaces(space_handling); + if (std::distance(it, end) >= (int64_t) token.size() && std::string(it, it + token.size()) == token) { + it += token.size(); + return token; + } + it = start; + return ""; + } + + std::shared_ptr parseExpression(bool allow_if_expr = true) { + auto left = parseLogicalOr(); + if (it == end) return left; + + if (!allow_if_expr) return left; + + static std::regex if_tok(R"(if\b)"); + if (consumeToken(if_tok).empty()) { + return left; + } + + auto location = get_location(); + auto [condition, else_expr] = parseIfExpression(); + return std::make_shared(location, std::move(condition), std::move(left), std::move(else_expr)); + } + + Location get_location() const { + return {template_str, (size_t) std::distance(start, it)}; + } + + std::pair, std::shared_ptr> parseIfExpression() { + auto condition = parseLogicalOr(); + if (!condition) throw std::runtime_error("Expected condition expression"); + + static std::regex else_tok(R"(else\b)"); + std::shared_ptr else_expr; + if (!consumeToken(else_tok).empty()) { + else_expr = parseExpression(); + if (!else_expr) throw std::runtime_error("Expected 'else' expression"); + } + return std::pair(std::move(condition), std::move(else_expr)); + } + + std::shared_ptr parseLogicalOr() { + auto left = parseLogicalAnd(); + if (!left) throw std::runtime_error("Expected left side of 'logical or' expression"); + + static std::regex or_tok(R"(or\b)"); + auto location = get_location(); + while (!consumeToken(or_tok).empty()) { + auto right = parseLogicalAnd(); + if (!right) throw std::runtime_error("Expected right side of 'or' expression"); + left = std::make_shared(location, std::move(left), std::move(right), BinaryOpExpr::Op::Or); + } + return left; + } + + std::shared_ptr parseLogicalNot() { + static std::regex not_tok(R"(not\b)"); + auto location = get_location(); + + if (!consumeToken(not_tok).empty()) { + auto sub = parseLogicalNot(); + if (!sub) throw std::runtime_error("Expected expression after 'not' keyword"); + return std::make_shared(location, std::move(sub), UnaryOpExpr::Op::LogicalNot); + } + return parseLogicalCompare(); + } + + std::shared_ptr parseLogicalAnd() { + auto left = parseLogicalNot(); + if (!left) throw std::runtime_error("Expected left side of 'logical and' expression"); + + static std::regex and_tok(R"(and\b)"); + auto location = get_location(); + while (!consumeToken(and_tok).empty()) { + auto right = parseLogicalNot(); + if (!right) throw std::runtime_error("Expected right side of 'and' expression"); + left = std::make_shared(location, std::move(left), std::move(right), BinaryOpExpr::Op::And); + } + return left; + } + + std::shared_ptr parseLogicalCompare() { + auto left = parseStringConcat(); + if (!left) throw std::runtime_error("Expected left side of 'logical compare' expression"); + + static std::regex compare_tok(R"(==|!=|<=?|>=?|in\b|is\b|not\s+in\b)"); + static std::regex not_tok(R"(not\b)"); + std::string op_str; + while (!(op_str = consumeToken(compare_tok)).empty()) { + auto location = get_location(); + if (op_str == "is") { + auto negated = !consumeToken(not_tok).empty(); + + auto identifier = parseIdentifier(); + if (!identifier) throw std::runtime_error("Expected identifier after 'is' keyword"); + + return std::make_shared( + left->location, + std::move(left), std::move(identifier), + negated ? BinaryOpExpr::Op::IsNot : BinaryOpExpr::Op::Is); + } + auto right = parseStringConcat(); + if (!right) throw std::runtime_error("Expected right side of 'logical compare' expression"); + BinaryOpExpr::Op op; + if (op_str == "==") op = BinaryOpExpr::Op::Eq; + else if (op_str == "!=") op = BinaryOpExpr::Op::Ne; + else if (op_str == "<") op = BinaryOpExpr::Op::Lt; + else if (op_str == ">") op = BinaryOpExpr::Op::Gt; + else if (op_str == "<=") op = BinaryOpExpr::Op::Le; + else if (op_str == ">=") op = BinaryOpExpr::Op::Ge; + else if (op_str == "in") op = BinaryOpExpr::Op::In; + else if (op_str.substr(0, 3) == "not") op = BinaryOpExpr::Op::NotIn; + else throw std::runtime_error("Unknown comparison operator: " + op_str); + left = std::make_shared(get_location(), std::move(left), std::move(right), op); + } + return left; + } + + Expression::Parameters parseParameters() { + consumeSpaces(); + if (consumeToken("(").empty()) throw std::runtime_error("Expected opening parenthesis in param list"); + + Expression::Parameters result; + + while (it != end) { + if (!consumeToken(")").empty()) { + return result; + } + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in call args"); + + if (auto ident = dynamic_cast(expr.get())) { + if (!consumeToken("=").empty()) { + auto value = parseExpression(); + if (!value) throw std::runtime_error("Expected expression in for named arg"); + result.emplace_back(ident->get_name(), std::move(value)); + } else { + result.emplace_back(ident->get_name(), nullptr); + } + } else { + result.emplace_back(std::string(), std::move(expr)); + } + if (consumeToken(",").empty()) { + if (consumeToken(")").empty()) { + throw std::runtime_error("Expected closing parenthesis in call args"); + } + return result; + } + } + throw std::runtime_error("Expected closing parenthesis in call args"); + } + + ArgumentsExpression parseCallArgs() { + consumeSpaces(); + if (consumeToken("(").empty()) throw std::runtime_error("Expected opening parenthesis in call args"); + + ArgumentsExpression result; + + while (it != end) { + if (!consumeToken(")").empty()) { + return result; + } + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in call args"); + + if (auto ident = dynamic_cast(expr.get())) { + if (!consumeToken("=").empty()) { + auto value = parseExpression(); + if (!value) throw std::runtime_error("Expected expression in for named arg"); + result.kwargs.emplace_back(ident->get_name(), std::move(value)); + } else { + result.args.emplace_back(std::move(expr)); + } + } else { + result.args.emplace_back(std::move(expr)); + } + if (consumeToken(",").empty()) { + if (consumeToken(")").empty()) { + throw std::runtime_error("Expected closing parenthesis in call args"); + } + return result; + } + } + throw std::runtime_error("Expected closing parenthesis in call args"); + } + + std::shared_ptr parseIdentifier() { + static std::regex ident_regex(R"((?!(?:not|is|and|or|del)\b)[a-zA-Z_]\w*)"); + auto location = get_location(); + auto ident = consumeToken(ident_regex); + if (ident.empty()) + return nullptr; + return std::make_shared(location, ident); + } + + std::shared_ptr parseStringConcat() { + auto left = parseMathPow(); + if (!left) throw std::runtime_error("Expected left side of 'string concat' expression"); + + static std::regex concat_tok(R"(~(?!\}))"); + if (!consumeToken(concat_tok).empty()) { + auto right = parseLogicalAnd(); + if (!right) throw std::runtime_error("Expected right side of 'string concat' expression"); + left = std::make_shared(get_location(), std::move(left), std::move(right), BinaryOpExpr::Op::StrConcat); + } + return left; + } + + std::shared_ptr parseMathPow() { + auto left = parseMathPlusMinus(); + if (!left) throw std::runtime_error("Expected left side of 'math pow' expression"); + + while (!consumeToken("**").empty()) { + auto right = parseMathPlusMinus(); + if (!right) throw std::runtime_error("Expected right side of 'math pow' expression"); + left = std::make_shared(get_location(), std::move(left), std::move(right), BinaryOpExpr::Op::MulMul); + } + return left; + } + + std::shared_ptr parseMathPlusMinus() { + static std::regex plus_minus_tok(R"(\+|-(?![}%#]\}))"); + + auto left = parseMathMulDiv(); + if (!left) throw std::runtime_error("Expected left side of 'math plus/minus' expression"); + std::string op_str; + while (!(op_str = consumeToken(plus_minus_tok)).empty()) { + auto right = parseMathMulDiv(); + if (!right) throw std::runtime_error("Expected right side of 'math plus/minus' expression"); + auto op = op_str == "+" ? BinaryOpExpr::Op::Add : BinaryOpExpr::Op::Sub; + left = std::make_shared(get_location(), std::move(left), std::move(right), op); + } + return left; + } + + std::shared_ptr parseMathMulDiv() { + auto left = parseMathUnaryPlusMinus(); + if (!left) throw std::runtime_error("Expected left side of 'math mul/div' expression"); + + static std::regex mul_div_tok(R"(\*\*?|//?|%(?!\}))"); + std::string op_str; + while (!(op_str = consumeToken(mul_div_tok)).empty()) { + auto right = parseMathUnaryPlusMinus(); + if (!right) throw std::runtime_error("Expected right side of 'math mul/div' expression"); + auto op = op_str == "*" ? BinaryOpExpr::Op::Mul + : op_str == "**" ? BinaryOpExpr::Op::MulMul + : op_str == "/" ? BinaryOpExpr::Op::Div + : op_str == "//" ? BinaryOpExpr::Op::DivDiv + : BinaryOpExpr::Op::Mod; + left = std::make_shared(get_location(), std::move(left), std::move(right), op); + } + + if (!consumeToken("|").empty()) { + auto expr = parseMathMulDiv(); + if (auto filter = dynamic_cast(expr.get())) { + filter->prepend(std::move(left)); + return expr; + } else { + std::vector> parts; + parts.emplace_back(std::move(left)); + parts.emplace_back(std::move(expr)); + return std::make_shared(get_location(), std::move(parts)); + } + } + return left; + } + + std::shared_ptr call_func(const std::string & name, ArgumentsExpression && args) const { + return std::make_shared(get_location(), std::make_shared(get_location(), name), std::move(args)); + } + + std::shared_ptr parseMathUnaryPlusMinus() { + static std::regex unary_plus_minus_tok(R"(\+|-(?![}%#]\}))"); + auto op_str = consumeToken(unary_plus_minus_tok); + auto expr = parseExpansion(); + if (!expr) throw std::runtime_error("Expected expr of 'unary plus/minus/expansion' expression"); + + if (!op_str.empty()) { + auto op = op_str == "+" ? UnaryOpExpr::Op::Plus : UnaryOpExpr::Op::Minus; + return std::make_shared(get_location(), std::move(expr), op); + } + return expr; + } + + std::shared_ptr parseExpansion() { + static std::regex expansion_tok(R"(\*\*?)"); + auto op_str = consumeToken(expansion_tok); + auto expr = parseValueExpression(); + if (op_str.empty()) return expr; + if (!expr) throw std::runtime_error("Expected expr of 'expansion' expression"); + return std::make_shared(get_location(), std::move(expr), op_str == "*" ? UnaryOpExpr::Op::Expansion : UnaryOpExpr::Op::ExpansionDict); + } + + std::shared_ptr parseValueExpression() { + auto parseValue = [&]() -> std::shared_ptr { + auto location = get_location(); + auto constant = parseConstant(); + if (constant) return std::make_shared(location, *constant); + + static std::regex null_regex(R"(null\b)"); + if (!consumeToken(null_regex).empty()) return std::make_shared(location, Value()); + + auto identifier = parseIdentifier(); + if (identifier) return identifier; + + auto braced = parseBracedExpressionOrArray(); + if (braced) return braced; + + auto array = parseArray(); + if (array) return array; + + auto dictionary = parseDictionary(); + if (dictionary) return dictionary; + + throw std::runtime_error("Expected value expression"); + }; + + auto value = parseValue(); + + while (it != end && consumeSpaces() && peekSymbols({ "[", "." })) { + if (!consumeToken("[").empty()) { + std::shared_ptr index; + if (!consumeToken(":").empty()) { + auto slice_end = parseExpression(); + index = std::make_shared(slice_end->location, nullptr, std::move(slice_end)); + } else { + auto slice_start = parseExpression(); + if (!consumeToken(":").empty()) { + consumeSpaces(); + if (peekSymbols({ "]" })) { + index = std::make_shared(slice_start->location, std::move(slice_start), nullptr); + } else { + auto slice_end = parseExpression(); + index = std::make_shared(slice_start->location, std::move(slice_start), std::move(slice_end)); + } + } else { + index = std::move(slice_start); + } + } + if (!index) throw std::runtime_error("Empty index in subscript"); + if (consumeToken("]").empty()) throw std::runtime_error("Expected closing bracket in subscript"); + + value = std::make_shared(value->location, std::move(value), std::move(index)); + } else if (!consumeToken(".").empty()) { + auto identifier = parseIdentifier(); + if (!identifier) throw std::runtime_error("Expected identifier in subscript"); + + consumeSpaces(); + if (peekSymbols({ "(" })) { + auto callParams = parseCallArgs(); + value = std::make_shared(identifier->location, std::move(value), std::move(identifier), std::move(callParams)); + } else { + auto key = std::make_shared(identifier->location, Value(identifier->get_name())); + value = std::make_shared(identifier->location, std::move(value), std::move(key)); + } + } + consumeSpaces(); + } + + if (peekSymbols({ "(" })) { + auto location = get_location(); + auto callParams = parseCallArgs(); + value = std::make_shared(location, std::move(value), std::move(callParams)); + } + return value; + } + + std::shared_ptr parseBracedExpressionOrArray() { + if (consumeToken("(").empty()) return nullptr; + + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in braced expression"); + + if (!consumeToken(")").empty()) { + return expr; // Drop the parentheses + } + + std::vector> tuple; + tuple.emplace_back(std::move(expr)); + + while (it != end) { + if (consumeToken(",").empty()) throw std::runtime_error("Expected comma in tuple"); + auto next = parseExpression(); + if (!next) throw std::runtime_error("Expected expression in tuple"); + tuple.push_back(std::move(next)); + + if (!consumeToken(")").empty()) { + return std::make_shared(get_location(), std::move(tuple)); + } + } + throw std::runtime_error("Expected closing parenthesis"); + } + + std::shared_ptr parseArray() { + if (consumeToken("[").empty()) return nullptr; + + std::vector> elements; + if (!consumeToken("]").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } + auto first_expr = parseExpression(); + if (!first_expr) throw std::runtime_error("Expected first expression in array"); + elements.push_back(std::move(first_expr)); + + while (it != end) { + if (!consumeToken(",").empty()) { + auto expr = parseExpression(); + if (!expr) throw std::runtime_error("Expected expression in array"); + elements.push_back(std::move(expr)); + } else if (!consumeToken("]").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } else { + throw std::runtime_error("Expected comma or closing bracket in array"); + } + } + throw std::runtime_error("Expected closing bracket"); + } + + std::shared_ptr parseDictionary() { + if (consumeToken("{").empty()) return nullptr; + + std::vector, std::shared_ptr>> elements; + if (!consumeToken("}").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } + + auto parseKeyValuePair = [&]() { + auto key = parseExpression(); + if (!key) throw std::runtime_error("Expected key in dictionary"); + if (consumeToken(":").empty()) throw std::runtime_error("Expected colon betweek key & value in dictionary"); + auto value = parseExpression(); + if (!value) throw std::runtime_error("Expected value in dictionary"); + elements.emplace_back(std::pair(std::move(key), std::move(value))); + }; + + parseKeyValuePair(); + + while (it != end) { + if (!consumeToken(",").empty()) { + parseKeyValuePair(); + } else if (!consumeToken("}").empty()) { + return std::make_shared(get_location(), std::move(elements)); + } else { + throw std::runtime_error("Expected comma or closing brace in dictionary"); + } + } + throw std::runtime_error("Expected closing brace"); + } + + SpaceHandling parsePreSpace(const std::string& s) const { + if (s == "-") + return SpaceHandling::Strip; + return SpaceHandling::Keep; + } + + SpaceHandling parsePostSpace(const std::string& s) const { + if (s == "-") return SpaceHandling::Strip; + return SpaceHandling::Keep; + } + + using TemplateTokenVector = std::vector>; + using TemplateTokenIterator = TemplateTokenVector::const_iterator; + + std::vector parseVarNames() { + static std::regex varnames_regex(R"(((?:\w+)(?:\s*,\s*(?:\w+))*)\s*)"); + + std::vector group; + if ((group = consumeTokenGroups(varnames_regex)).empty()) throw std::runtime_error("Expected variable names"); + std::vector varnames; + std::istringstream iss(group[1]); + std::string varname; + while (std::getline(iss, varname, ',')) { + varnames.push_back(strip(varname)); + } + return varnames; + } + + std::runtime_error unexpected(const TemplateToken & token) const { + return std::runtime_error("Unexpected " + TemplateToken::typeToString(token.type) + + error_location_suffix(*template_str, token.location.pos)); + } + std::runtime_error unterminated(const TemplateToken & token) const { + return std::runtime_error("Unterminated " + TemplateToken::typeToString(token.type) + + error_location_suffix(*template_str, token.location.pos)); + } + + TemplateTokenVector tokenize() { + static std::regex comment_tok(R"(\{#([-~]?)([\s\S]*?)([-~]?)#\})"); + static std::regex expr_open_regex(R"(\{\{([-~])?)"); + static std::regex block_open_regex(R"(^\{%([-~])?\s*)"); + static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter|break|continue)\b)"); + static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)"); + static std::regex expr_close_regex(R"(\s*([-~])?\}\})"); + static std::regex block_close_regex(R"(\s*([-~])?%\})"); + + TemplateTokenVector tokens; + std::vector group; + std::string text; + std::smatch match; + + try { + while (it != end) { + auto location = get_location(); + + if (!(group = consumeTokenGroups(comment_tok, SpaceHandling::Keep)).empty()) { + auto pre_space = parsePreSpace(group[1]); + auto content = group[2]; + auto post_space = parsePostSpace(group[3]); + tokens.push_back(std::make_unique(location, pre_space, post_space, content)); + } else if (!(group = consumeTokenGroups(expr_open_regex, SpaceHandling::Keep)).empty()) { + auto pre_space = parsePreSpace(group[1]); + auto expr = parseExpression(); + + if ((group = consumeTokenGroups(expr_close_regex)).empty()) { + throw std::runtime_error("Expected closing expression tag"); + } + + auto post_space = parsePostSpace(group[1]); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(expr))); + } else if (!(group = consumeTokenGroups(block_open_regex, SpaceHandling::Keep)).empty()) { + auto pre_space = parsePreSpace(group[1]); + + std::string keyword; + + auto parseBlockClose = [&]() -> SpaceHandling { + if ((group = consumeTokenGroups(block_close_regex)).empty()) throw std::runtime_error("Expected closing block tag"); + return parsePostSpace(group[1]); + }; + + if ((keyword = consumeToken(block_keyword_tok)).empty()) throw std::runtime_error("Expected block keyword"); + + if (keyword == "if") { + auto condition = parseExpression(); + if (!condition) throw std::runtime_error("Expected condition in if block"); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(condition))); + } else if (keyword == "elif") { + auto condition = parseExpression(); + if (!condition) throw std::runtime_error("Expected condition in elif block"); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(condition))); + } else if (keyword == "else") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "endif") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "for") { + static std::regex recursive_tok(R"(recursive\b)"); + static std::regex if_tok(R"(if\b)"); + + auto varnames = parseVarNames(); + static std::regex in_tok(R"(in\b)"); + if (consumeToken(in_tok).empty()) throw std::runtime_error("Expected 'in' keyword in for block"); + auto iterable = parseExpression(/* allow_if_expr = */ false); + if (!iterable) throw std::runtime_error("Expected iterable in for block"); + + std::shared_ptr condition; + if (!consumeToken(if_tok).empty()) { + condition = parseExpression(); + } + auto recursive = !consumeToken(recursive_tok).empty(); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(varnames), std::move(iterable), std::move(condition), recursive)); + } else if (keyword == "endfor") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "generation") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "endgeneration") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "set") { + static std::regex namespaced_var_regex(R"((\w+)\s*\.\s*(\w+))"); + + std::string ns; + std::vector var_names; + std::shared_ptr value; + if (!(group = consumeTokenGroups(namespaced_var_regex)).empty()) { + ns = group[1]; + var_names.push_back(group[2]); + + if (consumeToken("=").empty()) throw std::runtime_error("Expected equals sign in set block"); + + value = parseExpression(); + if (!value) throw std::runtime_error("Expected value in set block"); + } else { + var_names = parseVarNames(); + + if (!consumeToken("=").empty()) { + value = parseExpression(); + if (!value) throw std::runtime_error("Expected value in set block"); + } + } + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, ns, var_names, std::move(value))); + } else if (keyword == "endset") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "macro") { + auto macroname = parseIdentifier(); + if (!macroname) throw std::runtime_error("Expected macro name in macro block"); + auto params = parseParameters(); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(macroname), std::move(params))); + } else if (keyword == "endmacro") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "filter") { + auto filter = parseExpression(); + if (!filter) throw std::runtime_error("Expected expression in filter block"); + + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, std::move(filter))); + } else if (keyword == "endfilter") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space)); + } else if (keyword == "break" || keyword == "continue") { + auto post_space = parseBlockClose(); + tokens.push_back(std::make_unique(location, pre_space, post_space, keyword == "break" ? LoopControlType::Break : LoopControlType::Continue)); + } else { + throw std::runtime_error("Unexpected block: " + keyword); + } + } else if (std::regex_search(it, end, match, non_text_open_regex)) { + if (!match.position()) { + if (match[0] != "{#") + throw std::runtime_error("Internal error: Expected a comment"); + throw std::runtime_error("Missing end of comment tag"); + } + auto text_end = it + match.position(); + text = std::string(it, text_end); + it = text_end; + tokens.push_back(std::make_unique(location, SpaceHandling::Keep, SpaceHandling::Keep, text)); + } else { + text = std::string(it, end); + it = end; + tokens.push_back(std::make_unique(location, SpaceHandling::Keep, SpaceHandling::Keep, text)); + } + } + return tokens; + } catch (const std::exception & e) { + throw std::runtime_error(e.what() + error_location_suffix(*template_str, std::distance(start, it))); + } + } + + std::shared_ptr parseTemplate( + const TemplateTokenIterator & begin, + TemplateTokenIterator & it, + const TemplateTokenIterator & end, + bool fully = false) const { + std::vector> children; + while (it != end) { + const auto start = it; + const auto & token = *(it++); + if (auto if_token = dynamic_cast(token.get())) { + std::vector, std::shared_ptr>> cascade; + cascade.emplace_back(std::move(if_token->condition), parseTemplate(begin, it, end)); + + while (it != end && (*it)->type == TemplateToken::Type::Elif) { + auto elif_token = dynamic_cast((*(it++)).get()); + cascade.emplace_back(std::move(elif_token->condition), parseTemplate(begin, it, end)); + } + + if (it != end && (*it)->type == TemplateToken::Type::Else) { + cascade.emplace_back(nullptr, parseTemplate(begin, ++it, end)); + } + if (it == end || (*(it++))->type != TemplateToken::Type::EndIf) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(cascade))); + } else if (auto for_token = dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + auto else_body = std::shared_ptr(); + if (it != end && (*it)->type == TemplateToken::Type::Else) { + else_body = parseTemplate(begin, ++it, end); + } + if (it == end || (*(it++))->type != TemplateToken::Type::EndFor) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(for_token->var_names), std::move(for_token->iterable), std::move(for_token->condition), std::move(body), for_token->recursive, std::move(else_body))); + } else if (dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndGeneration) { + throw unterminated(**start); + } + // Treat as a no-op, as our scope is templates for inference, not training (`{% generation %}` wraps generated tokens for masking). + children.emplace_back(std::move(body)); + } else if (auto text_token = dynamic_cast(token.get())) { + SpaceHandling pre_space = (it - 1) != begin ? (*(it - 2))->post_space : SpaceHandling::Keep; + SpaceHandling post_space = it != end ? (*it)->pre_space : SpaceHandling::Keep; + + auto text = text_token->text; + if (post_space == SpaceHandling::Strip) { + static std::regex trailing_space_regex(R"(\s+$)"); + text = std::regex_replace(text, trailing_space_regex, ""); + } else if (options.lstrip_blocks && it != end) { + auto i = text.size(); + while (i > 0 && (text[i - 1] == ' ' || text[i - 1] == '\t')) i--; + if ((i == 0 && (it - 1) == begin) || (i > 0 && text[i - 1] == '\n')) { + text.resize(i); + } + } + if (pre_space == SpaceHandling::Strip) { + static std::regex leading_space_regex(R"(^\s+)"); + text = std::regex_replace(text, leading_space_regex, ""); + } else if (options.trim_blocks && (it - 1) != begin && !dynamic_cast((*(it - 2)).get())) { + if (text.length() > 0 && text[0] == '\n') { + text.erase(0, 1); + } + } + if (it == end && !options.keep_trailing_newline) { + auto i = text.size(); + if (i > 0 && text[i - 1] == '\n') { + i--; + if (i > 0 && text[i - 1] == '\r') i--; + text.resize(i); + } + } + children.emplace_back(std::make_shared(token->location, text)); + } else if (auto expr_token = dynamic_cast(token.get())) { + children.emplace_back(std::make_shared(token->location, std::move(expr_token->expr))); + } else if (auto set_token = dynamic_cast(token.get())) { + if (set_token->value) { + children.emplace_back(std::make_shared(token->location, set_token->ns, set_token->var_names, std::move(set_token->value))); + } else { + auto value_template = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndSet) { + throw unterminated(**start); + } + if (!set_token->ns.empty()) throw std::runtime_error("Namespaced set not supported in set with template value"); + if (set_token->var_names.size() != 1) throw std::runtime_error("Structural assignment not supported in set with template value"); + auto & name = set_token->var_names[0]; + children.emplace_back(std::make_shared(token->location, name, std::move(value_template))); + } + } else if (auto macro_token = dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndMacro) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(macro_token->name), std::move(macro_token->params), std::move(body))); + } else if (auto filter_token = dynamic_cast(token.get())) { + auto body = parseTemplate(begin, it, end); + if (it == end || (*(it++))->type != TemplateToken::Type::EndFilter) { + throw unterminated(**start); + } + children.emplace_back(std::make_shared(token->location, std::move(filter_token->filter), std::move(body))); + } else if (dynamic_cast(token.get())) { + // Ignore comments + } else if (auto ctrl_token = dynamic_cast(token.get())) { + children.emplace_back(std::make_shared(token->location, ctrl_token->control_type)); + } else if (dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get()) + || dynamic_cast(token.get())) { + it--; // unconsume the token + break; // exit the loop + } else { + throw unexpected(**(it-1)); + } + } + if (fully && it != end) { + throw unexpected(**it); + } + if (children.empty()) { + return std::make_shared(Location { template_str, 0 }, std::string()); + } else if (children.size() == 1) { + return std::move(children[0]); + } else { + return std::make_shared(children[0]->location(), std::move(children)); + } + } + +public: + + static std::shared_ptr parse(const std::string& template_str, const Options & options) { + Parser parser(std::make_shared(normalize_newlines(template_str)), options); + auto tokens = parser.tokenize(); + TemplateTokenIterator begin = tokens.begin(); + auto it = begin; + TemplateTokenIterator end = tokens.end(); + return parser.parseTemplate(begin, it, end, /* full= */ true); + } +}; + +static Value simple_function(const std::string & fn_name, const std::vector & params, const std::function &, Value & args)> & fn) { + std::map named_positions; + for (size_t i = 0, n = params.size(); i < n; i++) named_positions[params[i]] = i; + + return Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) -> Value { + auto args_obj = Value::object(); + std::vector provided_args(params.size()); + for (size_t i = 0, n = args.args.size(); i < n; i++) { + auto & arg = args.args[i]; + if (i < params.size()) { + args_obj.set(params[i], arg); + provided_args[i] = true; + } else { + throw std::runtime_error("Too many positional params for " + fn_name); + } + } + for (auto & [name, value] : args.kwargs) { + auto named_pos_it = named_positions.find(name); + if (named_pos_it == named_positions.end()) { + throw std::runtime_error("Unknown argument " + name + " for function " + fn_name); + } + provided_args[named_pos_it->second] = true; + args_obj.set(name, value); + } + return fn(context, args_obj); + }); +} + +inline std::shared_ptr Context::builtins() { + auto globals = Value::object(); + + globals.set("raise_exception", simple_function("raise_exception", { "message" }, [](const std::shared_ptr &, Value & args) -> Value { + throw std::runtime_error(args.at("message").get()); + })); + globals.set("tojson", simple_function("tojson", { "value", "indent" }, [](const std::shared_ptr &, Value & args) { + return Value(args.at("value").dump(args.get("indent", -1), /* tojson= */ true)); + })); + globals.set("items", simple_function("items", { "object" }, [](const std::shared_ptr &, Value & args) { + auto items = Value::array(); + if (args.contains("object")) { + auto & obj = args.at("object"); + if (obj.is_string()) { + auto json_obj = json::parse(obj.get()); + for (const auto & kv : json_obj.items()) { + items.push_back(Value::array({kv.key(), kv.value()})); + } + } else if (!obj.is_null()) { + for (auto & key : obj.keys()) { + items.push_back(Value::array({key, obj.at(key)})); + } + } + } + return items; + })); + globals.set("last", simple_function("last", { "items" }, [](const std::shared_ptr &, Value & args) { + auto items = args.at("items"); + if (!items.is_array()) throw std::runtime_error("object is not a list"); + if (items.size() == 0) return Value(); + return items.at(items.size() - 1); + })); + globals.set("trim", simple_function("trim", { "text" }, [](const std::shared_ptr &, Value & args) { + auto & text = args.at("text"); + return text.is_null() ? text : Value(strip(text.get())); + })); + globals.set("lower", simple_function("lower", { "text" }, [](const std::shared_ptr &, Value & args) { + auto text = args.at("text"); + if (text.is_null()) return text; + std::string res; + auto str = text.get(); + std::transform(str.begin(), str.end(), std::back_inserter(res), ::tolower); + return Value(res); + })); + globals.set("default", Value::callable([=](const std::shared_ptr &, ArgumentsValue & args) { + args.expectArgs("default", {2, 3}, {0, 1}); + auto & value = args.args[0]; + auto & default_value = args.args[1]; + bool boolean = false; + if (args.args.size() == 3) { + boolean = args.args[2].get(); + } else { + Value bv = args.get_named("boolean"); + if (!bv.is_null()) { + boolean = bv.get(); + } + } + return boolean ? (value.to_bool() ? value : default_value) : value.is_null() ? default_value : value; + })); + auto escape = simple_function("escape", { "text" }, [](const std::shared_ptr &, Value & args) { + return Value(html_escape(args.at("text").get())); + }); + globals.set("e", escape); + globals.set("escape", escape); + globals.set("joiner", simple_function("joiner", { "sep" }, [](const std::shared_ptr &, Value & args) { + auto sep = args.get("sep", ""); + auto first = std::make_shared(true); + return simple_function("", {}, [sep, first](const std::shared_ptr &, const Value &) -> Value { + if (*first) { + *first = false; + return ""; + } + return sep; + }); + return Value(html_escape(args.at("text").get())); + })); + globals.set("count", simple_function("count", { "items" }, [](const std::shared_ptr &, Value & args) { + return Value((int64_t) args.at("items").size()); + })); + globals.set("dictsort", simple_function("dictsort", { "value" }, [](const std::shared_ptr &, Value & args) { + if (args.size() != 1) throw std::runtime_error("dictsort expects exactly 1 argument (TODO: fix implementation)"); + auto & value = args.at("value"); + auto keys = value.keys(); + std::sort(keys.begin(), keys.end()); + auto res = Value::array(); + for (auto & key : keys) { + res.push_back(Value::array({key, value.at(key)})); + } + return res; + })); + globals.set("join", simple_function("join", { "items", "d" }, [](const std::shared_ptr &, Value & args) { + auto do_join = [](Value & items, const std::string & sep) { + if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump()); + std::ostringstream oss; + auto first = true; + for (size_t i = 0, n = items.size(); i < n; ++i) { + if (first) first = false; + else oss << sep; + oss << items.at(i).to_str(); + } + return Value(oss.str()); + }; + auto sep = args.get("d", ""); + if (args.contains("items")) { + auto & items = args.at("items"); + return do_join(items, sep); + } else { + return simple_function("", {"items"}, [sep, do_join](const std::shared_ptr &, Value & args) { + auto & items = args.at("items"); + if (!items.to_bool() || !items.is_array()) throw std::runtime_error("join expects an array for items, got: " + items.dump()); + return do_join(items, sep); + }); + } + })); + globals.set("namespace", Value::callable([=](const std::shared_ptr &, ArgumentsValue & args) { + auto ns = Value::object(); + args.expectArgs("namespace", {0, 0}, {0, (std::numeric_limits::max)()}); + for (auto & [name, value] : args.kwargs) { + ns.set(name, value); + } + return ns; + })); + auto equalto = simple_function("equalto", { "expected", "actual" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("actual") == args.at("expected"); + }); + globals.set("equalto", equalto); + globals.set("==", equalto); + globals.set("length", simple_function("length", { "items" }, [](const std::shared_ptr &, Value & args) -> Value { + auto & items = args.at("items"); + return (int64_t) items.size(); + })); + globals.set("safe", simple_function("safe", { "value" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("value").to_str(); + })); + globals.set("string", simple_function("string", { "value" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("value").to_str(); + })); + globals.set("int", simple_function("int", { "value" }, [](const std::shared_ptr &, Value & args) -> Value { + return args.at("value").to_int(); + })); + globals.set("list", simple_function("list", { "items" }, [](const std::shared_ptr &, Value & args) -> Value { + auto & items = args.at("items"); + if (!items.is_array()) throw std::runtime_error("object is not iterable"); + return items; + })); + globals.set("unique", simple_function("unique", { "items" }, [](const std::shared_ptr &, Value & args) -> Value { + auto & items = args.at("items"); + if (!items.is_array()) throw std::runtime_error("object is not iterable"); + std::unordered_set seen; + auto result = Value::array(); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto pair = seen.insert(items.at(i)); + if (pair.second) { + result.push_back(items.at(i)); + } + } + return result; + })); + auto make_filter = [](const Value & filter, Value & extra_args) -> Value { + return simple_function("", { "value" }, [=](const std::shared_ptr & context, Value & args) { + auto & value = args.at("value"); + ArgumentsValue actual_args; + actual_args.args.emplace_back(value); + for (size_t i = 0, n = extra_args.size(); i < n; i++) { + actual_args.args.emplace_back(extra_args.at(i)); + } + return filter.call(context, actual_args); + }); + }; + auto select_or_reject = [make_filter](bool is_select) { + return Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) { + args.expectArgs(is_select ? "select" : "reject", {2, (std::numeric_limits::max)()}, {0, 0}); + auto & items = args.args[0]; + if (items.is_null()) + return Value::array(); + if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump()); + + auto filter_fn = context->get(args.args[1]); + if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump()); + + auto filter_args = Value::array(); + for (size_t i = 2, n = args.args.size(); i < n; i++) { + filter_args.push_back(args.args[i]); + } + auto filter = make_filter(filter_fn, filter_args); + + auto res = Value::array(); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto & item = items.at(i); + ArgumentsValue filter_args; + filter_args.args.emplace_back(item); + auto pred_res = filter.call(context, filter_args); + if (pred_res.to_bool() == (is_select ? true : false)) { + res.push_back(item); + } + } + return res; + }); + }; + globals.set("select", select_or_reject(/* is_select= */ true)); + globals.set("reject", select_or_reject(/* is_select= */ false)); + globals.set("map", Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) { + auto res = Value::array(); + if (args.args.size() == 1 && + ((args.has_named("attribute") && args.kwargs.size() == 1) || (args.has_named("default") && args.kwargs.size() == 2))) { + auto & items = args.args[0]; + auto attr_name = args.get_named("attribute"); + auto default_value = args.get_named("default"); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto & item = items.at(i); + auto attr = item.get(attr_name); + res.push_back(attr.is_null() ? default_value : attr); + } + } else if (args.kwargs.empty() && args.args.size() >= 2) { + auto fn = context->get(args.args[1]); + if (fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump()); + ArgumentsValue filter_args { {Value()}, {} }; + for (size_t i = 2, n = args.args.size(); i < n; i++) { + filter_args.args.emplace_back(args.args[i]); + } + for (size_t i = 0, n = args.args[0].size(); i < n; i++) { + auto & item = args.args[0].at(i); + filter_args.args[0] = item; + res.push_back(fn.call(context, filter_args)); + } + } else { + throw std::runtime_error("Invalid or unsupported arguments for map"); + } + return res; + })); + globals.set("indent", simple_function("indent", { "text", "indent", "first" }, [](const std::shared_ptr &, Value & args) { + auto text = args.at("text").get(); + auto first = args.get("first", false); + std::string out; + std::string indent(args.get("indent", 0), ' '); + std::istringstream iss(text); + std::string line; + auto is_first = true; + while (std::getline(iss, line, '\n')) { + auto needs_indent = !is_first || first; + if (is_first) is_first = false; + else out += "\n"; + if (needs_indent) out += indent; + out += line; + } + if (!text.empty() && text.back() == '\n') out += "\n"; + return out; + })); + auto select_or_reject_attr = [](bool is_select) { + return Value::callable([=](const std::shared_ptr & context, ArgumentsValue & args) { + args.expectArgs(is_select ? "selectattr" : "rejectattr", {2, (std::numeric_limits::max)()}, {0, 0}); + auto & items = args.args[0]; + if (items.is_null()) + return Value::array(); + if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump()); + auto attr_name = args.args[1].get(); + + bool has_test = false; + Value test_fn; + ArgumentsValue test_args {{Value()}, {}}; + if (args.args.size() >= 3) { + has_test = true; + test_fn = context->get(args.args[2]); + if (test_fn.is_null()) throw std::runtime_error("Undefined test: " + args.args[2].dump()); + for (size_t i = 3, n = args.args.size(); i < n; i++) { + test_args.args.emplace_back(args.args[i]); + } + test_args.kwargs = args.kwargs; + } + + auto res = Value::array(); + for (size_t i = 0, n = items.size(); i < n; i++) { + auto & item = items.at(i); + auto attr = item.get(attr_name); + if (has_test) { + test_args.args[0] = attr; + if (test_fn.call(context, test_args).to_bool() == (is_select ? true : false)) { + res.push_back(item); + } + } else { + res.push_back(attr); + } + } + return res; + }); + }; + globals.set("selectattr", select_or_reject_attr(/* is_select= */ true)); + globals.set("rejectattr", select_or_reject_attr(/* is_select= */ false)); + globals.set("range", Value::callable([=](const std::shared_ptr &, ArgumentsValue & args) { + std::vector startEndStep(3); + std::vector param_set(3); + if (args.args.size() == 1) { + startEndStep[1] = args.args[0].get(); + param_set[1] = true; + } else { + for (size_t i = 0; i < args.args.size(); i++) { + auto & arg = args.args[i]; + auto v = arg.get(); + startEndStep[i] = v; + param_set[i] = true; + } + } + for (auto & [name, value] : args.kwargs) { + size_t i; + if (name == "start") i = 0; + else if (name == "end") i = 1; + else if (name == "step") i = 2; + else throw std::runtime_error("Unknown argument " + name + " for function range"); + + if (param_set[i]) { + throw std::runtime_error("Duplicate argument " + name + " for function range"); + } + startEndStep[i] = value.get(); + param_set[i] = true; + } + if (!param_set[1]) { + throw std::runtime_error("Missing required argument 'end' for function range"); + } + int64_t start = param_set[0] ? startEndStep[0] : 0; + int64_t end = startEndStep[1]; + int64_t step = param_set[2] ? startEndStep[2] : 1; + + auto res = Value::array(); + if (step > 0) { + for (int64_t i = start; i < end; i += step) { + res.push_back(Value(i)); + } + } else { + for (int64_t i = start; i > end; i += step) { + res.push_back(Value(i)); + } + } + return res; + })); + + return std::make_shared(std::move(globals)); +} + +inline std::shared_ptr Context::make(Value && values, const std::shared_ptr & parent) { + return std::make_shared(values.is_null() ? Value::object() : std::move(values), parent); +} + +} // namespace minja diff --git a/common/ngram-cache.cpp b/common/ngram-cache.cpp new file mode 100644 index 000000000..a057ae45f --- /dev/null +++ b/common/ngram-cache.cpp @@ -0,0 +1,285 @@ +#include "ngram-cache.h" +#include "common.h" +#include "log.h" + +#include +#include +#include +#include +#include + +void common_ngram_cache_update(common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, + std::vector & inp, int nnew, bool print_progress) { + const int64_t t_start_ms = ggml_time_ms(); + const int64_t inp_size = inp.size(); + + const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1); + int64_t n_done = 0; + + for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) { + const int64_t i_start = std::max(inp_size - nnew, ngram_size); + for (int64_t i = i_start; i < inp_size; ++i) { + const int64_t ngram_start = i - ngram_size; + common_ngram ngram(&inp[ngram_start], ngram_size); + const llama_token token = inp[i]; + + common_ngram_cache::iterator part_it = ngram_cache.find(ngram); + if (part_it == ngram_cache.end()) { + common_ngram_cache_part part; + part.emplace(token, 1); + ngram_cache.emplace(ngram, part); + } else { + common_ngram_cache_part::iterator token_count_it = part_it->second.find(token); + if (token_count_it == part_it->second.end()) { + part_it->second.emplace(token, 1); + } else { + token_count_it->second++; + } + } + ++n_done; + + if (print_progress && n_done % 10000000 == 0) { + const int64_t t_now_ms = ggml_time_ms(); + const int64_t eta_ms = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done; + const int64_t eta_min = eta_ms / (60*1000); + const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000; + + fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s); + } + } + } +} + +// Helper function to get a token from the combined, speculative sequence of inp and draft. +static llama_token get_token(const std::vector & inp, const std::vector & draft, const size_t i) { + return i < inp.size() ? inp[i] : draft[1 + i - inp.size()]; +} + +// If sample size or percentage are below these thresholds the draft is aborted early: +constexpr int draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2, 2, 1, 1}; +constexpr int draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50}; +constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2}; +constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66}; + +// Helper function that tries to draft a token from only the static ngram cache: +static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) { + common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + if (part_static_it == nc_static.end()) { + return LLAMA_TOKEN_NULL; + } + const common_ngram_cache_part part_static = part_static_it->second; + + int max_count_static = 0; + int sum_count_static = 0; + llama_token max_token = LLAMA_TOKEN_NULL; + + for (std::pair token_count_static : part_static) { + const llama_token token = token_count_static.first; + const int32_t count_static = token_count_static.second; + + if (count_static > max_count_static) { + max_token = token; + max_count_static = count_static; + } + sum_count_static += count_static; + } + + if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) { + return LLAMA_TOKEN_NULL; + } + if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) { + return LLAMA_TOKEN_NULL; + } + return max_token; +} + +// Try to draft a token from primary cache (context/dynamic), validate with static cache: +static llama_token try_draft( + common_ngram_cache & nc_primary, const std::vector & ngrams_primary, common_ngram_cache_part & part_static, + const int * min_sample_size, const int * min_percent) { + + llama_token drafted_token = LLAMA_TOKEN_NULL; + + for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) { + const common_ngram ngram_primary = ngrams_primary[i]; + + common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary); + if (part_primary_it == nc_primary.end()) { + continue; + } + const common_ngram_cache_part part_primary = part_primary_it->second; + + int max_count_primary = 0; + int max_count_static = 0; + int sum_count_primary = 0; + llama_token max_token = LLAMA_TOKEN_NULL; + + for (std::pair token_count_primary : part_primary) { + const llama_token token = token_count_primary.first; + + common_ngram_cache_part::iterator token_count_static_it = part_static.find(token); + + const int32_t count_primary = token_count_primary.second; + const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1; + + if (count_primary*count_static > max_count_primary*max_count_static) { + max_token = token; + max_count_primary = count_primary; + max_count_static = count_static; + } + sum_count_primary += count_primary; + } + + if (sum_count_primary < min_sample_size[i]) { + continue; + } + if (100*max_count_primary < min_percent[i]*sum_count_primary) { + continue;; + } + drafted_token = max_token; + } + + return drafted_token; +} + +void common_ngram_cache_draft( + std::vector & inp, std::vector & draft, int n_draft, int ngram_min, int ngram_max, + common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static +) { + GGML_ASSERT(draft.size() == 1); + const int inp_size = inp.size(); + + if (inp_size < LLAMA_NGRAM_STATIC) { + return; + } + + while ((int) draft.size()-1 < n_draft) { + llama_token drafted_token = LLAMA_TOKEN_NULL; + + const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1; + common_ngram ngram_static; + for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) { + ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j); + } + common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static); + common_ngram_cache_part part_static; + if (part_static_it != nc_static.end()) { + part_static = part_static_it->second; + } + + // cd = context + dynamic + std::vector ngrams_cd; + for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) { + const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1; + common_ngram ngram_cd; + for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) { + ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j); + } + ngrams_cd.push_back(ngram_cd); + } + if (drafted_token == LLAMA_TOKEN_NULL) { + drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax); + } + if (drafted_token == LLAMA_TOKEN_NULL) { + drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict); + } + if (drafted_token == LLAMA_TOKEN_NULL) { + drafted_token = try_draft(nc_static, ngram_static); + } + + if (drafted_token == LLAMA_TOKEN_NULL) { + break; + } + + LOG(" - draft candidate: token=%d\n", drafted_token); + draft.push_back(drafted_token); + } +} + +void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename) { + std::ofstream file_out(filename, std::ios::binary); + for (std::pair item : ngram_cache) { + const common_ngram ngram = item.first; + common_ngram_cache_part token_counts = item.second; + GGML_ASSERT(!token_counts.empty()); + const int32_t ntokens = token_counts.size(); + GGML_ASSERT(ntokens > 0); + + file_out.write(reinterpret_cast(&ngram), sizeof(common_ngram)); + file_out.write(reinterpret_cast(&ntokens), sizeof(int32_t)); + for (std::pair item2 : token_counts) { + const llama_token token = item2.first; + const int32_t count = item2.second; + GGML_ASSERT(count > 0); + + file_out.write(reinterpret_cast(&token), sizeof(llama_token)); + file_out.write(reinterpret_cast(&count), sizeof(int32_t)); + } + } + +} + +common_ngram_cache common_ngram_cache_load(std::string & filename) { + std::ifstream hashmap_file(filename, std::ios::binary); + if (!hashmap_file) { + throw std::ifstream::failure("Unable to open file " + filename); + } + common_ngram_cache ngram_cache; + + common_ngram ngram; + int32_t ntokens; + llama_token token; + int32_t count; + + char * ngramc = reinterpret_cast(&ngram); + char * ntokensc = reinterpret_cast(&ntokens); + char * tokenc = reinterpret_cast(&token); + char * countc = reinterpret_cast(&count); + while(hashmap_file.read(ngramc, sizeof(common_ngram))) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t))); + GGML_ASSERT(ntokens > 0); + common_ngram_cache_part token_counts; + + for (int i = 0; i < ntokens; ++i) { + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token))); + GGML_ASSERT(!hashmap_file.eof()); + GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t))); + GGML_ASSERT(count > 0); + token_counts.emplace(token, count); + } + + ngram_cache.emplace(ngram, token_counts); + } + GGML_ASSERT(hashmap_file.eof()); + + return ngram_cache; +} + +void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add) { + for (std::pair ngram_part : ngram_cache_add) { + const common_ngram ngram = ngram_part.first; + common_ngram_cache_part part = ngram_part.second; + + common_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram); + if (part_merged_it == ngram_cache_target.end()) { + ngram_cache_target.emplace(ngram, part); + continue; + } + + for (std::pair token_count : part) { + const llama_token token = token_count.first; + const int32_t count = token_count.second; + GGML_ASSERT(count > 0); + + common_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token); + if (token_count_merged_it == part_merged_it->second.end()) { + part_merged_it->second.emplace(token, count); + continue; + } + + token_count_merged_it->second += count; + } + } +} diff --git a/common/ngram-cache.h b/common/ngram-cache.h new file mode 100644 index 000000000..dfe012abe --- /dev/null +++ b/common/ngram-cache.h @@ -0,0 +1,101 @@ +#pragma once + +#include "llama.h" + +#include +#include +#include + +#define LLAMA_NGRAM_MIN 1 +#define LLAMA_NGRAM_MAX 4 +#define LLAMA_NGRAM_STATIC 2 + +// Data structures to map n-grams to empirical token probabilities: + +struct common_ngram { + llama_token tokens[LLAMA_NGRAM_MAX]; + + common_ngram() { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = LLAMA_TOKEN_NULL; + } + } + + common_ngram(const llama_token * input, const int ngram_size) { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL; + } + } + + bool operator==(const common_ngram & other) const { + for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) { + if (tokens[i] != other.tokens[i]) { + return false; + } + } + return true; + } +}; + +struct common_token_hash_function { + size_t operator()(const llama_token token) const { + // see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/ + return token * 11400714819323198485llu; + } +}; + +struct common_ngram_hash_function { + size_t operator()(const common_ngram & ngram) const { + size_t hash = common_token_hash_function{}(ngram.tokens[0]); + for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) { + hash ^= common_token_hash_function{}(ngram.tokens[i]); + } + return hash; + } +}; + +// token -> number of times token has been seen +typedef std::unordered_map common_ngram_cache_part; + +// n-gram -> empirical distribution of following tokens +typedef std::unordered_map common_ngram_cache; + + +// Update an ngram cache with tokens. +// ngram_cache: the cache to modify. +// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data. +// inp_data: the token sequence with which to update ngram_cache. +// nnew: how many new tokens have been appended to inp_data since the last call to this function. +// print_progress: whether to print progress to stderr. +// +// In order to get correct results inp_data can ONLY BE APPENDED TO. +// Changes in the middle need a complete rebuild. +void common_ngram_cache_update( + common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector & inp_data, int nnew, bool print_progress); + +// Try to draft tokens from ngram caches. +// inp: the tokens generated so far. +// draft: the token sequence to draft. Expected to initially contain the previously sampled token. +// n_draft: maximum number of tokens to add to draft. +// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic. +// nc_context: ngram cache based on current context. +// nc_dynamic: ngram cache based on previous user generations. +// nc_static: ngram cache generated from a large text corpus, used for validation. +void common_ngram_cache_draft( + std::vector & inp, std::vector & draft, int n_draft, int ngram_min, int ngram_max, + common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static); + +// Save an ngram cache to a file. +// ngram_cache: the ngram cache to save. +// filename: the path under which to save the ngram cache. +void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename); + +// Load an ngram cache saved with common_ngram_cache_save. +// filename: the path from which to load the ngram cache. +// returns: an ngram cache containing the information saved to filename. +common_ngram_cache common_ngram_cache_load(std::string & filename); + +// Merge two ngram caches. +// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add. +// ngram_cache_add: the ngram cache to add to ngram_cache_target. +void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add); diff --git a/common/sampling.cpp b/common/sampling.cpp index e67096bea..e4b21ca10 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -1,318 +1,526 @@ #include "sampling.h" -struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) { - struct llama_sampling_context * result = new llama_sampling_context(); +#include "common.h" - result->params = params; - result->grammar = nullptr; +#include +#include - // if there is a grammar, parse it - if (!params.grammar.empty()) { - result->parsed_grammar = grammar_parser::parse(params.grammar.c_str()); +// the ring buffer works similarly to std::deque, but with a fixed capacity +// TODO: deduplicate with llama-impl.h +template +struct ring_buffer { + ring_buffer(size_t cap) : capacity(cap), data(cap) {} - // will be empty (default) if there are parse errors - if (result->parsed_grammar.rules.empty()) { - fprintf(stderr, "%s: failed to parse grammar\n", __func__); - delete result; - return nullptr; + T & front() { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[first]; + } + + const T & front() const { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[first]; + } + + T & back() { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[pos]; + } + + const T & back() const { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + return data[pos]; + } + + void push_back(const T & value) { + if (sz == capacity) { + // advance the start when buffer is full + first = (first + 1) % capacity; + } else { + sz++; + } + data[pos] = value; + pos = (pos + 1) % capacity; + } + + T pop_front() { + if (sz == 0) { + throw std::runtime_error("ring buffer is empty"); + } + T value = data[first]; + first = (first + 1) % capacity; + sz--; + return value; + } + + const T & rat(size_t i) const { + if (i >= sz) { + throw std::runtime_error("ring buffer: index out of bounds"); + } + return data[(first + sz - i - 1) % capacity]; + } + + std::vector to_vector() const { + std::vector result; + result.reserve(sz); + for (size_t i = 0; i < sz; i++) { + result.push_back(data[(first + i) % capacity]); + } + return result; + } + + void clear() { + // here only reset the status of the buffer + sz = 0; + first = 0; + pos = 0; + } + + bool empty() const { + return sz == 0; + } + + size_t size() const { + return sz; + } + + size_t capacity = 0; + size_t sz = 0; + size_t first = 0; + size_t pos = 0; + std::vector data; +}; + +struct common_sampler { + common_params_sampling params; + + struct llama_sampler * grmr; + struct llama_sampler * chain; + + ring_buffer prev; + + std::vector cur; + + llama_token_data_array cur_p; + + void set_logits(struct llama_context * ctx, int idx) { + const auto * logits = llama_get_logits_ith(ctx, idx); + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_vocab = llama_vocab_n_tokens(vocab); + + cur.resize(n_vocab); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f}; } - std::vector grammar_rules(result->parsed_grammar.c_rules()); - - result->grammar = llama_grammar_init( - grammar_rules.data(), - grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root")); + cur_p = { cur.data(), cur.size(), -1, false }; } +}; - result->prev.resize(params.n_prev); - - return result; -} - -void llama_sampling_free(struct llama_sampling_context * ctx) { - if (ctx->grammar != NULL) { - llama_grammar_free(ctx->grammar); - } - - delete ctx; -} - -void llama_sampling_reset(llama_sampling_context * ctx) { - if (ctx->grammar != NULL) { - llama_grammar_free(ctx->grammar); - ctx->grammar = NULL; - } - - if (!ctx->parsed_grammar.rules.empty()) { - std::vector grammar_rules(ctx->parsed_grammar.c_rules()); - - ctx->grammar = llama_grammar_init( - grammar_rules.data(), - grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root")); - } - - std::fill(ctx->prev.begin(), ctx->prev.end(), 0); - ctx->cur.clear(); -} - -void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) { - if (dst->grammar) { - llama_grammar_free(dst->grammar); - dst->grammar = nullptr; - } - - if (src->grammar) { - dst->grammar = llama_grammar_copy(src->grammar); - } - - dst->prev = src->prev; -} - -llama_token llama_sampling_last(llama_sampling_context * ctx) { - return ctx->prev.back(); -} - -std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) { - const int size = ctx_sampling->prev.size(); - - n = std::min(n, size); - - std::string result; - - for (int i = size - n; i < size; i++) { - result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]); - } - - return result; -} - -std::string llama_sampling_print(const llama_sampling_params & params) { +std::string common_params_sampling::print() const { char result[1024]; snprintf(result, sizeof(result), "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n" - "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n" + "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n" + "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n" "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f", - params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present, - params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp, - params.mirostat, params.mirostat_eta, params.mirostat_tau); + penalty_last_n, penalty_repeat, penalty_freq, penalty_present, + dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, + top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp, + mirostat, mirostat_eta, mirostat_tau); return std::string(result); } -std::string llama_sampling_order_print(const llama_sampling_params & params) { - std::string result = "CFG -> Penalties "; +struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) { + const llama_vocab * vocab = llama_model_get_vocab(model); + + llama_sampler_chain_params lparams = llama_sampler_chain_default_params(); + + lparams.no_perf = params.no_perf; + + std::vector trigger_words; + trigger_words.reserve(params.grammar_trigger_words.size()); + for (const auto & str : params.grammar_trigger_words) { + trigger_words.push_back(str.word.c_str()); + } + + struct llama_sampler * grmr; + if (params.grammar.compare(0, 11, "%llguidance") == 0) { +#ifdef LLAMA_USE_LLGUIDANCE + grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()); +#else + GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); +#endif // LLAMA_USE_LLGUIDANCE + } else { + grmr = params.grammar_lazy + ? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root", + trigger_words.data(), trigger_words.size(), + params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size()) + : llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"); + } + + auto * result = new common_sampler { + /* .params = */ params, + /* .grmr = */ grmr, + /* .chain = */ llama_sampler_chain_init(lparams), + /* .prev = */ ring_buffer(std::max(32, params.n_prev)), + /* .cur = */ {}, + /* .cur_p = */ {}, + }; + + llama_sampler_chain_add(result->chain, + llama_sampler_init_logit_bias( + llama_vocab_n_tokens(vocab), + params.logit_bias.size(), + params.logit_bias.data())); + if (params.mirostat == 0) { - for (auto sampler_type : params.samplers_sequence) { - const auto sampler_type_name = sampler_type_to_name_string(sampler_type); - if (!sampler_type_name.empty()) { - result += "-> " + sampler_type_name + " "; + for (const auto & cnstr : params.samplers) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: + { + std::vector c_breakers; + c_breakers.reserve(params.dry_sequence_breakers.size()); + for (const auto & str : params.dry_sequence_breakers) { + c_breakers.push_back(str.c_str()); + } + + llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + } + break; + case COMMON_SAMPLER_TYPE_TOP_K: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + break; + case COMMON_SAMPLER_TYPE_TOP_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_MIN_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_XTC: + llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + break; + case COMMON_SAMPLER_TYPE_TYPICAL_P: + llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + break; + case COMMON_SAMPLER_TYPE_TEMPERATURE: + llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + break; + case COMMON_SAMPLER_TYPE_INFILL: + llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab)); + break; + case COMMON_SAMPLER_TYPE_PENALTIES: + llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + break; + default: + GGML_ASSERT(false && "unknown sampler type"); } } + llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); + } else if (params.mirostat == 1) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); + } else if (params.mirostat == 2) { + llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); + llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); } else { - result += "-> mirostat "; + GGML_ASSERT(false && "unknown mirostat version"); } return result; } -// no reasons to expose this function in header -static void sampler_queue( - struct llama_context * ctx_main, - const llama_sampling_params & params, - llama_token_data_array & cur_p, - size_t min_keep) { - const float temp = params.temp; - const float dynatemp_range = params.dynatemp_range; - const float dynatemp_exponent = params.dynatemp_exponent; - const int32_t top_k = params.top_k; - const float top_p = params.top_p; - const float min_p = params.min_p; - const float tfs_z = params.tfs_z; - const float typical_p = params.typical_p; - const std::vector & samplers_sequence = params.samplers_sequence; +void common_sampler_free(struct common_sampler * gsmpl) { + if (gsmpl) { + llama_sampler_free(gsmpl->grmr); - for (auto sampler_type : samplers_sequence) { - switch (sampler_type) { - case llama_sampler_type::TOP_K : llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break; - case llama_sampler_type::TFS_Z : llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break; - case llama_sampler_type::TYPICAL_P: llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break; - case llama_sampler_type::TOP_P : llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break; - case llama_sampler_type::MIN_P : llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break; - case llama_sampler_type::TEMPERATURE: - if (dynatemp_range > 0) { - float dynatemp_min = std::max(0.0f, temp - dynatemp_range); - float dynatemp_max = std::max(0.0f, temp + dynatemp_range); - llama_sample_entropy(ctx_main, &cur_p, dynatemp_min, dynatemp_max, dynatemp_exponent); - } else { - llama_sample_temp(ctx_main, &cur_p, temp); - } - break; - default : break; - } + llama_sampler_free(gsmpl->chain); + + delete gsmpl; } } -static llama_token llama_sampling_sample_impl( - struct llama_sampling_context * ctx_sampling, - struct llama_context * ctx_main, - struct llama_context * ctx_cfg, - const int idx, - bool is_resampling) { // Add a parameter to indicate if we are resampling - const llama_sampling_params & params = ctx_sampling->params; - - const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); - - const float temp = params.temp; - const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n; - const float penalty_repeat = params.penalty_repeat; - const float penalty_freq = params.penalty_freq; - const float penalty_present = params.penalty_present; - const int mirostat = params.mirostat; - const float mirostat_tau = params.mirostat_tau; - const float mirostat_eta = params.mirostat_eta; - const bool penalize_nl = params.penalize_nl; - - auto & prev = ctx_sampling->prev; - auto & cur = ctx_sampling->cur; - - llama_token id = 0; - - // Get a pointer to the logits - float * logits = llama_get_logits_ith(ctx_main, idx); - - // Declare original_logits at the beginning of the function scope - std::vector original_logits; - - if (!is_resampling) { - // Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this. - original_logits = std::vector(logits, logits + llama_n_vocab(llama_get_model(ctx_main))); +void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) { + if (accept_grammar) { + llama_sampler_accept(gsmpl->grmr, token); } - // apply params.logit_bias map - for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { - logits[it->first] += it->second; + llama_sampler_accept(gsmpl->chain, token); + + gsmpl->prev.push_back(token); +} + +void common_sampler_reset(struct common_sampler * gsmpl) { + llama_sampler_reset(gsmpl->grmr); + + llama_sampler_reset(gsmpl->chain); +} + +struct common_sampler * common_sampler_clone(common_sampler * gsmpl) { + return new common_sampler { + /* .params = */ gsmpl->params, + /* .grmr = */ llama_sampler_clone(gsmpl->grmr), + /* .chain = */ llama_sampler_clone(gsmpl->chain), + /* .prev = */ gsmpl->prev, + /* .cur = */ gsmpl->cur, + /* .cur_p = */ gsmpl->cur_p, + }; +} + +void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) { + // TODO: measure grammar performance + + if (gsmpl) { + llama_perf_sampler_print(gsmpl->chain); + } + if (ctx) { + llama_perf_context_print(ctx); + } +} + +llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) { + gsmpl->set_logits(ctx, idx); + + auto & grmr = gsmpl->grmr; + auto & chain = gsmpl->chain; + auto & cur_p = gsmpl->cur_p; // initialized by set_logits + + if (grammar_first) { + llama_sampler_apply(grmr, &cur_p); } - if (ctx_cfg) { - float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx); - llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale); + llama_sampler_apply(chain, &cur_p); + + GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration"); + + const llama_token id = cur_p.data[cur_p.selected].id; + + if (grammar_first) { + return id; } - cur.clear(); + // check if it the sampled token fits the grammar + { + llama_token_data single_token_data = { id, 1.0f, 0.0f }; + llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false }; - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + llama_sampler_apply(grmr, &single_token_data_array); + + const bool is_valid = single_token_data_array.data[0].logit != -INFINITY; + if (is_valid) { + return id; + } } - llama_token_data_array cur_p = { cur.data(), cur.size(), false }; + // resampling: + // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain + gsmpl->set_logits(ctx, idx); - // apply penalties - const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; - const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); - if (penalty_tokens_used_size) { - const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))]; + llama_sampler_apply(grmr, &cur_p); + llama_sampler_apply(chain, &cur_p); - llama_sample_repetition_penalties(ctx_main, &cur_p, - penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size, - penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present); + GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration"); - if (!penalize_nl) { - for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) { - cur_p.data[idx].logit = nl_logit; - break; + 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); +} + +// helpers + +llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) { + return &gsmpl->cur_p; +} + +llama_token common_sampler_last(const struct common_sampler * gsmpl) { + return gsmpl->prev.rat(0); +} + +std::string common_sampler_print(const struct common_sampler * gsmpl) { + std::string result = "logits "; + + for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) { + const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i); + result += std::string("-> ") + llama_sampler_name(smpl) + " "; + } + + return result; +} + +std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) { + n = std::min(n, (int) gsmpl->prev.size()); + + if (n <= 0) { + return ""; + } + + std::string result; + result.reserve(8*n); // 8 is the average length of a token [citation needed], TODO: compute this from the vocab + + for (int i = n - 1; i >= 0; i--) { + const llama_token id = gsmpl->prev.rat(i); + + GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen"); + + result += common_token_to_piece(ctx_main, id); + } + + return result; +} + +char common_sampler_type_to_chr(enum common_sampler_type cnstr) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return 'd'; + case COMMON_SAMPLER_TYPE_TOP_K: return 'k'; + case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y'; + case COMMON_SAMPLER_TYPE_TOP_P: return 'p'; + case COMMON_SAMPLER_TYPE_MIN_P: return 'm'; + case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't'; + case COMMON_SAMPLER_TYPE_XTC: return 'x'; + case COMMON_SAMPLER_TYPE_INFILL: return 'i'; + case COMMON_SAMPLER_TYPE_PENALTIES: return 'e'; + default : return '?'; + } +} + +std::string common_sampler_type_to_str(enum common_sampler_type cnstr) { + switch (cnstr) { + case COMMON_SAMPLER_TYPE_DRY: return "dry"; + case COMMON_SAMPLER_TYPE_TOP_K: return "top_k"; + case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p"; + case COMMON_SAMPLER_TYPE_TOP_P: return "top_p"; + case COMMON_SAMPLER_TYPE_MIN_P: return "min_p"; + case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature"; + case COMMON_SAMPLER_TYPE_XTC: return "xtc"; + case COMMON_SAMPLER_TYPE_INFILL: return "infill"; + case COMMON_SAMPLER_TYPE_PENALTIES: return "penalties"; + default : return ""; + } +} + +std::vector common_sampler_types_from_names(const std::vector & names, bool allow_alt_names) { + std::unordered_map sampler_canonical_name_map { + { "dry", COMMON_SAMPLER_TYPE_DRY }, + { "top_k", COMMON_SAMPLER_TYPE_TOP_K }, + { "top_p", COMMON_SAMPLER_TYPE_TOP_P }, + { "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "min_p", COMMON_SAMPLER_TYPE_MIN_P }, + { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE }, + { "xtc", COMMON_SAMPLER_TYPE_XTC }, + { "infill", COMMON_SAMPLER_TYPE_INFILL }, + { "penalties", COMMON_SAMPLER_TYPE_PENALTIES }, + }; + + // since samplers names are written multiple ways + // make it ready for both system names and input names + std::unordered_map sampler_alt_name_map { + { "top-k", COMMON_SAMPLER_TYPE_TOP_K }, + { "top-p", COMMON_SAMPLER_TYPE_TOP_P }, + { "nucleus", COMMON_SAMPLER_TYPE_TOP_P }, + { "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "typical", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "typ", COMMON_SAMPLER_TYPE_TYPICAL_P }, + { "min-p", COMMON_SAMPLER_TYPE_MIN_P }, + { "temp", COMMON_SAMPLER_TYPE_TEMPERATURE }, + }; + + std::vector samplers; + samplers.reserve(names.size()); + + for (const auto & name : names) { + auto sampler = sampler_canonical_name_map.find(name); + if (sampler != sampler_canonical_name_map.end()) { + samplers.push_back(sampler->second); + } else { + if (allow_alt_names) { + sampler = sampler_alt_name_map.find(name); + if (sampler != sampler_alt_name_map.end()) { + samplers.push_back(sampler->second); } } } } - // If we are in the resampling phase, apply grammar checks before sampling logic - if (is_resampling && ctx_sampling->grammar != NULL) { - llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); - } + return samplers; +} - if (temp < 0.0) { - // greedy sampling, with probs - llama_sample_softmax(ctx_main, &cur_p); - id = cur_p.data[0].id; - } else if (temp == 0.0) { - // greedy sampling, no probs - id = llama_sample_token_greedy(ctx_main, &cur_p); - } else { - if (mirostat == 1) { - const int mirostat_m = 100; - llama_sample_temp(ctx_main, &cur_p, temp); - id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu); - } else if (mirostat == 2) { - llama_sample_temp(ctx_main, &cur_p, temp); - id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu); - } else { - // temperature sampling - size_t min_keep = std::max(1, params.min_keep); +std::vector common_sampler_types_from_chars(const std::string & chars) { + std::unordered_map sampler_name_map = { + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL }, + { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES), COMMON_SAMPLER_TYPE_PENALTIES }, + }; - sampler_queue(ctx_main, params, cur_p, min_keep); + std::vector samplers; + samplers.reserve(chars.size()); - id = llama_sample_token(ctx_main, &cur_p); - - //{ - // const int n_top = 10; - // LOG("top %d candidates:\n", n_top); - - // for (int i = 0; i < n_top; i++) { - // const llama_token id = cur_p.data[i].id; - // (void)id; // To avoid a warning that id is unused when logging is disabled. - // LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p); - // } - //} - - //LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str()); + for (const auto & c : chars) { + const auto sampler = sampler_name_map.find(c); + if (sampler != sampler_name_map.end()) { + samplers.push_back(sampler->second); } } - if (ctx_sampling->grammar != NULL && !is_resampling) { - // Create an array with a single token data element for the sampled id - llama_token_data single_token_data = {id, logits[id], 0.0f}; - llama_token_data_array single_token_data_array = { &single_token_data, 1, false }; - - // Apply grammar constraints to the single token - llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar); - - // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY - bool is_valid = single_token_data_array.data[0].logit != -INFINITY; - - // If the token is not valid according to the grammar, perform resampling - if (!is_valid) { - LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str()); - - // Restore logits from the copy - std::copy(original_logits.begin(), original_logits.end(), logits); - - return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true); // Pass true for is_resampling - } - } - - return id; -} - -llama_token llama_sampling_sample( - struct llama_sampling_context * ctx_sampling, - struct llama_context * ctx_main, - struct llama_context * ctx_cfg, - const int idx) { - // Call the implementation function with is_resampling set to false by default - return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); -} - -void llama_sampling_accept( - struct llama_sampling_context * ctx_sampling, - struct llama_context * ctx_main, - llama_token id, - bool apply_grammar) { - ctx_sampling->prev.erase(ctx_sampling->prev.begin()); - ctx_sampling->prev.push_back(id); - - if (ctx_sampling->grammar != NULL && apply_grammar) { - llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id); - } + return samplers; } diff --git a/common/sampling.h b/common/sampling.h index 95d875394..2064421db 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -2,137 +2,106 @@ #include "llama.h" -#include "grammar-parser.h" +#include "common.h" #include #include -#include -// sampler types -enum class llama_sampler_type : char { - TOP_K = 'k', - TOP_P = 'p', - MIN_P = 'm', - TFS_Z = 'f', - TYPICAL_P = 'y', - TEMPERATURE = 't' -}; - -// sampling parameters -typedef struct llama_sampling_params { - int32_t n_prev = 64; // number of previous tokens to remember - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. - int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens - int32_t top_k = 40; // <= 0 to use vocab size - float top_p = 0.95f; // 1.0 = disabled - float min_p = 0.05f; // 0.0 = disabled - float tfs_z = 1.00f; // 1.0 = disabled - float typical_p = 1.00f; // 1.0 = disabled - float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities - float dynatemp_range = 0.00f; // 0.0 = disabled - float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler - int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float penalty_repeat = 1.10f; // 1.0 = disabled - float penalty_freq = 0.00f; // 0.0 = disabled - float penalty_present = 0.00f; // 0.0 = disabled - int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float mirostat_tau = 5.00f; // target entropy - float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = true; // consider newlines as a repeatable token - - std::vector samplers_sequence = { - llama_sampler_type::TOP_K, - llama_sampler_type::TFS_Z, - llama_sampler_type::TYPICAL_P, - llama_sampler_type::TOP_P, - llama_sampler_type::MIN_P, - llama_sampler_type::TEMPERATURE - }; - - std::string grammar; // optional BNF-like grammar to constrain sampling - - // Classifier-Free Guidance - // https://arxiv.org/abs/2306.17806 - std::string cfg_negative_prompt; // string to help guidance - float cfg_scale = 1.f; // how strong is guidance - - std::unordered_map logit_bias; // logit bias for specific tokens - - std::vector penalty_prompt_tokens; - bool use_penalty_prompt_tokens = false; -} llama_sampling_params; - -// general sampler context -// TODO: move to llama.h -struct llama_sampling_context { - // parameters that will be used for sampling - llama_sampling_params params; - - // mirostat sampler state - float mirostat_mu; - - llama_grammar * grammar; - - // internal - grammar_parser::parse_state parsed_grammar; - - // TODO: replace with ring-buffer - std::vector prev; - std::vector cur; -}; - -#include "common.h" - -// Create a new sampling context instance. -struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params); - -void llama_sampling_free(struct llama_sampling_context * ctx); - -// Reset the sampler context -// - clear prev tokens -// - reset grammar -void llama_sampling_reset(llama_sampling_context * ctx); - -// Copy the sampler context -void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst); - -// Get the last sampled token -llama_token llama_sampling_last(llama_sampling_context * ctx); - -// Get a string representation of the last sampled tokens -std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n); - -// Print sampling parameters into a string -std::string llama_sampling_print(const llama_sampling_params & params); - -// Print sampling order into a string -std::string llama_sampling_order_print(const llama_sampling_params & params); - -// this is a common sampling function used across the examples for convenience -// it can serve as a starting point for implementing your own sampling function -// Note: When using multiple sequences, it is the caller's responsibility to call -// llama_sampling_reset when a sequence ends +// common_sampler extends llama_sampler with additional functionality: // -// required: -// - ctx_main: context to use for sampling -// - ctx_sampling: sampling-specific context +// - grammar support +// - custom sampler logic based on the parameters +// - history of the last accepted tokens +// - performance metrics // -// optional: -// - ctx_cfg: context to use for classifier-free guidance -// - idx: sample from llama_get_logits_ith(ctx, idx) +// This goal is to have a common implementation of the sampling logic shared across the examples. +// For example, depending on the temperature, the sampling chain can be very simple (greedy) or more +// complex (top-k, top-p, etc). // -// returns: -// - token: sampled token -// - candidates: vector of candidate tokens +// Another example is related to the grammar. In general, the grammar constraints applied on the full +// vocabulary can be very taxing. To improve performance, the grammar can be applied only to the sampled +// token in order to verify if it fits the grammar. And only if the token doesn't fit the grammar, the +// grammar constraints are applied to the full vocabulary and the token is resampled. +// +// The common_sampler also maintains a container with the last accepted tokens. In the future, this can +// be moved into the core llama library. +// +// For convenience, the common_sampler also maintains a container with the current candidate tokens. +// This can be used to access the probabilities of the rest of the non-sampled tokens. +// +// TODO: measure grammar performance // -llama_token llama_sampling_sample( - struct llama_sampling_context * ctx_sampling, - struct llama_context * ctx_main, - struct llama_context * ctx_cfg, - int idx = 0); -void llama_sampling_accept( - struct llama_sampling_context * ctx_sampling, - struct llama_context * ctx_main, - llama_token id, - bool apply_grammar); +struct common_sampler; + +// llama_sampler API overloads + +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); + +// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar +void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar); +void common_sampler_reset (struct common_sampler * gsmpl); +struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl); + +// arguments can be nullptr to skip printing +void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl); + +// extended sampling implementation: +// +// - set logits +// - apply the configured sampler chain +// - check if the token fits the grammar (if any) +// - if not: resample by first applying the grammar constraints and then sampling again (slower path) +// +// if grammar_first is true, the grammar is applied before the samplers (slower) +// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar +// +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 + +// access the internal list of current candidate tokens +llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl); + +// get the last accepted token +llama_token common_sampler_last(const struct common_sampler * gsmpl); + +// print the sampler chain into a string +std::string common_sampler_print(const struct common_sampler * gsmpl); + +// get a string representation of the last accepted tokens +std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx, int n); + +char common_sampler_type_to_chr(enum common_sampler_type cnstr); +std::string common_sampler_type_to_str(enum common_sampler_type cnstr); + +std::vector common_sampler_types_from_names(const std::vector & names, bool allow_alt_names); +std::vector common_sampler_types_from_chars(const std::string & chars); + +llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, + const char * grammar_kind, const char * grammar_data); diff --git a/common/speculative.cpp b/common/speculative.cpp new file mode 100644 index 000000000..318e96ea3 --- /dev/null +++ b/common/speculative.cpp @@ -0,0 +1,277 @@ +#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) { + if (spec == nullptr) { + return; + } + + 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 struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt); + const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft); + + const bool vocab_type_tgt = llama_vocab_type(vocab_tgt); + LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt); + + const bool vocab_type_dft = llama_vocab_type(vocab_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_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) || + llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) || + llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) || + llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) { + LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__); + LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt)); + LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft)); + return false; + } + + { + const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt); + const int n_vocab_dft = llama_vocab_n_tokens(vocab_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_vocab_n_tokens(vocab_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_vocab_get_text(vocab_tgt, i); + const char * token_text_dft = llama_vocab_get_text(vocab_dft, i); + if (std::strcmp(token_text_tgt, token_text_dft) != 0) { + LOG_ERR("%s: draft vocab vocab must match target vocab 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/stb_image.h b/common/stb_image.h index 4766d7e67..9eedabedc 100644 --- a/common/stb_image.h +++ b/common/stb_image.h @@ -1,4 +1,4 @@ -/* stb_image - v2.28 - public domain image loader - http://nothings.org/stb +/* stb_image - v2.30 - public domain image loader - http://nothings.org/stb no warranty implied; use at your own risk Do this: @@ -48,6 +48,8 @@ LICENSE RECENT REVISION HISTORY: + 2.30 (2024-05-31) avoid erroneous gcc warning + 2.29 (2023-05-xx) optimizations 2.28 (2023-01-29) many error fixes, security errors, just tons of stuff 2.27 (2021-07-11) document stbi_info better, 16-bit PNM support, bug fixes 2.26 (2020-07-13) many minor fixes @@ -371,13 +373,14 @@ RECENT REVISION HISTORY: #define STBI_VERSION 1 -enum { - STBI_default = 0, // only used for desired_channels +enum +{ + STBI_default = 0, // only used for desired_channels - STBI_grey = 1, - STBI_grey_alpha = 2, - STBI_rgb = 3, - STBI_rgb_alpha = 4 + STBI_grey = 1, + STBI_grey_alpha = 2, + STBI_rgb = 3, + STBI_rgb_alpha = 4 }; #include @@ -405,11 +408,11 @@ extern "C" { // load image by filename, open file, or memory buffer // -typedef struct { - int (*read)(void * user, char * data, - int size); // fill 'data' with 'size' bytes. return number of bytes actually read - void (*skip)(void * user, int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative - int (*eof)(void * user); // returns nonzero if we are at end of file/data +typedef struct +{ + int (*read) (void *user,char *data,int size); // fill 'data' with 'size' bytes. return number of bytes actually read + void (*skip) (void *user,int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative + int (*eof) (void *user); // returns nonzero if we are at end of file/data } stbi_io_callbacks; //////////////////////////////////// @@ -417,24 +420,21 @@ typedef struct { // 8-bits-per-channel interface // -STBIDEF stbi_uc * stbi_load_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file, - int desired_channels); -STBIDEF stbi_uc * stbi_load_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, - int * channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *channels_in_file, int desired_channels); #ifndef STBI_NO_STDIO -STBIDEF stbi_uc * stbi_load(char const * filename, int * x, int * y, int * channels_in_file, int desired_channels); -STBIDEF stbi_uc * stbi_load_from_file(FILE * f, int * x, int * y, int * channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); // for stbi_load_from_file, file pointer is left pointing immediately after image #endif #ifndef STBI_NO_GIF -STBIDEF stbi_uc * stbi_load_gif_from_memory(stbi_uc const * buffer, int len, int ** delays, int * x, int * y, int * z, - int * comp, int req_comp); +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); #endif #ifdef STBI_WINDOWS_UTF8 -STBIDEF int stbi_convert_wchar_to_utf8(char * buffer, size_t bufferlen, const wchar_t * input); +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input); #endif //////////////////////////////////// @@ -442,14 +442,12 @@ STBIDEF int stbi_convert_wchar_to_utf8(char * buffer, size_t bufferlen, const wc // 16-bits-per-channel interface // -STBIDEF stbi_us * stbi_load_16_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file, - int desired_channels); -STBIDEF stbi_us * stbi_load_16_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, - int * channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); #ifndef STBI_NO_STDIO -STBIDEF stbi_us * stbi_load_16(char const * filename, int * x, int * y, int * channels_in_file, int desired_channels); -STBIDEF stbi_us * stbi_load_from_file_16(FILE * f, int * x, int * y, int * channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16 (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); #endif //////////////////////////////////// @@ -457,55 +455,56 @@ STBIDEF stbi_us * stbi_load_from_file_16(FILE * f, int * x, int * y, int * chann // float-per-channel interface // #ifndef STBI_NO_LINEAR -STBIDEF float * stbi_loadf_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file, - int desired_channels); -STBIDEF float * stbi_loadf_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * channels_in_file, - int desired_channels); + STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); -#ifndef STBI_NO_STDIO -STBIDEF float * stbi_loadf(char const * filename, int * x, int * y, int * channels_in_file, int desired_channels); -STBIDEF float * stbi_loadf_from_file(FILE * f, int * x, int * y, int * channels_in_file, int desired_channels); -#endif + #ifndef STBI_NO_STDIO + STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); + #endif #endif #ifndef STBI_NO_HDR -STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); -STBIDEF void stbi_hdr_to_ldr_scale(float scale); + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); + STBIDEF void stbi_hdr_to_ldr_scale(float scale); #endif // STBI_NO_HDR #ifndef STBI_NO_LINEAR -STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); -STBIDEF void stbi_ldr_to_hdr_scale(float scale); + STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); + STBIDEF void stbi_ldr_to_hdr_scale(float scale); #endif // STBI_NO_LINEAR // stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR -STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const * clbk, void * user); -STBIDEF int stbi_is_hdr_from_memory(stbi_uc const * buffer, int len); +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len); #ifndef STBI_NO_STDIO -STBIDEF int stbi_is_hdr(char const * filename); -STBIDEF int stbi_is_hdr_from_file(FILE * f); +STBIDEF int stbi_is_hdr (char const *filename); +STBIDEF int stbi_is_hdr_from_file(FILE *f); #endif // STBI_NO_STDIO + // get a VERY brief reason for failure // on most compilers (and ALL modern mainstream compilers) this is threadsafe -STBIDEF const char * stbi_failure_reason(void); +STBIDEF const char *stbi_failure_reason (void); // free the loaded image -- this is just free() -STBIDEF void stbi_image_free(void * retval_from_stbi_load); +STBIDEF void stbi_image_free (void *retval_from_stbi_load); // get image dimensions & components without fully decoding -STBIDEF int stbi_info_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp); -STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * comp); -STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const * buffer, int len); -STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const * clbk, void * user); +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); #ifndef STBI_NO_STDIO -STBIDEF int stbi_info(char const * filename, int * x, int * y, int * comp); -STBIDEF int stbi_info_from_file(FILE * f, int * x, int * y, int * comp); -STBIDEF int stbi_is_16_bit(char const * filename); -STBIDEF int stbi_is_16_bit_from_file(FILE * f); +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); #endif + + // for image formats that explicitly notate that they have premultiplied alpha, // we just return the colors as stored in the file. set this flag to force // unpremultiplication. results are undefined if the unpremultiply overflow. @@ -527,14 +526,14 @@ STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_fli // ZLIB client - used by PNG, available for other purposes -STBIDEF char * stbi_zlib_decode_malloc_guesssize(const char * buffer, int len, int initial_size, int * outlen); -STBIDEF char * stbi_zlib_decode_malloc_guesssize_headerflag(const char * buffer, int len, int initial_size, int * outlen, - int parse_header); -STBIDEF char * stbi_zlib_decode_malloc(const char * buffer, int len, int * outlen); -STBIDEF int stbi_zlib_decode_buffer(char * obuffer, int olen, const char * ibuffer, int ilen); +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen); +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header); +STBIDEF char *stbi_zlib_decode_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + +STBIDEF char *stbi_zlib_decode_noheader_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); -STBIDEF char * stbi_zlib_decode_noheader_malloc(const char * buffer, int len, int * outlen); -STBIDEF int stbi_zlib_decode_noheader_buffer(char * obuffer, int olen, const char * ibuffer, int ilen); #ifdef __cplusplus } @@ -547,50 +546,52 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char * obuffer, int olen, const cha #ifdef STB_IMAGE_IMPLEMENTATION -#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) || defined(STBI_ONLY_TGA) || \ - defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || \ - defined(STBI_ONLY_PNM) || defined(STBI_ONLY_ZLIB) -#ifndef STBI_ONLY_JPEG -#define STBI_NO_JPEG -#endif -#ifndef STBI_ONLY_PNG -#define STBI_NO_PNG -#endif -#ifndef STBI_ONLY_BMP -#define STBI_NO_BMP -#endif -#ifndef STBI_ONLY_PSD -#define STBI_NO_PSD -#endif -#ifndef STBI_ONLY_TGA -#define STBI_NO_TGA -#endif -#ifndef STBI_ONLY_GIF -#define STBI_NO_GIF -#endif -#ifndef STBI_ONLY_HDR -#define STBI_NO_HDR -#endif -#ifndef STBI_ONLY_PIC -#define STBI_NO_PIC -#endif -#ifndef STBI_ONLY_PNM -#define STBI_NO_PNM -#endif +#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) \ + || defined(STBI_ONLY_TGA) || defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) \ + || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || defined(STBI_ONLY_PNM) \ + || defined(STBI_ONLY_ZLIB) + #ifndef STBI_ONLY_JPEG + #define STBI_NO_JPEG + #endif + #ifndef STBI_ONLY_PNG + #define STBI_NO_PNG + #endif + #ifndef STBI_ONLY_BMP + #define STBI_NO_BMP + #endif + #ifndef STBI_ONLY_PSD + #define STBI_NO_PSD + #endif + #ifndef STBI_ONLY_TGA + #define STBI_NO_TGA + #endif + #ifndef STBI_ONLY_GIF + #define STBI_NO_GIF + #endif + #ifndef STBI_ONLY_HDR + #define STBI_NO_HDR + #endif + #ifndef STBI_ONLY_PIC + #define STBI_NO_PIC + #endif + #ifndef STBI_ONLY_PNM + #define STBI_NO_PNM + #endif #endif #if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB) #define STBI_NO_ZLIB #endif -#include + #include #include // ptrdiff_t on osx #include #include +#include #if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) -#include // ldexp, pow +#include // ldexp, pow #endif #ifndef STBI_NO_STDIO @@ -608,54 +609,55 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char * obuffer, int olen, const cha #define STBI_EXTERN extern #endif + #ifndef _MSC_VER -#ifdef __cplusplus -#define stbi_inline inline + #ifdef __cplusplus + #define stbi_inline inline + #else + #define stbi_inline + #endif #else -#define stbi_inline -#endif -#else -#define stbi_inline __forceinline + #define stbi_inline __forceinline #endif #ifndef STBI_NO_THREAD_LOCALS -#if defined(__cplusplus) && __cplusplus >= 201103L -#define STBI_THREAD_LOCAL thread_local -#elif defined(__GNUC__) && __GNUC__ < 5 -#define STBI_THREAD_LOCAL __thread -#elif defined(_MSC_VER) -#define STBI_THREAD_LOCAL __declspec(thread) -#elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 201112L && !defined(__STDC_NO_THREADS__) -#define STBI_THREAD_LOCAL _Thread_local -#endif + #if defined(__cplusplus) && __cplusplus >= 201103L + #define STBI_THREAD_LOCAL thread_local + #elif defined(__GNUC__) && __GNUC__ < 5 + #define STBI_THREAD_LOCAL __thread + #elif defined(_MSC_VER) + #define STBI_THREAD_LOCAL __declspec(thread) + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L && !defined(__STDC_NO_THREADS__) + #define STBI_THREAD_LOCAL _Thread_local + #endif -#ifndef STBI_THREAD_LOCAL -#if defined(__GNUC__) -#define STBI_THREAD_LOCAL __thread -#endif -#endif + #ifndef STBI_THREAD_LOCAL + #if defined(__GNUC__) + #define STBI_THREAD_LOCAL __thread + #endif + #endif #endif #if defined(_MSC_VER) || defined(__SYMBIAN32__) typedef unsigned short stbi__uint16; -typedef signed short stbi__int16; -typedef unsigned int stbi__uint32; -typedef signed int stbi__int32; +typedef signed short stbi__int16; +typedef unsigned int stbi__uint32; +typedef signed int stbi__int32; #else #include typedef uint16_t stbi__uint16; -typedef int16_t stbi__int16; +typedef int16_t stbi__int16; typedef uint32_t stbi__uint32; -typedef int32_t stbi__int32; +typedef int32_t stbi__int32; #endif // should produce compiler error if size is wrong -typedef unsigned char validate_uint32[sizeof(stbi__uint32) == 4 ? 1 : -1]; +typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; #ifdef _MSC_VER -#define STBI_NOTUSED(v) (void)(v) +#define STBI_NOTUSED(v) (void)(v) #else -#define STBI_NOTUSED(v) (void)sizeof(v) +#define STBI_NOTUSED(v) (void)sizeof(v) #endif #ifdef _MSC_VER @@ -663,9 +665,9 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32) == 4 ? 1 : -1]; #endif #ifdef STBI_HAS_LROTL -#define stbi_lrot(x, y) _lrotl(x, y) + #define stbi_lrot(x,y) _lrotl(x,y) #else -#define stbi_lrot(x, y) (((x) << (y)) | ((x) >> (-(y)&31))) + #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (-(y) & 31))) #endif #if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) @@ -677,13 +679,13 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32) == 4 ? 1 : -1]; #endif #ifndef STBI_MALLOC -#define STBI_MALLOC(sz) malloc(sz) -#define STBI_REALLOC(p, newsz) realloc(p, newsz) -#define STBI_FREE(p) free(p) +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) #endif #ifndef STBI_REALLOC_SIZED -#define STBI_REALLOC_SIZED(p, oldsz, newsz) STBI_REALLOC(p, newsz) +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) #endif // x86/x64 detection @@ -725,31 +727,34 @@ typedef unsigned char validate_uint32[sizeof(stbi__uint32) == 4 ? 1 : -1]; #ifdef _MSC_VER -#if _MSC_VER >= 1400 // not VC6 -#include // __cpuid -static int stbi__cpuid3(void) { - int info[4]; - __cpuid(info, 1); - return info[3]; +#if _MSC_VER >= 1400 // not VC6 +#include // __cpuid +static int stbi__cpuid3(void) +{ + int info[4]; + __cpuid(info,1); + return info[3]; } #else -static int stbi__cpuid3(void) { - int res; - __asm { +static int stbi__cpuid3(void) +{ + int res; + __asm { mov eax,1 cpuid mov res,edx - } - return res; + } + return res; } #endif #define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name #if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) -static int stbi__sse2_available(void) { - int info3 = stbi__cpuid3(); - return ((info3 >> 26) & 1) != 0; +static int stbi__sse2_available(void) +{ + int info3 = stbi__cpuid3(); + return ((info3 >> 26) & 1) != 0; } #endif @@ -757,11 +762,12 @@ static int stbi__sse2_available(void) { #define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) #if !defined(STBI_NO_JPEG) && defined(STBI_SSE2) -static int stbi__sse2_available(void) { - // If we're even attempting to compile this on GCC/Clang, that means - // -msse2 is on, which means the compiler is allowed to use SSE2 - // instructions at will, and so are we. - return 1; +static int stbi__sse2_available(void) +{ + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; } #endif @@ -796,162 +802,190 @@ static int stbi__sse2_available(void) { // stbi__context structure is our basic context used by all images, so it // contains all the IO context, plus some basic image information -typedef struct { - stbi__uint32 img_x, img_y; - int img_n, img_out_n; +typedef struct +{ + stbi__uint32 img_x, img_y; + int img_n, img_out_n; - stbi_io_callbacks io; - void * io_user_data; + stbi_io_callbacks io; + void *io_user_data; - int read_from_callbacks; - int buflen; - stbi_uc buffer_start[128]; - int callback_already_read; + int read_from_callbacks; + int buflen; + stbi_uc buffer_start[128]; + int callback_already_read; - stbi_uc *img_buffer, *img_buffer_end; - stbi_uc *img_buffer_original, *img_buffer_original_end; + stbi_uc *img_buffer, *img_buffer_end; + stbi_uc *img_buffer_original, *img_buffer_original_end; } stbi__context; -static void stbi__refill_buffer(stbi__context * s); + +static void stbi__refill_buffer(stbi__context *s); // initialize a memory-decode context -static void stbi__start_mem(stbi__context * s, stbi_uc const * buffer, int len) { - s->io.read = NULL; - s->read_from_callbacks = 0; - s->callback_already_read = 0; - s->img_buffer = s->img_buffer_original = (stbi_uc *)buffer; - s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *)buffer + len; +static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) +{ + s->io.read = NULL; + s->read_from_callbacks = 0; + s->callback_already_read = 0; + s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; } // initialize a callback-based context -static void stbi__start_callbacks(stbi__context * s, stbi_io_callbacks * c, void * user) { - s->io = *c; - s->io_user_data = user; - s->buflen = sizeof(s->buffer_start); - s->read_from_callbacks = 1; - s->callback_already_read = 0; - s->img_buffer = s->img_buffer_original = s->buffer_start; - stbi__refill_buffer(s); - s->img_buffer_original_end = s->img_buffer_end; +static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void *user) +{ + s->io = *c; + s->io_user_data = user; + s->buflen = sizeof(s->buffer_start); + s->read_from_callbacks = 1; + s->callback_already_read = 0; + s->img_buffer = s->img_buffer_original = s->buffer_start; + stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; } #ifndef STBI_NO_STDIO -static int stbi__stdio_read(void * user, char * data, int size) { return (int)fread(data, 1, size, (FILE *)user); } - -static void stbi__stdio_skip(void * user, int n) { - int ch; - fseek((FILE *)user, n, SEEK_CUR); - ch = fgetc((FILE *)user); /* have to read a byte to reset feof()'s flag */ - if (ch != EOF) { - ungetc(ch, (FILE *)user); /* push byte back onto stream if valid. */ - } +static int stbi__stdio_read(void *user, char *data, int size) +{ + return (int) fread(data,1,size,(FILE*) user); } -static int stbi__stdio_eof(void * user) { return feof((FILE *)user) || ferror((FILE *)user); } +static void stbi__stdio_skip(void *user, int n) +{ + int ch; + fseek((FILE*) user, n, SEEK_CUR); + ch = fgetc((FILE*) user); /* have to read a byte to reset feof()'s flag */ + if (ch != EOF) { + ungetc(ch, (FILE *) user); /* push byte back onto stream if valid. */ + } +} -static stbi_io_callbacks stbi__stdio_callbacks = { - stbi__stdio_read, - stbi__stdio_skip, - stbi__stdio_eof, +static int stbi__stdio_eof(void *user) +{ + return feof((FILE*) user) || ferror((FILE *) user); +} + +static stbi_io_callbacks stbi__stdio_callbacks = +{ + stbi__stdio_read, + stbi__stdio_skip, + stbi__stdio_eof, }; -static void stbi__start_file(stbi__context * s, FILE * f) { stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *)f); } +static void stbi__start_file(stbi__context *s, FILE *f) +{ + stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *) f); +} -// static void stop_file(stbi__context *s) { } +//static void stop_file(stbi__context *s) { } #endif // !STBI_NO_STDIO -static void stbi__rewind(stbi__context * s) { - // conceptually rewind SHOULD rewind to the beginning of the stream, - // but we just rewind to the beginning of the initial buffer, because - // we only use it after doing 'test', which only ever looks at at most 92 bytes - s->img_buffer = s->img_buffer_original; - s->img_buffer_end = s->img_buffer_original_end; +static void stbi__rewind(stbi__context *s) +{ + // conceptually rewind SHOULD rewind to the beginning of the stream, + // but we just rewind to the beginning of the initial buffer, because + // we only use it after doing 'test', which only ever looks at at most 92 bytes + s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; } -enum { STBI_ORDER_RGB, STBI_ORDER_BGR }; +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; -typedef struct { - int bits_per_channel; - int num_channels; - int channel_order; +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; } stbi__result_info; #ifndef STBI_NO_JPEG -static int stbi__jpeg_test(stbi__context * s); -static void * stbi__jpeg_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static int stbi__jpeg_info(stbi__context * s, int * x, int * y, int * comp); +static int stbi__jpeg_test(stbi__context *s); +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PNG -static int stbi__png_test(stbi__context * s); -static void * stbi__png_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static int stbi__png_info(stbi__context * s, int * x, int * y, int * comp); -static int stbi__png_is16(stbi__context * s); +static int stbi__png_test(stbi__context *s); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); #endif #ifndef STBI_NO_BMP -static int stbi__bmp_test(stbi__context * s); -static void * stbi__bmp_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static int stbi__bmp_info(stbi__context * s, int * x, int * y, int * comp); +static int stbi__bmp_test(stbi__context *s); +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_TGA -static int stbi__tga_test(stbi__context * s); -static void * stbi__tga_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static int stbi__tga_info(stbi__context * s, int * x, int * y, int * comp); +static int stbi__tga_test(stbi__context *s); +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PSD -static int stbi__psd_test(stbi__context * s); -static void * stbi__psd_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri, int bpc); -static int stbi__psd_info(stbi__context * s, int * x, int * y, int * comp); -static int stbi__psd_is16(stbi__context * s); +static int stbi__psd_test(stbi__context *s); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); #endif #ifndef STBI_NO_HDR -static int stbi__hdr_test(stbi__context * s); -static float * stbi__hdr_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static int stbi__hdr_info(stbi__context * s, int * x, int * y, int * comp); +static int stbi__hdr_test(stbi__context *s); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PIC -static int stbi__pic_test(stbi__context * s); -static void * stbi__pic_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static int stbi__pic_info(stbi__context * s, int * x, int * y, int * comp); +static int stbi__pic_test(stbi__context *s); +static void *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_GIF -static int stbi__gif_test(stbi__context * s); -static void * stbi__gif_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static void * stbi__load_gif_main(stbi__context * s, int ** delays, int * x, int * y, int * z, int * comp, int req_comp); -static int stbi__gif_info(stbi__context * s, int * x, int * y, int * comp); +static int stbi__gif_test(stbi__context *s); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); #endif #ifndef STBI_NO_PNM -static int stbi__pnm_test(stbi__context * s); -static void * stbi__pnm_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri); -static int stbi__pnm_info(stbi__context * s, int * x, int * y, int * comp); -static int stbi__pnm_is16(stbi__context * s); +static int stbi__pnm_test(stbi__context *s); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__pnm_is16(stbi__context *s); #endif static #ifdef STBI_THREAD_LOCAL - STBI_THREAD_LOCAL +STBI_THREAD_LOCAL #endif - const char * stbi__g_failure_reason; +const char *stbi__g_failure_reason; -STBIDEF const char * stbi_failure_reason(void) { return stbi__g_failure_reason; } +STBIDEF const char *stbi_failure_reason(void) +{ + return stbi__g_failure_reason; +} #ifndef STBI_NO_FAILURE_STRINGS -static int stbi__err(const char * str) { - stbi__g_failure_reason = str; - return 0; +static int stbi__err(const char *str) +{ + stbi__g_failure_reason = str; + return 0; } #endif -static void * stbi__malloc(size_t size) { return STBI_MALLOC(size); } +static void *stbi__malloc(size_t size) +{ + return STBI_MALLOC(size); +} // stb_image uses ints pervasively, including for offset calculations. // therefore the largest decoded image size we can support with the @@ -965,88 +999,88 @@ static void * stbi__malloc(size_t size) { return STBI_MALLOC(size); } // return 1 if the sum is valid, 0 on overflow. // negative terms are considered invalid. -static int stbi__addsizes_valid(int a, int b) { - if (b < 0) - return 0; - // now 0 <= b <= INT_MAX, hence also - // 0 <= INT_MAX - b <= INTMAX. - // And "a + b <= INT_MAX" (which might overflow) is the - // same as a <= INT_MAX - b (no overflow) - return a <= INT_MAX - b; +static int stbi__addsizes_valid(int a, int b) +{ + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; } // returns 1 if the product is valid, 0 on overflow. // negative factors are considered invalid. -static int stbi__mul2sizes_valid(int a, int b) { - if (a < 0 || b < 0) - return 0; - if (b == 0) - return 1; // mul-by-0 is always safe - // portable way to check for no overflows in a*b - return a <= INT_MAX / b; +static int stbi__mul2sizes_valid(int a, int b) +{ + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX/b; } #if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR) // returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow -static int stbi__mad2sizes_valid(int a, int b, int add) { - return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a * b, add); +static int stbi__mad2sizes_valid(int a, int b, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); } #endif // returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow -static int stbi__mad3sizes_valid(int a, int b, int c, int add) { - return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a * b, c) && stbi__addsizes_valid(a * b * c, add); +static int stbi__mad3sizes_valid(int a, int b, int c, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__addsizes_valid(a*b*c, add); } // returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow #if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM) -static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) { - return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a * b, c) && stbi__mul2sizes_valid(a * b * c, d) && - stbi__addsizes_valid(a * b * c * d, add); +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); } #endif #if !defined(STBI_NO_JPEG) || !defined(STBI_NO_PNG) || !defined(STBI_NO_TGA) || !defined(STBI_NO_HDR) // mallocs with size overflow checking -static void * stbi__malloc_mad2(int a, int b, int add) { - if (!stbi__mad2sizes_valid(a, b, add)) - return NULL; - return stbi__malloc(a * b + add); +static void *stbi__malloc_mad2(int a, int b, int add) +{ + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a*b + add); } #endif -static void * stbi__malloc_mad3(int a, int b, int c, int add) { - if (!stbi__mad3sizes_valid(a, b, c, add)) - return NULL; - return stbi__malloc(a * b * c + add); +static void *stbi__malloc_mad3(int a, int b, int c, int add) +{ + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a*b*c + add); } #if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) || !defined(STBI_NO_PNM) -static void * stbi__malloc_mad4(int a, int b, int c, int d, int add) { - if (!stbi__mad4sizes_valid(a, b, c, d, add)) - return NULL; - return stbi__malloc(a * b * c * d + add); +static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) +{ + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a*b*c*d + add); } #endif // returns 1 if the sum of two signed ints is valid (between -2^31 and 2^31-1 inclusive), 0 on overflow. -static int stbi__addints_valid(int a, int b) { - if ((a >= 0) != (b >= 0)) - return 1; // a and b have different signs, so no overflow - if (a < 0 && b < 0) - return a >= INT_MIN - b; // same as a + b >= INT_MIN; INT_MIN - b cannot overflow since b < 0. - return a <= INT_MAX - b; +static int stbi__addints_valid(int a, int b) +{ + if ((a >= 0) != (b >= 0)) return 1; // a and b have different signs, so no overflow + if (a < 0 && b < 0) return a >= INT_MIN - b; // same as a + b >= INT_MIN; INT_MIN - b cannot overflow since b < 0. + return a <= INT_MAX - b; } -// returns 1 if the product of two signed shorts is valid, 0 on overflow. -static int stbi__mul2shorts_valid(short a, short b) { - if (b == 0 || b == -1) - return 1; // multiplication by 0 is always 0; check for -1 so SHRT_MIN/b doesn't overflow - if ((a >= 0) == (b >= 0)) - return a <= SHRT_MAX / b; // product is positive, so similar to mul2sizes_valid - if (b < 0) - return a <= SHRT_MIN / b; // same as a * b >= SHRT_MIN - return a >= SHRT_MIN / b; +// returns 1 if the product of two ints fits in a signed short, 0 on overflow. +static int stbi__mul2shorts_valid(int a, int b) +{ + if (b == 0 || b == -1) return 1; // multiplication by 0 is always 0; check for -1 so SHRT_MIN/b doesn't overflow + if ((a >= 0) == (b >= 0)) return a <= SHRT_MAX/b; // product is positive, so similar to mul2sizes_valid + if (b < 0) return a <= SHRT_MIN / b; // same as a * b >= SHRT_MIN + return a >= SHRT_MIN / b; } // stbi__err - error @@ -1054,411 +1088,423 @@ static int stbi__mul2shorts_valid(short a, short b) { // stbi__errpuc - error returning pointer to unsigned char #ifdef STBI_NO_FAILURE_STRINGS -#define stbi__err(x, y) 0 + #define stbi__err(x,y) 0 #elif defined(STBI_FAILURE_USERMSG) -#define stbi__err(x, y) stbi__err(y) + #define stbi__err(x,y) stbi__err(y) #else -#define stbi__err(x, y) stbi__err(x) + #define stbi__err(x,y) stbi__err(x) #endif -#define stbi__errpf(x, y) ((float *)(size_t)(stbi__err(x, y) ? NULL : NULL)) -#define stbi__errpuc(x, y) ((unsigned char *)(size_t)(stbi__err(x, y) ? NULL : NULL)) +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) -STBIDEF void stbi_image_free(void * retval_from_stbi_load) { STBI_FREE(retval_from_stbi_load); } +STBIDEF void stbi_image_free(void *retval_from_stbi_load) +{ + STBI_FREE(retval_from_stbi_load); +} #ifndef STBI_NO_LINEAR -static float * stbi__ldr_to_hdr(stbi_uc * data, int x, int y, int comp); +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp); #endif #ifndef STBI_NO_HDR -static stbi_uc * stbi__hdr_to_ldr(float * data, int x, int y, int comp); +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp); #endif static int stbi__vertically_flip_on_load_global = 0; -STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) { - stbi__vertically_flip_on_load_global = flag_true_if_should_flip; +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) +{ + stbi__vertically_flip_on_load_global = flag_true_if_should_flip; } #ifndef STBI_THREAD_LOCAL -#define stbi__vertically_flip_on_load stbi__vertically_flip_on_load_global +#define stbi__vertically_flip_on_load stbi__vertically_flip_on_load_global #else static STBI_THREAD_LOCAL int stbi__vertically_flip_on_load_local, stbi__vertically_flip_on_load_set; -STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip) { - stbi__vertically_flip_on_load_local = flag_true_if_should_flip; - stbi__vertically_flip_on_load_set = 1; +STBIDEF void stbi_set_flip_vertically_on_load_thread(int flag_true_if_should_flip) +{ + stbi__vertically_flip_on_load_local = flag_true_if_should_flip; + stbi__vertically_flip_on_load_set = 1; } -#define stbi__vertically_flip_on_load \ - (stbi__vertically_flip_on_load_set ? stbi__vertically_flip_on_load_local : stbi__vertically_flip_on_load_global) +#define stbi__vertically_flip_on_load (stbi__vertically_flip_on_load_set \ + ? stbi__vertically_flip_on_load_local \ + : stbi__vertically_flip_on_load_global) #endif // STBI_THREAD_LOCAL -static void * stbi__load_main(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri, int bpc) { - memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields - ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed - ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order - ri->num_channels = 0; +static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; -// test the formats with a very explicit header first (at least a FOURCC -// or distinctive magic number first) -#ifndef STBI_NO_PNG - if (stbi__png_test(s)) - return stbi__png_load(s, x, y, comp, req_comp, ri); -#endif -#ifndef STBI_NO_BMP - if (stbi__bmp_test(s)) - return stbi__bmp_load(s, x, y, comp, req_comp, ri); -#endif -#ifndef STBI_NO_GIF - if (stbi__gif_test(s)) - return stbi__gif_load(s, x, y, comp, req_comp, ri); -#endif -#ifndef STBI_NO_PSD - if (stbi__psd_test(s)) - return stbi__psd_load(s, x, y, comp, req_comp, ri, bpc); -#else - STBI_NOTUSED(bpc); -#endif -#ifndef STBI_NO_PIC - if (stbi__pic_test(s)) - return stbi__pic_load(s, x, y, comp, req_comp, ri); -#endif + // test the formats with a very explicit header first (at least a FOURCC + // or distinctive magic number first) + #ifndef STBI_NO_PNG + if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_BMP + if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_GIF + if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PSD + if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); + #else + STBI_NOTUSED(bpc); + #endif + #ifndef STBI_NO_PIC + if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); + #endif -// then the formats that can end up attempting to load with just 1 or 2 -// bytes matching expectations; these are prone to false positives, so -// try them later -#ifndef STBI_NO_JPEG - if (stbi__jpeg_test(s)) - return stbi__jpeg_load(s, x, y, comp, req_comp, ri); -#endif -#ifndef STBI_NO_PNM - if (stbi__pnm_test(s)) - return stbi__pnm_load(s, x, y, comp, req_comp, ri); -#endif + // then the formats that can end up attempting to load with just 1 or 2 + // bytes matching expectations; these are prone to false positives, so + // try them later + #ifndef STBI_NO_JPEG + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNM + if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); + #endif -#ifndef STBI_NO_HDR - if (stbi__hdr_test(s)) { - float * hdr = stbi__hdr_load(s, x, y, comp, req_comp, ri); - return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); - } -#endif + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri); + return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); + } + #endif -#ifndef STBI_NO_TGA - // test tga last because it's a crappy test! - if (stbi__tga_test(s)) - return stbi__tga_load(s, x, y, comp, req_comp, ri); -#endif + #ifndef STBI_NO_TGA + // test tga last because it's a crappy test! + if (stbi__tga_test(s)) + return stbi__tga_load(s,x,y,comp,req_comp, ri); + #endif - return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); + return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); } -static stbi_uc * stbi__convert_16_to_8(stbi__uint16 * orig, int w, int h, int channels) { - int i; - int img_len = w * h * channels; - stbi_uc * reduced; +static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi_uc *reduced; - reduced = (stbi_uc *)stbi__malloc(img_len); - if (reduced == NULL) - return stbi__errpuc("outofmem", "Out of memory"); + reduced = (stbi_uc *) stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); - for (i = 0; i < img_len; ++i) - reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling - STBI_FREE(orig); - return reduced; + STBI_FREE(orig); + return reduced; } -static stbi__uint16 * stbi__convert_8_to_16(stbi_uc * orig, int w, int h, int channels) { - int i; - int img_len = w * h * channels; - stbi__uint16 * enlarged; +static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi__uint16 *enlarged; - enlarged = (stbi__uint16 *)stbi__malloc(img_len * 2); - if (enlarged == NULL) - return (stbi__uint16 *)stbi__errpuc("outofmem", "Out of memory"); + enlarged = (stbi__uint16 *) stbi__malloc(img_len*2); + if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); - for (i = 0; i < img_len; ++i) - enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff - STBI_FREE(orig); - return enlarged; + STBI_FREE(orig); + return enlarged; } -static void stbi__vertical_flip(void * image, int w, int h, int bytes_per_pixel) { - int row; - size_t bytes_per_row = (size_t)w * bytes_per_pixel; - stbi_uc temp[2048]; - stbi_uc * bytes = (stbi_uc *)image; +static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) +{ + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc *bytes = (stbi_uc *)image; - for (row = 0; row < (h >> 1); row++) { - stbi_uc * row0 = bytes + row * bytes_per_row; - stbi_uc * row1 = bytes + (h - row - 1) * bytes_per_row; - // swap row0 with row1 - size_t bytes_left = bytes_per_row; - while (bytes_left) { - size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); - memcpy(temp, row0, bytes_copy); - memcpy(row0, row1, bytes_copy); - memcpy(row1, temp, bytes_copy); - row0 += bytes_copy; - row1 += bytes_copy; - bytes_left -= bytes_copy; - } - } + for (row = 0; row < (h>>1); row++) { + stbi_uc *row0 = bytes + row*bytes_per_row; + stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; + } + } } #ifndef STBI_NO_GIF -static void stbi__vertical_flip_slices(void * image, int w, int h, int z, int bytes_per_pixel) { - int slice; - int slice_size = w * h * bytes_per_pixel; +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; - stbi_uc * bytes = (stbi_uc *)image; - for (slice = 0; slice < z; ++slice) { - stbi__vertical_flip(bytes, w, h, bytes_per_pixel); - bytes += slice_size; - } + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } } #endif -static unsigned char * stbi__load_and_postprocess_8bit(stbi__context * s, int * x, int * y, int * comp, int req_comp) { - stbi__result_info ri; - void * result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); +static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); - if (result == NULL) - return NULL; + if (result == NULL) + return NULL; - // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. - STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); + // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. + STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); - if (ri.bits_per_channel != 8) { - result = stbi__convert_16_to_8((stbi__uint16 *)result, *x, *y, req_comp == 0 ? *comp : req_comp); - ri.bits_per_channel = 8; - } + if (ri.bits_per_channel != 8) { + result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } - // @TODO: move stbi__convert_format to here + // @TODO: move stbi__convert_format to here - if (stbi__vertically_flip_on_load) { - int channels = req_comp ? req_comp : *comp; - stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); - } + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } - return (unsigned char *)result; + return (unsigned char *) result; } -static stbi__uint16 * stbi__load_and_postprocess_16bit(stbi__context * s, int * x, int * y, int * comp, int req_comp) { - stbi__result_info ri; - void * result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); +static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); - if (result == NULL) - return NULL; + if (result == NULL) + return NULL; - // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. - STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); + // it is the responsibility of the loaders to make sure we get either 8 or 16 bit. + STBI_ASSERT(ri.bits_per_channel == 8 || ri.bits_per_channel == 16); - if (ri.bits_per_channel != 16) { - result = stbi__convert_8_to_16((stbi_uc *)result, *x, *y, req_comp == 0 ? *comp : req_comp); - ri.bits_per_channel = 16; - } + if (ri.bits_per_channel != 16) { + result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } - // @TODO: move stbi__convert_format16 to here - // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision - if (stbi__vertically_flip_on_load) { - int channels = req_comp ? req_comp : *comp; - stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); - } + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } - return (stbi__uint16 *)result; + return (stbi__uint16 *) result; } #if !defined(STBI_NO_HDR) && !defined(STBI_NO_LINEAR) -static void stbi__float_postprocess(float * result, int * x, int * y, int * comp, int req_comp) { - if (stbi__vertically_flip_on_load && result != NULL) { - int channels = req_comp ? req_comp : *comp; - stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); - } +static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) +{ + if (stbi__vertically_flip_on_load && result != NULL) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); + } } #endif #ifndef STBI_NO_STDIO #if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) -STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char * str, - int cbmb, wchar_t * widestr, int cchwide); -STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, - const wchar_t * widestr, int cchwide, char * str, int cbmb, - const char * defchar, int * used_default); +STBI_EXTERN __declspec(dllimport) int __stdcall MultiByteToWideChar(unsigned int cp, unsigned long flags, const char *str, int cbmb, wchar_t *widestr, int cchwide); +STBI_EXTERN __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int cp, unsigned long flags, const wchar_t *widestr, int cchwide, char *str, int cbmb, const char *defchar, int *used_default); #endif #if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) -STBIDEF int stbi_convert_wchar_to_utf8(char * buffer, size_t bufferlen, const wchar_t * input) { - return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int)bufferlen, NULL, NULL); +STBIDEF int stbi_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input) +{ + return WideCharToMultiByte(65001 /* UTF8 */, 0, input, -1, buffer, (int) bufferlen, NULL, NULL); } #endif -static FILE * stbi__fopen(char const * filename, char const * mode) { - FILE * f; +static FILE *stbi__fopen(char const *filename, char const *mode) +{ + FILE *f; #if defined(_WIN32) && defined(STBI_WINDOWS_UTF8) - wchar_t wMode[64]; - wchar_t wFilename[1024]; - if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename) / sizeof(*wFilename))) - return 0; + wchar_t wMode[64]; + wchar_t wFilename[1024]; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, filename, -1, wFilename, sizeof(wFilename)/sizeof(*wFilename))) + return 0; - if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode) / sizeof(*wMode))) - return 0; + if (0 == MultiByteToWideChar(65001 /* UTF8 */, 0, mode, -1, wMode, sizeof(wMode)/sizeof(*wMode))) + return 0; #if defined(_MSC_VER) && _MSC_VER >= 1400 - if (0 != _wfopen_s(&f, wFilename, wMode)) - f = 0; + if (0 != _wfopen_s(&f, wFilename, wMode)) + f = 0; #else - f = _wfopen(wFilename, wMode); + f = _wfopen(wFilename, wMode); #endif #elif defined(_MSC_VER) && _MSC_VER >= 1400 - if (0 != fopen_s(&f, filename, mode)) - f = 0; + if (0 != fopen_s(&f, filename, mode)) + f=0; #else - f = fopen(filename, mode); + f = fopen(filename, mode); #endif - return f; + return f; } -STBIDEF stbi_uc * stbi_load(char const * filename, int * x, int * y, int * comp, int req_comp) { - FILE * f = stbi__fopen(filename, "rb"); - unsigned char * result; - if (!f) - return stbi__errpuc("can't fopen", "Unable to open file"); - result = stbi_load_from_file(f, x, y, comp, req_comp); - fclose(f); - return result; + +STBIDEF stbi_uc *stbi_load(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + unsigned char *result; + if (!f) return stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; } -STBIDEF stbi_uc * stbi_load_from_file(FILE * f, int * x, int * y, int * comp, int req_comp) { - unsigned char * result; - stbi__context s; - stbi__start_file(&s, f); - result = stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp); - if (result) { - // need to 'unget' all the characters in the IO buffer - fseek(f, -(int)(s.img_buffer_end - s.img_buffer), SEEK_CUR); - } - return result; +STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; } -STBIDEF stbi__uint16 * stbi_load_from_file_16(FILE * f, int * x, int * y, int * comp, int req_comp) { - stbi__uint16 * result; - stbi__context s; - stbi__start_file(&s, f); - result = stbi__load_and_postprocess_16bit(&s, x, y, comp, req_comp); - if (result) { - // need to 'unget' all the characters in the IO buffer - fseek(f, -(int)(s.img_buffer_end - s.img_buffer), SEEK_CUR); - } - return result; +STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__uint16 *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; } -STBIDEF stbi_us * stbi_load_16(char const * filename, int * x, int * y, int * comp, int req_comp) { - FILE * f = stbi__fopen(filename, "rb"); - stbi__uint16 * result; - if (!f) - return (stbi_us *)stbi__errpuc("can't fopen", "Unable to open file"); - result = stbi_load_from_file_16(f, x, y, comp, req_comp); - fclose(f); - return result; +STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + stbi__uint16 *result; + if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f,x,y,comp,req_comp); + fclose(f); + return result; } -#endif //! STBI_NO_STDIO -STBIDEF stbi_us * stbi_load_16_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * channels_in_file, - int desired_channels) { - stbi__context s; - stbi__start_mem(&s, buffer, len); - return stbi__load_and_postprocess_16bit(&s, x, y, channels_in_file, desired_channels); +#endif //!STBI_NO_STDIO + +STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); } -STBIDEF stbi_us * stbi_load_16_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, - int * channels_in_file, int desired_channels) { - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); - return stbi__load_and_postprocess_16bit(&s, x, y, channels_in_file, desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); } -STBIDEF stbi_uc * stbi_load_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp, int req_comp) { - stbi__context s; - stbi__start_mem(&s, buffer, len); - return stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp); +STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); } -STBIDEF stbi_uc * stbi_load_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * comp, - int req_comp) { - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); - return stbi__load_and_postprocess_8bit(&s, x, y, comp, req_comp); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); } #ifndef STBI_NO_GIF -STBIDEF stbi_uc * stbi_load_gif_from_memory(stbi_uc const * buffer, int len, int ** delays, int * x, int * y, int * z, - int * comp, int req_comp) { - unsigned char * result; - stbi__context s; - stbi__start_mem(&s, buffer, len); +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); - result = (unsigned char *)stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); - if (stbi__vertically_flip_on_load) { - stbi__vertical_flip_slices(result, *x, *y, *z, *comp); - } + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } - return result; + return result; } #endif #ifndef STBI_NO_LINEAR -static float * stbi__loadf_main(stbi__context * s, int * x, int * y, int * comp, int req_comp) { - unsigned char * data; -#ifndef STBI_NO_HDR - if (stbi__hdr_test(s)) { - stbi__result_info ri; - float * hdr_data = stbi__hdr_load(s, x, y, comp, req_comp, &ri); - if (hdr_data) - stbi__float_postprocess(hdr_data, x, y, comp, req_comp); - return hdr_data; - } -#endif - data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); - if (data) - return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); - return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); +static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *data; + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + stbi__result_info ri; + float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri); + if (hdr_data) + stbi__float_postprocess(hdr_data,x,y,comp,req_comp); + return hdr_data; + } + #endif + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); + if (data) + return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); + return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); } -STBIDEF float * stbi_loadf_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp, int req_comp) { - stbi__context s; - stbi__start_mem(&s, buffer, len); - return stbi__loadf_main(&s, x, y, comp, req_comp); +STBIDEF float *stbi_loadf_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__loadf_main(&s,x,y,comp,req_comp); } -STBIDEF float * stbi_loadf_from_callbacks(stbi_io_callbacks const * clbk, void * user, int * x, int * y, int * comp, - int req_comp) { - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); - return stbi__loadf_main(&s, x, y, comp, req_comp); +STBIDEF float *stbi_loadf_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__loadf_main(&s,x,y,comp,req_comp); } #ifndef STBI_NO_STDIO -STBIDEF float * stbi_loadf(char const * filename, int * x, int * y, int * comp, int req_comp) { - float * result; - FILE * f = stbi__fopen(filename, "rb"); - if (!f) - return stbi__errpf("can't fopen", "Unable to open file"); - result = stbi_loadf_from_file(f, x, y, comp, req_comp); - fclose(f); - return result; +STBIDEF float *stbi_loadf(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + float *result; + FILE *f = stbi__fopen(filename, "rb"); + if (!f) return stbi__errpf("can't fopen", "Unable to open file"); + result = stbi_loadf_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; } -STBIDEF float * stbi_loadf_from_file(FILE * f, int * x, int * y, int * comp, int req_comp) { - stbi__context s; - stbi__start_file(&s, f); - return stbi__loadf_main(&s, x, y, comp, req_comp); +STBIDEF float *stbi_loadf_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_file(&s,f); + return stbi__loadf_main(&s,x,y,comp,req_comp); } #endif // !STBI_NO_STDIO @@ -1468,208 +1514,222 @@ STBIDEF float * stbi_loadf_from_file(FILE * f, int * x, int * y, int * comp, int // defined, for API simplicity; if STBI_NO_LINEAR is defined, it always // reports false! -STBIDEF int stbi_is_hdr_from_memory(stbi_uc const * buffer, int len) { -#ifndef STBI_NO_HDR - stbi__context s; - stbi__start_mem(&s, buffer, len); - return stbi__hdr_test(&s); -#else - STBI_NOTUSED(buffer); - STBI_NOTUSED(len); - return 0; -#endif +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(buffer); + STBI_NOTUSED(len); + return 0; + #endif } #ifndef STBI_NO_STDIO -STBIDEF int stbi_is_hdr(char const * filename) { - FILE * f = stbi__fopen(filename, "rb"); - int result = 0; - if (f) { - result = stbi_is_hdr_from_file(f); - fclose(f); - } - return result; +STBIDEF int stbi_is_hdr (char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result=0; + if (f) { + result = stbi_is_hdr_from_file(f); + fclose(f); + } + return result; } -STBIDEF int stbi_is_hdr_from_file(FILE * f) { -#ifndef STBI_NO_HDR - long pos = ftell(f); - int res; - stbi__context s; - stbi__start_file(&s, f); - res = stbi__hdr_test(&s); - fseek(f, pos, SEEK_SET); - return res; -#else - STBI_NOTUSED(f); - return 0; -#endif +STBIDEF int stbi_is_hdr_from_file(FILE *f) +{ + #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; + stbi__context s; + stbi__start_file(&s,f); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; + #else + STBI_NOTUSED(f); + return 0; + #endif } #endif // !STBI_NO_STDIO -STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const * clbk, void * user) { -#ifndef STBI_NO_HDR - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); - return stbi__hdr_test(&s); -#else - STBI_NOTUSED(clbk); - STBI_NOTUSED(user); - return 0; -#endif +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); + return 0; + #endif } #ifndef STBI_NO_LINEAR -static float stbi__l2h_gamma = 2.2f, stbi__l2h_scale = 1.0f; +static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; -STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } -STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } +STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } +STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } #endif -static float stbi__h2l_gamma_i = 1.0f / 2.2f, stbi__h2l_scale_i = 1.0f; +static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; + +STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } +STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } -STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1 / gamma; } -STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1 / scale; } ////////////////////////////////////////////////////////////////////////////// // // Common code used by all image loaders // -enum { STBI__SCAN_load = 0, STBI__SCAN_type, STBI__SCAN_header }; +enum +{ + STBI__SCAN_load=0, + STBI__SCAN_type, + STBI__SCAN_header +}; -static void stbi__refill_buffer(stbi__context * s) { - int n = (s->io.read)(s->io_user_data, (char *)s->buffer_start, s->buflen); - s->callback_already_read += (int)(s->img_buffer - s->img_buffer_original); - if (n == 0) { - // at end of file, treat same as if from memory, but need to handle case - // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file - s->read_from_callbacks = 0; - s->img_buffer = s->buffer_start; - s->img_buffer_end = s->buffer_start + 1; - *s->img_buffer = 0; - } else { - s->img_buffer = s->buffer_start; - s->img_buffer_end = s->buffer_start + n; - } +static void stbi__refill_buffer(stbi__context *s) +{ + int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen); + s->callback_already_read += (int) (s->img_buffer - s->img_buffer_original); + if (n == 0) { + // at end of file, treat same as if from memory, but need to handle case + // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file + s->read_from_callbacks = 0; + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start+1; + *s->img_buffer = 0; + } else { + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start + n; + } } -stbi_inline static stbi_uc stbi__get8(stbi__context * s) { - if (s->img_buffer < s->img_buffer_end) - return *s->img_buffer++; - if (s->read_from_callbacks) { - stbi__refill_buffer(s); - return *s->img_buffer++; - } - return 0; +stbi_inline static stbi_uc stbi__get8(stbi__context *s) +{ + if (s->img_buffer < s->img_buffer_end) + return *s->img_buffer++; + if (s->read_from_callbacks) { + stbi__refill_buffer(s); + return *s->img_buffer++; + } + return 0; } #if defined(STBI_NO_JPEG) && defined(STBI_NO_HDR) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) // nothing #else -stbi_inline static int stbi__at_eof(stbi__context * s) { - if (s->io.read) { - if (!(s->io.eof)(s->io_user_data)) - return 0; - // if feof() is true, check if buffer = end - // special case: we've only got the special 0 character at the end - if (s->read_from_callbacks == 0) - return 1; - } +stbi_inline static int stbi__at_eof(stbi__context *s) +{ + if (s->io.read) { + if (!(s->io.eof)(s->io_user_data)) return 0; + // if feof() is true, check if buffer = end + // special case: we've only got the special 0 character at the end + if (s->read_from_callbacks == 0) return 1; + } - return s->img_buffer >= s->img_buffer_end; + return s->img_buffer >= s->img_buffer_end; } #endif -#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && \ - defined(STBI_NO_GIF) && defined(STBI_NO_PIC) +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) // nothing #else -static void stbi__skip(stbi__context * s, int n) { - if (n == 0) - return; // already there! - if (n < 0) { - s->img_buffer = s->img_buffer_end; - return; - } - if (s->io.read) { - int blen = (int)(s->img_buffer_end - s->img_buffer); - if (blen < n) { - s->img_buffer = s->img_buffer_end; - (s->io.skip)(s->io_user_data, n - blen); - return; - } - } - s->img_buffer += n; +static void stbi__skip(stbi__context *s, int n) +{ + if (n == 0) return; // already there! + if (n < 0) { + s->img_buffer = s->img_buffer_end; + return; + } + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + s->img_buffer = s->img_buffer_end; + (s->io.skip)(s->io_user_data, n - blen); + return; + } + } + s->img_buffer += n; } #endif #if defined(STBI_NO_PNG) && defined(STBI_NO_TGA) && defined(STBI_NO_HDR) && defined(STBI_NO_PNM) // nothing #else -static int stbi__getn(stbi__context * s, stbi_uc * buffer, int n) { - if (s->io.read) { - int blen = (int)(s->img_buffer_end - s->img_buffer); - if (blen < n) { - int res, count; +static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) +{ + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + int res, count; - memcpy(buffer, s->img_buffer, blen); + memcpy(buffer, s->img_buffer, blen); - count = (s->io.read)(s->io_user_data, (char *)buffer + blen, n - blen); - res = (count == (n - blen)); - s->img_buffer = s->img_buffer_end; - return res; - } - } + count = (s->io.read)(s->io_user_data, (char*) buffer + blen, n - blen); + res = (count == (n-blen)); + s->img_buffer = s->img_buffer_end; + return res; + } + } - if (s->img_buffer + n <= s->img_buffer_end) { - memcpy(buffer, s->img_buffer, n); - s->img_buffer += n; - return 1; - } else - return 0; + if (s->img_buffer+n <= s->img_buffer_end) { + memcpy(buffer, s->img_buffer, n); + s->img_buffer += n; + return 1; + } else + return 0; } #endif #if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC) // nothing #else -static int stbi__get16be(stbi__context * s) { - int z = stbi__get8(s); - return (z << 8) + stbi__get8(s); +static int stbi__get16be(stbi__context *s) +{ + int z = stbi__get8(s); + return (z << 8) + stbi__get8(s); } #endif #if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) && defined(STBI_NO_PIC) // nothing #else -static stbi__uint32 stbi__get32be(stbi__context * s) { - stbi__uint32 z = stbi__get16be(s); - return (z << 16) + stbi__get16be(s); +static stbi__uint32 stbi__get32be(stbi__context *s) +{ + stbi__uint32 z = stbi__get16be(s); + return (z << 16) + stbi__get16be(s); } #endif #if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) // nothing #else -static int stbi__get16le(stbi__context * s) { - int z = stbi__get8(s); - return z + (stbi__get8(s) << 8); +static int stbi__get16le(stbi__context *s) +{ + int z = stbi__get8(s); + return z + (stbi__get8(s) << 8); } #endif #ifndef STBI_NO_BMP -static stbi__uint32 stbi__get32le(stbi__context * s) { - stbi__uint32 z = stbi__get16le(s); - z += (stbi__uint32)stbi__get16le(s) << 16; - return z; +static stbi__uint32 stbi__get32le(stbi__context *s) +{ + stbi__uint32 z = stbi__get16le(s); + z += (stbi__uint32)stbi__get16le(s) << 16; + return z; } #endif -#define STBI__BYTECAST(x) ((stbi_uc)((x)&255)) // truncate int to byte without warnings +#define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings -#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && \ - defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +#if defined(STBI_NO_JPEG) && defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) // nothing #else ////////////////////////////////////////////////////////////////////////////// @@ -1683,264 +1743,169 @@ static stbi__uint32 stbi__get32le(stbi__context * s) { // assume data buffer is malloced, so malloc a new one and free that one // only failure mode is malloc failing -static stbi_uc stbi__compute_y(int r, int g, int b) { return (stbi_uc)(((r * 77) + (g * 150) + (29 * b)) >> 8); } +static stbi_uc stbi__compute_y(int r, int g, int b) +{ + return (stbi_uc) (((r*77) + (g*150) + (29*b)) >> 8); +} #endif -#if defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && \ - defined(STBI_NO_PIC) && defined(STBI_NO_PNM) +#if defined(STBI_NO_PNG) && defined(STBI_NO_BMP) && defined(STBI_NO_PSD) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) && defined(STBI_NO_PIC) && defined(STBI_NO_PNM) // nothing #else -static unsigned char * stbi__convert_format(unsigned char * data, int img_n, int req_comp, unsigned int x, unsigned int y) { - int i, j; - unsigned char * good; +static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + unsigned char *good; - if (req_comp == img_n) - return data; - STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); - good = (unsigned char *)stbi__malloc_mad3(req_comp, x, y, 0); - if (good == NULL) { - STBI_FREE(data); - return stbi__errpuc("outofmem", "Out of memory"); - } + good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0); + if (good == NULL) { + STBI_FREE(data); + return stbi__errpuc("outofmem", "Out of memory"); + } - for (j = 0; j < (int)y; ++j) { - unsigned char * src = data + j * x * img_n; - unsigned char * dest = good + j * x * req_comp; + for (j=0; j < (int) y; ++j) { + unsigned char *src = data + j * x * img_n ; + unsigned char *dest = good + j * x * req_comp; -#define STBI__COMBO(a, b) ((a)*8 + (b)) -#define STBI__CASE(a, b) \ - case STBI__COMBO(a, b): \ - for (i = x - 1; i >= 0; --i, src += a, dest += b) - // convert source image with img_n components to one with req_comp components; - // avoid switch per pixel, so use switch per scanline and massive macros - switch (STBI__COMBO(img_n, req_comp)) { - STBI__CASE(1, 2) { - dest[0] = src[0]; - dest[1] = 255; - } - break; - STBI__CASE(1, 3) { dest[0] = dest[1] = dest[2] = src[0]; } - break; - STBI__CASE(1, 4) { - dest[0] = dest[1] = dest[2] = src[0]; - dest[3] = 255; - } - break; - STBI__CASE(2, 1) { dest[0] = src[0]; } - break; - STBI__CASE(2, 3) { dest[0] = dest[1] = dest[2] = src[0]; } - break; - STBI__CASE(2, 4) { - dest[0] = dest[1] = dest[2] = src[0]; - dest[3] = src[1]; - } - break; - STBI__CASE(3, 4) { - dest[0] = src[0]; - dest[1] = src[1]; - dest[2] = src[2]; - dest[3] = 255; - } - break; - STBI__CASE(3, 1) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); } - break; - STBI__CASE(3, 2) { - dest[0] = stbi__compute_y(src[0], src[1], src[2]); - dest[1] = 255; - } - break; - STBI__CASE(4, 1) { dest[0] = stbi__compute_y(src[0], src[1], src[2]); } - break; - STBI__CASE(4, 2) { - dest[0] = stbi__compute_y(src[0], src[1], src[2]); - dest[1] = src[3]; - } - break; - STBI__CASE(4, 3) { - dest[0] = src[0]; - dest[1] = src[1]; - dest[2] = src[2]; - } - break; - default: - STBI_ASSERT(0); - STBI_FREE(data); - STBI_FREE(good); - return stbi__errpuc("unsupported", "Unsupported format conversion"); - } -#undef STBI__CASE - } + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=255; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=255; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=255; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = 255; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); STBI_FREE(data); STBI_FREE(good); return stbi__errpuc("unsupported", "Unsupported format conversion"); + } + #undef STBI__CASE + } - STBI_FREE(data); - return good; + STBI_FREE(data); + return good; } #endif #if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) // nothing #else -static stbi__uint16 stbi__compute_y_16(int r, int g, int b) { return (stbi__uint16)(((r * 77) + (g * 150) + (29 * b)) >> 8); } +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) +{ + return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); +} #endif #if defined(STBI_NO_PNG) && defined(STBI_NO_PSD) // nothing #else -static stbi__uint16 * stbi__convert_format16(stbi__uint16 * data, int img_n, int req_comp, unsigned int x, unsigned int y) { - int i, j; - stbi__uint16 * good; +static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + stbi__uint16 *good; - if (req_comp == img_n) - return data; - STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); - good = (stbi__uint16 *)stbi__malloc(req_comp * x * y * 2); - if (good == NULL) { - STBI_FREE(data); - return (stbi__uint16 *)stbi__errpuc("outofmem", "Out of memory"); - } + good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + } - for (j = 0; j < (int)y; ++j) { - stbi__uint16 * src = data + j * x * img_n; - stbi__uint16 * dest = good + j * x * req_comp; + for (j=0; j < (int) y; ++j) { + stbi__uint16 *src = data + j * x * img_n ; + stbi__uint16 *dest = good + j * x * req_comp; -#define STBI__COMBO(a, b) ((a)*8 + (b)) -#define STBI__CASE(a, b) \ - case STBI__COMBO(a, b): \ - for (i = x - 1; i >= 0; --i, src += a, dest += b) - // convert source image with img_n components to one with req_comp components; - // avoid switch per pixel, so use switch per scanline and massive macros - switch (STBI__COMBO(img_n, req_comp)) { - STBI__CASE(1, 2) { - dest[0] = src[0]; - dest[1] = 0xffff; - } - break; - STBI__CASE(1, 3) { dest[0] = dest[1] = dest[2] = src[0]; } - break; - STBI__CASE(1, 4) { - dest[0] = dest[1] = dest[2] = src[0]; - dest[3] = 0xffff; - } - break; - STBI__CASE(2, 1) { dest[0] = src[0]; } - break; - STBI__CASE(2, 3) { dest[0] = dest[1] = dest[2] = src[0]; } - break; - STBI__CASE(2, 4) { - dest[0] = dest[1] = dest[2] = src[0]; - dest[3] = src[1]; - } - break; - STBI__CASE(3, 4) { - dest[0] = src[0]; - dest[1] = src[1]; - dest[2] = src[2]; - dest[3] = 0xffff; - } - break; - STBI__CASE(3, 1) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); } - break; - STBI__CASE(3, 2) { - dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); - dest[1] = 0xffff; - } - break; - STBI__CASE(4, 1) { dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); } - break; - STBI__CASE(4, 2) { - dest[0] = stbi__compute_y_16(src[0], src[1], src[2]); - dest[1] = src[3]; - } - break; - STBI__CASE(4, 3) { - dest[0] = src[0]; - dest[1] = src[1]; - dest[2] = src[2]; - } - break; - default: - STBI_ASSERT(0); - STBI_FREE(data); - STBI_FREE(good); - return (stbi__uint16 *)stbi__errpuc("unsupported", "Unsupported format conversion"); - } -#undef STBI__CASE - } + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0]; dest[1]=0xffff; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=0xffff; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0]; dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2];dest[3]=0xffff; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = 0xffff; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0];dest[1]=src[1];dest[2]=src[2]; } break; + default: STBI_ASSERT(0); STBI_FREE(data); STBI_FREE(good); return (stbi__uint16*) stbi__errpuc("unsupported", "Unsupported format conversion"); + } + #undef STBI__CASE + } - STBI_FREE(data); - return good; + STBI_FREE(data); + return good; } #endif #ifndef STBI_NO_LINEAR -static float * stbi__ldr_to_hdr(stbi_uc * data, int x, int y, int comp) { - int i, k, n; - float * output; - if (!data) - return NULL; - output = (float *)stbi__malloc_mad4(x, y, comp, sizeof(float), 0); - if (output == NULL) { - STBI_FREE(data); - return stbi__errpf("outofmem", "Out of memory"); - } - // compute number of non-alpha components - if (comp & 1) - n = comp; - else - n = comp - 1; - for (i = 0; i < x * y; ++i) { - for (k = 0; k < n; ++k) { - output[i * comp + k] = (float)(pow(data[i * comp + k] / 255.0f, stbi__l2h_gamma) * stbi__l2h_scale); - } - } - if (n < comp) { - for (i = 0; i < x * y; ++i) { - output[i * comp + n] = data[i * comp + n] / 255.0f; - } - } - STBI_FREE(data); - return output; +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) +{ + int i,k,n; + float *output; + if (!data) return NULL; + output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale); + } + } + if (n < comp) { + for (i=0; i < x*y; ++i) { + output[i*comp + n] = data[i*comp + n]/255.0f; + } + } + STBI_FREE(data); + return output; } #endif #ifndef STBI_NO_HDR -#define stbi__float2int(x) ((int)(x)) -static stbi_uc * stbi__hdr_to_ldr(float * data, int x, int y, int comp) { - int i, k, n; - stbi_uc * output; - if (!data) - return NULL; - output = (stbi_uc *)stbi__malloc_mad3(x, y, comp, 0); - if (output == NULL) { - STBI_FREE(data); - return stbi__errpuc("outofmem", "Out of memory"); - } - // compute number of non-alpha components - if (comp & 1) - n = comp; - else - n = comp - 1; - for (i = 0; i < x * y; ++i) { - for (k = 0; k < n; ++k) { - float z = (float)pow(data[i * comp + k] * stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; - if (z < 0) - z = 0; - if (z > 255) - z = 255; - output[i * comp + k] = (stbi_uc)stbi__float2int(z); - } - if (k < comp) { - float z = data[i * comp + k] * 255 + 0.5f; - if (z < 0) - z = 0; - if (z > 255) - z = 255; - output[i * comp + k] = (stbi_uc)stbi__float2int(z); - } - } - STBI_FREE(data); - return output; +#define stbi__float2int(x) ((int) (x)) +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) +{ + int i,k,n; + stbi_uc *output; + if (!data) return NULL; + output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + float z = (float) pow(data[i*comp+k]*stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + if (k < comp) { + float z = data[i*comp+k] * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + } + STBI_FREE(data); + return output; } #endif @@ -1968,783 +1933,763 @@ static stbi_uc * stbi__hdr_to_ldr(float * data, int x, int y, int comp) { #ifndef STBI_NO_JPEG // huffman decoding acceleration -#define FAST_BITS 9 // larger handles more cases; smaller stomps less cache +#define FAST_BITS 9 // larger handles more cases; smaller stomps less cache -typedef struct { - stbi_uc fast[1 << FAST_BITS]; - // weirdly, repacking this into AoS is a 10% speed loss, instead of a win - stbi__uint16 code[256]; - stbi_uc values[256]; - stbi_uc size[257]; - unsigned int maxcode[18]; - int delta[17]; // old 'firstsymbol' - old 'firstcode' +typedef struct +{ + stbi_uc fast[1 << FAST_BITS]; + // weirdly, repacking this into AoS is a 10% speed loss, instead of a win + stbi__uint16 code[256]; + stbi_uc values[256]; + stbi_uc size[257]; + unsigned int maxcode[18]; + int delta[17]; // old 'firstsymbol' - old 'firstcode' } stbi__huffman; -typedef struct { - stbi__context * s; - stbi__huffman huff_dc[4]; - stbi__huffman huff_ac[4]; - stbi__uint16 dequant[4][64]; - stbi__int16 fast_ac[4][1 << FAST_BITS]; +typedef struct +{ + stbi__context *s; + stbi__huffman huff_dc[4]; + stbi__huffman huff_ac[4]; + stbi__uint16 dequant[4][64]; + stbi__int16 fast_ac[4][1 << FAST_BITS]; - // sizes for components, interleaved MCUs - int img_h_max, img_v_max; - int img_mcu_x, img_mcu_y; - int img_mcu_w, img_mcu_h; +// sizes for components, interleaved MCUs + int img_h_max, img_v_max; + int img_mcu_x, img_mcu_y; + int img_mcu_w, img_mcu_h; - // definition of jpeg image component - struct { - int id; - int h, v; - int tq; - int hd, ha; - int dc_pred; +// definition of jpeg image component + struct + { + int id; + int h,v; + int tq; + int hd,ha; + int dc_pred; - int x, y, w2, h2; - stbi_uc * data; - void *raw_data, *raw_coeff; - stbi_uc * linebuf; - short * coeff; // progressive only - int coeff_w, coeff_h; // number of 8x8 coefficient blocks - } img_comp[4]; + int x,y,w2,h2; + stbi_uc *data; + void *raw_data, *raw_coeff; + stbi_uc *linebuf; + short *coeff; // progressive only + int coeff_w, coeff_h; // number of 8x8 coefficient blocks + } img_comp[4]; - stbi__uint32 code_buffer; // jpeg entropy-coded buffer - int code_bits; // number of valid bits - unsigned char marker; // marker seen while filling entropy buffer - int nomore; // flag if we saw a marker so must stop + stbi__uint32 code_buffer; // jpeg entropy-coded buffer + int code_bits; // number of valid bits + unsigned char marker; // marker seen while filling entropy buffer + int nomore; // flag if we saw a marker so must stop - int progressive; - int spec_start; - int spec_end; - int succ_high; - int succ_low; - int eob_run; - int jfif; - int app14_color_transform; // Adobe APP14 tag - int rgb; + int progressive; + int spec_start; + int spec_end; + int succ_high; + int succ_low; + int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; - int scan_n, order[4]; - int restart_interval, todo; + int scan_n, order[4]; + int restart_interval, todo; - // kernels - void (*idct_block_kernel)(stbi_uc * out, int out_stride, short data[64]); - void (*YCbCr_to_RGB_kernel)(stbi_uc * out, const stbi_uc * y, const stbi_uc * pcb, const stbi_uc * pcr, int count, - int step); - stbi_uc * (*resample_row_hv_2_kernel)(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs); +// kernels + void (*idct_block_kernel)(stbi_uc *out, int out_stride, short data[64]); + void (*YCbCr_to_RGB_kernel)(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step); + stbi_uc *(*resample_row_hv_2_kernel)(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs); } stbi__jpeg; -static int stbi__build_huffman(stbi__huffman * h, int * count) { - int i, j, k = 0; - unsigned int code; - // build size list for each symbol (from JPEG spec) - for (i = 0; i < 16; ++i) { - for (j = 0; j < count[i]; ++j) { - h->size[k++] = (stbi_uc)(i + 1); - if (k >= 257) - return stbi__err("bad size list", "Corrupt JPEG"); - } - } - h->size[k] = 0; +static int stbi__build_huffman(stbi__huffman *h, int *count) +{ + int i,j,k=0; + unsigned int code; + // build size list for each symbol (from JPEG spec) + for (i=0; i < 16; ++i) { + for (j=0; j < count[i]; ++j) { + h->size[k++] = (stbi_uc) (i+1); + if(k >= 257) return stbi__err("bad size list","Corrupt JPEG"); + } + } + h->size[k] = 0; - // compute actual symbols (from jpeg spec) - code = 0; - k = 0; - for (j = 1; j <= 16; ++j) { - // compute delta to add to code to compute symbol id - h->delta[j] = k - code; - if (h->size[k] == j) { - while (h->size[k] == j) - h->code[k++] = (stbi__uint16)(code++); - if (code - 1 >= (1u << j)) - return stbi__err("bad code lengths", "Corrupt JPEG"); - } - // compute largest code + 1 for this size, preshifted as needed later - h->maxcode[j] = code << (16 - j); - code <<= 1; - } - h->maxcode[j] = 0xffffffff; + // compute actual symbols (from jpeg spec) + code = 0; + k = 0; + for(j=1; j <= 16; ++j) { + // compute delta to add to code to compute symbol id + h->delta[j] = k - code; + if (h->size[k] == j) { + while (h->size[k] == j) + h->code[k++] = (stbi__uint16) (code++); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + } + // compute largest code + 1 for this size, preshifted as needed later + h->maxcode[j] = code << (16-j); + code <<= 1; + } + h->maxcode[j] = 0xffffffff; - // build non-spec acceleration table; 255 is flag for not-accelerated - memset(h->fast, 255, 1 << FAST_BITS); - for (i = 0; i < k; ++i) { - int s = h->size[i]; - if (s <= FAST_BITS) { - int c = h->code[i] << (FAST_BITS - s); - int m = 1 << (FAST_BITS - s); - for (j = 0; j < m; ++j) { - h->fast[c + j] = (stbi_uc)i; - } - } - } - return 1; + // build non-spec acceleration table; 255 is flag for not-accelerated + memset(h->fast, 255, 1 << FAST_BITS); + for (i=0; i < k; ++i) { + int s = h->size[i]; + if (s <= FAST_BITS) { + int c = h->code[i] << (FAST_BITS-s); + int m = 1 << (FAST_BITS-s); + for (j=0; j < m; ++j) { + h->fast[c+j] = (stbi_uc) i; + } + } + } + return 1; } // build a table that decodes both magnitude and value of small ACs in // one go. -static void stbi__build_fast_ac(stbi__int16 * fast_ac, stbi__huffman * h) { - int i; - for (i = 0; i < (1 << FAST_BITS); ++i) { - stbi_uc fast = h->fast[i]; - fast_ac[i] = 0; - if (fast < 255) { - int rs = h->values[fast]; - int run = (rs >> 4) & 15; - int magbits = rs & 15; - int len = h->size[fast]; +static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) +{ + int i; + for (i=0; i < (1 << FAST_BITS); ++i) { + stbi_uc fast = h->fast[i]; + fast_ac[i] = 0; + if (fast < 255) { + int rs = h->values[fast]; + int run = (rs >> 4) & 15; + int magbits = rs & 15; + int len = h->size[fast]; - if (magbits && len + magbits <= FAST_BITS) { - // magnitude code followed by receive_extend code - int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); - int m = 1 << (magbits - 1); - if (k < m) - k += (~0U << magbits) + 1; - // if the result is small enough, we can fit it in fast_ac table - if (k >= -128 && k <= 127) - fast_ac[i] = (stbi__int16)((k * 256) + (run * 16) + (len + magbits)); - } - } - } + if (magbits && len + magbits <= FAST_BITS) { + // magnitude code followed by receive_extend code + int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); + int m = 1 << (magbits - 1); + if (k < m) k += (~0U << magbits) + 1; + // if the result is small enough, we can fit it in fast_ac table + if (k >= -128 && k <= 127) + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); + } + } + } } -static void stbi__grow_buffer_unsafe(stbi__jpeg * j) { - do { - unsigned int b = j->nomore ? 0 : stbi__get8(j->s); - if (b == 0xff) { - int c = stbi__get8(j->s); - while (c == 0xff) - c = stbi__get8(j->s); // consume fill bytes - if (c != 0) { - j->marker = (unsigned char)c; - j->nomore = 1; - return; - } - } - j->code_buffer |= b << (24 - j->code_bits); - j->code_bits += 8; - } while (j->code_bits <= 24); +static void stbi__grow_buffer_unsafe(stbi__jpeg *j) +{ + do { + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); + if (b == 0xff) { + int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes + if (c != 0) { + j->marker = (unsigned char) c; + j->nomore = 1; + return; + } + } + j->code_buffer |= b << (24 - j->code_bits); + j->code_bits += 8; + } while (j->code_bits <= 24); } // (1 << n) - 1 -static const stbi__uint32 stbi__bmask[17] = {0, 1, 3, 7, 15, 31, 63, 127, 255, - 511, 1023, 2047, 4095, 8191, 16383, 32767, 65535}; +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; // decode a jpeg huffman value from the bitstream -stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg * j, stbi__huffman * h) { - unsigned int temp; - int c, k; +stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) +{ + unsigned int temp; + int c,k; - if (j->code_bits < 16) - stbi__grow_buffer_unsafe(j); + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); - // look at the top FAST_BITS and determine what symbol ID it is, - // if the code is <= FAST_BITS - c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1); - k = h->fast[c]; - if (k < 255) { - int s = h->size[k]; - if (s > j->code_bits) - return -1; - j->code_buffer <<= s; - j->code_bits -= s; - return h->values[k]; - } + // look at the top FAST_BITS and determine what symbol ID it is, + // if the code is <= FAST_BITS + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + k = h->fast[c]; + if (k < 255) { + int s = h->size[k]; + if (s > j->code_bits) + return -1; + j->code_buffer <<= s; + j->code_bits -= s; + return h->values[k]; + } - // naive test is to shift the code_buffer down so k bits are - // valid, then test against maxcode. To speed this up, we've - // preshifted maxcode left so that it has (16-k) 0s at the - // end; in other words, regardless of the number of bits, it - // wants to be compared against something shifted to have 16; - // that way we don't need to shift inside the loop. - temp = j->code_buffer >> 16; - for (k = FAST_BITS + 1;; ++k) - if (temp < h->maxcode[k]) - break; - if (k == 17) { - // error! code not found - j->code_bits -= 16; - return -1; - } + // naive test is to shift the code_buffer down so k bits are + // valid, then test against maxcode. To speed this up, we've + // preshifted maxcode left so that it has (16-k) 0s at the + // end; in other words, regardless of the number of bits, it + // wants to be compared against something shifted to have 16; + // that way we don't need to shift inside the loop. + temp = j->code_buffer >> 16; + for (k=FAST_BITS+1 ; ; ++k) + if (temp < h->maxcode[k]) + break; + if (k == 17) { + // error! code not found + j->code_bits -= 16; + return -1; + } - if (k > j->code_bits) - return -1; + if (k > j->code_bits) + return -1; - // convert the huffman code to the symbol id - c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; - if (c < 0 || c >= 256) // symbol id out of bounds! - return -1; - STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); + // convert the huffman code to the symbol id + c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; + if(c < 0 || c >= 256) // symbol id out of bounds! + return -1; + STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); - // convert the id to a symbol - j->code_bits -= k; - j->code_buffer <<= k; - return h->values[c]; + // convert the id to a symbol + j->code_bits -= k; + j->code_buffer <<= k; + return h->values[c]; } // bias[n] = (-1<code_bits < n) - stbi__grow_buffer_unsafe(j); - if (j->code_bits < n) - return 0; // ran out of bits from stream, return 0s intead of continuing +stbi_inline static int stbi__extend_receive(stbi__jpeg *j, int n) +{ + unsigned int k; + int sgn; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + if (j->code_bits < n) return 0; // ran out of bits from stream, return 0s intead of continuing - sgn = j->code_buffer >> 31; // sign bit always in MSB; 0 if MSB clear (positive), 1 if MSB set (negative) - k = stbi_lrot(j->code_buffer, n); - j->code_buffer = k & ~stbi__bmask[n]; - k &= stbi__bmask[n]; - j->code_bits -= n; - return k + (stbi__jbias[n] & (sgn - 1)); + sgn = j->code_buffer >> 31; // sign bit always in MSB; 0 if MSB clear (positive), 1 if MSB set (negative) + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k + (stbi__jbias[n] & (sgn - 1)); } // get some unsigned bits -stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg * j, int n) { - unsigned int k; - if (j->code_bits < n) - stbi__grow_buffer_unsafe(j); - if (j->code_bits < n) - return 0; // ran out of bits from stream, return 0s intead of continuing - k = stbi_lrot(j->code_buffer, n); - j->code_buffer = k & ~stbi__bmask[n]; - k &= stbi__bmask[n]; - j->code_bits -= n; - return k; +stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n) +{ + unsigned int k; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + if (j->code_bits < n) return 0; // ran out of bits from stream, return 0s intead of continuing + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k; } -stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg * j) { - unsigned int k; - if (j->code_bits < 1) - stbi__grow_buffer_unsafe(j); - if (j->code_bits < 1) - return 0; // ran out of bits from stream, return 0s intead of continuing - k = j->code_buffer; - j->code_buffer <<= 1; - --j->code_bits; - return k & 0x80000000; +stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j) +{ + unsigned int k; + if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); + if (j->code_bits < 1) return 0; // ran out of bits from stream, return 0s intead of continuing + k = j->code_buffer; + j->code_buffer <<= 1; + --j->code_bits; + return k & 0x80000000; } // given a value that's at position X in the zigzag stream, // where does it appear in the 8x8 matrix coded as row-major? -static const stbi_uc stbi__jpeg_dezigzag[64 + 15] = { - 0, 1, 8, 16, 9, 2, 3, 10, 17, 24, 32, 25, 18, 11, 4, 5, 12, 19, 26, 33, 40, 48, 41, 34, 27, 20, 13, 6, 7, 14, 21, 28, 35, - 42, 49, 56, 57, 50, 43, 36, 29, 22, 15, 23, 30, 37, 44, 51, 58, 59, 52, 45, 38, 31, 39, 46, 53, 60, 61, 54, 47, 55, 62, 63, - // let corrupt input sample past end - 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63}; +static const stbi_uc stbi__jpeg_dezigzag[64+15] = +{ + 0, 1, 8, 16, 9, 2, 3, 10, + 17, 24, 32, 25, 18, 11, 4, 5, + 12, 19, 26, 33, 40, 48, 41, 34, + 27, 20, 13, 6, 7, 14, 21, 28, + 35, 42, 49, 56, 57, 50, 43, 36, + 29, 22, 15, 23, 30, 37, 44, 51, + 58, 59, 52, 45, 38, 31, 39, 46, + 53, 60, 61, 54, 47, 55, 62, 63, + // let corrupt input sample past end + 63, 63, 63, 63, 63, 63, 63, 63, + 63, 63, 63, 63, 63, 63, 63 +}; // decode one 64-entry block-- -static int stbi__jpeg_decode_block(stbi__jpeg * j, short data[64], stbi__huffman * hdc, stbi__huffman * hac, stbi__int16 * fac, - int b, stbi__uint16 * dequant) { - int diff, dc, k; - int t; +static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman *hdc, stbi__huffman *hac, stbi__int16 *fac, int b, stbi__uint16 *dequant) +{ + int diff,dc,k; + int t; - if (j->code_bits < 16) - stbi__grow_buffer_unsafe(j); - t = stbi__jpeg_huff_decode(j, hdc); - if (t < 0 || t > 15) - return stbi__err("bad huffman code", "Corrupt JPEG"); + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0 || t > 15) return stbi__err("bad huffman code","Corrupt JPEG"); - // 0 all the ac values now so we can do it 32-bits at a time - memset(data, 0, 64 * sizeof(data[0])); + // 0 all the ac values now so we can do it 32-bits at a time + memset(data,0,64*sizeof(data[0])); - diff = t ? stbi__extend_receive(j, t) : 0; - if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) - return stbi__err("bad delta", "Corrupt JPEG"); - dc = j->img_comp[b].dc_pred + diff; - j->img_comp[b].dc_pred = dc; - if (!stbi__mul2shorts_valid(dc, dequant[0])) - return stbi__err("can't merge dc and ac", "Corrupt JPEG"); - data[0] = (short)(dc * dequant[0]); + diff = t ? stbi__extend_receive(j, t) : 0; + if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) return stbi__err("bad delta","Corrupt JPEG"); + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + if (!stbi__mul2shorts_valid(dc, dequant[0])) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + data[0] = (short) (dc * dequant[0]); - // decode AC components, see JPEG spec - k = 1; - do { - unsigned int zig; - int c, r, s; - if (j->code_bits < 16) - stbi__grow_buffer_unsafe(j); - c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1); - r = fac[c]; - if (r) { // fast-AC path - k += (r >> 4) & 15; // run - s = r & 15; // combined length - if (s > j->code_bits) - return stbi__err("bad huffman code", "Combined length longer than code bits available"); - j->code_buffer <<= s; - j->code_bits -= s; + // decode AC components, see JPEG spec + k = 1; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + if (s > j->code_bits) return stbi__err("bad huffman code", "Combined length longer than code bits available"); + j->code_buffer <<= s; + j->code_bits -= s; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) * dequant[zig]); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (rs != 0xf0) break; // end block + k += 16; + } else { + k += r; // decode into unzigzag'd location zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short)((r >> 8) * dequant[zig]); - } else { - int rs = stbi__jpeg_huff_decode(j, hac); - if (rs < 0) - return stbi__err("bad huffman code", "Corrupt JPEG"); - s = rs & 15; - r = rs >> 4; - if (s == 0) { - if (rs != 0xf0) - break; // end block - k += 16; - } else { - k += r; - // decode into unzigzag'd location - zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short)(stbi__extend_receive(j, s) * dequant[zig]); - } - } - } while (k < 64); - return 1; + data[zig] = (short) (stbi__extend_receive(j,s) * dequant[zig]); + } + } + } while (k < 64); + return 1; } -static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg * j, short data[64], stbi__huffman * hdc, int b) { - int diff, dc; - int t; - if (j->spec_end != 0) - return stbi__err("can't merge dc and ac", "Corrupt JPEG"); +static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__huffman *hdc, int b) +{ + int diff,dc; + int t; + if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); - if (j->code_bits < 16) - stbi__grow_buffer_unsafe(j); + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); - if (j->succ_high == 0) { - // first scan for DC coefficient, must be first - memset(data, 0, 64 * sizeof(data[0])); // 0 all the ac values now - t = stbi__jpeg_huff_decode(j, hdc); - if (t < 0 || t > 15) - return stbi__err("can't merge dc and ac", "Corrupt JPEG"); - diff = t ? stbi__extend_receive(j, t) : 0; + if (j->succ_high == 0) { + // first scan for DC coefficient, must be first + memset(data,0,64*sizeof(data[0])); // 0 all the ac values now + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0 || t > 15) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + diff = t ? stbi__extend_receive(j, t) : 0; - if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) - return stbi__err("bad delta", "Corrupt JPEG"); - dc = j->img_comp[b].dc_pred + diff; - j->img_comp[b].dc_pred = dc; - if (!stbi__mul2shorts_valid(dc, 1 << j->succ_low)) - return stbi__err("can't merge dc and ac", "Corrupt JPEG"); - data[0] = (short)(dc * (1 << j->succ_low)); - } else { - // refinement scan for DC coefficient - if (stbi__jpeg_get_bit(j)) - data[0] += (short)(1 << j->succ_low); - } - return 1; + if (!stbi__addints_valid(j->img_comp[b].dc_pred, diff)) return stbi__err("bad delta", "Corrupt JPEG"); + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + if (!stbi__mul2shorts_valid(dc, 1 << j->succ_low)) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + data[0] = (short) (dc * (1 << j->succ_low)); + } else { + // refinement scan for DC coefficient + if (stbi__jpeg_get_bit(j)) + data[0] += (short) (1 << j->succ_low); + } + return 1; } // @OPTIMIZE: store non-zigzagged during the decode passes, // and only de-zigzag when dequantizing -static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg * j, short data[64], stbi__huffman * hac, stbi__int16 * fac) { - int k; - if (j->spec_start == 0) - return stbi__err("can't merge dc and ac", "Corrupt JPEG"); +static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__huffman *hac, stbi__int16 *fac) +{ + int k; + if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); - if (j->succ_high == 0) { - int shift = j->succ_low; + if (j->succ_high == 0) { + int shift = j->succ_low; - if (j->eob_run) { - --j->eob_run; - return 1; - } + if (j->eob_run) { + --j->eob_run; + return 1; + } - k = j->spec_start; - do { - unsigned int zig; - int c, r, s; - if (j->code_bits < 16) - stbi__grow_buffer_unsafe(j); - c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS) - 1); - r = fac[c]; - if (r) { // fast-AC path - k += (r >> 4) & 15; // run - s = r & 15; // combined length - if (s > j->code_bits) - return stbi__err("bad huffman code", "Combined length longer than code bits available"); - j->code_buffer <<= s; - j->code_bits -= s; - zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short)((r >> 8) * (1 << shift)); + k = j->spec_start; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + if (s > j->code_bits) return stbi__err("bad huffman code", "Combined length longer than code bits available"); + j->code_buffer <<= s; + j->code_bits -= s; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) * (1 << shift)); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r); + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + --j->eob_run; + break; + } + k += 16; } else { - int rs = stbi__jpeg_huff_decode(j, hac); - if (rs < 0) - return stbi__err("bad huffman code", "Corrupt JPEG"); - s = rs & 15; - r = rs >> 4; - if (s == 0) { - if (r < 15) { - j->eob_run = (1 << r); - if (r) - j->eob_run += stbi__jpeg_get_bits(j, r); - --j->eob_run; - break; - } - k += 16; - } else { - k += r; - zig = stbi__jpeg_dezigzag[k++]; - data[zig] = (short)(stbi__extend_receive(j, s) * (1 << shift)); - } + k += r; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) * (1 << shift)); } - } while (k <= j->spec_end); - } else { - // refinement scan for these AC coefficients + } + } while (k <= j->spec_end); + } else { + // refinement scan for these AC coefficients - short bit = (short)(1 << j->succ_low); + short bit = (short) (1 << j->succ_low); - if (j->eob_run) { - --j->eob_run; - for (k = j->spec_start; k <= j->spec_end; ++k) { - short * p = &data[stbi__jpeg_dezigzag[k]]; - if (*p != 0) - if (stbi__jpeg_get_bit(j)) - if ((*p & bit) == 0) { - if (*p > 0) - *p += bit; - else - *p -= bit; - } + if (j->eob_run) { + --j->eob_run; + for (k = j->spec_start; k <= j->spec_end; ++k) { + short *p = &data[stbi__jpeg_dezigzag[k]]; + if (*p != 0) + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } + } else { + k = j->spec_start; + do { + int r,s; + int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r) - 1; + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + r = 64; // force end of block + } else { + // r=15 s=0 should write 16 0s, so we just do + // a run of 15 0s and then write s (which is 0), + // so we don't have to do anything special here + } + } else { + if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG"); + // sign bit + if (stbi__jpeg_get_bit(j)) + s = bit; + else + s = -bit; } - } else { - k = j->spec_start; - do { - int r, s; - int rs = stbi__jpeg_huff_decode( - j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh - if (rs < 0) - return stbi__err("bad huffman code", "Corrupt JPEG"); - s = rs & 15; - r = rs >> 4; - if (s == 0) { - if (r < 15) { - j->eob_run = (1 << r) - 1; - if (r) - j->eob_run += stbi__jpeg_get_bits(j, r); - r = 64; // force end of block - } else { - // r=15 s=0 should write 16 0s, so we just do - // a run of 15 0s and then write s (which is 0), - // so we don't have to do anything special here - } - } else { - if (s != 1) - return stbi__err("bad huffman code", "Corrupt JPEG"); - // sign bit - if (stbi__jpeg_get_bit(j)) - s = bit; - else - s = -bit; - } - // advance by r - while (k <= j->spec_end) { - short * p = &data[stbi__jpeg_dezigzag[k++]]; - if (*p != 0) { - if (stbi__jpeg_get_bit(j)) - if ((*p & bit) == 0) { - if (*p > 0) - *p += bit; - else - *p -= bit; - } - } else { - if (r == 0) { - *p = (short)s; - break; - } - --r; - } - } - } while (k <= j->spec_end); - } - } - return 1; + // advance by r + while (k <= j->spec_end) { + short *p = &data[stbi__jpeg_dezigzag[k++]]; + if (*p != 0) { + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } else { + if (r == 0) { + *p = (short) s; + break; + } + --r; + } + } + } while (k <= j->spec_end); + } + } + return 1; } // take a -128..127 value and stbi__clamp it and convert to 0..255 -stbi_inline static stbi_uc stbi__clamp(int x) { - // trick to use a single test to catch both cases - if ((unsigned int)x > 255) { - if (x < 0) - return 0; - if (x > 255) - return 255; - } - return (stbi_uc)x; +stbi_inline static stbi_uc stbi__clamp(int x) +{ + // trick to use a single test to catch both cases + if ((unsigned int) x > 255) { + if (x < 0) return 0; + if (x > 255) return 255; + } + return (stbi_uc) x; } -#define stbi__f2f(x) ((int)(((x)*4096 + 0.5))) -#define stbi__fsh(x) ((x)*4096) +#define stbi__f2f(x) ((int) (((x) * 4096 + 0.5))) +#define stbi__fsh(x) ((x) * 4096) // derived from jidctint -- DCT_ISLOW -#define STBI__IDCT_1D(s0, s1, s2, s3, s4, s5, s6, s7) \ - int t0, t1, t2, t3, p1, p2, p3, p4, p5, x0, x1, x2, x3; \ - p2 = s2; \ - p3 = s6; \ - p1 = (p2 + p3) * stbi__f2f(0.5411961f); \ - t2 = p1 + p3 * stbi__f2f(-1.847759065f); \ - t3 = p1 + p2 * stbi__f2f(0.765366865f); \ - p2 = s0; \ - p3 = s4; \ - t0 = stbi__fsh(p2 + p3); \ - t1 = stbi__fsh(p2 - p3); \ - x0 = t0 + t3; \ - x3 = t0 - t3; \ - x1 = t1 + t2; \ - x2 = t1 - t2; \ - t0 = s7; \ - t1 = s5; \ - t2 = s3; \ - t3 = s1; \ - p3 = t0 + t2; \ - p4 = t1 + t3; \ - p1 = t0 + t3; \ - p2 = t1 + t2; \ - p5 = (p3 + p4) * stbi__f2f(1.175875602f); \ - t0 = t0 * stbi__f2f(0.298631336f); \ - t1 = t1 * stbi__f2f(2.053119869f); \ - t2 = t2 * stbi__f2f(3.072711026f); \ - t3 = t3 * stbi__f2f(1.501321110f); \ - p1 = p5 + p1 * stbi__f2f(-0.899976223f); \ - p2 = p5 + p2 * stbi__f2f(-2.562915447f); \ - p3 = p3 * stbi__f2f(-1.961570560f); \ - p4 = p4 * stbi__f2f(-0.390180644f); \ - t3 += p1 + p4; \ - t2 += p2 + p3; \ - t1 += p2 + p4; \ - t0 += p1 + p3; +#define STBI__IDCT_1D(s0,s1,s2,s3,s4,s5,s6,s7) \ + int t0,t1,t2,t3,p1,p2,p3,p4,p5,x0,x1,x2,x3; \ + p2 = s2; \ + p3 = s6; \ + p1 = (p2+p3) * stbi__f2f(0.5411961f); \ + t2 = p1 + p3*stbi__f2f(-1.847759065f); \ + t3 = p1 + p2*stbi__f2f( 0.765366865f); \ + p2 = s0; \ + p3 = s4; \ + t0 = stbi__fsh(p2+p3); \ + t1 = stbi__fsh(p2-p3); \ + x0 = t0+t3; \ + x3 = t0-t3; \ + x1 = t1+t2; \ + x2 = t1-t2; \ + t0 = s7; \ + t1 = s5; \ + t2 = s3; \ + t3 = s1; \ + p3 = t0+t2; \ + p4 = t1+t3; \ + p1 = t0+t3; \ + p2 = t1+t2; \ + p5 = (p3+p4)*stbi__f2f( 1.175875602f); \ + t0 = t0*stbi__f2f( 0.298631336f); \ + t1 = t1*stbi__f2f( 2.053119869f); \ + t2 = t2*stbi__f2f( 3.072711026f); \ + t3 = t3*stbi__f2f( 1.501321110f); \ + p1 = p5 + p1*stbi__f2f(-0.899976223f); \ + p2 = p5 + p2*stbi__f2f(-2.562915447f); \ + p3 = p3*stbi__f2f(-1.961570560f); \ + p4 = p4*stbi__f2f(-0.390180644f); \ + t3 += p1+p4; \ + t2 += p2+p3; \ + t1 += p2+p4; \ + t0 += p1+p3; -static void stbi__idct_block(stbi_uc * out, int out_stride, short data[64]) { - int i, val[64], *v = val; - stbi_uc * o; - short * d = data; +static void stbi__idct_block(stbi_uc *out, int out_stride, short data[64]) +{ + int i,val[64],*v=val; + stbi_uc *o; + short *d = data; - // columns - for (i = 0; i < 8; ++i, ++d, ++v) { - // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing - if (d[8] == 0 && d[16] == 0 && d[24] == 0 && d[32] == 0 && d[40] == 0 && d[48] == 0 && d[56] == 0) { - // no shortcut 0 seconds - // (1|2|3|4|5|6|7)==0 0 seconds - // all separate -0.047 seconds - // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds - int dcterm = d[0] * 4; - v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; - } else { - STBI__IDCT_1D(d[0], d[8], d[16], d[24], d[32], d[40], d[48], d[56]) - // constants scaled things up by 1<<12; let's bring them back - // down, but keep 2 extra bits of precision - x0 += 512; - x1 += 512; - x2 += 512; - x3 += 512; - v[0] = (x0 + t3) >> 10; - v[56] = (x0 - t3) >> 10; - v[8] = (x1 + t2) >> 10; - v[48] = (x1 - t2) >> 10; - v[16] = (x2 + t1) >> 10; - v[40] = (x2 - t1) >> 10; - v[24] = (x3 + t0) >> 10; - v[32] = (x3 - t0) >> 10; - } - } + // columns + for (i=0; i < 8; ++i,++d, ++v) { + // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing + if (d[ 8]==0 && d[16]==0 && d[24]==0 && d[32]==0 + && d[40]==0 && d[48]==0 && d[56]==0) { + // no shortcut 0 seconds + // (1|2|3|4|5|6|7)==0 0 seconds + // all separate -0.047 seconds + // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds + int dcterm = d[0]*4; + v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; + } else { + STBI__IDCT_1D(d[ 0],d[ 8],d[16],d[24],d[32],d[40],d[48],d[56]) + // constants scaled things up by 1<<12; let's bring them back + // down, but keep 2 extra bits of precision + x0 += 512; x1 += 512; x2 += 512; x3 += 512; + v[ 0] = (x0+t3) >> 10; + v[56] = (x0-t3) >> 10; + v[ 8] = (x1+t2) >> 10; + v[48] = (x1-t2) >> 10; + v[16] = (x2+t1) >> 10; + v[40] = (x2-t1) >> 10; + v[24] = (x3+t0) >> 10; + v[32] = (x3-t0) >> 10; + } + } - for (i = 0, v = val, o = out; i < 8; ++i, v += 8, o += out_stride) { - // no fast case since the first 1D IDCT spread components out - STBI__IDCT_1D(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]) - // constants scaled things up by 1<<12, plus we had 1<<2 from first - // loop, plus horizontal and vertical each scale by sqrt(8) so together - // we've got an extra 1<<3, so 1<<17 total we need to remove. - // so we want to round that, which means adding 0.5 * 1<<17, - // aka 65536. Also, we'll end up with -128 to 127 that we want - // to encode as 0..255 by adding 128, so we'll add that before the shift - x0 += 65536 + (128 << 17); - x1 += 65536 + (128 << 17); - x2 += 65536 + (128 << 17); - x3 += 65536 + (128 << 17); - // tried computing the shifts into temps, or'ing the temps to see - // if any were out of range, but that was slower - o[0] = stbi__clamp((x0 + t3) >> 17); - o[7] = stbi__clamp((x0 - t3) >> 17); - o[1] = stbi__clamp((x1 + t2) >> 17); - o[6] = stbi__clamp((x1 - t2) >> 17); - o[2] = stbi__clamp((x2 + t1) >> 17); - o[5] = stbi__clamp((x2 - t1) >> 17); - o[3] = stbi__clamp((x3 + t0) >> 17); - o[4] = stbi__clamp((x3 - t0) >> 17); - } + for (i=0, v=val, o=out; i < 8; ++i,v+=8,o+=out_stride) { + // no fast case since the first 1D IDCT spread components out + STBI__IDCT_1D(v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7]) + // constants scaled things up by 1<<12, plus we had 1<<2 from first + // loop, plus horizontal and vertical each scale by sqrt(8) so together + // we've got an extra 1<<3, so 1<<17 total we need to remove. + // so we want to round that, which means adding 0.5 * 1<<17, + // aka 65536. Also, we'll end up with -128 to 127 that we want + // to encode as 0..255 by adding 128, so we'll add that before the shift + x0 += 65536 + (128<<17); + x1 += 65536 + (128<<17); + x2 += 65536 + (128<<17); + x3 += 65536 + (128<<17); + // tried computing the shifts into temps, or'ing the temps to see + // if any were out of range, but that was slower + o[0] = stbi__clamp((x0+t3) >> 17); + o[7] = stbi__clamp((x0-t3) >> 17); + o[1] = stbi__clamp((x1+t2) >> 17); + o[6] = stbi__clamp((x1-t2) >> 17); + o[2] = stbi__clamp((x2+t1) >> 17); + o[5] = stbi__clamp((x2-t1) >> 17); + o[3] = stbi__clamp((x3+t0) >> 17); + o[4] = stbi__clamp((x3-t0) >> 17); + } } #ifdef STBI_SSE2 // sse2 integer IDCT. not the fastest possible implementation but it // produces bit-identical results to the generic C version so it's // fully "transparent". -static void stbi__idct_simd(stbi_uc * out, int out_stride, short data[64]) { - // This is constructed to match our regular (generic) integer IDCT exactly. - __m128i row0, row1, row2, row3, row4, row5, row6, row7; - __m128i tmp; +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + // This is constructed to match our regular (generic) integer IDCT exactly. + __m128i row0, row1, row2, row3, row4, row5, row6, row7; + __m128i tmp; -// dot product constant: even elems=x, odd elems=y -#define dct_const(x, y) _mm_setr_epi16((x), (y), (x), (y), (x), (y), (x), (y)) + // dot product constant: even elems=x, odd elems=y + #define dct_const(x,y) _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y)) -// out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) -// out(1) = c1[even]*x + c1[odd]*y -#define dct_rot(out0, out1, x, y, c0, c1) \ - __m128i c0##lo = _mm_unpacklo_epi16((x), (y)); \ - __m128i c0##hi = _mm_unpackhi_epi16((x), (y)); \ - __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \ - __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \ - __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \ - __m128i out1##_h = _mm_madd_epi16(c0##hi, c1) + // out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) + // out(1) = c1[even]*x + c1[odd]*y + #define dct_rot(out0,out1, x,y,c0,c1) \ + __m128i c0##lo = _mm_unpacklo_epi16((x),(y)); \ + __m128i c0##hi = _mm_unpackhi_epi16((x),(y)); \ + __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \ + __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \ + __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \ + __m128i out1##_h = _mm_madd_epi16(c0##hi, c1) -// out = in << 12 (in 16-bit, out 32-bit) -#define dct_widen(out, in) \ - __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \ - __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4) + // out = in << 12 (in 16-bit, out 32-bit) + #define dct_widen(out, in) \ + __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \ + __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4) -// wide add -#define dct_wadd(out, a, b) \ - __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \ - __m128i out##_h = _mm_add_epi32(a##_h, b##_h) + // wide add + #define dct_wadd(out, a, b) \ + __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_add_epi32(a##_h, b##_h) -// wide sub -#define dct_wsub(out, a, b) \ - __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \ - __m128i out##_h = _mm_sub_epi32(a##_h, b##_h) + // wide sub + #define dct_wsub(out, a, b) \ + __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_sub_epi32(a##_h, b##_h) -// butterfly a/b, add bias, then shift by "s" and pack -#define dct_bfly32o(out0, out1, a, b, bias, s) \ - { \ - __m128i abiased_l = _mm_add_epi32(a##_l, bias); \ - __m128i abiased_h = _mm_add_epi32(a##_h, bias); \ - dct_wadd(sum, abiased, b); \ - dct_wsub(dif, abiased, b); \ - out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \ - out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \ - } + // butterfly a/b, add bias, then shift by "s" and pack + #define dct_bfly32o(out0, out1, a,b,bias,s) \ + { \ + __m128i abiased_l = _mm_add_epi32(a##_l, bias); \ + __m128i abiased_h = _mm_add_epi32(a##_h, bias); \ + dct_wadd(sum, abiased, b); \ + dct_wsub(dif, abiased, b); \ + out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \ + out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \ + } -// 8-bit interleave step (for transposes) -#define dct_interleave8(a, b) \ - tmp = a; \ - a = _mm_unpacklo_epi8(a, b); \ - b = _mm_unpackhi_epi8(tmp, b) + // 8-bit interleave step (for transposes) + #define dct_interleave8(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi8(a, b); \ + b = _mm_unpackhi_epi8(tmp, b) -// 16-bit interleave step (for transposes) -#define dct_interleave16(a, b) \ - tmp = a; \ - a = _mm_unpacklo_epi16(a, b); \ - b = _mm_unpackhi_epi16(tmp, b) + // 16-bit interleave step (for transposes) + #define dct_interleave16(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi16(a, b); \ + b = _mm_unpackhi_epi16(tmp, b) -#define dct_pass(bias, shift) \ - { \ - /* even part */ \ - dct_rot(t2e, t3e, row2, row6, rot0_0, rot0_1); \ - __m128i sum04 = _mm_add_epi16(row0, row4); \ - __m128i dif04 = _mm_sub_epi16(row0, row4); \ - dct_widen(t0e, sum04); \ - dct_widen(t1e, dif04); \ - dct_wadd(x0, t0e, t3e); \ - dct_wsub(x3, t0e, t3e); \ - dct_wadd(x1, t1e, t2e); \ - dct_wsub(x2, t1e, t2e); \ - /* odd part */ \ - dct_rot(y0o, y2o, row7, row3, rot2_0, rot2_1); \ - dct_rot(y1o, y3o, row5, row1, rot3_0, rot3_1); \ - __m128i sum17 = _mm_add_epi16(row1, row7); \ - __m128i sum35 = _mm_add_epi16(row3, row5); \ - dct_rot(y4o, y5o, sum17, sum35, rot1_0, rot1_1); \ - dct_wadd(x4, y0o, y4o); \ - dct_wadd(x5, y1o, y5o); \ - dct_wadd(x6, y2o, y5o); \ - dct_wadd(x7, y3o, y4o); \ - dct_bfly32o(row0, row7, x0, x7, bias, shift); \ - dct_bfly32o(row1, row6, x1, x6, bias, shift); \ - dct_bfly32o(row2, row5, x2, x5, bias, shift); \ - dct_bfly32o(row3, row4, x3, x4, bias, shift); \ - } + #define dct_pass(bias,shift) \ + { \ + /* even part */ \ + dct_rot(t2e,t3e, row2,row6, rot0_0,rot0_1); \ + __m128i sum04 = _mm_add_epi16(row0, row4); \ + __m128i dif04 = _mm_sub_epi16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + dct_rot(y0o,y2o, row7,row3, rot2_0,rot2_1); \ + dct_rot(y1o,y3o, row5,row1, rot3_0,rot3_1); \ + __m128i sum17 = _mm_add_epi16(row1, row7); \ + __m128i sum35 = _mm_add_epi16(row3, row5); \ + dct_rot(y4o,y5o, sum17,sum35, rot1_0,rot1_1); \ + dct_wadd(x4, y0o, y4o); \ + dct_wadd(x5, y1o, y5o); \ + dct_wadd(x6, y2o, y5o); \ + dct_wadd(x7, y3o, y4o); \ + dct_bfly32o(row0,row7, x0,x7,bias,shift); \ + dct_bfly32o(row1,row6, x1,x6,bias,shift); \ + dct_bfly32o(row2,row5, x2,x5,bias,shift); \ + dct_bfly32o(row3,row4, x3,x4,bias,shift); \ + } - __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); - __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f(0.765366865f), stbi__f2f(0.5411961f)); - __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); - __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); - __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f(0.298631336f), stbi__f2f(-1.961570560f)); - __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f(3.072711026f)); - __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f(2.053119869f), stbi__f2f(-0.390180644f)); - __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f(1.501321110f)); + __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); + __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f( 0.765366865f), stbi__f2f(0.5411961f)); + __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); + __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); + __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f( 0.298631336f), stbi__f2f(-1.961570560f)); + __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f( 3.072711026f)); + __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f( 2.053119869f), stbi__f2f(-0.390180644f)); + __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f( 1.501321110f)); - // rounding biases in column/row passes, see stbi__idct_block for explanation. - __m128i bias_0 = _mm_set1_epi32(512); - __m128i bias_1 = _mm_set1_epi32(65536 + (128 << 17)); + // rounding biases in column/row passes, see stbi__idct_block for explanation. + __m128i bias_0 = _mm_set1_epi32(512); + __m128i bias_1 = _mm_set1_epi32(65536 + (128<<17)); - // load - row0 = _mm_load_si128((const __m128i *)(data + 0 * 8)); - row1 = _mm_load_si128((const __m128i *)(data + 1 * 8)); - row2 = _mm_load_si128((const __m128i *)(data + 2 * 8)); - row3 = _mm_load_si128((const __m128i *)(data + 3 * 8)); - row4 = _mm_load_si128((const __m128i *)(data + 4 * 8)); - row5 = _mm_load_si128((const __m128i *)(data + 5 * 8)); - row6 = _mm_load_si128((const __m128i *)(data + 6 * 8)); - row7 = _mm_load_si128((const __m128i *)(data + 7 * 8)); + // load + row0 = _mm_load_si128((const __m128i *) (data + 0*8)); + row1 = _mm_load_si128((const __m128i *) (data + 1*8)); + row2 = _mm_load_si128((const __m128i *) (data + 2*8)); + row3 = _mm_load_si128((const __m128i *) (data + 3*8)); + row4 = _mm_load_si128((const __m128i *) (data + 4*8)); + row5 = _mm_load_si128((const __m128i *) (data + 5*8)); + row6 = _mm_load_si128((const __m128i *) (data + 6*8)); + row7 = _mm_load_si128((const __m128i *) (data + 7*8)); - // column pass - dct_pass(bias_0, 10); + // column pass + dct_pass(bias_0, 10); - { - // 16bit 8x8 transpose pass 1 - dct_interleave16(row0, row4); - dct_interleave16(row1, row5); - dct_interleave16(row2, row6); - dct_interleave16(row3, row7); + { + // 16bit 8x8 transpose pass 1 + dct_interleave16(row0, row4); + dct_interleave16(row1, row5); + dct_interleave16(row2, row6); + dct_interleave16(row3, row7); - // transpose pass 2 - dct_interleave16(row0, row2); - dct_interleave16(row1, row3); - dct_interleave16(row4, row6); - dct_interleave16(row5, row7); + // transpose pass 2 + dct_interleave16(row0, row2); + dct_interleave16(row1, row3); + dct_interleave16(row4, row6); + dct_interleave16(row5, row7); - // transpose pass 3 - dct_interleave16(row0, row1); - dct_interleave16(row2, row3); - dct_interleave16(row4, row5); - dct_interleave16(row6, row7); - } + // transpose pass 3 + dct_interleave16(row0, row1); + dct_interleave16(row2, row3); + dct_interleave16(row4, row5); + dct_interleave16(row6, row7); + } - // row pass - dct_pass(bias_1, 17); + // row pass + dct_pass(bias_1, 17); - { - // pack - __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 - __m128i p1 = _mm_packus_epi16(row2, row3); - __m128i p2 = _mm_packus_epi16(row4, row5); - __m128i p3 = _mm_packus_epi16(row6, row7); + { + // pack + __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 + __m128i p1 = _mm_packus_epi16(row2, row3); + __m128i p2 = _mm_packus_epi16(row4, row5); + __m128i p3 = _mm_packus_epi16(row6, row7); - // 8bit 8x8 transpose pass 1 - dct_interleave8(p0, p2); // a0e0a1e1... - dct_interleave8(p1, p3); // c0g0c1g1... + // 8bit 8x8 transpose pass 1 + dct_interleave8(p0, p2); // a0e0a1e1... + dct_interleave8(p1, p3); // c0g0c1g1... - // transpose pass 2 - dct_interleave8(p0, p1); // a0c0e0g0... - dct_interleave8(p2, p3); // b0d0f0h0... + // transpose pass 2 + dct_interleave8(p0, p1); // a0c0e0g0... + dct_interleave8(p2, p3); // b0d0f0h0... - // transpose pass 3 - dct_interleave8(p0, p2); // a0b0c0d0... - dct_interleave8(p1, p3); // a4b4c4d4... + // transpose pass 3 + dct_interleave8(p0, p2); // a0b0c0d0... + dct_interleave8(p1, p3); // a4b4c4d4... - // store - _mm_storel_epi64((__m128i *)out, p0); - out += out_stride; - _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p0, 0x4e)); - out += out_stride; - _mm_storel_epi64((__m128i *)out, p2); - out += out_stride; - _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p2, 0x4e)); - out += out_stride; - _mm_storel_epi64((__m128i *)out, p1); - out += out_stride; - _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p1, 0x4e)); - out += out_stride; - _mm_storel_epi64((__m128i *)out, p3); - out += out_stride; - _mm_storel_epi64((__m128i *)out, _mm_shuffle_epi32(p3, 0x4e)); - } + // store + _mm_storel_epi64((__m128i *) out, p0); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p2); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p1); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p3); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p3, 0x4e)); + } #undef dct_const #undef dct_rot @@ -2763,235 +2708,198 @@ static void stbi__idct_simd(stbi_uc * out, int out_stride, short data[64]) { // NEON integer IDCT. should produce bit-identical // results to the generic C version. -static void stbi__idct_simd(stbi_uc * out, int out_stride, short data[64]) { - int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; - int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); - int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); - int16x4_t rot0_2 = vdup_n_s16(stbi__f2f(0.765366865f)); - int16x4_t rot1_0 = vdup_n_s16(stbi__f2f(1.175875602f)); - int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); - int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); - int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); - int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); - int16x4_t rot3_0 = vdup_n_s16(stbi__f2f(0.298631336f)); - int16x4_t rot3_1 = vdup_n_s16(stbi__f2f(2.053119869f)); - int16x4_t rot3_2 = vdup_n_s16(stbi__f2f(3.072711026f)); - int16x4_t rot3_3 = vdup_n_s16(stbi__f2f(1.501321110f)); + int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); + int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); + int16x4_t rot0_2 = vdup_n_s16(stbi__f2f( 0.765366865f)); + int16x4_t rot1_0 = vdup_n_s16(stbi__f2f( 1.175875602f)); + int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); + int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); + int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); + int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); + int16x4_t rot3_0 = vdup_n_s16(stbi__f2f( 0.298631336f)); + int16x4_t rot3_1 = vdup_n_s16(stbi__f2f( 2.053119869f)); + int16x4_t rot3_2 = vdup_n_s16(stbi__f2f( 3.072711026f)); + int16x4_t rot3_3 = vdup_n_s16(stbi__f2f( 1.501321110f)); -#define dct_long_mul(out, inq, coeff) \ - int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \ - int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff) +#define dct_long_mul(out, inq, coeff) \ + int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff) -#define dct_long_mac(out, acc, inq, coeff) \ - int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \ - int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff) +#define dct_long_mac(out, acc, inq, coeff) \ + int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff) -#define dct_widen(out, inq) \ - int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \ - int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12) +#define dct_widen(out, inq) \ + int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \ + int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12) // wide add -#define dct_wadd(out, a, b) \ - int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \ - int32x4_t out##_h = vaddq_s32(a##_h, b##_h) +#define dct_wadd(out, a, b) \ + int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vaddq_s32(a##_h, b##_h) // wide sub -#define dct_wsub(out, a, b) \ - int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \ - int32x4_t out##_h = vsubq_s32(a##_h, b##_h) +#define dct_wsub(out, a, b) \ + int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vsubq_s32(a##_h, b##_h) // butterfly a/b, then shift using "shiftop" by "s" and pack -#define dct_bfly32o(out0, out1, a, b, shiftop, s) \ - { \ - dct_wadd(sum, a, b); \ - dct_wsub(dif, a, b); \ - out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \ - out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \ - } +#define dct_bfly32o(out0,out1, a,b,shiftop,s) \ + { \ + dct_wadd(sum, a, b); \ + dct_wsub(dif, a, b); \ + out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \ + out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \ + } -#define dct_pass(shiftop, shift) \ - { \ - /* even part */ \ - int16x8_t sum26 = vaddq_s16(row2, row6); \ - dct_long_mul(p1e, sum26, rot0_0); \ - dct_long_mac(t2e, p1e, row6, rot0_1); \ - dct_long_mac(t3e, p1e, row2, rot0_2); \ - int16x8_t sum04 = vaddq_s16(row0, row4); \ - int16x8_t dif04 = vsubq_s16(row0, row4); \ - dct_widen(t0e, sum04); \ - dct_widen(t1e, dif04); \ - dct_wadd(x0, t0e, t3e); \ - dct_wsub(x3, t0e, t3e); \ - dct_wadd(x1, t1e, t2e); \ - dct_wsub(x2, t1e, t2e); \ - /* odd part */ \ - int16x8_t sum15 = vaddq_s16(row1, row5); \ - int16x8_t sum17 = vaddq_s16(row1, row7); \ - int16x8_t sum35 = vaddq_s16(row3, row5); \ - int16x8_t sum37 = vaddq_s16(row3, row7); \ - int16x8_t sumodd = vaddq_s16(sum17, sum35); \ - dct_long_mul(p5o, sumodd, rot1_0); \ - dct_long_mac(p1o, p5o, sum17, rot1_1); \ - dct_long_mac(p2o, p5o, sum35, rot1_2); \ - dct_long_mul(p3o, sum37, rot2_0); \ - dct_long_mul(p4o, sum15, rot2_1); \ - dct_wadd(sump13o, p1o, p3o); \ - dct_wadd(sump24o, p2o, p4o); \ - dct_wadd(sump23o, p2o, p3o); \ - dct_wadd(sump14o, p1o, p4o); \ - dct_long_mac(x4, sump13o, row7, rot3_0); \ - dct_long_mac(x5, sump24o, row5, rot3_1); \ - dct_long_mac(x6, sump23o, row3, rot3_2); \ - dct_long_mac(x7, sump14o, row1, rot3_3); \ - dct_bfly32o(row0, row7, x0, x7, shiftop, shift); \ - dct_bfly32o(row1, row6, x1, x6, shiftop, shift); \ - dct_bfly32o(row2, row5, x2, x5, shiftop, shift); \ - dct_bfly32o(row3, row4, x3, x4, shiftop, shift); \ - } +#define dct_pass(shiftop, shift) \ + { \ + /* even part */ \ + int16x8_t sum26 = vaddq_s16(row2, row6); \ + dct_long_mul(p1e, sum26, rot0_0); \ + dct_long_mac(t2e, p1e, row6, rot0_1); \ + dct_long_mac(t3e, p1e, row2, rot0_2); \ + int16x8_t sum04 = vaddq_s16(row0, row4); \ + int16x8_t dif04 = vsubq_s16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + int16x8_t sum15 = vaddq_s16(row1, row5); \ + int16x8_t sum17 = vaddq_s16(row1, row7); \ + int16x8_t sum35 = vaddq_s16(row3, row5); \ + int16x8_t sum37 = vaddq_s16(row3, row7); \ + int16x8_t sumodd = vaddq_s16(sum17, sum35); \ + dct_long_mul(p5o, sumodd, rot1_0); \ + dct_long_mac(p1o, p5o, sum17, rot1_1); \ + dct_long_mac(p2o, p5o, sum35, rot1_2); \ + dct_long_mul(p3o, sum37, rot2_0); \ + dct_long_mul(p4o, sum15, rot2_1); \ + dct_wadd(sump13o, p1o, p3o); \ + dct_wadd(sump24o, p2o, p4o); \ + dct_wadd(sump23o, p2o, p3o); \ + dct_wadd(sump14o, p1o, p4o); \ + dct_long_mac(x4, sump13o, row7, rot3_0); \ + dct_long_mac(x5, sump24o, row5, rot3_1); \ + dct_long_mac(x6, sump23o, row3, rot3_2); \ + dct_long_mac(x7, sump14o, row1, rot3_3); \ + dct_bfly32o(row0,row7, x0,x7,shiftop,shift); \ + dct_bfly32o(row1,row6, x1,x6,shiftop,shift); \ + dct_bfly32o(row2,row5, x2,x5,shiftop,shift); \ + dct_bfly32o(row3,row4, x3,x4,shiftop,shift); \ + } - // load - row0 = vld1q_s16(data + 0 * 8); - row1 = vld1q_s16(data + 1 * 8); - row2 = vld1q_s16(data + 2 * 8); - row3 = vld1q_s16(data + 3 * 8); - row4 = vld1q_s16(data + 4 * 8); - row5 = vld1q_s16(data + 5 * 8); - row6 = vld1q_s16(data + 6 * 8); - row7 = vld1q_s16(data + 7 * 8); + // load + row0 = vld1q_s16(data + 0*8); + row1 = vld1q_s16(data + 1*8); + row2 = vld1q_s16(data + 2*8); + row3 = vld1q_s16(data + 3*8); + row4 = vld1q_s16(data + 4*8); + row5 = vld1q_s16(data + 5*8); + row6 = vld1q_s16(data + 6*8); + row7 = vld1q_s16(data + 7*8); - // add DC bias - row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); + // add DC bias + row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); - // column pass - dct_pass(vrshrn_n_s32, 10); + // column pass + dct_pass(vrshrn_n_s32, 10); - // 16bit 8x8 transpose - { + // 16bit 8x8 transpose + { // these three map to a single VTRN.16, VTRN.32, and VSWP, respectively. // whether compilers actually get this is another story, sadly. -#define dct_trn16(x, y) \ - { \ - int16x8x2_t t = vtrnq_s16(x, y); \ - x = t.val[0]; \ - y = t.val[1]; \ - } -#define dct_trn32(x, y) \ - { \ - int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); \ - x = vreinterpretq_s16_s32(t.val[0]); \ - y = vreinterpretq_s16_s32(t.val[1]); \ - } -#define dct_trn64(x, y) \ - { \ - int16x8_t x0 = x; \ - int16x8_t y0 = y; \ - x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); \ - y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); \ - } +#define dct_trn16(x, y) { int16x8x2_t t = vtrnq_s16(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn32(x, y) { int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); x = vreinterpretq_s16_s32(t.val[0]); y = vreinterpretq_s16_s32(t.val[1]); } +#define dct_trn64(x, y) { int16x8_t x0 = x; int16x8_t y0 = y; x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); } - // pass 1 - dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 - dct_trn16(row2, row3); - dct_trn16(row4, row5); - dct_trn16(row6, row7); + // pass 1 + dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 + dct_trn16(row2, row3); + dct_trn16(row4, row5); + dct_trn16(row6, row7); - // pass 2 - dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 - dct_trn32(row1, row3); - dct_trn32(row4, row6); - dct_trn32(row5, row7); + // pass 2 + dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 + dct_trn32(row1, row3); + dct_trn32(row4, row6); + dct_trn32(row5, row7); - // pass 3 - dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 - dct_trn64(row1, row5); - dct_trn64(row2, row6); - dct_trn64(row3, row7); + // pass 3 + dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 + dct_trn64(row1, row5); + dct_trn64(row2, row6); + dct_trn64(row3, row7); #undef dct_trn16 #undef dct_trn32 #undef dct_trn64 - } + } - // row pass - // vrshrn_n_s32 only supports shifts up to 16, we need - // 17. so do a non-rounding shift of 16 first then follow - // up with a rounding shift by 1. - dct_pass(vshrn_n_s32, 16); + // row pass + // vrshrn_n_s32 only supports shifts up to 16, we need + // 17. so do a non-rounding shift of 16 first then follow + // up with a rounding shift by 1. + dct_pass(vshrn_n_s32, 16); - { - // pack and round - uint8x8_t p0 = vqrshrun_n_s16(row0, 1); - uint8x8_t p1 = vqrshrun_n_s16(row1, 1); - uint8x8_t p2 = vqrshrun_n_s16(row2, 1); - uint8x8_t p3 = vqrshrun_n_s16(row3, 1); - uint8x8_t p4 = vqrshrun_n_s16(row4, 1); - uint8x8_t p5 = vqrshrun_n_s16(row5, 1); - uint8x8_t p6 = vqrshrun_n_s16(row6, 1); - uint8x8_t p7 = vqrshrun_n_s16(row7, 1); + { + // pack and round + uint8x8_t p0 = vqrshrun_n_s16(row0, 1); + uint8x8_t p1 = vqrshrun_n_s16(row1, 1); + uint8x8_t p2 = vqrshrun_n_s16(row2, 1); + uint8x8_t p3 = vqrshrun_n_s16(row3, 1); + uint8x8_t p4 = vqrshrun_n_s16(row4, 1); + uint8x8_t p5 = vqrshrun_n_s16(row5, 1); + uint8x8_t p6 = vqrshrun_n_s16(row6, 1); + uint8x8_t p7 = vqrshrun_n_s16(row7, 1); - // again, these can translate into one instruction, but often don't. -#define dct_trn8_8(x, y) \ - { \ - uint8x8x2_t t = vtrn_u8(x, y); \ - x = t.val[0]; \ - y = t.val[1]; \ - } -#define dct_trn8_16(x, y) \ - { \ - uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); \ - x = vreinterpret_u8_u16(t.val[0]); \ - y = vreinterpret_u8_u16(t.val[1]); \ - } -#define dct_trn8_32(x, y) \ - { \ - uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); \ - x = vreinterpret_u8_u32(t.val[0]); \ - y = vreinterpret_u8_u32(t.val[1]); \ - } + // again, these can translate into one instruction, but often don't. +#define dct_trn8_8(x, y) { uint8x8x2_t t = vtrn_u8(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn8_16(x, y) { uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); x = vreinterpret_u8_u16(t.val[0]); y = vreinterpret_u8_u16(t.val[1]); } +#define dct_trn8_32(x, y) { uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); x = vreinterpret_u8_u32(t.val[0]); y = vreinterpret_u8_u32(t.val[1]); } - // sadly can't use interleaved stores here since we only write - // 8 bytes to each scan line! + // sadly can't use interleaved stores here since we only write + // 8 bytes to each scan line! - // 8x8 8-bit transpose pass 1 - dct_trn8_8(p0, p1); - dct_trn8_8(p2, p3); - dct_trn8_8(p4, p5); - dct_trn8_8(p6, p7); + // 8x8 8-bit transpose pass 1 + dct_trn8_8(p0, p1); + dct_trn8_8(p2, p3); + dct_trn8_8(p4, p5); + dct_trn8_8(p6, p7); - // pass 2 - dct_trn8_16(p0, p2); - dct_trn8_16(p1, p3); - dct_trn8_16(p4, p6); - dct_trn8_16(p5, p7); + // pass 2 + dct_trn8_16(p0, p2); + dct_trn8_16(p1, p3); + dct_trn8_16(p4, p6); + dct_trn8_16(p5, p7); - // pass 3 - dct_trn8_32(p0, p4); - dct_trn8_32(p1, p5); - dct_trn8_32(p2, p6); - dct_trn8_32(p3, p7); + // pass 3 + dct_trn8_32(p0, p4); + dct_trn8_32(p1, p5); + dct_trn8_32(p2, p6); + dct_trn8_32(p3, p7); - // store - vst1_u8(out, p0); - out += out_stride; - vst1_u8(out, p1); - out += out_stride; - vst1_u8(out, p2); - out += out_stride; - vst1_u8(out, p3); - out += out_stride; - vst1_u8(out, p4); - out += out_stride; - vst1_u8(out, p5); - out += out_stride; - vst1_u8(out, p6); - out += out_stride; - vst1_u8(out, p7); + // store + vst1_u8(out, p0); out += out_stride; + vst1_u8(out, p1); out += out_stride; + vst1_u8(out, p2); out += out_stride; + vst1_u8(out, p3); out += out_stride; + vst1_u8(out, p4); out += out_stride; + vst1_u8(out, p5); out += out_stride; + vst1_u8(out, p6); out += out_stride; + vst1_u8(out, p7); #undef dct_trn8_8 #undef dct_trn8_16 #undef dct_trn8_32 - } + } #undef dct_long_mul #undef dct_long_mac @@ -3004,1267 +2912,1169 @@ static void stbi__idct_simd(stbi_uc * out, int out_stride, short data[64]) { #endif // STBI_NEON -#define STBI__MARKER_none 0xff +#define STBI__MARKER_none 0xff // if there's a pending marker from the entropy stream, return that // otherwise, fetch from the stream and get a marker. if there's no // marker, return 0xff, which is never a valid marker value -static stbi_uc stbi__get_marker(stbi__jpeg * j) { - stbi_uc x; - if (j->marker != STBI__MARKER_none) { - x = j->marker; - j->marker = STBI__MARKER_none; - return x; - } - x = stbi__get8(j->s); - if (x != 0xff) - return STBI__MARKER_none; - while (x == 0xff) - x = stbi__get8(j->s); // consume repeated 0xff fill bytes - return x; +static stbi_uc stbi__get_marker(stbi__jpeg *j) +{ + stbi_uc x; + if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; } + x = stbi__get8(j->s); + if (x != 0xff) return STBI__MARKER_none; + while (x == 0xff) + x = stbi__get8(j->s); // consume repeated 0xff fill bytes + return x; } // in each scan, we'll have scan_n components, and the order // of the components is specified by order[] -#define STBI__RESTART(x) ((x) >= 0xd0 && (x) <= 0xd7) +#define STBI__RESTART(x) ((x) >= 0xd0 && (x) <= 0xd7) // after a restart interval, stbi__jpeg_reset the entropy decoder and // the dc prediction -static void stbi__jpeg_reset(stbi__jpeg * j) { - j->code_bits = 0; - j->code_buffer = 0; - j->nomore = 0; - j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; - j->marker = STBI__MARKER_none; - j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; - j->eob_run = 0; - // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, - // since we don't even allow 1<<30 pixels +static void stbi__jpeg_reset(stbi__jpeg *j) +{ + j->code_bits = 0; + j->code_buffer = 0; + j->nomore = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; + j->marker = STBI__MARKER_none; + j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; + j->eob_run = 0; + // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, + // since we don't even allow 1<<30 pixels } -static int stbi__parse_entropy_coded_data(stbi__jpeg * z) { - stbi__jpeg_reset(z); - if (!z->progressive) { - if (z->scan_n == 1) { - int i, j; - STBI_SIMD_ALIGN(short, data[64]); - int n = z->order[0]; - // non-interleaved data, we just need to process one block at a time, - // in trivial scanline order - // number of blocks to do just depends on how many actual "pixels" this - // component has, independent of interleaved MCU blocking and such - int w = (z->img_comp[n].x + 7) >> 3; - int h = (z->img_comp[n].y + 7) >> 3; - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - int ha = z->img_comp[n].ha; - if (!stbi__jpeg_decode_block(z, data, z->huff_dc + z->img_comp[n].hd, z->huff_ac + ha, z->fast_ac[ha], n, - z->dequant[z->img_comp[n].tq])) - return 0; - z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * j * 8 + i * 8, z->img_comp[n].w2, data); - // every data block is an MCU, so countdown the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) - stbi__grow_buffer_unsafe(z); - // if it's NOT a restart, then just bail, so we get corrupt data - // rather than no data - if (!STBI__RESTART(z->marker)) - return 1; - stbi__jpeg_reset(z); - } - } +static int stbi__parse_entropy_coded_data(stbi__jpeg *z) +{ + stbi__jpeg_reset(z); + if (!z->progressive) { + if (z->scan_n == 1) { + int i,j; + STBI_SIMD_ALIGN(short, data[64]); + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + // if it's NOT a restart, then just bail, so we get corrupt data + // rather than no data + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } } - return 1; - } else { // interleaved - int i, j, k, x, y; - STBI_SIMD_ALIGN(short, data[64]); - for (j = 0; j < z->img_mcu_y; ++j) { - for (i = 0; i < z->img_mcu_x; ++i) { - // scan an interleaved mcu... process scan_n components in order - for (k = 0; k < z->scan_n; ++k) { - int n = z->order[k]; - // scan out an mcu's worth of this component; that's just determined - // by the basic H and V specified for the component - for (y = 0; y < z->img_comp[n].v; ++y) { - for (x = 0; x < z->img_comp[n].h; ++x) { - int x2 = (i * z->img_comp[n].h + x) * 8; - int y2 = (j * z->img_comp[n].v + y) * 8; - int ha = z->img_comp[n].ha; - if (!stbi__jpeg_decode_block(z, data, z->huff_dc + z->img_comp[n].hd, z->huff_ac + ha, - z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) - return 0; - z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * y2 + x2, z->img_comp[n].w2, - data); - } - } - } - // after all interleaved components, that's an interleaved MCU, - // so now count down the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) - stbi__grow_buffer_unsafe(z); - if (!STBI__RESTART(z->marker)) - return 1; - stbi__jpeg_reset(z); - } - } - } - return 1; - } - } else { - if (z->scan_n == 1) { - int i, j; - int n = z->order[0]; - // non-interleaved data, we just need to process one block at a time, - // in trivial scanline order - // number of blocks to do just depends on how many actual "pixels" this - // component has, independent of interleaved MCU blocking and such - int w = (z->img_comp[n].x + 7) >> 3; - int h = (z->img_comp[n].y + 7) >> 3; - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - short * data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); - if (z->spec_start == 0) { - if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) - return 0; - } else { + } + return 1; + } else { // interleaved + int i,j,k,x,y; + STBI_SIMD_ALIGN(short, data[64]); + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x)*8; + int y2 = (j*z->img_comp[n].v + y)*8; int ha = z->img_comp[n].ha; - if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) - return 0; - } - // every data block is an MCU, so countdown the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) - stbi__grow_buffer_unsafe(z); - if (!STBI__RESTART(z->marker)) - return 1; - stbi__jpeg_reset(z); - } - } + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*y2+x2, z->img_comp[n].w2, data); + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } } - return 1; - } else { // interleaved - int i, j, k, x, y; - for (j = 0; j < z->img_mcu_y; ++j) { - for (i = 0; i < z->img_mcu_x; ++i) { - // scan an interleaved mcu... process scan_n components in order - for (k = 0; k < z->scan_n; ++k) { - int n = z->order[k]; - // scan out an mcu's worth of this component; that's just determined - // by the basic H and V specified for the component - for (y = 0; y < z->img_comp[n].v; ++y) { - for (x = 0; x < z->img_comp[n].h; ++x) { - int x2 = (i * z->img_comp[n].h + x); - int y2 = (j * z->img_comp[n].v + y); - short * data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); - if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) - return 0; - } - } - } - // after all interleaved components, that's an interleaved MCU, - // so now count down the restart interval - if (--z->todo <= 0) { - if (z->code_bits < 24) - stbi__grow_buffer_unsafe(z); - if (!STBI__RESTART(z->marker)) - return 1; - stbi__jpeg_reset(z); - } - } + } + return 1; + } + } else { + if (z->scan_n == 1) { + int i,j; + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + if (z->spec_start == 0) { + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } else { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) + return 0; + } + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } } - return 1; - } - } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x); + int y2 = (j*z->img_comp[n].v + y); + short *data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } } -static void stbi__jpeg_dequantize(short * data, stbi__uint16 * dequant) { - int i; - for (i = 0; i < 64; ++i) - data[i] *= dequant[i]; +static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant) +{ + int i; + for (i=0; i < 64; ++i) + data[i] *= dequant[i]; } -static void stbi__jpeg_finish(stbi__jpeg * z) { - if (z->progressive) { - // dequantize and idct the data - int i, j, n; - for (n = 0; n < z->s->img_n; ++n) { - int w = (z->img_comp[n].x + 7) >> 3; - int h = (z->img_comp[n].y + 7) >> 3; - for (j = 0; j < h; ++j) { - for (i = 0; i < w; ++i) { - short * data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); - stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); - z->idct_block_kernel(z->img_comp[n].data + z->img_comp[n].w2 * j * 8 + i * 8, z->img_comp[n].w2, data); - } +static void stbi__jpeg_finish(stbi__jpeg *z) +{ + if (z->progressive) { + // dequantize and idct the data + int i,j,n; + for (n=0; n < z->s->img_n; ++n) { + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); } - } - } + } + } + } } -static int stbi__process_marker(stbi__jpeg * z, int m) { - int L; - switch (m) { - case STBI__MARKER_none: // no marker found - return stbi__err("expected marker", "Corrupt JPEG"); +static int stbi__process_marker(stbi__jpeg *z, int m) +{ + int L; + switch (m) { + case STBI__MARKER_none: // no marker found + return stbi__err("expected marker","Corrupt JPEG"); - case 0xDD: // DRI - specify restart interval - if (stbi__get16be(z->s) != 4) - return stbi__err("bad DRI len", "Corrupt JPEG"); - z->restart_interval = stbi__get16be(z->s); - return 1; + case 0xDD: // DRI - specify restart interval + if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len","Corrupt JPEG"); + z->restart_interval = stbi__get16be(z->s); + return 1; - case 0xDB: // DQT - define quantization table - L = stbi__get16be(z->s) - 2; - while (L > 0) { + case 0xDB: // DQT - define quantization table + L = stbi__get16be(z->s)-2; + while (L > 0) { int q = stbi__get8(z->s); int p = q >> 4, sixteen = (p != 0); - int t = q & 15, i; - if (p != 0 && p != 1) - return stbi__err("bad DQT type", "Corrupt JPEG"); - if (t > 3) - return stbi__err("bad DQT table", "Corrupt JPEG"); + int t = q & 15,i; + if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG"); + if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); - for (i = 0; i < 64; ++i) - z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + for (i=0; i < 64; ++i) + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); L -= (sixteen ? 129 : 65); - } - return L == 0; + } + return L==0; - case 0xC4: // DHT - define huffman table - L = stbi__get16be(z->s) - 2; - while (L > 0) { - stbi_uc * v; - int sizes[16], i, n = 0; + case 0xC4: // DHT - define huffman table + L = stbi__get16be(z->s)-2; + while (L > 0) { + stbi_uc *v; + int sizes[16],i,n=0; int q = stbi__get8(z->s); int tc = q >> 4; int th = q & 15; - if (tc > 1 || th > 3) - return stbi__err("bad DHT header", "Corrupt JPEG"); - for (i = 0; i < 16; ++i) { - sizes[i] = stbi__get8(z->s); - n += sizes[i]; + if (tc > 1 || th > 3) return stbi__err("bad DHT header","Corrupt JPEG"); + for (i=0; i < 16; ++i) { + sizes[i] = stbi__get8(z->s); + n += sizes[i]; } - if (n > 256) - return stbi__err("bad DHT header", "Corrupt JPEG"); // Loop over i < n would write past end of values! + if(n > 256) return stbi__err("bad DHT header","Corrupt JPEG"); // Loop over i < n would write past end of values! L -= 17; if (tc == 0) { - if (!stbi__build_huffman(z->huff_dc + th, sizes)) - return 0; - v = z->huff_dc[th].values; + if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0; + v = z->huff_dc[th].values; } else { - if (!stbi__build_huffman(z->huff_ac + th, sizes)) - return 0; - v = z->huff_ac[th].values; + if (!stbi__build_huffman(z->huff_ac+th, sizes)) return 0; + v = z->huff_ac[th].values; } - for (i = 0; i < n; ++i) - v[i] = stbi__get8(z->s); + for (i=0; i < n; ++i) + v[i] = stbi__get8(z->s); if (tc != 0) - stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); + stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); L -= n; - } - return L == 0; - } + } + return L==0; + } - // check for comment block or APP blocks - if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { - L = stbi__get16be(z->s); - if (L < 2) { - if (m == 0xFE) - return stbi__err("bad COM len", "Corrupt JPEG"); - else - return stbi__err("bad APP len", "Corrupt JPEG"); - } - L -= 2; + // check for comment block or APP blocks + if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len","Corrupt JPEG"); + else + return stbi__err("bad APP len","Corrupt JPEG"); + } + L -= 2; - if (m == 0xE0 && L >= 5) { // JFIF APP0 segment - static const unsigned char tag[5] = {'J', 'F', 'I', 'F', '\0'}; - int ok = 1; - int i; - for (i = 0; i < 5; ++i) - if (stbi__get8(z->s) != tag[i]) - ok = 0; - L -= 5; - if (ok) - z->jfif = 1; - } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment - static const unsigned char tag[6] = {'A', 'd', 'o', 'b', 'e', '\0'}; - int ok = 1; - int i; - for (i = 0; i < 6; ++i) - if (stbi__get8(z->s) != tag[i]) - ok = 0; + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = {'J','F','I','F','\0'}; + int ok = 1; + int i; + for (i=0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = {'A','d','o','b','e','\0'}; + int ok = 1; + int i; + for (i=0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform L -= 6; - if (ok) { - stbi__get8(z->s); // version - stbi__get16be(z->s); // flags0 - stbi__get16be(z->s); // flags1 - z->app14_color_transform = stbi__get8(z->s); // color transform - L -= 6; - } - } + } + } - stbi__skip(z->s, L); - return 1; - } + stbi__skip(z->s, L); + return 1; + } - return stbi__err("unknown marker", "Corrupt JPEG"); + return stbi__err("unknown marker","Corrupt JPEG"); } // after we see SOS -static int stbi__process_scan_header(stbi__jpeg * z) { - int i; - int Ls = stbi__get16be(z->s); - z->scan_n = stbi__get8(z->s); - if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int)z->s->img_n) - return stbi__err("bad SOS component count", "Corrupt JPEG"); - if (Ls != 6 + 2 * z->scan_n) - return stbi__err("bad SOS len", "Corrupt JPEG"); - for (i = 0; i < z->scan_n; ++i) { - int id = stbi__get8(z->s), which; - int q = stbi__get8(z->s); - for (which = 0; which < z->s->img_n; ++which) - if (z->img_comp[which].id == id) - break; - if (which == z->s->img_n) - return 0; // no match - z->img_comp[which].hd = q >> 4; - if (z->img_comp[which].hd > 3) - return stbi__err("bad DC huff", "Corrupt JPEG"); - z->img_comp[which].ha = q & 15; - if (z->img_comp[which].ha > 3) - return stbi__err("bad AC huff", "Corrupt JPEG"); - z->order[i] = which; - } +static int stbi__process_scan_header(stbi__jpeg *z) +{ + int i; + int Ls = stbi__get16be(z->s); + z->scan_n = stbi__get8(z->s); + if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int) z->s->img_n) return stbi__err("bad SOS component count","Corrupt JPEG"); + if (Ls != 6+2*z->scan_n) return stbi__err("bad SOS len","Corrupt JPEG"); + for (i=0; i < z->scan_n; ++i) { + int id = stbi__get8(z->s), which; + int q = stbi__get8(z->s); + for (which = 0; which < z->s->img_n; ++which) + if (z->img_comp[which].id == id) + break; + if (which == z->s->img_n) return 0; // no match + z->img_comp[which].hd = q >> 4; if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff","Corrupt JPEG"); + z->img_comp[which].ha = q & 15; if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff","Corrupt JPEG"); + z->order[i] = which; + } - { - int aa; - z->spec_start = stbi__get8(z->s); - z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 - aa = stbi__get8(z->s); - z->succ_high = (aa >> 4); - z->succ_low = (aa & 15); - if (z->progressive) { - if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) - return stbi__err("bad SOS", "Corrupt JPEG"); - } else { - if (z->spec_start != 0) - return stbi__err("bad SOS", "Corrupt JPEG"); - if (z->succ_high != 0 || z->succ_low != 0) - return stbi__err("bad SOS", "Corrupt JPEG"); - z->spec_end = 63; - } - } + { + int aa; + z->spec_start = stbi__get8(z->s); + z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 + aa = stbi__get8(z->s); + z->succ_high = (aa >> 4); + z->succ_low = (aa & 15); + if (z->progressive) { + if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) + return stbi__err("bad SOS", "Corrupt JPEG"); + } else { + if (z->spec_start != 0) return stbi__err("bad SOS","Corrupt JPEG"); + if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS","Corrupt JPEG"); + z->spec_end = 63; + } + } - return 1; + return 1; } -static int stbi__free_jpeg_components(stbi__jpeg * z, int ncomp, int why) { - int i; - for (i = 0; i < ncomp; ++i) { - if (z->img_comp[i].raw_data) { - STBI_FREE(z->img_comp[i].raw_data); - z->img_comp[i].raw_data = NULL; - z->img_comp[i].data = NULL; - } - if (z->img_comp[i].raw_coeff) { - STBI_FREE(z->img_comp[i].raw_coeff); - z->img_comp[i].raw_coeff = 0; - z->img_comp[i].coeff = 0; - } - if (z->img_comp[i].linebuf) { - STBI_FREE(z->img_comp[i].linebuf); - z->img_comp[i].linebuf = NULL; - } - } - return why; +static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why) +{ + int i; + for (i=0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; } -static int stbi__process_frame_header(stbi__jpeg * z, int scan) { - stbi__context * s = z->s; - int Lf, p, i, q, h_max = 1, v_max = 1, c; - Lf = stbi__get16be(s); - if (Lf < 11) - return stbi__err("bad SOF len", "Corrupt JPEG"); // JPEG - p = stbi__get8(s); - if (p != 8) - return stbi__err("only 8-bit", "JPEG format not supported: 8-bit only"); // JPEG baseline - s->img_y = stbi__get16be(s); - if (s->img_y == 0) - return stbi__err("no header height", - "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG - s->img_x = stbi__get16be(s); - if (s->img_x == 0) - return stbi__err("0 width", "Corrupt JPEG"); // JPEG requires - if (s->img_y > STBI_MAX_DIMENSIONS) - return stbi__err("too large", "Very large image (corrupt?)"); - if (s->img_x > STBI_MAX_DIMENSIONS) - return stbi__err("too large", "Very large image (corrupt?)"); - c = stbi__get8(s); - if (c != 3 && c != 1 && c != 4) - return stbi__err("bad component count", "Corrupt JPEG"); - s->img_n = c; - for (i = 0; i < c; ++i) { - z->img_comp[i].data = NULL; - z->img_comp[i].linebuf = NULL; - } +static int stbi__process_frame_header(stbi__jpeg *z, int scan) +{ + stbi__context *s = z->s; + int Lf,p,i,q, h_max=1,v_max=1,c; + Lf = stbi__get16be(s); if (Lf < 11) return stbi__err("bad SOF len","Corrupt JPEG"); // JPEG + p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline + s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG + s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + c = stbi__get8(s); + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); + s->img_n = c; + for (i=0; i < c; ++i) { + z->img_comp[i].data = NULL; + z->img_comp[i].linebuf = NULL; + } - if (Lf != 8 + 3 * s->img_n) - return stbi__err("bad SOF len", "Corrupt JPEG"); + if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); - z->rgb = 0; - for (i = 0; i < s->img_n; ++i) { - static const unsigned char rgb[3] = {'R', 'G', 'B'}; - z->img_comp[i].id = stbi__get8(s); - if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) - ++z->rgb; - q = stbi__get8(s); - z->img_comp[i].h = (q >> 4); - if (!z->img_comp[i].h || z->img_comp[i].h > 4) - return stbi__err("bad H", "Corrupt JPEG"); - z->img_comp[i].v = q & 15; - if (!z->img_comp[i].v || z->img_comp[i].v > 4) - return stbi__err("bad V", "Corrupt JPEG"); - z->img_comp[i].tq = stbi__get8(s); - if (z->img_comp[i].tq > 3) - return stbi__err("bad TQ", "Corrupt JPEG"); - } + z->rgb = 0; + for (i=0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; + z->img_comp[i].id = stbi__get8(s); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; + q = stbi__get8(s); + z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); + z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); + z->img_comp[i].tq = stbi__get8(s); if (z->img_comp[i].tq > 3) return stbi__err("bad TQ","Corrupt JPEG"); + } - if (scan != STBI__SCAN_load) - return 1; + if (scan != STBI__SCAN_load) return 1; - if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) - return stbi__err("too large", "Image too large to decode"); + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); - for (i = 0; i < s->img_n; ++i) { - if (z->img_comp[i].h > h_max) - h_max = z->img_comp[i].h; - if (z->img_comp[i].v > v_max) - v_max = z->img_comp[i].v; - } + for (i=0; i < s->img_n; ++i) { + if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; + if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; + } - // check that plane subsampling factors are integer ratios; our resamplers can't deal with fractional ratios - // and I've never seen a non-corrupted JPEG file actually use them - for (i = 0; i < s->img_n; ++i) { - if (h_max % z->img_comp[i].h != 0) - return stbi__err("bad H", "Corrupt JPEG"); - if (v_max % z->img_comp[i].v != 0) - return stbi__err("bad V", "Corrupt JPEG"); - } + // check that plane subsampling factors are integer ratios; our resamplers can't deal with fractional ratios + // and I've never seen a non-corrupted JPEG file actually use them + for (i=0; i < s->img_n; ++i) { + if (h_max % z->img_comp[i].h != 0) return stbi__err("bad H","Corrupt JPEG"); + if (v_max % z->img_comp[i].v != 0) return stbi__err("bad V","Corrupt JPEG"); + } - // compute interleaved mcu info - z->img_h_max = h_max; - z->img_v_max = v_max; - z->img_mcu_w = h_max * 8; - z->img_mcu_h = v_max * 8; - // these sizes can't be more than 17 bits - z->img_mcu_x = (s->img_x + z->img_mcu_w - 1) / z->img_mcu_w; - z->img_mcu_y = (s->img_y + z->img_mcu_h - 1) / z->img_mcu_h; + // compute interleaved mcu info + z->img_h_max = h_max; + z->img_v_max = v_max; + z->img_mcu_w = h_max * 8; + z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits + z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; + z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; - for (i = 0; i < s->img_n; ++i) { - // number of effective pixels (e.g. for non-interleaved MCU) - z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max - 1) / h_max; - z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max - 1) / v_max; - // to simplify generation, we'll allocate enough memory to decode - // the bogus oversized data from using interleaved MCUs and their - // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't - // discard the extra data until colorspace conversion - // - // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) - // so these muls can't overflow with 32-bit ints (which we require) - z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; - z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; - z->img_comp[i].coeff = 0; - z->img_comp[i].raw_coeff = 0; - z->img_comp[i].linebuf = NULL; - z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); - if (z->img_comp[i].raw_data == NULL) - return stbi__free_jpeg_components(z, i + 1, stbi__err("outofmem", "Out of memory")); - // align blocks for idct using mmx/sse - z->img_comp[i].data = (stbi_uc *)(((size_t)z->img_comp[i].raw_data + 15) & ~15); - if (z->progressive) { - // w2, h2 are multiples of 8 (see above) - z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; - z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; - z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); - if (z->img_comp[i].raw_coeff == NULL) - return stbi__free_jpeg_components(z, i + 1, stbi__err("outofmem", "Out of memory")); - z->img_comp[i].coeff = (short *)(((size_t)z->img_comp[i].raw_coeff + 15) & ~15); - } - } + for (i=0; i < s->img_n; ++i) { + // number of effective pixels (e.g. for non-interleaved MCU) + z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max-1) / h_max; + z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max-1) / v_max; + // to simplify generation, we'll allocate enough memory to decode + // the bogus oversized data from using interleaved MCUs and their + // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't + // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) + z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; + z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + // align blocks for idct using mmx/sse + z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); + if (z->progressive) { + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); + } + } - return 1; + return 1; } // use comparisons since in some cases we handle more than one case (e.g. SOF) -#define stbi__DNL(x) ((x) == 0xdc) -#define stbi__SOI(x) ((x) == 0xd8) -#define stbi__EOI(x) ((x) == 0xd9) -#define stbi__SOF(x) ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2) -#define stbi__SOS(x) ((x) == 0xda) +#define stbi__DNL(x) ((x) == 0xdc) +#define stbi__SOI(x) ((x) == 0xd8) +#define stbi__EOI(x) ((x) == 0xd9) +#define stbi__SOF(x) ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2) +#define stbi__SOS(x) ((x) == 0xda) -#define stbi__SOF_progressive(x) ((x) == 0xc2) +#define stbi__SOF_progressive(x) ((x) == 0xc2) -static int stbi__decode_jpeg_header(stbi__jpeg * z, int scan) { - int m; - z->jfif = 0; - z->app14_color_transform = -1; // valid values are 0,1,2 - z->marker = STBI__MARKER_none; // initialize cached marker to empty - m = stbi__get_marker(z); - if (!stbi__SOI(m)) - return stbi__err("no SOI", "Corrupt JPEG"); - if (scan == STBI__SCAN_type) - return 1; - m = stbi__get_marker(z); - while (!stbi__SOF(m)) { - if (!stbi__process_marker(z, m)) - return 0; - m = stbi__get_marker(z); - while (m == STBI__MARKER_none) { - // some files have extra padding after their blocks, so ok, we'll scan - if (stbi__at_eof(z->s)) - return stbi__err("no SOF", "Corrupt JPEG"); - m = stbi__get_marker(z); - } - } - z->progressive = stbi__SOF_progressive(m); - if (!stbi__process_frame_header(z, scan)) - return 0; - return 1; +static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) +{ + int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 + z->marker = STBI__MARKER_none; // initialize cached marker to empty + m = stbi__get_marker(z); + if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); + if (scan == STBI__SCAN_type) return 1; + m = stbi__get_marker(z); + while (!stbi__SOF(m)) { + if (!stbi__process_marker(z,m)) return 0; + m = stbi__get_marker(z); + while (m == STBI__MARKER_none) { + // some files have extra padding after their blocks, so ok, we'll scan + if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG"); + m = stbi__get_marker(z); + } + } + z->progressive = stbi__SOF_progressive(m); + if (!stbi__process_frame_header(z, scan)) return 0; + return 1; } -static int stbi__skip_jpeg_junk_at_end(stbi__jpeg * j) { - // some JPEGs have junk at end, skip over it but if we find what looks - // like a valid marker, resume there - while (!stbi__at_eof(j->s)) { - int x = stbi__get8(j->s); - while (x == 255) { // might be a marker - if (stbi__at_eof(j->s)) - return STBI__MARKER_none; - x = stbi__get8(j->s); - if (x != 0x00 && x != 0xff) { - // not a stuffed zero or lead-in to another marker, looks - // like an actual marker, return it - return x; - } - // stuffed zero has x=0 now which ends the loop, meaning we go - // back to regular scan loop. - // repeated 0xff keeps trying to read the next byte of the marker. - } - } - return STBI__MARKER_none; +static stbi_uc stbi__skip_jpeg_junk_at_end(stbi__jpeg *j) +{ + // some JPEGs have junk at end, skip over it but if we find what looks + // like a valid marker, resume there + while (!stbi__at_eof(j->s)) { + stbi_uc x = stbi__get8(j->s); + while (x == 0xff) { // might be a marker + if (stbi__at_eof(j->s)) return STBI__MARKER_none; + x = stbi__get8(j->s); + if (x != 0x00 && x != 0xff) { + // not a stuffed zero or lead-in to another marker, looks + // like an actual marker, return it + return x; + } + // stuffed zero has x=0 now which ends the loop, meaning we go + // back to regular scan loop. + // repeated 0xff keeps trying to read the next byte of the marker. + } + } + return STBI__MARKER_none; } // decode image to YCbCr format -static int stbi__decode_jpeg_image(stbi__jpeg * j) { - int m; - for (m = 0; m < 4; m++) { - j->img_comp[m].raw_data = NULL; - j->img_comp[m].raw_coeff = NULL; - } - j->restart_interval = 0; - if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) - return 0; - m = stbi__get_marker(j); - while (!stbi__EOI(m)) { - if (stbi__SOS(m)) { - if (!stbi__process_scan_header(j)) - return 0; - if (!stbi__parse_entropy_coded_data(j)) - return 0; - if (j->marker == STBI__MARKER_none) { - j->marker = stbi__skip_jpeg_junk_at_end(j); - // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 - } +static int stbi__decode_jpeg_image(stbi__jpeg *j) +{ + int m; + for (m = 0; m < 4; m++) { + j->img_comp[m].raw_data = NULL; + j->img_comp[m].raw_coeff = NULL; + } + j->restart_interval = 0; + if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0; + m = stbi__get_marker(j); + while (!stbi__EOI(m)) { + if (stbi__SOS(m)) { + if (!stbi__process_scan_header(j)) return 0; + if (!stbi__parse_entropy_coded_data(j)) return 0; + if (j->marker == STBI__MARKER_none ) { + j->marker = stbi__skip_jpeg_junk_at_end(j); + // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 + } + m = stbi__get_marker(j); + if (STBI__RESTART(m)) m = stbi__get_marker(j); - if (STBI__RESTART(m)) - m = stbi__get_marker(j); - } else if (stbi__DNL(m)) { - int Ld = stbi__get16be(j->s); - stbi__uint32 NL = stbi__get16be(j->s); - if (Ld != 4) - return stbi__err("bad DNL len", "Corrupt JPEG"); - if (NL != j->s->img_y) - return stbi__err("bad DNL height", "Corrupt JPEG"); - m = stbi__get_marker(j); - } else { - if (!stbi__process_marker(j, m)) - return 1; - m = stbi__get_marker(j); - } - } - if (j->progressive) - stbi__jpeg_finish(j); - return 1; + } else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); + m = stbi__get_marker(j); + } else { + if (!stbi__process_marker(j, m)) return 1; + m = stbi__get_marker(j); + } + } + if (j->progressive) + stbi__jpeg_finish(j); + return 1; } // static jfif-centered resampling (across block boundaries) -typedef stbi_uc * (*resample_row_func)(stbi_uc * out, stbi_uc * in0, stbi_uc * in1, int w, int hs); +typedef stbi_uc *(*resample_row_func)(stbi_uc *out, stbi_uc *in0, stbi_uc *in1, + int w, int hs); -#define stbi__div4(x) ((stbi_uc)((x) >> 2)) +#define stbi__div4(x) ((stbi_uc) ((x) >> 2)) -static stbi_uc * resample_row_1(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) { - STBI_NOTUSED(out); - STBI_NOTUSED(in_far); - STBI_NOTUSED(w); - STBI_NOTUSED(hs); - return in_near; +static stbi_uc *resample_row_1(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + STBI_NOTUSED(out); + STBI_NOTUSED(in_far); + STBI_NOTUSED(w); + STBI_NOTUSED(hs); + return in_near; } -static stbi_uc * stbi__resample_row_v_2(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) { - // need to generate two samples vertically for every one in input - int i; - STBI_NOTUSED(hs); - for (i = 0; i < w; ++i) - out[i] = stbi__div4(3 * in_near[i] + in_far[i] + 2); - return out; +static stbi_uc* stbi__resample_row_v_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples vertically for every one in input + int i; + STBI_NOTUSED(hs); + for (i=0; i < w; ++i) + out[i] = stbi__div4(3*in_near[i] + in_far[i] + 2); + return out; } -static stbi_uc * stbi__resample_row_h_2(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) { - // need to generate two samples horizontally for every one in input - int i; - stbi_uc * input = in_near; +static stbi_uc* stbi__resample_row_h_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples horizontally for every one in input + int i; + stbi_uc *input = in_near; - if (w == 1) { - // if only one sample, can't do any interpolation - out[0] = out[1] = input[0]; - return out; - } + if (w == 1) { + // if only one sample, can't do any interpolation + out[0] = out[1] = input[0]; + return out; + } - out[0] = input[0]; - out[1] = stbi__div4(input[0] * 3 + input[1] + 2); - for (i = 1; i < w - 1; ++i) { - int n = 3 * input[i] + 2; - out[i * 2 + 0] = stbi__div4(n + input[i - 1]); - out[i * 2 + 1] = stbi__div4(n + input[i + 1]); - } - out[i * 2 + 0] = stbi__div4(input[w - 2] * 3 + input[w - 1] + 2); - out[i * 2 + 1] = input[w - 1]; + out[0] = input[0]; + out[1] = stbi__div4(input[0]*3 + input[1] + 2); + for (i=1; i < w-1; ++i) { + int n = 3*input[i]+2; + out[i*2+0] = stbi__div4(n+input[i-1]); + out[i*2+1] = stbi__div4(n+input[i+1]); + } + out[i*2+0] = stbi__div4(input[w-2]*3 + input[w-1] + 2); + out[i*2+1] = input[w-1]; - STBI_NOTUSED(in_far); - STBI_NOTUSED(hs); + STBI_NOTUSED(in_far); + STBI_NOTUSED(hs); - return out; + return out; } -#define stbi__div16(x) ((stbi_uc)((x) >> 4)) +#define stbi__div16(x) ((stbi_uc) ((x) >> 4)) -static stbi_uc * stbi__resample_row_hv_2(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) { - // need to generate 2x2 samples for every one in input - int i, t0, t1; - if (w == 1) { - out[0] = out[1] = stbi__div4(3 * in_near[0] + in_far[0] + 2); - return out; - } +static stbi_uc *stbi__resample_row_hv_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i,t0,t1; + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } - t1 = 3 * in_near[0] + in_far[0]; - out[0] = stbi__div4(t1 + 2); - for (i = 1; i < w; ++i) { - t0 = t1; - t1 = 3 * in_near[i] + in_far[i]; - out[i * 2 - 1] = stbi__div16(3 * t0 + t1 + 8); - out[i * 2] = stbi__div16(3 * t1 + t0 + 8); - } - out[w * 2 - 1] = stbi__div4(t1 + 2); + t1 = 3*in_near[0] + in_far[0]; + out[0] = stbi__div4(t1+2); + for (i=1; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); - STBI_NOTUSED(hs); + STBI_NOTUSED(hs); - return out; + return out; } #if defined(STBI_SSE2) || defined(STBI_NEON) -static stbi_uc * stbi__resample_row_hv_2_simd(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) { - // need to generate 2x2 samples for every one in input - int i = 0, t0, t1; +static stbi_uc *stbi__resample_row_hv_2_simd(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i=0,t0,t1; - if (w == 1) { - out[0] = out[1] = stbi__div4(3 * in_near[0] + in_far[0] + 2); - return out; - } + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } - t1 = 3 * in_near[0] + in_far[0]; - // process groups of 8 pixels for as long as we can. - // note we can't handle the last pixel in a row in this loop - // because we need to handle the filter boundary conditions. - for (; i < ((w - 1) & ~7); i += 8) { + t1 = 3*in_near[0] + in_far[0]; + // process groups of 8 pixels for as long as we can. + // note we can't handle the last pixel in a row in this loop + // because we need to handle the filter boundary conditions. + for (; i < ((w-1) & ~7); i += 8) { #if defined(STBI_SSE2) - // load and perform the vertical filtering pass - // this uses 3*x + y = 4*x + (y - x) - __m128i zero = _mm_setzero_si128(); - __m128i farb = _mm_loadl_epi64((__m128i *)(in_far + i)); - __m128i nearb = _mm_loadl_epi64((__m128i *)(in_near + i)); - __m128i farw = _mm_unpacklo_epi8(farb, zero); - __m128i nearw = _mm_unpacklo_epi8(nearb, zero); - __m128i diff = _mm_sub_epi16(farw, nearw); - __m128i nears = _mm_slli_epi16(nearw, 2); - __m128i curr = _mm_add_epi16(nears, diff); // current row + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + __m128i zero = _mm_setzero_si128(); + __m128i farb = _mm_loadl_epi64((__m128i *) (in_far + i)); + __m128i nearb = _mm_loadl_epi64((__m128i *) (in_near + i)); + __m128i farw = _mm_unpacklo_epi8(farb, zero); + __m128i nearw = _mm_unpacklo_epi8(nearb, zero); + __m128i diff = _mm_sub_epi16(farw, nearw); + __m128i nears = _mm_slli_epi16(nearw, 2); + __m128i curr = _mm_add_epi16(nears, diff); // current row - // horizontal filter works the same based on shifted vers of current - // row. "prev" is current row shifted right by 1 pixel; we need to - // insert the previous pixel value (from t1). - // "next" is current row shifted left by 1 pixel, with first pixel - // of next block of 8 pixels added in. - __m128i prv0 = _mm_slli_si128(curr, 2); - __m128i nxt0 = _mm_srli_si128(curr, 2); - __m128i prev = _mm_insert_epi16(prv0, t1, 0); - __m128i next = _mm_insert_epi16(nxt0, 3 * in_near[i + 8] + in_far[i + 8], 7); + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + __m128i prv0 = _mm_slli_si128(curr, 2); + __m128i nxt0 = _mm_srli_si128(curr, 2); + __m128i prev = _mm_insert_epi16(prv0, t1, 0); + __m128i next = _mm_insert_epi16(nxt0, 3*in_near[i+8] + in_far[i+8], 7); - // horizontal filter, polyphase implementation since it's convenient: - // even pixels = 3*cur + prev = cur*4 + (prev - cur) - // odd pixels = 3*cur + next = cur*4 + (next - cur) - // note the shared term. - __m128i bias = _mm_set1_epi16(8); - __m128i curs = _mm_slli_epi16(curr, 2); - __m128i prvd = _mm_sub_epi16(prev, curr); - __m128i nxtd = _mm_sub_epi16(next, curr); - __m128i curb = _mm_add_epi16(curs, bias); - __m128i even = _mm_add_epi16(prvd, curb); - __m128i odd = _mm_add_epi16(nxtd, curb); + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + __m128i bias = _mm_set1_epi16(8); + __m128i curs = _mm_slli_epi16(curr, 2); + __m128i prvd = _mm_sub_epi16(prev, curr); + __m128i nxtd = _mm_sub_epi16(next, curr); + __m128i curb = _mm_add_epi16(curs, bias); + __m128i even = _mm_add_epi16(prvd, curb); + __m128i odd = _mm_add_epi16(nxtd, curb); - // interleave even and odd pixels, then undo scaling. - __m128i int0 = _mm_unpacklo_epi16(even, odd); - __m128i int1 = _mm_unpackhi_epi16(even, odd); - __m128i de0 = _mm_srli_epi16(int0, 4); - __m128i de1 = _mm_srli_epi16(int1, 4); + // interleave even and odd pixels, then undo scaling. + __m128i int0 = _mm_unpacklo_epi16(even, odd); + __m128i int1 = _mm_unpackhi_epi16(even, odd); + __m128i de0 = _mm_srli_epi16(int0, 4); + __m128i de1 = _mm_srli_epi16(int1, 4); - // pack and write output - __m128i outv = _mm_packus_epi16(de0, de1); - _mm_storeu_si128((__m128i *)(out + i * 2), outv); + // pack and write output + __m128i outv = _mm_packus_epi16(de0, de1); + _mm_storeu_si128((__m128i *) (out + i*2), outv); #elif defined(STBI_NEON) - // load and perform the vertical filtering pass - // this uses 3*x + y = 4*x + (y - x) - uint8x8_t farb = vld1_u8(in_far + i); - uint8x8_t nearb = vld1_u8(in_near + i); - int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); - int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); - int16x8_t curr = vaddq_s16(nears, diff); // current row + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + uint8x8_t farb = vld1_u8(in_far + i); + uint8x8_t nearb = vld1_u8(in_near + i); + int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); + int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); + int16x8_t curr = vaddq_s16(nears, diff); // current row - // horizontal filter works the same based on shifted vers of current - // row. "prev" is current row shifted right by 1 pixel; we need to - // insert the previous pixel value (from t1). - // "next" is current row shifted left by 1 pixel, with first pixel - // of next block of 8 pixels added in. - int16x8_t prv0 = vextq_s16(curr, curr, 7); - int16x8_t nxt0 = vextq_s16(curr, curr, 1); - int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); - int16x8_t next = vsetq_lane_s16(3 * in_near[i + 8] + in_far[i + 8], nxt0, 7); + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + int16x8_t prv0 = vextq_s16(curr, curr, 7); + int16x8_t nxt0 = vextq_s16(curr, curr, 1); + int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); + int16x8_t next = vsetq_lane_s16(3*in_near[i+8] + in_far[i+8], nxt0, 7); - // horizontal filter, polyphase implementation since it's convenient: - // even pixels = 3*cur + prev = cur*4 + (prev - cur) - // odd pixels = 3*cur + next = cur*4 + (next - cur) - // note the shared term. - int16x8_t curs = vshlq_n_s16(curr, 2); - int16x8_t prvd = vsubq_s16(prev, curr); - int16x8_t nxtd = vsubq_s16(next, curr); - int16x8_t even = vaddq_s16(curs, prvd); - int16x8_t odd = vaddq_s16(curs, nxtd); + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + int16x8_t curs = vshlq_n_s16(curr, 2); + int16x8_t prvd = vsubq_s16(prev, curr); + int16x8_t nxtd = vsubq_s16(next, curr); + int16x8_t even = vaddq_s16(curs, prvd); + int16x8_t odd = vaddq_s16(curs, nxtd); - // undo scaling and round, then store with even/odd phases interleaved - uint8x8x2_t o; - o.val[0] = vqrshrun_n_s16(even, 4); - o.val[1] = vqrshrun_n_s16(odd, 4); - vst2_u8(out + i * 2, o); + // undo scaling and round, then store with even/odd phases interleaved + uint8x8x2_t o; + o.val[0] = vqrshrun_n_s16(even, 4); + o.val[1] = vqrshrun_n_s16(odd, 4); + vst2_u8(out + i*2, o); #endif - // "previous" value for next iter - t1 = 3 * in_near[i + 7] + in_far[i + 7]; - } + // "previous" value for next iter + t1 = 3*in_near[i+7] + in_far[i+7]; + } - t0 = t1; - t1 = 3 * in_near[i] + in_far[i]; - out[i * 2] = stbi__div16(3 * t1 + t0 + 8); + t0 = t1; + t1 = 3*in_near[i] + in_far[i]; + out[i*2] = stbi__div16(3*t1 + t0 + 8); - for (++i; i < w; ++i) { - t0 = t1; - t1 = 3 * in_near[i] + in_far[i]; - out[i * 2 - 1] = stbi__div16(3 * t0 + t1 + 8); - out[i * 2] = stbi__div16(3 * t1 + t0 + 8); - } - out[w * 2 - 1] = stbi__div4(t1 + 2); + for (++i; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); - STBI_NOTUSED(hs); + STBI_NOTUSED(hs); - return out; + return out; } #endif -static stbi_uc * stbi__resample_row_generic(stbi_uc * out, stbi_uc * in_near, stbi_uc * in_far, int w, int hs) { - // resample with nearest-neighbor - int i, j; - STBI_NOTUSED(in_far); - for (i = 0; i < w; ++i) - for (j = 0; j < hs; ++j) - out[i * hs + j] = in_near[i]; - return out; +static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // resample with nearest-neighbor + int i,j; + STBI_NOTUSED(in_far); + for (i=0; i < w; ++i) + for (j=0; j < hs; ++j) + out[i*hs+j] = in_near[i]; + return out; } // this is a reduced-precision calculation of YCbCr-to-RGB introduced // to make sure the code produces the same results in both SIMD and scalar -#define stbi__float2fixed(x) (((int)((x)*4096.0f + 0.5f)) << 8) -static void stbi__YCbCr_to_RGB_row(stbi_uc * out, const stbi_uc * y, const stbi_uc * pcb, const stbi_uc * pcr, int count, - int step) { - int i; - for (i = 0; i < count; ++i) { - int y_fixed = (y[i] << 20) + (1 << 19); // rounding - int r, g, b; - int cr = pcr[i] - 128; - int cb = pcb[i] - 128; - r = y_fixed + cr * stbi__float2fixed(1.40200f); - g = y_fixed + (cr * -stbi__float2fixed(0.71414f)) + ((cb * -stbi__float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb * stbi__float2fixed(1.77200f); - r >>= 20; - g >>= 20; - b >>= 20; - if ((unsigned)r > 255) { - if (r < 0) - r = 0; - else - r = 255; - } - if ((unsigned)g > 255) { - if (g < 0) - g = 0; - else - g = 255; - } - if ((unsigned)b > 255) { - if (b < 0) - b = 0; - else - b = 255; - } - out[0] = (stbi_uc)r; - out[1] = (stbi_uc)g; - out[2] = (stbi_uc)b; - out[3] = 255; - out += step; - } +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) +{ + int i; + for (i=0; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } } #if defined(STBI_SSE2) || defined(STBI_NEON) -static void stbi__YCbCr_to_RGB_simd(stbi_uc * out, stbi_uc const * y, stbi_uc const * pcb, stbi_uc const * pcr, int count, - int step) { - int i = 0; +static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) +{ + int i = 0; #ifdef STBI_SSE2 - // step == 3 is pretty ugly on the final interleave, and i'm not convinced - // it's useful in practice (you wouldn't use it for textures, for example). - // so just accelerate step == 4 case. - if (step == 4) { - // this is a fairly straightforward implementation and not super-optimized. - __m128i signflip = _mm_set1_epi8(-0x80); - __m128i cr_const0 = _mm_set1_epi16((short)(1.40200f * 4096.0f + 0.5f)); - __m128i cr_const1 = _mm_set1_epi16(-(short)(0.71414f * 4096.0f + 0.5f)); - __m128i cb_const0 = _mm_set1_epi16(-(short)(0.34414f * 4096.0f + 0.5f)); - __m128i cb_const1 = _mm_set1_epi16((short)(1.77200f * 4096.0f + 0.5f)); - __m128i y_bias = _mm_set1_epi8((char)(unsigned char)128); - __m128i xw = _mm_set1_epi16(255); // alpha channel + // step == 3 is pretty ugly on the final interleave, and i'm not convinced + // it's useful in practice (you wouldn't use it for textures, for example). + // so just accelerate step == 4 case. + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + __m128i signflip = _mm_set1_epi8(-0x80); + __m128i cr_const0 = _mm_set1_epi16( (short) ( 1.40200f*4096.0f+0.5f)); + __m128i cr_const1 = _mm_set1_epi16( - (short) ( 0.71414f*4096.0f+0.5f)); + __m128i cb_const0 = _mm_set1_epi16( - (short) ( 0.34414f*4096.0f+0.5f)); + __m128i cb_const1 = _mm_set1_epi16( (short) ( 1.77200f*4096.0f+0.5f)); + __m128i y_bias = _mm_set1_epi8((char) (unsigned char) 128); + __m128i xw = _mm_set1_epi16(255); // alpha channel - for (; i + 7 < count; i += 8) { - // load - __m128i y_bytes = _mm_loadl_epi64((__m128i *)(y + i)); - __m128i cr_bytes = _mm_loadl_epi64((__m128i *)(pcr + i)); - __m128i cb_bytes = _mm_loadl_epi64((__m128i *)(pcb + i)); - __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 - __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 + for (; i+7 < count; i += 8) { + // load + __m128i y_bytes = _mm_loadl_epi64((__m128i *) (y+i)); + __m128i cr_bytes = _mm_loadl_epi64((__m128i *) (pcr+i)); + __m128i cb_bytes = _mm_loadl_epi64((__m128i *) (pcb+i)); + __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 + __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 - // unpack to short (and left-shift cr, cb by 8) - __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); - __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); - __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); + // unpack to short (and left-shift cr, cb by 8) + __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); + __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); + __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); - // color transform - __m128i yws = _mm_srli_epi16(yw, 4); - __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); - __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); - __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); - __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); - __m128i rws = _mm_add_epi16(cr0, yws); - __m128i gwt = _mm_add_epi16(cb0, yws); - __m128i bws = _mm_add_epi16(yws, cb1); - __m128i gws = _mm_add_epi16(gwt, cr1); + // color transform + __m128i yws = _mm_srli_epi16(yw, 4); + __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); + __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); + __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); + __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); + __m128i rws = _mm_add_epi16(cr0, yws); + __m128i gwt = _mm_add_epi16(cb0, yws); + __m128i bws = _mm_add_epi16(yws, cb1); + __m128i gws = _mm_add_epi16(gwt, cr1); - // descale - __m128i rw = _mm_srai_epi16(rws, 4); - __m128i bw = _mm_srai_epi16(bws, 4); - __m128i gw = _mm_srai_epi16(gws, 4); + // descale + __m128i rw = _mm_srai_epi16(rws, 4); + __m128i bw = _mm_srai_epi16(bws, 4); + __m128i gw = _mm_srai_epi16(gws, 4); - // back to byte, set up for transpose - __m128i brb = _mm_packus_epi16(rw, bw); - __m128i gxb = _mm_packus_epi16(gw, xw); + // back to byte, set up for transpose + __m128i brb = _mm_packus_epi16(rw, bw); + __m128i gxb = _mm_packus_epi16(gw, xw); - // transpose to interleave channels - __m128i t0 = _mm_unpacklo_epi8(brb, gxb); - __m128i t1 = _mm_unpackhi_epi8(brb, gxb); - __m128i o0 = _mm_unpacklo_epi16(t0, t1); - __m128i o1 = _mm_unpackhi_epi16(t0, t1); + // transpose to interleave channels + __m128i t0 = _mm_unpacklo_epi8(brb, gxb); + __m128i t1 = _mm_unpackhi_epi8(brb, gxb); + __m128i o0 = _mm_unpacklo_epi16(t0, t1); + __m128i o1 = _mm_unpackhi_epi16(t0, t1); - // store - _mm_storeu_si128((__m128i *)(out + 0), o0); - _mm_storeu_si128((__m128i *)(out + 16), o1); - out += 32; - } - } + // store + _mm_storeu_si128((__m128i *) (out + 0), o0); + _mm_storeu_si128((__m128i *) (out + 16), o1); + out += 32; + } + } #endif #ifdef STBI_NEON - // in this version, step=3 support would be easy to add. but is there demand? - if (step == 4) { - // this is a fairly straightforward implementation and not super-optimized. - uint8x8_t signflip = vdup_n_u8(0x80); - int16x8_t cr_const0 = vdupq_n_s16((short)(1.40200f * 4096.0f + 0.5f)); - int16x8_t cr_const1 = vdupq_n_s16(-(short)(0.71414f * 4096.0f + 0.5f)); - int16x8_t cb_const0 = vdupq_n_s16(-(short)(0.34414f * 4096.0f + 0.5f)); - int16x8_t cb_const1 = vdupq_n_s16((short)(1.77200f * 4096.0f + 0.5f)); + // in this version, step=3 support would be easy to add. but is there demand? + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + uint8x8_t signflip = vdup_n_u8(0x80); + int16x8_t cr_const0 = vdupq_n_s16( (short) ( 1.40200f*4096.0f+0.5f)); + int16x8_t cr_const1 = vdupq_n_s16( - (short) ( 0.71414f*4096.0f+0.5f)); + int16x8_t cb_const0 = vdupq_n_s16( - (short) ( 0.34414f*4096.0f+0.5f)); + int16x8_t cb_const1 = vdupq_n_s16( (short) ( 1.77200f*4096.0f+0.5f)); - for (; i + 7 < count; i += 8) { - // load - uint8x8_t y_bytes = vld1_u8(y + i); - uint8x8_t cr_bytes = vld1_u8(pcr + i); - uint8x8_t cb_bytes = vld1_u8(pcb + i); - int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); - int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); + for (; i+7 < count; i += 8) { + // load + uint8x8_t y_bytes = vld1_u8(y + i); + uint8x8_t cr_bytes = vld1_u8(pcr + i); + uint8x8_t cb_bytes = vld1_u8(pcb + i); + int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); + int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); - // expand to s16 - int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); - int16x8_t crw = vshll_n_s8(cr_biased, 7); - int16x8_t cbw = vshll_n_s8(cb_biased, 7); + // expand to s16 + int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); + int16x8_t crw = vshll_n_s8(cr_biased, 7); + int16x8_t cbw = vshll_n_s8(cb_biased, 7); - // color transform - int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); - int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); - int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); - int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); - int16x8_t rws = vaddq_s16(yws, cr0); - int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); - int16x8_t bws = vaddq_s16(yws, cb1); + // color transform + int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); + int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); + int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); + int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); + int16x8_t rws = vaddq_s16(yws, cr0); + int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); + int16x8_t bws = vaddq_s16(yws, cb1); - // undo scaling, round, convert to byte - uint8x8x4_t o; - o.val[0] = vqrshrun_n_s16(rws, 4); - o.val[1] = vqrshrun_n_s16(gws, 4); - o.val[2] = vqrshrun_n_s16(bws, 4); - o.val[3] = vdup_n_u8(255); + // undo scaling, round, convert to byte + uint8x8x4_t o; + o.val[0] = vqrshrun_n_s16(rws, 4); + o.val[1] = vqrshrun_n_s16(gws, 4); + o.val[2] = vqrshrun_n_s16(bws, 4); + o.val[3] = vdup_n_u8(255); - // store, interleaving r/g/b/a - vst4_u8(out, o); - out += 8 * 4; - } - } + // store, interleaving r/g/b/a + vst4_u8(out, o); + out += 8*4; + } + } #endif - for (; i < count; ++i) { - int y_fixed = (y[i] << 20) + (1 << 19); // rounding - int r, g, b; - int cr = pcr[i] - 128; - int cb = pcb[i] - 128; - r = y_fixed + cr * stbi__float2fixed(1.40200f); - g = y_fixed + cr * -stbi__float2fixed(0.71414f) + ((cb * -stbi__float2fixed(0.34414f)) & 0xffff0000); - b = y_fixed + cb * stbi__float2fixed(1.77200f); - r >>= 20; - g >>= 20; - b >>= 20; - if ((unsigned)r > 255) { - if (r < 0) - r = 0; - else - r = 255; - } - if ((unsigned)g > 255) { - if (g < 0) - g = 0; - else - g = 255; - } - if ((unsigned)b > 255) { - if (b < 0) - b = 0; - else - b = 255; - } - out[0] = (stbi_uc)r; - out[1] = (stbi_uc)g; - out[2] = (stbi_uc)b; - out[3] = 255; - out += step; - } + for (; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } } #endif // set up the kernels -static void stbi__setup_jpeg(stbi__jpeg * j) { - j->idct_block_kernel = stbi__idct_block; - j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; - j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; +static void stbi__setup_jpeg(stbi__jpeg *j) +{ + j->idct_block_kernel = stbi__idct_block; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; #ifdef STBI_SSE2 - if (stbi__sse2_available()) { - j->idct_block_kernel = stbi__idct_simd; - j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; - } + if (stbi__sse2_available()) { + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + } #endif #ifdef STBI_NEON - j->idct_block_kernel = stbi__idct_simd; - j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; - j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; #endif } // clean up the temporary component buffers -static void stbi__cleanup_jpeg(stbi__jpeg * j) { stbi__free_jpeg_components(j, j->s->img_n, 0); } +static void stbi__cleanup_jpeg(stbi__jpeg *j) +{ + stbi__free_jpeg_components(j, j->s->img_n, 0); +} -typedef struct { - resample_row_func resample; - stbi_uc *line0, *line1; - int hs, vs; // expansion factor in each axis - int w_lores; // horizontal pixels pre-expansion - int ystep; // how far through vertical expansion we are - int ypos; // which pre-expansion row we're on +typedef struct +{ + resample_row_func resample; + stbi_uc *line0,*line1; + int hs,vs; // expansion factor in each axis + int w_lores; // horizontal pixels pre-expansion + int ystep; // how far through vertical expansion we are + int ypos; // which pre-expansion row we're on } stbi__resample; // fast 0..255 * 0..255 => 0..255 rounded multiplication -static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) { - unsigned int t = x * y + 128; - return (stbi_uc)((t + (t >> 8)) >> 8); +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x*y + 128; + return (stbi_uc) ((t + (t >>8)) >> 8); } -static stbi_uc * load_jpeg_image(stbi__jpeg * z, int * out_x, int * out_y, int * comp, int req_comp) { - int n, decode_n, is_rgb; - z->s->img_n = 0; // make stbi__cleanup_jpeg safe +static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) +{ + int n, decode_n, is_rgb; + z->s->img_n = 0; // make stbi__cleanup_jpeg safe - // validate req_comp - if (req_comp < 0 || req_comp > 4) - return stbi__errpuc("bad req_comp", "Internal error"); + // validate req_comp + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); - // load a jpeg image from whichever source, but leave in YCbCr format - if (!stbi__decode_jpeg_image(z)) { - stbi__cleanup_jpeg(z); - return NULL; - } + // load a jpeg image from whichever source, but leave in YCbCr format + if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } - // determine actual number of components to generate - n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + // determine actual number of components to generate + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; - is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); - if (z->s->img_n == 3 && n < 3 && !is_rgb) - decode_n = 1; - else - decode_n = z->s->img_n; + if (z->s->img_n == 3 && n < 3 && !is_rgb) + decode_n = 1; + else + decode_n = z->s->img_n; - // nothing to do if no components requested; check this now to avoid - // accessing uninitialized coutput[0] later - if (decode_n <= 0) { - stbi__cleanup_jpeg(z); - return NULL; - } + // nothing to do if no components requested; check this now to avoid + // accessing uninitialized coutput[0] later + if (decode_n <= 0) { stbi__cleanup_jpeg(z); return NULL; } - // resample and color-convert - { - int k; - unsigned int i, j; - stbi_uc * output; - stbi_uc * coutput[4] = {NULL, NULL, NULL, NULL}; + // resample and color-convert + { + int k; + unsigned int i,j; + stbi_uc *output; + stbi_uc *coutput[4] = { NULL, NULL, NULL, NULL }; - stbi__resample res_comp[4]; + stbi__resample res_comp[4]; - for (k = 0; k < decode_n; ++k) { - stbi__resample * r = &res_comp[k]; + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; - // allocate line buffer big enough for upsampling off the edges - // with upsample factor of 4 - z->img_comp[k].linebuf = (stbi_uc *)stbi__malloc(z->s->img_x + 3); - if (!z->img_comp[k].linebuf) { - stbi__cleanup_jpeg(z); - return stbi__errpuc("outofmem", "Out of memory"); + // allocate line buffer big enough for upsampling off the edges + // with upsample factor of 4 + z->img_comp[k].linebuf = (stbi_uc *) stbi__malloc(z->s->img_x + 3); + if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + r->hs = z->img_h_max / z->img_comp[k].h; + r->vs = z->img_v_max / z->img_comp[k].v; + r->ystep = r->vs >> 1; + r->w_lores = (z->s->img_x + r->hs-1) / r->hs; + r->ypos = 0; + r->line0 = r->line1 = z->img_comp[k].data; + + if (r->hs == 1 && r->vs == 1) r->resample = resample_row_1; + else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2; + else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2; + else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel; + else r->resample = stbi__resample_row_generic; + } + + // can't error after this so, this is safe + output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); + if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + // now go ahead and resample + for (j=0; j < z->s->img_y; ++j) { + stbi_uc *out = output + n * z->s->img_x * j; + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + int y_bot = r->ystep >= (r->vs >> 1); + coutput[k] = r->resample(z->img_comp[k].linebuf, + y_bot ? r->line1 : r->line0, + y_bot ? r->line0 : r->line1, + r->w_lores, r->hs); + if (++r->ystep >= r->vs) { + r->ystep = 0; + r->line0 = r->line1; + if (++r->ypos < z->img_comp[k].y) + r->line1 += z->img_comp[k].w2; } - - r->hs = z->img_h_max / z->img_comp[k].h; - r->vs = z->img_v_max / z->img_comp[k].v; - r->ystep = r->vs >> 1; - r->w_lores = (z->s->img_x + r->hs - 1) / r->hs; - r->ypos = 0; - r->line0 = r->line1 = z->img_comp[k].data; - - if (r->hs == 1 && r->vs == 1) - r->resample = resample_row_1; - else if (r->hs == 1 && r->vs == 2) - r->resample = stbi__resample_row_v_2; - else if (r->hs == 2 && r->vs == 1) - r->resample = stbi__resample_row_h_2; - else if (r->hs == 2 && r->vs == 2) - r->resample = z->resample_row_hv_2_kernel; - else - r->resample = stbi__resample_row_generic; - } - - // can't error after this so, this is safe - output = (stbi_uc *)stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); - if (!output) { - stbi__cleanup_jpeg(z); - return stbi__errpuc("outofmem", "Out of memory"); - } - - // now go ahead and resample - for (j = 0; j < z->s->img_y; ++j) { - stbi_uc * out = output + n * z->s->img_x * j; - for (k = 0; k < decode_n; ++k) { - stbi__resample * r = &res_comp[k]; - int y_bot = r->ystep >= (r->vs >> 1); - coutput[k] = r->resample(z->img_comp[k].linebuf, y_bot ? r->line1 : r->line0, y_bot ? r->line0 : r->line1, - r->w_lores, r->hs); - if (++r->ystep >= r->vs) { - r->ystep = 0; - r->line0 = r->line1; - if (++r->ypos < z->img_comp[k].y) - r->line1 += z->img_comp[k].w2; - } - } - if (n >= 3) { - stbi_uc * y = coutput[0]; - if (z->s->img_n == 3) { - if (is_rgb) { - for (i = 0; i < z->s->img_x; ++i) { - out[0] = y[i]; - out[1] = coutput[1][i]; - out[2] = coutput[2][i]; - out[3] = 255; - out += n; - } - } else { - z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); - } - } else if (z->s->img_n == 4) { - if (z->app14_color_transform == 0) { // CMYK - for (i = 0; i < z->s->img_x; ++i) { - stbi_uc m = coutput[3][i]; - out[0] = stbi__blinn_8x8(coutput[0][i], m); - out[1] = stbi__blinn_8x8(coutput[1][i], m); - out[2] = stbi__blinn_8x8(coutput[2][i], m); - out[3] = 255; - out += n; - } - } else if (z->app14_color_transform == 2) { // YCCK - z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); - for (i = 0; i < z->s->img_x; ++i) { - stbi_uc m = coutput[3][i]; - out[0] = stbi__blinn_8x8(255 - out[0], m); - out[1] = stbi__blinn_8x8(255 - out[1], m); - out[2] = stbi__blinn_8x8(255 - out[2], m); - out += n; - } - } else { // YCbCr + alpha? Ignore the fourth channel for now - z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); - } - } else - for (i = 0; i < z->s->img_x; ++i) { - out[0] = out[1] = out[2] = y[i]; - out[3] = 255; // not used if n==3 - out += n; - } + } + if (n >= 3) { + stbi_uc *y = coutput[0]; + if (z->s->img_n == 3) { + if (is_rgb) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else + for (i=0; i < z->s->img_x; ++i) { + out[0] = out[1] = out[2] = y[i]; + out[3] = 255; // not used if n==3 + out += n; + } + } else { + if (is_rgb) { + if (n == 1) + for (i=0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i=0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } } else { - if (is_rgb) { - if (n == 1) - for (i = 0; i < z->s->img_x; ++i) - *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); - else { - for (i = 0; i < z->s->img_x; ++i, out += 2) { - out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); - out[1] = 255; - } - } - } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { - for (i = 0; i < z->s->img_x; ++i) { - stbi_uc m = coutput[3][i]; - stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); - stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); - stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); - out[0] = stbi__compute_y(r, g, b); - out[1] = 255; - out += n; - } - } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { - for (i = 0; i < z->s->img_x; ++i) { - out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); - out[1] = 255; - out += n; - } - } else { - stbi_uc * y = coutput[0]; - if (n == 1) - for (i = 0; i < z->s->img_x; ++i) - out[i] = y[i]; - else - for (i = 0; i < z->s->img_x; ++i) { - *out++ = y[i]; - *out++ = 255; - } - } + stbi_uc *y = coutput[0]; + if (n == 1) + for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i=0; i < z->s->img_x; ++i) { *out++ = y[i]; *out++ = 255; } } - } - stbi__cleanup_jpeg(z); - *out_x = z->s->img_x; - *out_y = z->s->img_y; - if (comp) - *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output - return output; - } + } + } + stbi__cleanup_jpeg(z); + *out_x = z->s->img_x; + *out_y = z->s->img_y; + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output + return output; + } } -static void * stbi__jpeg_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) { - unsigned char * result; - stbi__jpeg * j = (stbi__jpeg *)stbi__malloc(sizeof(stbi__jpeg)); - if (!j) - return stbi__errpuc("outofmem", "Out of memory"); - memset(j, 0, sizeof(stbi__jpeg)); - STBI_NOTUSED(ri); - j->s = s; - stbi__setup_jpeg(j); - result = load_jpeg_image(j, x, y, comp, req_comp); - STBI_FREE(j); - return result; +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + if (!j) return stbi__errpuc("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x,y,comp,req_comp); + STBI_FREE(j); + return result; } -static int stbi__jpeg_test(stbi__context * s) { - int r; - stbi__jpeg * j = (stbi__jpeg *)stbi__malloc(sizeof(stbi__jpeg)); - if (!j) - return stbi__err("outofmem", "Out of memory"); - memset(j, 0, sizeof(stbi__jpeg)); - j->s = s; - stbi__setup_jpeg(j); - r = stbi__decode_jpeg_header(j, STBI__SCAN_type); - stbi__rewind(s); - STBI_FREE(j); - return r; +static int stbi__jpeg_test(stbi__context *s) +{ + int r; + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + if (!j) return stbi__err("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); + stbi__rewind(s); + STBI_FREE(j); + return r; } -static int stbi__jpeg_info_raw(stbi__jpeg * j, int * x, int * y, int * comp) { - if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { - stbi__rewind(j->s); - return 0; - } - if (x) - *x = j->s->img_x; - if (y) - *y = j->s->img_y; - if (comp) - *comp = j->s->img_n >= 3 ? 3 : 1; - return 1; +static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) +{ + if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { + stbi__rewind( j->s ); + return 0; + } + if (x) *x = j->s->img_x; + if (y) *y = j->s->img_y; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; + return 1; } -static int stbi__jpeg_info(stbi__context * s, int * x, int * y, int * comp) { - int result; - stbi__jpeg * j = (stbi__jpeg *)(stbi__malloc(sizeof(stbi__jpeg))); - if (!j) - return stbi__err("outofmem", "Out of memory"); - memset(j, 0, sizeof(stbi__jpeg)); - j->s = s; - result = stbi__jpeg_info_raw(j, x, y, comp); - STBI_FREE(j); - return result; +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) +{ + int result; + stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + if (!j) return stbi__err("outofmem", "Out of memory"); + memset(j, 0, sizeof(stbi__jpeg)); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; } #endif @@ -4278,81 +4088,84 @@ static int stbi__jpeg_info(stbi__context * s, int * x, int * y, int * comp) { #ifndef STBI_NO_ZLIB // fast-way is faster to check than jpeg huffman, but slow way is slower -#define STBI__ZFAST_BITS 9 // accelerate all cases in default tables -#define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) +#define STBI__ZFAST_BITS 9 // accelerate all cases in default tables +#define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) #define STBI__ZNSYMS 288 // number of symbols in literal/length alphabet // zlib-style huffman encoding // (jpegs packs from left, zlib from right, so can't share code) -typedef struct { - stbi__uint16 fast[1 << STBI__ZFAST_BITS]; - stbi__uint16 firstcode[16]; - int maxcode[17]; - stbi__uint16 firstsymbol[16]; - stbi_uc size[STBI__ZNSYMS]; - stbi__uint16 value[STBI__ZNSYMS]; +typedef struct +{ + stbi__uint16 fast[1 << STBI__ZFAST_BITS]; + stbi__uint16 firstcode[16]; + int maxcode[17]; + stbi__uint16 firstsymbol[16]; + stbi_uc size[STBI__ZNSYMS]; + stbi__uint16 value[STBI__ZNSYMS]; } stbi__zhuffman; -stbi_inline static int stbi__bitreverse16(int n) { - n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); - n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); - n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); - n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); - return n; +stbi_inline static int stbi__bitreverse16(int n) +{ + n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); + n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); + n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); + n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); + return n; } -stbi_inline static int stbi__bit_reverse(int v, int bits) { - STBI_ASSERT(bits <= 16); - // to bit reverse n bits, reverse 16 and shift - // e.g. 11 bits, bit reverse and shift away 5 - return stbi__bitreverse16(v) >> (16 - bits); +stbi_inline static int stbi__bit_reverse(int v, int bits) +{ + STBI_ASSERT(bits <= 16); + // to bit reverse n bits, reverse 16 and shift + // e.g. 11 bits, bit reverse and shift away 5 + return stbi__bitreverse16(v) >> (16-bits); } -static int stbi__zbuild_huffman(stbi__zhuffman * z, const stbi_uc * sizelist, int num) { - int i, k = 0; - int code, next_code[16], sizes[17]; +static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num) +{ + int i,k=0; + int code, next_code[16], sizes[17]; - // DEFLATE spec for generating codes - memset(sizes, 0, sizeof(sizes)); - memset(z->fast, 0, sizeof(z->fast)); - for (i = 0; i < num; ++i) - ++sizes[sizelist[i]]; - sizes[0] = 0; - for (i = 1; i < 16; ++i) - if (sizes[i] > (1 << i)) - return stbi__err("bad sizes", "Corrupt PNG"); - code = 0; - for (i = 1; i < 16; ++i) { - next_code[i] = code; - z->firstcode[i] = (stbi__uint16)code; - z->firstsymbol[i] = (stbi__uint16)k; - code = (code + sizes[i]); - if (sizes[i]) - if (code - 1 >= (1 << i)) - return stbi__err("bad codelengths", "Corrupt PNG"); - z->maxcode[i] = code << (16 - i); // preshift for inner loop - code <<= 1; - k += sizes[i]; - } - z->maxcode[16] = 0x10000; // sentinel - for (i = 0; i < num; ++i) { - int s = sizelist[i]; - if (s) { - int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; - stbi__uint16 fastv = (stbi__uint16)((s << 9) | i); - z->size[c] = (stbi_uc)s; - z->value[c] = (stbi__uint16)i; - if (s <= STBI__ZFAST_BITS) { - int j = stbi__bit_reverse(next_code[s], s); - while (j < (1 << STBI__ZFAST_BITS)) { - z->fast[j] = fastv; - j += (1 << s); - } + // DEFLATE spec for generating codes + memset(sizes, 0, sizeof(sizes)); + memset(z->fast, 0, sizeof(z->fast)); + for (i=0; i < num; ++i) + ++sizes[sizelist[i]]; + sizes[0] = 0; + for (i=1; i < 16; ++i) + if (sizes[i] > (1 << i)) + return stbi__err("bad sizes", "Corrupt PNG"); + code = 0; + for (i=1; i < 16; ++i) { + next_code[i] = code; + z->firstcode[i] = (stbi__uint16) code; + z->firstsymbol[i] = (stbi__uint16) k; + code = (code + sizes[i]); + if (sizes[i]) + if (code-1 >= (1 << i)) return stbi__err("bad codelengths","Corrupt PNG"); + z->maxcode[i] = code << (16-i); // preshift for inner loop + code <<= 1; + k += sizes[i]; + } + z->maxcode[16] = 0x10000; // sentinel + for (i=0; i < num; ++i) { + int s = sizelist[i]; + if (s) { + int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; + stbi__uint16 fastv = (stbi__uint16) ((s << 9) | i); + z->size [c] = (stbi_uc ) s; + z->value[c] = (stbi__uint16) i; + if (s <= STBI__ZFAST_BITS) { + int j = stbi__bit_reverse(next_code[s],s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); } - ++next_code[s]; - } - } - return 1; + } + ++next_code[s]; + } + } + return 1; } // zlib-from-memory implementation for PNG reading @@ -4361,298 +4174,297 @@ static int stbi__zbuild_huffman(stbi__zhuffman * z, const stbi_uc * sizelist, in // we require PNG read all the IDATs and combine them into a single // memory buffer -typedef struct { - stbi_uc *zbuffer, *zbuffer_end; - int num_bits; - stbi__uint32 code_buffer; +typedef struct +{ + stbi_uc *zbuffer, *zbuffer_end; + int num_bits; + int hit_zeof_once; + stbi__uint32 code_buffer; - char * zout; - char * zout_start; - char * zout_end; - int z_expandable; + char *zout; + char *zout_start; + char *zout_end; + int z_expandable; - stbi__zhuffman z_length, z_distance; + stbi__zhuffman z_length, z_distance; } stbi__zbuf; -stbi_inline static int stbi__zeof(stbi__zbuf * z) { return (z->zbuffer >= z->zbuffer_end); } - -stbi_inline static stbi_uc stbi__zget8(stbi__zbuf * z) { return stbi__zeof(z) ? 0 : *z->zbuffer++; } - -static void stbi__fill_bits(stbi__zbuf * z) { - do { - if (z->code_buffer >= (1U << z->num_bits)) { - z->zbuffer = z->zbuffer_end; /* treat this as EOF so we fail. */ - return; - } - z->code_buffer |= (unsigned int)stbi__zget8(z) << z->num_bits; - z->num_bits += 8; - } while (z->num_bits <= 24); -} - -stbi_inline static unsigned int stbi__zreceive(stbi__zbuf * z, int n) { - unsigned int k; - if (z->num_bits < n) - stbi__fill_bits(z); - k = z->code_buffer & ((1 << n) - 1); - z->code_buffer >>= n; - z->num_bits -= n; - return k; -} - -static int stbi__zhuffman_decode_slowpath(stbi__zbuf * a, stbi__zhuffman * z) { - int b, s, k; - // not resolved by fast table, so compute it the slow way - // use jpeg approach, which requires MSbits at top - k = stbi__bit_reverse(a->code_buffer, 16); - for (s = STBI__ZFAST_BITS + 1;; ++s) - if (k < z->maxcode[s]) - break; - if (s >= 16) - return -1; // invalid code! - // code size is s, so: - b = (k >> (16 - s)) - z->firstcode[s] + z->firstsymbol[s]; - if (b >= STBI__ZNSYMS) - return -1; // some data was corrupt somewhere! - if (z->size[b] != s) - return -1; // was originally an assert, but report failure instead. - a->code_buffer >>= s; - a->num_bits -= s; - return z->value[b]; -} - -stbi_inline static int stbi__zhuffman_decode(stbi__zbuf * a, stbi__zhuffman * z) { - int b, s; - if (a->num_bits < 16) { - if (stbi__zeof(a)) { - return -1; /* report error for unexpected end of data. */ - } - stbi__fill_bits(a); - } - b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; - if (b) { - s = b >> 9; - a->code_buffer >>= s; - a->num_bits -= s; - return b & 511; - } - return stbi__zhuffman_decode_slowpath(a, z); -} - -static int stbi__zexpand(stbi__zbuf * z, char * zout, int n) // need to make room for n bytes +stbi_inline static int stbi__zeof(stbi__zbuf *z) { - char * q; - unsigned int cur, limit, old_limit; - z->zout = zout; - if (!z->z_expandable) - return stbi__err("output buffer limit", "Corrupt PNG"); - cur = (unsigned int)(z->zout - z->zout_start); - limit = old_limit = (unsigned)(z->zout_end - z->zout_start); - if (UINT_MAX - cur < (unsigned)n) - return stbi__err("outofmem", "Out of memory"); - while (cur + n > limit) { - if (limit > UINT_MAX / 2) - return stbi__err("outofmem", "Out of memory"); - limit *= 2; - } - q = (char *)STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); - STBI_NOTUSED(old_limit); - if (q == NULL) - return stbi__err("outofmem", "Out of memory"); - z->zout_start = q; - z->zout = q + cur; - z->zout_end = q + limit; - return 1; + return (z->zbuffer >= z->zbuffer_end); } -static const int stbi__zlength_base[31] = {3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 17, 19, 23, 27, 31, - 35, 43, 51, 59, 67, 83, 99, 115, 131, 163, 195, 227, 258, 0, 0}; - -static const int stbi__zlength_extra[31] = {0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, - 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0, 0, 0}; - -static const int stbi__zdist_base[32] = {1, 2, 3, 4, 5, 7, 9, 13, 17, 25, 33, - 49, 65, 97, 129, 193, 257, 385, 513, 769, 1025, 1537, - 2049, 3073, 4097, 6145, 8193, 12289, 16385, 24577, 0, 0}; - -static const int stbi__zdist_extra[32] = {0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, - 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13}; - -static int stbi__parse_huffman_block(stbi__zbuf * a) { - char * zout = a->zout; - for (;;) { - int z = stbi__zhuffman_decode(a, &a->z_length); - if (z < 256) { - if (z < 0) - return stbi__err("bad huffman code", "Corrupt PNG"); // error in huffman codes - if (zout >= a->zout_end) { - if (!stbi__zexpand(a, zout, 1)) - return 0; - zout = a->zout; - } - *zout++ = (char)z; - } else { - stbi_uc * p; - int len, dist; - if (z == 256) { - a->zout = zout; - return 1; - } - if (z >= 286) - return stbi__err("bad huffman code", - "Corrupt PNG"); // per DEFLATE, length codes 286 and 287 must not appear in compressed data - z -= 257; - len = stbi__zlength_base[z]; - if (stbi__zlength_extra[z]) - len += stbi__zreceive(a, stbi__zlength_extra[z]); - z = stbi__zhuffman_decode(a, &a->z_distance); - if (z < 0 || z >= 30) - return stbi__err("bad huffman code", - "Corrupt PNG"); // per DEFLATE, distance codes 30 and 31 must not appear in compressed data - dist = stbi__zdist_base[z]; - if (stbi__zdist_extra[z]) - dist += stbi__zreceive(a, stbi__zdist_extra[z]); - if (zout - a->zout_start < dist) - return stbi__err("bad dist", "Corrupt PNG"); - if (zout + len > a->zout_end) { - if (!stbi__zexpand(a, zout, len)) - return 0; - zout = a->zout; - } - p = (stbi_uc *)(zout - dist); - if (dist == 1) { // run of one byte; common in images. - stbi_uc v = *p; - if (len) { - do - *zout++ = v; - while (--len); - } - } else { - if (len) { - do - *zout++ = *p++; - while (--len); - } - } - } - } +stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z) +{ + return stbi__zeof(z) ? 0 : *z->zbuffer++; } -static int stbi__compute_huffman_codes(stbi__zbuf * a) { - static const stbi_uc length_dezigzag[19] = {16, 17, 18, 0, 8, 7, 9, 6, 10, 5, 11, 4, 12, 3, 13, 2, 14, 1, 15}; - stbi__zhuffman z_codelength; - stbi_uc lencodes[286 + 32 + 137]; // padding for maximum single op - stbi_uc codelength_sizes[19]; - int i, n; +static void stbi__fill_bits(stbi__zbuf *z) +{ + do { + if (z->code_buffer >= (1U << z->num_bits)) { + z->zbuffer = z->zbuffer_end; /* treat this as EOF so we fail. */ + return; + } + z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; + z->num_bits += 8; + } while (z->num_bits <= 24); +} - int hlit = stbi__zreceive(a, 5) + 257; - int hdist = stbi__zreceive(a, 5) + 1; - int hclen = stbi__zreceive(a, 4) + 4; - int ntot = hlit + hdist; +stbi_inline static unsigned int stbi__zreceive(stbi__zbuf *z, int n) +{ + unsigned int k; + if (z->num_bits < n) stbi__fill_bits(z); + k = z->code_buffer & ((1 << n) - 1); + z->code_buffer >>= n; + z->num_bits -= n; + return k; +} - memset(codelength_sizes, 0, sizeof(codelength_sizes)); - for (i = 0; i < hclen; ++i) { - int s = stbi__zreceive(a, 3); - codelength_sizes[length_dezigzag[i]] = (stbi_uc)s; - } - if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) - return 0; +static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s,k; + // not resolved by fast table, so compute it the slow way + // use jpeg approach, which requires MSbits at top + k = stbi__bit_reverse(a->code_buffer, 16); + for (s=STBI__ZFAST_BITS+1; ; ++s) + if (k < z->maxcode[s]) + break; + if (s >= 16) return -1; // invalid code! + // code size is s, so: + b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s]; + if (b >= STBI__ZNSYMS) return -1; // some data was corrupt somewhere! + if (z->size[b] != s) return -1; // was originally an assert, but report failure instead. + a->code_buffer >>= s; + a->num_bits -= s; + return z->value[b]; +} - n = 0; - while (n < ntot) { - int c = stbi__zhuffman_decode(a, &z_codelength); - if (c < 0 || c >= 19) +stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s; + if (a->num_bits < 16) { + if (stbi__zeof(a)) { + if (!a->hit_zeof_once) { + // This is the first time we hit eof, insert 16 extra padding btis + // to allow us to keep going; if we actually consume any of them + // though, that is invalid data. This is caught later. + a->hit_zeof_once = 1; + a->num_bits += 16; // add 16 implicit zero bits + } else { + // We already inserted our extra 16 padding bits and are again + // out, this stream is actually prematurely terminated. + return -1; + } + } else { + stbi__fill_bits(a); + } + } + b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; + if (b) { + s = b >> 9; + a->code_buffer >>= s; + a->num_bits -= s; + return b & 511; + } + return stbi__zhuffman_decode_slowpath(a, z); +} + +static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes +{ + char *q; + unsigned int cur, limit, old_limit; + z->zout = zout; + if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); + cur = (unsigned int) (z->zout - z->zout_start); + limit = old_limit = (unsigned) (z->zout_end - z->zout_start); + if (UINT_MAX - cur < (unsigned) n) return stbi__err("outofmem", "Out of memory"); + while (cur + n > limit) { + if(limit > UINT_MAX / 2) return stbi__err("outofmem", "Out of memory"); + limit *= 2; + } + q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); + if (q == NULL) return stbi__err("outofmem", "Out of memory"); + z->zout_start = q; + z->zout = q + cur; + z->zout_end = q + limit; + return 1; +} + +static const int stbi__zlength_base[31] = { + 3,4,5,6,7,8,9,10,11,13, + 15,17,19,23,27,31,35,43,51,59, + 67,83,99,115,131,163,195,227,258,0,0 }; + +static const int stbi__zlength_extra[31]= +{ 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; + +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; + +static const int stbi__zdist_extra[32] = +{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; + +static int stbi__parse_huffman_block(stbi__zbuf *a) +{ + char *zout = a->zout; + for(;;) { + int z = stbi__zhuffman_decode(a, &a->z_length); + if (z < 256) { + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); // error in huffman codes + if (zout >= a->zout_end) { + if (!stbi__zexpand(a, zout, 1)) return 0; + zout = a->zout; + } + *zout++ = (char) z; + } else { + stbi_uc *p; + int len,dist; + if (z == 256) { + a->zout = zout; + if (a->hit_zeof_once && a->num_bits < 16) { + // The first time we hit zeof, we inserted 16 extra zero bits into our bit + // buffer so the decoder can just do its speculative decoding. But if we + // actually consumed any of those bits (which is the case when num_bits < 16), + // the stream actually read past the end so it is malformed. + return stbi__err("unexpected end","Corrupt PNG"); + } + return 1; + } + if (z >= 286) return stbi__err("bad huffman code","Corrupt PNG"); // per DEFLATE, length codes 286 and 287 must not appear in compressed data + z -= 257; + len = stbi__zlength_base[z]; + if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); + z = stbi__zhuffman_decode(a, &a->z_distance); + if (z < 0 || z >= 30) return stbi__err("bad huffman code","Corrupt PNG"); // per DEFLATE, distance codes 30 and 31 must not appear in compressed data + dist = stbi__zdist_base[z]; + if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); + if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); + if (len > a->zout_end - zout) { + if (!stbi__zexpand(a, zout, len)) return 0; + zout = a->zout; + } + p = (stbi_uc *) (zout - dist); + if (dist == 1) { // run of one byte; common in images. + stbi_uc v = *p; + if (len) { do *zout++ = v; while (--len); } + } else { + if (len) { do *zout++ = *p++; while (--len); } + } + } + } +} + +static int stbi__compute_huffman_codes(stbi__zbuf *a) +{ + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + stbi__zhuffman z_codelength; + stbi_uc lencodes[286+32+137];//padding for maximum single op + stbi_uc codelength_sizes[19]; + int i,n; + + int hlit = stbi__zreceive(a,5) + 257; + int hdist = stbi__zreceive(a,5) + 1; + int hclen = stbi__zreceive(a,4) + 4; + int ntot = hlit + hdist; + + memset(codelength_sizes, 0, sizeof(codelength_sizes)); + for (i=0; i < hclen; ++i) { + int s = stbi__zreceive(a,3); + codelength_sizes[length_dezigzag[i]] = (stbi_uc) s; + } + if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; + + n = 0; + while (n < ntot) { + int c = stbi__zhuffman_decode(a, &z_codelength); + if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); + if (c < 16) + lencodes[n++] = (stbi_uc) c; + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a,2)+3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n-1]; + } else if (c == 17) { + c = stbi__zreceive(a,3)+3; + } else if (c == 18) { + c = stbi__zreceive(a,7)+11; + } else { return stbi__err("bad codelengths", "Corrupt PNG"); - if (c < 16) - lencodes[n++] = (stbi_uc)c; - else { - stbi_uc fill = 0; - if (c == 16) { - c = stbi__zreceive(a, 2) + 3; - if (n == 0) - return stbi__err("bad codelengths", "Corrupt PNG"); - fill = lencodes[n - 1]; - } else if (c == 17) { - c = stbi__zreceive(a, 3) + 3; - } else if (c == 18) { - c = stbi__zreceive(a, 7) + 11; - } else { - return stbi__err("bad codelengths", "Corrupt PNG"); - } - if (ntot - n < c) - return stbi__err("bad codelengths", "Corrupt PNG"); - memset(lencodes + n, fill, c); - n += c; - } - } - if (n != ntot) - return stbi__err("bad codelengths", "Corrupt PNG"); - if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) - return 0; - if (!stbi__zbuild_huffman(&a->z_distance, lencodes + hlit, hdist)) - return 0; - return 1; + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes+n, fill, c); + n += c; + } + } + if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG"); + if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; + return 1; } -static int stbi__parse_uncompressed_block(stbi__zbuf * a) { - stbi_uc header[4]; - int len, nlen, k; - if (a->num_bits & 7) - stbi__zreceive(a, a->num_bits & 7); // discard - // drain the bit-packed data into header - k = 0; - while (a->num_bits > 0) { - header[k++] = (stbi_uc)(a->code_buffer & 255); // suppress MSVC run-time check - a->code_buffer >>= 8; - a->num_bits -= 8; - } - if (a->num_bits < 0) - return stbi__err("zlib corrupt", "Corrupt PNG"); - // now fill header the normal way - while (k < 4) - header[k++] = stbi__zget8(a); - len = header[1] * 256 + header[0]; - nlen = header[3] * 256 + header[2]; - if (nlen != (len ^ 0xffff)) - return stbi__err("zlib corrupt", "Corrupt PNG"); - if (a->zbuffer + len > a->zbuffer_end) - return stbi__err("read past buffer", "Corrupt PNG"); - if (a->zout + len > a->zout_end) - if (!stbi__zexpand(a, a->zout, len)) - return 0; - memcpy(a->zout, a->zbuffer, len); - a->zbuffer += len; - a->zout += len; - return 1; +static int stbi__parse_uncompressed_block(stbi__zbuf *a) +{ + stbi_uc header[4]; + int len,nlen,k; + if (a->num_bits & 7) + stbi__zreceive(a, a->num_bits & 7); // discard + // drain the bit-packed data into header + k = 0; + while (a->num_bits > 0) { + header[k++] = (stbi_uc) (a->code_buffer & 255); // suppress MSVC run-time check + a->code_buffer >>= 8; + a->num_bits -= 8; + } + if (a->num_bits < 0) return stbi__err("zlib corrupt","Corrupt PNG"); + // now fill header the normal way + while (k < 4) + header[k++] = stbi__zget8(a); + len = header[1] * 256 + header[0]; + nlen = header[3] * 256 + header[2]; + if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt","Corrupt PNG"); + if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer","Corrupt PNG"); + if (a->zout + len > a->zout_end) + if (!stbi__zexpand(a, a->zout, len)) return 0; + memcpy(a->zout, a->zbuffer, len); + a->zbuffer += len; + a->zout += len; + return 1; } -static int stbi__parse_zlib_header(stbi__zbuf * a) { - int cmf = stbi__zget8(a); - int cm = cmf & 15; - /* int cinfo = cmf >> 4; */ - int flg = stbi__zget8(a); - if (stbi__zeof(a)) - return stbi__err("bad zlib header", "Corrupt PNG"); // zlib spec - if ((cmf * 256 + flg) % 31 != 0) - return stbi__err("bad zlib header", "Corrupt PNG"); // zlib spec - if (flg & 32) - return stbi__err("no preset dict", "Corrupt PNG"); // preset dictionary not allowed in png - if (cm != 8) - return stbi__err("bad compression", "Corrupt PNG"); // DEFLATE required for png - // window = 1 << (8 + cinfo)... but who cares, we fully buffer output - return 1; +static int stbi__parse_zlib_header(stbi__zbuf *a) +{ + int cmf = stbi__zget8(a); + int cm = cmf & 15; + /* int cinfo = cmf >> 4; */ + int flg = stbi__zget8(a); + if (stbi__zeof(a)) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec + if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec + if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png + if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png + // window = 1 << (8 + cinfo)... but who cares, we fully buffer output + return 1; } -static const stbi_uc stbi__zdefault_length[STBI__ZNSYMS] = { - 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, - 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, - 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, - 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, - 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, - 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, - 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, - 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8}; -static const stbi_uc stbi__zdefault_distance[32] = {5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, - 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5}; +static const stbi_uc stbi__zdefault_length[STBI__ZNSYMS] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; /* Init algorithm: { @@ -4666,122 +4478,118 @@ Init algorithm: } */ -static int stbi__parse_zlib(stbi__zbuf * a, int parse_header) { - int final, type; - if (parse_header) - if (!stbi__parse_zlib_header(a)) - return 0; - a->num_bits = 0; - a->code_buffer = 0; - do { - final = stbi__zreceive(a, 1); - type = stbi__zreceive(a, 2); - if (type == 0) { - if (!stbi__parse_uncompressed_block(a)) - return 0; - } else if (type == 3) { - return 0; - } else { - if (type == 1) { - // use fixed code lengths - if (!stbi__zbuild_huffman(&a->z_length, stbi__zdefault_length, STBI__ZNSYMS)) - return 0; - if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) - return 0; - } else { - if (!stbi__compute_huffman_codes(a)) - return 0; - } - if (!stbi__parse_huffman_block(a)) - return 0; - } - } while (!final); - return 1; +static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) +{ + int final, type; + if (parse_header) + if (!stbi__parse_zlib_header(a)) return 0; + a->num_bits = 0; + a->code_buffer = 0; + a->hit_zeof_once = 0; + do { + final = stbi__zreceive(a,1); + type = stbi__zreceive(a,2); + if (type == 0) { + if (!stbi__parse_uncompressed_block(a)) return 0; + } else if (type == 3) { + return 0; + } else { + if (type == 1) { + // use fixed code lengths + if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , STBI__ZNSYMS)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; + } else { + if (!stbi__compute_huffman_codes(a)) return 0; + } + if (!stbi__parse_huffman_block(a)) return 0; + } + } while (!final); + return 1; } -static int stbi__do_zlib(stbi__zbuf * a, char * obuf, int olen, int exp, int parse_header) { - a->zout_start = obuf; - a->zout = obuf; - a->zout_end = obuf + olen; - a->z_expandable = exp; +static int stbi__do_zlib(stbi__zbuf *a, char *obuf, int olen, int exp, int parse_header) +{ + a->zout_start = obuf; + a->zout = obuf; + a->zout_end = obuf + olen; + a->z_expandable = exp; - return stbi__parse_zlib(a, parse_header); + return stbi__parse_zlib(a, parse_header); } -STBIDEF char * stbi_zlib_decode_malloc_guesssize(const char * buffer, int len, int initial_size, int * outlen) { - stbi__zbuf a; - char * p = (char *)stbi__malloc(initial_size); - if (p == NULL) - return NULL; - a.zbuffer = (stbi_uc *)buffer; - a.zbuffer_end = (stbi_uc *)buffer + len; - if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { - if (outlen) - *outlen = (int)(a.zout - a.zout_start); - return a.zout_start; - } else { - STBI_FREE(a.zout_start); - return NULL; - } +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } } -STBIDEF char * stbi_zlib_decode_malloc(char const * buffer, int len, int * outlen) { - return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); +STBIDEF char *stbi_zlib_decode_malloc(char const *buffer, int len, int *outlen) +{ + return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); } -STBIDEF char * stbi_zlib_decode_malloc_guesssize_headerflag(const char * buffer, int len, int initial_size, int * outlen, - int parse_header) { - stbi__zbuf a; - char * p = (char *)stbi__malloc(initial_size); - if (p == NULL) - return NULL; - a.zbuffer = (stbi_uc *)buffer; - a.zbuffer_end = (stbi_uc *)buffer + len; - if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { - if (outlen) - *outlen = (int)(a.zout - a.zout_start); - return a.zout_start; - } else { - STBI_FREE(a.zout_start); - return NULL; - } +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } } -STBIDEF int stbi_zlib_decode_buffer(char * obuffer, int olen, char const * ibuffer, int ilen) { - stbi__zbuf a; - a.zbuffer = (stbi_uc *)ibuffer; - a.zbuffer_end = (stbi_uc *)ibuffer + ilen; - if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) - return (int)(a.zout - a.zout_start); - else - return -1; +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, char const *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) + return (int) (a.zout - a.zout_start); + else + return -1; } -STBIDEF char * stbi_zlib_decode_noheader_malloc(char const * buffer, int len, int * outlen) { - stbi__zbuf a; - char * p = (char *)stbi__malloc(16384); - if (p == NULL) - return NULL; - a.zbuffer = (stbi_uc *)buffer; - a.zbuffer_end = (stbi_uc *)buffer + len; - if (stbi__do_zlib(&a, p, 16384, 1, 0)) { - if (outlen) - *outlen = (int)(a.zout - a.zout_start); - return a.zout_start; - } else { - STBI_FREE(a.zout_start); - return NULL; - } +STBIDEF char *stbi_zlib_decode_noheader_malloc(char const *buffer, int len, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(16384); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer+len; + if (stbi__do_zlib(&a, p, 16384, 1, 0)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } } -STBIDEF int stbi_zlib_decode_noheader_buffer(char * obuffer, int olen, const char * ibuffer, int ilen) { - stbi__zbuf a; - a.zbuffer = (stbi_uc *)ibuffer; - a.zbuffer_end = (stbi_uc *)ibuffer + ilen; - if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) - return (int)(a.zout - a.zout_start); - else - return -1; +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) + return (int) (a.zout - a.zout_start); + else + return -1; } #endif @@ -4796,1303 +4604,1131 @@ STBIDEF int stbi_zlib_decode_noheader_buffer(char * obuffer, int olen, const cha // - uses stb_zlib, a PD zlib implementation with fast huffman decoding #ifndef STBI_NO_PNG -typedef struct { - stbi__uint32 length; - stbi__uint32 type; +typedef struct +{ + stbi__uint32 length; + stbi__uint32 type; } stbi__pngchunk; -static stbi__pngchunk stbi__get_chunk_header(stbi__context * s) { - stbi__pngchunk c; - c.length = stbi__get32be(s); - c.type = stbi__get32be(s); - return c; +static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) +{ + stbi__pngchunk c; + c.length = stbi__get32be(s); + c.type = stbi__get32be(s); + return c; } -static int stbi__check_png_header(stbi__context * s) { - static const stbi_uc png_sig[8] = {137, 80, 78, 71, 13, 10, 26, 10}; - int i; - for (i = 0; i < 8; ++i) - if (stbi__get8(s) != png_sig[i]) - return stbi__err("bad png sig", "Not a PNG"); - return 1; +static int stbi__check_png_header(stbi__context *s) +{ + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + int i; + for (i=0; i < 8; ++i) + if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); + return 1; } -typedef struct { - stbi__context * s; - stbi_uc *idata, *expanded, *out; - int depth; +typedef struct +{ + stbi__context *s; + stbi_uc *idata, *expanded, *out; + int depth; } stbi__png; + enum { - STBI__F_none = 0, - STBI__F_sub = 1, - STBI__F_up = 2, - STBI__F_avg = 3, - STBI__F_paeth = 4, - // synthetic filters used for first scanline to avoid needing a dummy row of 0s - STBI__F_avg_first, - STBI__F_paeth_first + STBI__F_none=0, + STBI__F_sub=1, + STBI__F_up=2, + STBI__F_avg=3, + STBI__F_paeth=4, + // synthetic filter used for first scanline to avoid needing a dummy row of 0s + STBI__F_avg_first }; -static stbi_uc first_row_filter[5] = {STBI__F_none, STBI__F_sub, STBI__F_none, STBI__F_avg_first, STBI__F_paeth_first}; +static stbi_uc first_row_filter[5] = +{ + STBI__F_none, + STBI__F_sub, + STBI__F_none, + STBI__F_avg_first, + STBI__F_sub // Paeth with b=c=0 turns out to be equivalent to sub +}; -static int stbi__paeth(int a, int b, int c) { - int p = a + b - c; - int pa = abs(p - a); - int pb = abs(p - b); - int pc = abs(p - c); - if (pa <= pb && pa <= pc) - return a; - if (pb <= pc) - return b; - return c; +static int stbi__paeth(int a, int b, int c) +{ + // This formulation looks very different from the reference in the PNG spec, but is + // actually equivalent and has favorable data dependencies and admits straightforward + // generation of branch-free code, which helps performance significantly. + int thresh = c*3 - (a + b); + int lo = a < b ? a : b; + int hi = a < b ? b : a; + int t0 = (hi <= thresh) ? lo : c; + int t1 = (thresh <= lo) ? hi : t0; + return t1; } -static const stbi_uc stbi__depth_scale_table[9] = {0, 0xff, 0x55, 0, 0x11, 0, 0, 0, 0x01}; +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; + +// adds an extra all-255 alpha channel +// dest == src is legal +// img_n must be 1 or 3 +static void stbi__create_png_alpha_expand8(stbi_uc *dest, stbi_uc *src, stbi__uint32 x, int img_n) +{ + int i; + // must process data backwards since we allow dest==src + if (img_n == 1) { + for (i=x-1; i >= 0; --i) { + dest[i*2+1] = 255; + dest[i*2+0] = src[i]; + } + } else { + STBI_ASSERT(img_n == 3); + for (i=x-1; i >= 0; --i) { + dest[i*4+3] = 255; + dest[i*4+2] = src[i*3+2]; + dest[i*4+1] = src[i*3+1]; + dest[i*4+0] = src[i*3+0]; + } + } +} // create the png data from post-deflated data -static int stbi__create_png_image_raw(stbi__png * a, stbi_uc * raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, - stbi__uint32 y, int depth, int color) { - int bytes = (depth == 16 ? 2 : 1); - stbi__context * s = a->s; - stbi__uint32 i, j, stride = x * out_n * bytes; - stbi__uint32 img_len, img_width_bytes; - int k; - int img_n = s->img_n; // copy it into a local for later +static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) +{ + int bytes = (depth == 16 ? 2 : 1); + stbi__context *s = a->s; + stbi__uint32 i,j,stride = x*out_n*bytes; + stbi__uint32 img_len, img_width_bytes; + stbi_uc *filter_buf; + int all_ok = 1; + int k; + int img_n = s->img_n; // copy it into a local for later - int output_bytes = out_n * bytes; - int filter_bytes = img_n * bytes; - int width = x; + int output_bytes = out_n*bytes; + int filter_bytes = img_n*bytes; + int width = x; - STBI_ASSERT(out_n == s->img_n || out_n == s->img_n + 1); - a->out = (stbi_uc *)stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into - if (!a->out) - return stbi__err("outofmem", "Out of memory"); + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); + a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into + if (!a->out) return stbi__err("outofmem", "Out of memory"); - if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) - return stbi__err("too large", "Corrupt PNG"); - img_width_bytes = (((img_n * x * depth) + 7) >> 3); - img_len = (img_width_bytes + 1) * y; + // note: error exits here don't need to clean up a->out individually, + // stbi__do_png always does on error. + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); + img_width_bytes = (((img_n * x * depth) + 7) >> 3); + if (!stbi__mad2sizes_valid(img_width_bytes, y, img_width_bytes)) return stbi__err("too large", "Corrupt PNG"); + img_len = (img_width_bytes + 1) * y; - // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, - // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), - // so just check for raw_len < img_len always. - if (raw_len < img_len) - return stbi__err("not enough pixels", "Corrupt PNG"); + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); - for (j = 0; j < y; ++j) { - stbi_uc * cur = a->out + stride * j; - stbi_uc * prior; - int filter = *raw++; + // Allocate two scan lines worth of filter workspace buffer. + filter_buf = (stbi_uc *) stbi__malloc_mad2(img_width_bytes, 2, 0); + if (!filter_buf) return stbi__err("outofmem", "Out of memory"); - if (filter > 4) - return stbi__err("invalid filter", "Corrupt PNG"); + // Filtering for low-bit-depth images + if (depth < 8) { + filter_bytes = 1; + width = img_width_bytes; + } - if (depth < 8) { - if (img_width_bytes > x) - return stbi__err("invalid width", "Corrupt PNG"); - cur += x * out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place - filter_bytes = 1; - width = img_width_bytes; - } - prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above + for (j=0; j < y; ++j) { + // cur/prior filter buffers alternate + stbi_uc *cur = filter_buf + (j & 1)*img_width_bytes; + stbi_uc *prior = filter_buf + (~j & 1)*img_width_bytes; + stbi_uc *dest = a->out + stride*j; + int nk = width * filter_bytes; + int filter = *raw++; - // if first row, use special filter that doesn't sample previous row - if (j == 0) - filter = first_row_filter[filter]; + // check filter type + if (filter > 4) { + all_ok = stbi__err("invalid filter","Corrupt PNG"); + break; + } - // handle first byte explicitly - for (k = 0; k < filter_bytes; ++k) { - switch (filter) { - case STBI__F_none: - cur[k] = raw[k]; - break; - case STBI__F_sub: - cur[k] = raw[k]; - break; - case STBI__F_up: - cur[k] = STBI__BYTECAST(raw[k] + prior[k]); - break; - case STBI__F_avg: - cur[k] = STBI__BYTECAST(raw[k] + (prior[k] >> 1)); - break; - case STBI__F_paeth: - cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0, prior[k], 0)); - break; - case STBI__F_avg_first: - cur[k] = raw[k]; - break; - case STBI__F_paeth_first: - cur[k] = raw[k]; - break; + // if first row, use special filter that doesn't sample previous row + if (j == 0) filter = first_row_filter[filter]; + + // perform actual filtering + switch (filter) { + case STBI__F_none: + memcpy(cur, raw, nk); + break; + case STBI__F_sub: + memcpy(cur, raw, filter_bytes); + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); + break; + case STBI__F_up: + for (k = 0; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + prior[k]); + break; + case STBI__F_avg: + for (k = 0; k < filter_bytes; ++k) + cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); + break; + case STBI__F_paeth: + for (k = 0; k < filter_bytes; ++k) + cur[k] = STBI__BYTECAST(raw[k] + prior[k]); // prior[k] == stbi__paeth(0,prior[k],0) + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes], prior[k], prior[k-filter_bytes])); + break; + case STBI__F_avg_first: + memcpy(cur, raw, filter_bytes); + for (k = filter_bytes; k < nk; ++k) + cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); + break; + } + + raw += nk; + + // expand decoded bits in cur to dest, also adding an extra alpha channel if desired + if (depth < 8) { + stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range + stbi_uc *in = cur; + stbi_uc *out = dest; + stbi_uc inb = 0; + stbi__uint32 nsmp = x*img_n; + + // expand bits to bytes first + if (depth == 4) { + for (i=0; i < nsmp; ++i) { + if ((i & 1) == 0) inb = *in++; + *out++ = scale * (inb >> 4); + inb <<= 4; } - } - - if (depth == 8) { - if (img_n != out_n) - cur[img_n] = 255; // first pixel - raw += img_n; - cur += out_n; - prior += out_n; - } else if (depth == 16) { - if (img_n != out_n) { - cur[filter_bytes] = 255; // first pixel top byte - cur[filter_bytes + 1] = 255; // first pixel bottom byte + } else if (depth == 2) { + for (i=0; i < nsmp; ++i) { + if ((i & 3) == 0) inb = *in++; + *out++ = scale * (inb >> 6); + inb <<= 2; } - raw += filter_bytes; - cur += output_bytes; - prior += output_bytes; - } else { - raw += 1; - cur += 1; - prior += 1; - } - - // this is a little gross, so that we don't switch per-pixel or per-component - if (depth < 8 || img_n == out_n) { - int nk = (width - 1) * filter_bytes; -#define STBI__CASE(f) \ - case f: \ - for (k = 0; k < nk; ++k) - switch (filter) { - // "none" filter turns into a memcpy here; make that explicit. - case STBI__F_none: - memcpy(cur, raw, nk); - break; - STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k - filter_bytes]); } - break; - STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } - break; - STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k - filter_bytes]) >> 1)); } - break; - STBI__CASE(STBI__F_paeth) { - cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - filter_bytes], prior[k], prior[k - filter_bytes])); - } - break; - STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k - filter_bytes] >> 1)); } - break; - STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - filter_bytes], 0, 0)); } - break; + } else { + STBI_ASSERT(depth == 1); + for (i=0; i < nsmp; ++i) { + if ((i & 7) == 0) inb = *in++; + *out++ = scale * (inb >> 7); + inb <<= 1; } -#undef STBI__CASE - raw += nk; - } else { - STBI_ASSERT(img_n + 1 == out_n); -#define STBI__CASE(f) \ - case f: \ - for (i = x - 1; i >= 1; --i, cur[filter_bytes] = 255, raw += filter_bytes, cur += output_bytes, prior += output_bytes) \ - for (k = 0; k < filter_bytes; ++k) - switch (filter) { - STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } - break; - STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k - output_bytes]); } - break; - STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } - break; - STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k - output_bytes]) >> 1)); } - break; - STBI__CASE(STBI__F_paeth) { - cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - output_bytes], prior[k], prior[k - output_bytes])); - } - break; - STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k - output_bytes] >> 1)); } - break; - STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k - output_bytes], 0, 0)); } - break; + } + + // insert alpha=255 values if desired + if (img_n != out_n) + stbi__create_png_alpha_expand8(dest, dest, x, img_n); + } else if (depth == 8) { + if (img_n == out_n) + memcpy(dest, cur, x*img_n); + else + stbi__create_png_alpha_expand8(dest, cur, x, img_n); + } else if (depth == 16) { + // convert the image data from big-endian to platform-native + stbi__uint16 *dest16 = (stbi__uint16*)dest; + stbi__uint32 nsmp = x*img_n; + + if (img_n == out_n) { + for (i = 0; i < nsmp; ++i, ++dest16, cur += 2) + *dest16 = (cur[0] << 8) | cur[1]; + } else { + STBI_ASSERT(img_n+1 == out_n); + if (img_n == 1) { + for (i = 0; i < x; ++i, dest16 += 2, cur += 2) { + dest16[0] = (cur[0] << 8) | cur[1]; + dest16[1] = 0xffff; + } + } else { + STBI_ASSERT(img_n == 3); + for (i = 0; i < x; ++i, dest16 += 4, cur += 6) { + dest16[0] = (cur[0] << 8) | cur[1]; + dest16[1] = (cur[2] << 8) | cur[3]; + dest16[2] = (cur[4] << 8) | cur[5]; + dest16[3] = 0xffff; + } } -#undef STBI__CASE + } + } + } - // the loop above sets the high byte of the pixels' alpha, but for - // 16 bit png files we also need the low byte set. we'll do that here. - if (depth == 16) { - cur = a->out + stride * j; // start at the beginning of the row again - for (i = 0; i < x; ++i, cur += output_bytes) { - cur[filter_bytes + 1] = 255; - } - } - } - } + STBI_FREE(filter_buf); + if (!all_ok) return 0; - // we make a separate pass to expand bits to pixels; for performance, - // this could run two scanlines behind the above code, so it won't - // intefere with filtering but will still be in the cache. - if (depth < 8) { - for (j = 0; j < y; ++j) { - stbi_uc * cur = a->out + stride * j; - stbi_uc * in = a->out + stride * j + x * out_n - img_width_bytes; - // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for - // 1/2/4-bit png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that - // will be skipped in the later loop - stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range - - // note that the final byte might overshoot and write more data than desired. - // we can allocate enough data that this never writes out of memory, but it - // could also overwrite the next scanline. can it overwrite non-empty data - // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. - // so we need to explicitly clamp the final ones - - if (depth == 4) { - for (k = x * img_n; k >= 2; k -= 2, ++in) { - *cur++ = scale * ((*in >> 4)); - *cur++ = scale * ((*in) & 0x0f); - } - if (k > 0) - *cur++ = scale * ((*in >> 4)); - } else if (depth == 2) { - for (k = x * img_n; k >= 4; k -= 4, ++in) { - *cur++ = scale * ((*in >> 6)); - *cur++ = scale * ((*in >> 4) & 0x03); - *cur++ = scale * ((*in >> 2) & 0x03); - *cur++ = scale * ((*in) & 0x03); - } - if (k > 0) - *cur++ = scale * ((*in >> 6)); - if (k > 1) - *cur++ = scale * ((*in >> 4) & 0x03); - if (k > 2) - *cur++ = scale * ((*in >> 2) & 0x03); - } else if (depth == 1) { - for (k = x * img_n; k >= 8; k -= 8, ++in) { - *cur++ = scale * ((*in >> 7)); - *cur++ = scale * ((*in >> 6) & 0x01); - *cur++ = scale * ((*in >> 5) & 0x01); - *cur++ = scale * ((*in >> 4) & 0x01); - *cur++ = scale * ((*in >> 3) & 0x01); - *cur++ = scale * ((*in >> 2) & 0x01); - *cur++ = scale * ((*in >> 1) & 0x01); - *cur++ = scale * ((*in) & 0x01); - } - if (k > 0) - *cur++ = scale * ((*in >> 7)); - if (k > 1) - *cur++ = scale * ((*in >> 6) & 0x01); - if (k > 2) - *cur++ = scale * ((*in >> 5) & 0x01); - if (k > 3) - *cur++ = scale * ((*in >> 4) & 0x01); - if (k > 4) - *cur++ = scale * ((*in >> 3) & 0x01); - if (k > 5) - *cur++ = scale * ((*in >> 2) & 0x01); - if (k > 6) - *cur++ = scale * ((*in >> 1) & 0x01); - } - if (img_n != out_n) { - int q; - // insert alpha = 255 - cur = a->out + stride * j; - if (img_n == 1) { - for (q = x - 1; q >= 0; --q) { - cur[q * 2 + 1] = 255; - cur[q * 2 + 0] = cur[q]; - } - } else { - STBI_ASSERT(img_n == 3); - for (q = x - 1; q >= 0; --q) { - cur[q * 4 + 3] = 255; - cur[q * 4 + 2] = cur[q * 3 + 2]; - cur[q * 4 + 1] = cur[q * 3 + 1]; - cur[q * 4 + 0] = cur[q * 3 + 0]; - } - } - } - } - } else if (depth == 16) { - // force the image data from big-endian to platform-native. - // this is done in a separate pass due to the decoding relying - // on the data being untouched, but could probably be done - // per-line during decode if care is taken. - stbi_uc * cur = a->out; - stbi__uint16 * cur16 = (stbi__uint16 *)cur; - - for (i = 0; i < x * y * out_n; ++i, cur16++, cur += 2) { - *cur16 = (cur[0] << 8) | cur[1]; - } - } - - return 1; + return 1; } -static int stbi__create_png_image(stbi__png * a, stbi_uc * image_data, stbi__uint32 image_data_len, int out_n, int depth, - int color, int interlaced) { - int bytes = (depth == 16 ? 2 : 1); - int out_bytes = out_n * bytes; - stbi_uc * final; - int p; - if (!interlaced) - return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); +static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) +{ + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; + stbi_uc *final; + int p; + if (!interlaced) + return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); - // de-interlacing - final = (stbi_uc *)stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); - if (!final) - return stbi__err("outofmem", "Out of memory"); - for (p = 0; p < 7; ++p) { - int xorig[] = {0, 4, 0, 2, 0, 1, 0}; - int yorig[] = {0, 0, 4, 0, 2, 0, 1}; - int xspc[] = {8, 8, 4, 4, 2, 2, 1}; - int yspc[] = {8, 8, 8, 4, 4, 2, 2}; - int i, j, x, y; - // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 - x = (a->s->img_x - xorig[p] + xspc[p] - 1) / xspc[p]; - y = (a->s->img_y - yorig[p] + yspc[p] - 1) / yspc[p]; - if (x && y) { - stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; - if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { - STBI_FREE(final); - return 0; + // de-interlacing + final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); + if (!final) return stbi__err("outofmem", "Out of memory"); + for (p=0; p < 7; ++p) { + int xorig[] = { 0,4,0,2,0,1,0 }; + int yorig[] = { 0,0,4,0,2,0,1 }; + int xspc[] = { 8,8,4,4,2,2,1 }; + int yspc[] = { 8,8,8,4,4,2,2 }; + int i,j,x,y; + // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 + x = (a->s->img_x - xorig[p] + xspc[p]-1) / xspc[p]; + y = (a->s->img_y - yorig[p] + yspc[p]-1) / yspc[p]; + if (x && y) { + stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; + if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { + STBI_FREE(final); + return 0; + } + for (j=0; j < y; ++j) { + for (i=0; i < x; ++i) { + int out_y = j*yspc[p]+yorig[p]; + int out_x = i*xspc[p]+xorig[p]; + memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes, + a->out + (j*x+i)*out_bytes, out_bytes); } - for (j = 0; j < y; ++j) { - for (i = 0; i < x; ++i) { - int out_y = j * yspc[p] + yorig[p]; - int out_x = i * xspc[p] + xorig[p]; - memcpy(final + out_y * a->s->img_x * out_bytes + out_x * out_bytes, a->out + (j * x + i) * out_bytes, - out_bytes); - } - } - STBI_FREE(a->out); - image_data += img_len; - image_data_len -= img_len; - } - } - a->out = final; + } + STBI_FREE(a->out); + image_data += img_len; + image_data_len -= img_len; + } + } + a->out = final; - return 1; + return 1; } -static int stbi__compute_transparency(stbi__png * z, stbi_uc tc[3], int out_n) { - stbi__context * s = z->s; - stbi__uint32 i, pixel_count = s->img_x * s->img_y; - stbi_uc * p = z->out; +static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; - // compute color-based transparency, assuming we've - // already got 255 as the alpha value in the output - STBI_ASSERT(out_n == 2 || out_n == 4); + // compute color-based transparency, assuming we've + // already got 255 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); - if (out_n == 2) { - for (i = 0; i < pixel_count; ++i) { - p[1] = (p[0] == tc[0] ? 0 : 255); - p += 2; - } - } else { - for (i = 0; i < pixel_count; ++i) { - if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) - p[3] = 0; - p += 4; - } - } - return 1; + if (out_n == 2) { + for (i=0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 255); + p += 2; + } + } else { + for (i=0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; } -static int stbi__compute_transparency16(stbi__png * z, stbi__uint16 tc[3], int out_n) { - stbi__context * s = z->s; - stbi__uint32 i, pixel_count = s->img_x * s->img_y; - stbi__uint16 * p = (stbi__uint16 *)z->out; +static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16 *p = (stbi__uint16*) z->out; - // compute color-based transparency, assuming we've - // already got 65535 as the alpha value in the output - STBI_ASSERT(out_n == 2 || out_n == 4); + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); - if (out_n == 2) { - for (i = 0; i < pixel_count; ++i) { - p[1] = (p[0] == tc[0] ? 0 : 65535); - p += 2; - } - } else { - for (i = 0; i < pixel_count; ++i) { - if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) - p[3] = 0; - p += 4; - } - } - return 1; + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; } -static int stbi__expand_png_palette(stbi__png * a, stbi_uc * palette, int len, int pal_img_n) { - stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; - stbi_uc *p, *temp_out, *orig = a->out; +static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) +{ + stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; + stbi_uc *p, *temp_out, *orig = a->out; - p = (stbi_uc *)stbi__malloc_mad2(pixel_count, pal_img_n, 0); - if (p == NULL) - return stbi__err("outofmem", "Out of memory"); + p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0); + if (p == NULL) return stbi__err("outofmem", "Out of memory"); - // between here and free(out) below, exitting would leak - temp_out = p; + // between here and free(out) below, exitting would leak + temp_out = p; - if (pal_img_n == 3) { - for (i = 0; i < pixel_count; ++i) { - int n = orig[i] * 4; - p[0] = palette[n]; - p[1] = palette[n + 1]; - p[2] = palette[n + 2]; - p += 3; - } - } else { - for (i = 0; i < pixel_count; ++i) { - int n = orig[i] * 4; - p[0] = palette[n]; - p[1] = palette[n + 1]; - p[2] = palette[n + 2]; - p[3] = palette[n + 3]; - p += 4; - } - } - STBI_FREE(a->out); - a->out = temp_out; + if (pal_img_n == 3) { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p += 3; + } + } else { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p[3] = palette[n+3]; + p += 4; + } + } + STBI_FREE(a->out); + a->out = temp_out; - STBI_NOTUSED(len); + STBI_NOTUSED(len); - return 1; + return 1; } static int stbi__unpremultiply_on_load_global = 0; static int stbi__de_iphone_flag_global = 0; -STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) { - stbi__unpremultiply_on_load_global = flag_true_if_should_unpremultiply; +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) +{ + stbi__unpremultiply_on_load_global = flag_true_if_should_unpremultiply; } -STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) { - stbi__de_iphone_flag_global = flag_true_if_should_convert; +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag_global = flag_true_if_should_convert; } #ifndef STBI_THREAD_LOCAL -#define stbi__unpremultiply_on_load stbi__unpremultiply_on_load_global -#define stbi__de_iphone_flag stbi__de_iphone_flag_global +#define stbi__unpremultiply_on_load stbi__unpremultiply_on_load_global +#define stbi__de_iphone_flag stbi__de_iphone_flag_global #else static STBI_THREAD_LOCAL int stbi__unpremultiply_on_load_local, stbi__unpremultiply_on_load_set; static STBI_THREAD_LOCAL int stbi__de_iphone_flag_local, stbi__de_iphone_flag_set; -STBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply) { - stbi__unpremultiply_on_load_local = flag_true_if_should_unpremultiply; - stbi__unpremultiply_on_load_set = 1; +STBIDEF void stbi_set_unpremultiply_on_load_thread(int flag_true_if_should_unpremultiply) +{ + stbi__unpremultiply_on_load_local = flag_true_if_should_unpremultiply; + stbi__unpremultiply_on_load_set = 1; } -STBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert) { - stbi__de_iphone_flag_local = flag_true_if_should_convert; - stbi__de_iphone_flag_set = 1; +STBIDEF void stbi_convert_iphone_png_to_rgb_thread(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag_local = flag_true_if_should_convert; + stbi__de_iphone_flag_set = 1; } -#define stbi__unpremultiply_on_load \ - (stbi__unpremultiply_on_load_set ? stbi__unpremultiply_on_load_local : stbi__unpremultiply_on_load_global) -#define stbi__de_iphone_flag (stbi__de_iphone_flag_set ? stbi__de_iphone_flag_local : stbi__de_iphone_flag_global) +#define stbi__unpremultiply_on_load (stbi__unpremultiply_on_load_set \ + ? stbi__unpremultiply_on_load_local \ + : stbi__unpremultiply_on_load_global) +#define stbi__de_iphone_flag (stbi__de_iphone_flag_set \ + ? stbi__de_iphone_flag_local \ + : stbi__de_iphone_flag_global) #endif // STBI_THREAD_LOCAL -static void stbi__de_iphone(stbi__png * z) { - stbi__context * s = z->s; - stbi__uint32 i, pixel_count = s->img_x * s->img_y; - stbi_uc * p = z->out; +static void stbi__de_iphone(stbi__png *z) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; - if (s->img_out_n == 3) { // convert bgr to rgb - for (i = 0; i < pixel_count; ++i) { + if (s->img_out_n == 3) { // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 3; + } + } else { + STBI_ASSERT(s->img_out_n == 4); + if (stbi__unpremultiply_on_load) { + // convert bgr to rgb and unpremultiply + for (i=0; i < pixel_count; ++i) { + stbi_uc a = p[3]; + stbi_uc t = p[0]; + if (a) { + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = ( t * 255 + half) / a; + } else { + p[0] = p[2]; + p[2] = t; + } + p += 4; + } + } else { + // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { stbi_uc t = p[0]; p[0] = p[2]; p[2] = t; - p += 3; - } - } else { - STBI_ASSERT(s->img_out_n == 4); - if (stbi__unpremultiply_on_load) { - // convert bgr to rgb and unpremultiply - for (i = 0; i < pixel_count; ++i) { - stbi_uc a = p[3]; - stbi_uc t = p[0]; - if (a) { - stbi_uc half = a / 2; - p[0] = (p[2] * 255 + half) / a; - p[1] = (p[1] * 255 + half) / a; - p[2] = (t * 255 + half) / a; - } else { - p[0] = p[2]; - p[2] = t; - } - p += 4; - } - } else { - // convert bgr to rgb - for (i = 0; i < pixel_count; ++i) { - stbi_uc t = p[0]; - p[0] = p[2]; - p[2] = t; - p += 4; - } - } - } + p += 4; + } + } + } } -#define STBI__PNG_TYPE(a, b, c, d) (((unsigned)(a) << 24) + ((unsigned)(b) << 16) + ((unsigned)(c) << 8) + (unsigned)(d)) +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) -static int stbi__parse_png_file(stbi__png * z, int scan, int req_comp) { - stbi_uc palette[1024], pal_img_n = 0; - stbi_uc has_trans = 0, tc[3] = {0}; - stbi__uint16 tc16[3]; - stbi__uint32 ioff = 0, idata_limit = 0, i, pal_len = 0; - int first = 1, k, interlace = 0, color = 0, is_iphone = 0; - stbi__context * s = z->s; +static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) +{ + stbi_uc palette[1024], pal_img_n=0; + stbi_uc has_trans=0, tc[3]={0}; + stbi__uint16 tc16[3]; + stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; + int first=1,k,interlace=0, color=0, is_iphone=0; + stbi__context *s = z->s; - z->expanded = NULL; - z->idata = NULL; - z->out = NULL; + z->expanded = NULL; + z->idata = NULL; + z->out = NULL; - if (!stbi__check_png_header(s)) - return 0; + if (!stbi__check_png_header(s)) return 0; - if (scan == STBI__SCAN_type) - return 1; + if (scan == STBI__SCAN_type) return 1; - for (;;) { - stbi__pngchunk c = stbi__get_chunk_header(s); - switch (c.type) { - case STBI__PNG_TYPE('C', 'g', 'B', 'I'): + for (;;) { + stbi__pngchunk c = stbi__get_chunk_header(s); + switch (c.type) { + case STBI__PNG_TYPE('C','g','B','I'): is_iphone = 1; stbi__skip(s, c.length); break; - case STBI__PNG_TYPE('I', 'H', 'D', 'R'): { - int comp, filter; - if (!first) - return stbi__err("multiple IHDR", "Corrupt PNG"); + case STBI__PNG_TYPE('I','H','D','R'): { + int comp,filter; + if (!first) return stbi__err("multiple IHDR","Corrupt PNG"); first = 0; - if (c.length != 13) - return stbi__err("bad IHDR len", "Corrupt PNG"); + if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); s->img_x = stbi__get32be(s); s->img_y = stbi__get32be(s); - if (s->img_y > STBI_MAX_DIMENSIONS) - return stbi__err("too large", "Very large image (corrupt?)"); - if (s->img_x > STBI_MAX_DIMENSIONS) - return stbi__err("too large", "Very large image (corrupt?)"); - z->depth = stbi__get8(s); - if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) - return stbi__err("1/2/4/8/16-bit only", "PNG not supported: 1/2/4/8/16-bit only"); - color = stbi__get8(s); - if (color > 6) - return stbi__err("bad ctype", "Corrupt PNG"); - if (color == 3 && z->depth == 16) - return stbi__err("bad ctype", "Corrupt PNG"); - if (color == 3) - pal_img_n = 3; - else if (color & 1) - return stbi__err("bad ctype", "Corrupt PNG"); - comp = stbi__get8(s); - if (comp) - return stbi__err("bad comp method", "Corrupt PNG"); - filter = stbi__get8(s); - if (filter) - return stbi__err("bad filter method", "Corrupt PNG"); - interlace = stbi__get8(s); - if (interlace > 1) - return stbi__err("bad interlace method", "Corrupt PNG"); - if (!s->img_x || !s->img_y) - return stbi__err("0-pixel image", "Corrupt PNG"); + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); + color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); + comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); + filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); + interlace = stbi__get8(s); if (interlace>1) return stbi__err("bad interlace method","Corrupt PNG"); + if (!s->img_x || !s->img_y) return stbi__err("0-pixel image","Corrupt PNG"); if (!pal_img_n) { - s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); - if ((1 << 30) / s->img_x / s->img_n < s->img_y) - return stbi__err("too large", "Image too large to decode"); + s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); + if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); } else { - // if paletted, then pal_n is our final components, and - // img_n is # components to decompress/filter. - s->img_n = 1; - if ((1 << 30) / s->img_x / 4 < s->img_y) - return stbi__err("too large", "Corrupt PNG"); + // if paletted, then pal_n is our final components, and + // img_n is # components to decompress/filter. + s->img_n = 1; + if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG"); } // even with SCAN_header, have to scan to see if we have a tRNS break; - } + } - case STBI__PNG_TYPE('P', 'L', 'T', 'E'): { - if (first) - return stbi__err("first not IHDR", "Corrupt PNG"); - if (c.length > 256 * 3) - return stbi__err("invalid PLTE", "Corrupt PNG"); + case STBI__PNG_TYPE('P','L','T','E'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (c.length > 256*3) return stbi__err("invalid PLTE","Corrupt PNG"); pal_len = c.length / 3; - if (pal_len * 3 != c.length) - return stbi__err("invalid PLTE", "Corrupt PNG"); - for (i = 0; i < pal_len; ++i) { - palette[i * 4 + 0] = stbi__get8(s); - palette[i * 4 + 1] = stbi__get8(s); - palette[i * 4 + 2] = stbi__get8(s); - palette[i * 4 + 3] = 255; + if (pal_len * 3 != c.length) return stbi__err("invalid PLTE","Corrupt PNG"); + for (i=0; i < pal_len; ++i) { + palette[i*4+0] = stbi__get8(s); + palette[i*4+1] = stbi__get8(s); + palette[i*4+2] = stbi__get8(s); + palette[i*4+3] = 255; } break; - } + } - case STBI__PNG_TYPE('t', 'R', 'N', 'S'): { - if (first) - return stbi__err("first not IHDR", "Corrupt PNG"); - if (z->idata) - return stbi__err("tRNS after IDAT", "Corrupt PNG"); + case STBI__PNG_TYPE('t','R','N','S'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (z->idata) return stbi__err("tRNS after IDAT","Corrupt PNG"); if (pal_img_n) { - if (scan == STBI__SCAN_header) { - s->img_n = 4; - return 1; - } - if (pal_len == 0) - return stbi__err("tRNS before PLTE", "Corrupt PNG"); - if (c.length > pal_len) - return stbi__err("bad tRNS len", "Corrupt PNG"); - pal_img_n = 4; - for (i = 0; i < c.length; ++i) - palette[i * 4 + 3] = stbi__get8(s); + if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; } + if (pal_len == 0) return stbi__err("tRNS before PLTE","Corrupt PNG"); + if (c.length > pal_len) return stbi__err("bad tRNS len","Corrupt PNG"); + pal_img_n = 4; + for (i=0; i < c.length; ++i) + palette[i*4+3] = stbi__get8(s); } else { - if (!(s->img_n & 1)) - return stbi__err("tRNS with alpha", "Corrupt PNG"); - if (c.length != (stbi__uint32)s->img_n * 2) - return stbi__err("bad tRNS len", "Corrupt PNG"); - has_trans = 1; - // non-paletted with tRNS = constant alpha. if header-scanning, we can stop now. - if (scan == STBI__SCAN_header) { - ++s->img_n; - return 1; - } - if (z->depth == 16) { - for (k = 0; k < s->img_n; ++k) - tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is - } else { - for (k = 0; k < s->img_n; ++k) - tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * - stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger - } + if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); + if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); + has_trans = 1; + // non-paletted with tRNS = constant alpha. if header-scanning, we can stop now. + if (scan == STBI__SCAN_header) { ++s->img_n; return 1; } + if (z->depth == 16) { + for (k = 0; k < s->img_n && k < 3; ++k) // extra loop test to suppress false GCC warning + tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } else { + for (k = 0; k < s->img_n && k < 3; ++k) + tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } } break; - } + } - case STBI__PNG_TYPE('I', 'D', 'A', 'T'): { - if (first) - return stbi__err("first not IHDR", "Corrupt PNG"); - if (pal_img_n && !pal_len) - return stbi__err("no PLTE", "Corrupt PNG"); + case STBI__PNG_TYPE('I','D','A','T'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG"); if (scan == STBI__SCAN_header) { - // header scan definitely stops at first IDAT - if (pal_img_n) - s->img_n = pal_img_n; - return 1; + // header scan definitely stops at first IDAT + if (pal_img_n) + s->img_n = pal_img_n; + return 1; } - if (c.length > (1u << 30)) - return stbi__err("IDAT size limit", "IDAT section larger than 2^30 bytes"); - if ((int)(ioff + c.length) < (int)ioff) - return 0; + if (c.length > (1u << 30)) return stbi__err("IDAT size limit", "IDAT section larger than 2^30 bytes"); + if ((int)(ioff + c.length) < (int)ioff) return 0; if (ioff + c.length > idata_limit) { - stbi__uint32 idata_limit_old = idata_limit; - stbi_uc * p; - if (idata_limit == 0) - idata_limit = c.length > 4096 ? c.length : 4096; - while (ioff + c.length > idata_limit) - idata_limit *= 2; - STBI_NOTUSED(idata_limit_old); - p = (stbi_uc *)STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); - if (p == NULL) - return stbi__err("outofmem", "Out of memory"); - z->idata = p; + stbi__uint32 idata_limit_old = idata_limit; + stbi_uc *p; + if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; + while (ioff + c.length > idata_limit) + idata_limit *= 2; + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + z->idata = p; } - if (!stbi__getn(s, z->idata + ioff, c.length)) - return stbi__err("outofdata", "Corrupt PNG"); + if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); ioff += c.length; break; - } + } - case STBI__PNG_TYPE('I', 'E', 'N', 'D'): { + case STBI__PNG_TYPE('I','E','N','D'): { stbi__uint32 raw_len, bpl; - if (first) - return stbi__err("first not IHDR", "Corrupt PNG"); - if (scan != STBI__SCAN_load) - return 1; - if (z->idata == NULL) - return stbi__err("no IDAT", "Corrupt PNG"); + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (scan != STBI__SCAN_load) return 1; + if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); // initial guess for decoded data size to avoid unnecessary reallocs bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; - z->expanded = (stbi_uc *)stbi_zlib_decode_malloc_guesssize_headerflag((char *)z->idata, ioff, raw_len, - (int *)&raw_len, !is_iphone); - if (z->expanded == NULL) - return 0; // zlib should set error - STBI_FREE(z->idata); - z->idata = NULL; - if ((req_comp == s->img_n + 1 && req_comp != 3 && !pal_img_n) || has_trans) - s->img_out_n = s->img_n + 1; + z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); + if (z->expanded == NULL) return 0; // zlib should set error + STBI_FREE(z->idata); z->idata = NULL; + if ((req_comp == s->img_n+1 && req_comp != 3 && !pal_img_n) || has_trans) + s->img_out_n = s->img_n+1; else - s->img_out_n = s->img_n; - if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) - return 0; + s->img_out_n = s->img_n; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; if (has_trans) { - if (z->depth == 16) { - if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) - return 0; - } else { - if (!stbi__compute_transparency(z, tc, s->img_out_n)) - return 0; - } + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } } if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) - stbi__de_iphone(z); + stbi__de_iphone(z); if (pal_img_n) { - // pal_img_n == 3 or 4 - s->img_n = pal_img_n; // record the actual colors we had - s->img_out_n = pal_img_n; - if (req_comp >= 3) - s->img_out_n = req_comp; - if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) - return 0; + // pal_img_n == 3 or 4 + s->img_n = pal_img_n; // record the actual colors we had + s->img_out_n = pal_img_n; + if (req_comp >= 3) s->img_out_n = req_comp; + if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) + return 0; } else if (has_trans) { - // non-paletted image with tRNS -> source image has (constant) alpha - ++s->img_n; + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; } - STBI_FREE(z->expanded); - z->expanded = NULL; + STBI_FREE(z->expanded); z->expanded = NULL; // end of PNG chunk, read and skip CRC stbi__get32be(s); return 1; - } + } - default: + default: // if critical, fail - if (first) - return stbi__err("first not IHDR", "Corrupt PNG"); + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); if ((c.type & (1 << 29)) == 0) { -#ifndef STBI_NO_FAILURE_STRINGS - // not threadsafe - static char invalid_chunk[] = "XXXX PNG chunk not known"; - invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); - invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); - invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); - invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); -#endif - return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); + #ifndef STBI_NO_FAILURE_STRINGS + // not threadsafe + static char invalid_chunk[] = "XXXX PNG chunk not known"; + invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); + invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); + invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); + invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); + #endif + return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); } stbi__skip(s, c.length); break; - } - // end of PNG chunk, read and skip CRC - stbi__get32be(s); - } + } + // end of PNG chunk, read and skip CRC + stbi__get32be(s); + } } -static void * stbi__do_png(stbi__png * p, int * x, int * y, int * n, int req_comp, stbi__result_info * ri) { - void * result = NULL; - if (req_comp < 0 || req_comp > 4) - return stbi__errpuc("bad req_comp", "Internal error"); - if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { - if (p->depth <= 8) - ri->bits_per_channel = 8; - else if (p->depth == 16) - ri->bits_per_channel = 16; - else - return stbi__errpuc("bad bits_per_channel", "PNG not supported: unsupported color depth"); - result = p->out; - p->out = NULL; - if (req_comp && req_comp != p->s->img_out_n) { - if (ri->bits_per_channel == 8) - result = stbi__convert_format((unsigned char *)result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); - else - result = stbi__convert_format16((stbi__uint16 *)result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); - p->s->img_out_n = req_comp; - if (result == NULL) - return result; - } - *x = p->s->img_x; - *y = p->s->img_y; - if (n) - *n = p->s->img_n; - } - STBI_FREE(p->out); - p->out = NULL; - STBI_FREE(p->expanded); - p->expanded = NULL; - STBI_FREE(p->idata); - p->idata = NULL; +static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri) +{ + void *result=NULL; + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth <= 8) + ri->bits_per_channel = 8; + else if (p->depth == 16) + ri->bits_per_channel = 16; + else + return stbi__errpuc("bad bits_per_channel", "PNG not supported: unsupported color depth"); + result = p->out; + p->out = NULL; + if (req_comp && req_comp != p->s->img_out_n) { + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + p->s->img_out_n = req_comp; + if (result == NULL) return result; + } + *x = p->s->img_x; + *y = p->s->img_y; + if (n) *n = p->s->img_n; + } + STBI_FREE(p->out); p->out = NULL; + STBI_FREE(p->expanded); p->expanded = NULL; + STBI_FREE(p->idata); p->idata = NULL; - return result; + return result; } -static void * stbi__png_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) { - stbi__png p; - p.s = s; - return stbi__do_png(&p, x, y, comp, req_comp, ri); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi__png p; + p.s = s; + return stbi__do_png(&p, x,y,comp,req_comp, ri); } -static int stbi__png_test(stbi__context * s) { - int r; - r = stbi__check_png_header(s); - stbi__rewind(s); - return r; +static int stbi__png_test(stbi__context *s) +{ + int r; + r = stbi__check_png_header(s); + stbi__rewind(s); + return r; } -static int stbi__png_info_raw(stbi__png * p, int * x, int * y, int * comp) { - if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { - stbi__rewind(p->s); - return 0; - } - if (x) - *x = p->s->img_x; - if (y) - *y = p->s->img_y; - if (comp) - *comp = p->s->img_n; - return 1; +static int stbi__png_info_raw(stbi__png *p, int *x, int *y, int *comp) +{ + if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { + stbi__rewind( p->s ); + return 0; + } + if (x) *x = p->s->img_x; + if (y) *y = p->s->img_y; + if (comp) *comp = p->s->img_n; + return 1; } -static int stbi__png_info(stbi__context * s, int * x, int * y, int * comp) { - stbi__png p; - p.s = s; - return stbi__png_info_raw(&p, x, y, comp); +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__png p; + p.s = s; + return stbi__png_info_raw(&p, x, y, comp); } -static int stbi__png_is16(stbi__context * s) { - stbi__png p; - p.s = s; - if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) - return 0; - if (p.depth != 16) { - stbi__rewind(p.s); - return 0; - } - return 1; +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; } #endif // Microsoft/Windows BMP image #ifndef STBI_NO_BMP -static int stbi__bmp_test_raw(stbi__context * s) { - int r; - int sz; - if (stbi__get8(s) != 'B') - return 0; - if (stbi__get8(s) != 'M') - return 0; - stbi__get32le(s); // discard filesize - stbi__get16le(s); // discard reserved - stbi__get16le(s); // discard reserved - stbi__get32le(s); // discard data offset - sz = stbi__get32le(s); - r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); - return r; +static int stbi__bmp_test_raw(stbi__context *s) +{ + int r; + int sz; + if (stbi__get8(s) != 'B') return 0; + if (stbi__get8(s) != 'M') return 0; + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + stbi__get32le(s); // discard data offset + sz = stbi__get32le(s); + r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); + return r; } -static int stbi__bmp_test(stbi__context * s) { - int r = stbi__bmp_test_raw(s); - stbi__rewind(s); - return r; +static int stbi__bmp_test(stbi__context *s) +{ + int r = stbi__bmp_test_raw(s); + stbi__rewind(s); + return r; } + // returns 0..31 for the highest set bit -static int stbi__high_bit(unsigned int z) { - int n = 0; - if (z == 0) - return -1; - if (z >= 0x10000) { - n += 16; - z >>= 16; - } - if (z >= 0x00100) { - n += 8; - z >>= 8; - } - if (z >= 0x00010) { - n += 4; - z >>= 4; - } - if (z >= 0x00004) { - n += 2; - z >>= 2; - } - if (z >= 0x00002) { - n += 1; /* >>= 1;*/ - } - return n; +static int stbi__high_bit(unsigned int z) +{ + int n=0; + if (z == 0) return -1; + if (z >= 0x10000) { n += 16; z >>= 16; } + if (z >= 0x00100) { n += 8; z >>= 8; } + if (z >= 0x00010) { n += 4; z >>= 4; } + if (z >= 0x00004) { n += 2; z >>= 2; } + if (z >= 0x00002) { n += 1;/* >>= 1;*/ } + return n; } -static int stbi__bitcount(unsigned int a) { - a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 - a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 - a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits - a = (a + (a >> 8)); // max 16 per 8 bits - a = (a + (a >> 16)); // max 32 per 8 bits - return a & 0xff; +static int stbi__bitcount(unsigned int a) +{ + a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 + a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 + a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits + a = (a + (a >> 8)); // max 16 per 8 bits + a = (a + (a >> 16)); // max 32 per 8 bits + return a & 0xff; } // extract an arbitrarily-aligned N-bit value (N=bits) // from v, and then make it 8-bits long and fractionally // extend it to full full range. -static int stbi__shiftsigned(unsigned int v, int shift, int bits) { - static unsigned int mul_table[9] = { - 0, - 0xff /*0b11111111*/, - 0x55 /*0b01010101*/, - 0x49 /*0b01001001*/, - 0x11 /*0b00010001*/, - 0x21 /*0b00100001*/, - 0x41 /*0b01000001*/, - 0x81 /*0b10000001*/, - 0x01 /*0b00000001*/, - }; - static unsigned int shift_table[9] = { - 0, 0, 0, 1, 0, 2, 4, 6, 0, - }; - if (shift < 0) - v <<= -shift; - else - v >>= shift; - STBI_ASSERT(v < 256); - v >>= (8 - bits); - STBI_ASSERT(bits >= 0 && bits <= 8); - return (int)((unsigned)v * mul_table[bits]) >> shift_table[bits]; +static int stbi__shiftsigned(unsigned int v, int shift, int bits) +{ + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; } -typedef struct { - int bpp, offset, hsz; - unsigned int mr, mg, mb, ma, all_a; - int extra_read; +typedef struct +{ + int bpp, offset, hsz; + unsigned int mr,mg,mb,ma, all_a; + int extra_read; } stbi__bmp_data; -static int stbi__bmp_set_mask_defaults(stbi__bmp_data * info, int compress) { - // BI_BITFIELDS specifies masks explicitly, don't override - if (compress == 3) - return 1; +static int stbi__bmp_set_mask_defaults(stbi__bmp_data *info, int compress) +{ + // BI_BITFIELDS specifies masks explicitly, don't override + if (compress == 3) + return 1; - if (compress == 0) { - if (info->bpp == 16) { - info->mr = 31u << 10; - info->mg = 31u << 5; - info->mb = 31u << 0; - } else if (info->bpp == 32) { - info->mr = 0xffu << 16; - info->mg = 0xffu << 8; - info->mb = 0xffu << 0; - info->ma = 0xffu << 24; - info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 - } else { - // otherwise, use defaults, which is all-0 - info->mr = info->mg = info->mb = info->ma = 0; - } - return 1; - } - return 0; // error + if (compress == 0) { + if (info->bpp == 16) { + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; + } else if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 + } else { + // otherwise, use defaults, which is all-0 + info->mr = info->mg = info->mb = info->ma = 0; + } + return 1; + } + return 0; // error } -static void * stbi__bmp_parse_header(stbi__context * s, stbi__bmp_data * info) { - int hsz; - if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') - return stbi__errpuc("not BMP", "Corrupt BMP"); - stbi__get32le(s); // discard filesize - stbi__get16le(s); // discard reserved - stbi__get16le(s); // discard reserved - info->offset = stbi__get32le(s); - info->hsz = hsz = stbi__get32le(s); - info->mr = info->mg = info->mb = info->ma = 0; - info->extra_read = 14; +static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) +{ + int hsz; + if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + info->extra_read = 14; - if (info->offset < 0) - return stbi__errpuc("bad BMP", "bad BMP"); + if (info->offset < 0) return stbi__errpuc("bad BMP", "bad BMP"); - if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) - return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); - if (hsz == 12) { - s->img_x = stbi__get16le(s); - s->img_y = stbi__get16le(s); - } else { - s->img_x = stbi__get32le(s); - s->img_y = stbi__get32le(s); - } - if (stbi__get16le(s) != 1) - return stbi__errpuc("bad BMP", "bad BMP"); - info->bpp = stbi__get16le(s); - if (hsz != 12) { - int compress = stbi__get32le(s); - if (compress == 1 || compress == 2) - return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); - if (compress >= 4) - return stbi__errpuc("BMP JPEG/PNG", - "BMP type not supported: unsupported compression"); // this includes PNG/JPEG modes - if (compress == 3 && info->bpp != 16 && info->bpp != 32) - return stbi__errpuc("bad BMP", "bad BMP"); // bitfields requires 16 or 32 bits/pixel - stbi__get32le(s); // discard sizeof - stbi__get32le(s); // discard hres - stbi__get32le(s); // discard vres - stbi__get32le(s); // discard colorsused - stbi__get32le(s); // discard max important - if (hsz == 40 || hsz == 56) { - if (hsz == 56) { - stbi__get32le(s); - stbi__get32le(s); - stbi__get32le(s); - stbi__get32le(s); - } - if (info->bpp == 16 || info->bpp == 32) { - if (compress == 0) { - stbi__bmp_set_mask_defaults(info, compress); - } else if (compress == 3) { - info->mr = stbi__get32le(s); - info->mg = stbi__get32le(s); - info->mb = stbi__get32le(s); - info->extra_read += 12; - // not documented, but generated by photoshop and handled by mspaint - if (info->mr == info->mg && info->mg == info->mb) { - // ?!?!? - return stbi__errpuc("bad BMP", "bad BMP"); - } - } else - return stbi__errpuc("bad BMP", "bad BMP"); - } - } else { - // V4/V5 header - int i; - if (hsz != 108 && hsz != 124) - return stbi__errpuc("bad BMP", "bad BMP"); - info->mr = stbi__get32le(s); - info->mg = stbi__get32le(s); - info->mb = stbi__get32le(s); - info->ma = stbi__get32le(s); - if (compress != 3) // override mr/mg/mb unless in BI_BITFIELDS mode, as per docs - stbi__bmp_set_mask_defaults(info, compress); - stbi__get32le(s); // discard color space - for (i = 0; i < 12; ++i) - stbi__get32le(s); // discard color space parameters - if (hsz == 124) { - stbi__get32le(s); // discard rendering intent - stbi__get32le(s); // discard offset of profile data - stbi__get32le(s); // discard size of profile data - stbi__get32le(s); // discard reserved - } - } - } - return (void *)1; + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); + if (hsz == 12) { + s->img_x = stbi__get16le(s); + s->img_y = stbi__get16le(s); + } else { + s->img_x = stbi__get32le(s); + s->img_y = stbi__get32le(s); + } + if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); + if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); + if (compress >= 4) return stbi__errpuc("BMP JPEG/PNG", "BMP type not supported: unsupported compression"); // this includes PNG/JPEG modes + if (compress == 3 && info->bpp != 16 && info->bpp != 32) return stbi__errpuc("bad BMP", "bad BMP"); // bitfields requires 16 or 32 bits/pixel + stbi__get32le(s); // discard sizeof + stbi__get32le(s); // discard hres + stbi__get32le(s); // discard vres + stbi__get32le(s); // discard colorsused + stbi__get32le(s); // discard max important + if (hsz == 40 || hsz == 56) { + if (hsz == 56) { + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + } + if (info->bpp == 16 || info->bpp == 32) { + if (compress == 0) { + stbi__bmp_set_mask_defaults(info, compress); + } else if (compress == 3) { + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->extra_read += 12; + // not documented, but generated by photoshop and handled by mspaint + if (info->mr == info->mg && info->mg == info->mb) { + // ?!?!? + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else { + // V4/V5 header + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); + if (compress != 3) // override mr/mg/mb unless in BI_BITFIELDS mode, as per docs + stbi__bmp_set_mask_defaults(info, compress); + stbi__get32le(s); // discard color space + for (i=0; i < 12; ++i) + stbi__get32le(s); // discard color space parameters + if (hsz == 124) { + stbi__get32le(s); // discard rendering intent + stbi__get32le(s); // discard offset of profile data + stbi__get32le(s); // discard size of profile data + stbi__get32le(s); // discard reserved + } + } + } + return (void *) 1; } -static void * stbi__bmp_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) { - stbi_uc * out; - unsigned int mr = 0, mg = 0, mb = 0, ma = 0, all_a; - stbi_uc pal[256][4]; - int psize = 0, i, j, width; - int flip_vertically, pad, target; - stbi__bmp_data info; - STBI_NOTUSED(ri); - info.all_a = 255; - if (stbi__bmp_parse_header(s, &info) == NULL) - return NULL; // error code already set +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + unsigned int mr=0,mg=0,mb=0,ma=0, all_a; + stbi_uc pal[256][4]; + int psize=0,i,j,width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); - flip_vertically = ((int)s->img_y) > 0; - s->img_y = abs((int)s->img_y); + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set - if (s->img_y > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); - if (s->img_x > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); + flip_vertically = ((int) s->img_y) > 0; + s->img_y = abs((int) s->img_y); - mr = info.mr; - mg = info.mg; - mb = info.mb; - ma = info.ma; - all_a = info.all_a; + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); - if (info.hsz == 12) { - if (info.bpp < 24) - psize = (info.offset - info.extra_read - 24) / 3; - } else { - if (info.bpp < 16) - psize = (info.offset - info.extra_read - info.hsz) >> 2; - } - if (psize == 0) { - // accept some number of extra bytes after the header, but if the offset points either to before - // the header ends or implies a large amount of extra data, reject the file as malformed - int bytes_read_so_far = s->callback_already_read + (int)(s->img_buffer - s->img_buffer_original); - int header_limit = 1024; // max we actually read is below 256 bytes currently. - int extra_data_limit = 256 * 4; // what ordinarily goes here is a palette; 256 entries*4 bytes is its max size. - if (bytes_read_so_far <= 0 || bytes_read_so_far > header_limit) { - return stbi__errpuc("bad header", "Corrupt BMP"); - } - // we established that bytes_read_so_far is positive and sensible. - // the first half of this test rejects offsets that are either too small positives, or - // negative, and guarantees that info.offset >= bytes_read_so_far > 0. this in turn - // ensures the number computed in the second half of the test can't overflow. - if (info.offset < bytes_read_so_far || info.offset - bytes_read_so_far > extra_data_limit) { - return stbi__errpuc("bad offset", "Corrupt BMP"); - } else { - stbi__skip(s, info.offset - bytes_read_so_far); - } - } + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; - if (info.bpp == 24 && ma == 0xff000000) - s->img_n = 3; - else - s->img_n = ma ? 4 : 3; - if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 - target = req_comp; - else - target = s->img_n; // if they want monochrome, we'll post-convert + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - info.extra_read - 24) / 3; + } else { + if (info.bpp < 16) + psize = (info.offset - info.extra_read - info.hsz) >> 2; + } + if (psize == 0) { + // accept some number of extra bytes after the header, but if the offset points either to before + // the header ends or implies a large amount of extra data, reject the file as malformed + int bytes_read_so_far = s->callback_already_read + (int)(s->img_buffer - s->img_buffer_original); + int header_limit = 1024; // max we actually read is below 256 bytes currently. + int extra_data_limit = 256*4; // what ordinarily goes here is a palette; 256 entries*4 bytes is its max size. + if (bytes_read_so_far <= 0 || bytes_read_so_far > header_limit) { + return stbi__errpuc("bad header", "Corrupt BMP"); + } + // we established that bytes_read_so_far is positive and sensible. + // the first half of this test rejects offsets that are either too small positives, or + // negative, and guarantees that info.offset >= bytes_read_so_far > 0. this in turn + // ensures the number computed in the second half of the test can't overflow. + if (info.offset < bytes_read_so_far || info.offset - bytes_read_so_far > extra_data_limit) { + return stbi__errpuc("bad offset", "Corrupt BMP"); + } else { + stbi__skip(s, info.offset - bytes_read_so_far); + } + } - // sanity-check size - if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) - return stbi__errpuc("too large", "Corrupt BMP"); + if (info.bpp == 24 && ma == 0xff000000) + s->img_n = 3; + else + s->img_n = ma ? 4 : 3; + if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 + target = req_comp; + else + target = s->img_n; // if they want monochrome, we'll post-convert - out = (stbi_uc *)stbi__malloc_mad3(target, s->img_x, s->img_y, 0); - if (!out) - return stbi__errpuc("outofmem", "Out of memory"); - if (info.bpp < 16) { - int z = 0; - if (psize == 0 || psize > 256) { - STBI_FREE(out); - return stbi__errpuc("invalid", "Corrupt BMP"); - } - for (i = 0; i < psize; ++i) { - pal[i][2] = stbi__get8(s); - pal[i][1] = stbi__get8(s); - pal[i][0] = stbi__get8(s); - if (info.hsz != 12) - stbi__get8(s); - pal[i][3] = 255; - } - stbi__skip(s, info.offset - info.extra_read - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); - if (info.bpp == 1) - width = (s->img_x + 7) >> 3; - else if (info.bpp == 4) - width = (s->img_x + 1) >> 1; - else if (info.bpp == 8) - width = s->img_x; - else { - STBI_FREE(out); - return stbi__errpuc("bad bpp", "Corrupt BMP"); - } - pad = (-width) & 3; - if (info.bpp == 1) { - for (j = 0; j < (int)s->img_y; ++j) { - int bit_offset = 7, v = stbi__get8(s); - for (i = 0; i < (int)s->img_x; ++i) { - int color = (v >> bit_offset) & 0x1; - out[z++] = pal[color][0]; - out[z++] = pal[color][1]; - out[z++] = pal[color][2]; - if (target == 4) - out[z++] = 255; - if (i + 1 == (int)s->img_x) - break; - if ((--bit_offset) < 0) { - bit_offset = 7; - v = stbi__get8(s); - } - } - stbi__skip(s, pad); - } - } else { - for (j = 0; j < (int)s->img_y; ++j) { - for (i = 0; i < (int)s->img_x; i += 2) { - int v = stbi__get8(s), v2 = 0; - if (info.bpp == 4) { - v2 = v & 15; - v >>= 4; - } - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) - out[z++] = 255; - if (i + 1 == (int)s->img_x) - break; - v = (info.bpp == 8) ? stbi__get8(s) : v2; - out[z++] = pal[v][0]; - out[z++] = pal[v][1]; - out[z++] = pal[v][2]; - if (target == 4) - out[z++] = 255; - } - stbi__skip(s, pad); - } - } - } else { - int rshift = 0, gshift = 0, bshift = 0, ashift = 0, rcount = 0, gcount = 0, bcount = 0, acount = 0; - int z = 0; - int easy = 0; - stbi__skip(s, info.offset - info.extra_read - info.hsz); - if (info.bpp == 24) - width = 3 * s->img_x; - else if (info.bpp == 16) - width = 2 * s->img_x; - else /* bpp = 32 and pad = 0 */ - width = 0; - pad = (-width) & 3; - if (info.bpp == 24) { - easy = 1; - } else if (info.bpp == 32) { - if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) - easy = 2; - } - if (!easy) { - if (!mr || !mg || !mb) { - STBI_FREE(out); - return stbi__errpuc("bad masks", "Corrupt BMP"); - } - // right shift amt to put high bit in position #7 - rshift = stbi__high_bit(mr) - 7; - rcount = stbi__bitcount(mr); - gshift = stbi__high_bit(mg) - 7; - gcount = stbi__bitcount(mg); - bshift = stbi__high_bit(mb) - 7; - bcount = stbi__bitcount(mb); - ashift = stbi__high_bit(ma) - 7; - acount = stbi__bitcount(ma); - if (rcount > 8 || gcount > 8 || bcount > 8 || acount > 8) { - STBI_FREE(out); - return stbi__errpuc("bad masks", "Corrupt BMP"); - } - } - for (j = 0; j < (int)s->img_y; ++j) { - if (easy) { - for (i = 0; i < (int)s->img_x; ++i) { - unsigned char a; - out[z + 2] = stbi__get8(s); - out[z + 1] = stbi__get8(s); - out[z + 0] = stbi__get8(s); - z += 3; - a = (easy == 2 ? stbi__get8(s) : 255); - all_a |= a; - if (target == 4) - out[z++] = a; - } - } else { - int bpp = info.bpp; - for (i = 0; i < (int)s->img_x; ++i) { - stbi__uint32 v = (bpp == 16 ? (stbi__uint32)stbi__get16le(s) : stbi__get32le(s)); - unsigned int a; - out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); - out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); - out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); - a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); - all_a |= a; - if (target == 4) - out[z++] = STBI__BYTECAST(a); - } + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (info.bpp < 16) { + int z=0; + if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } + for (i=0; i < psize; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); + pal[i][3] = 255; + } + stbi__skip(s, info.offset - info.extra_read - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; + else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } + pad = (-width)&3; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } } stbi__skip(s, pad); - } - } - - // if alpha channel is all 0s, replace with all 255s - if (target == 4 && all_a == 0) - for (i = 4 * s->img_x * s->img_y - 1; i >= 0; i -= 4) - out[i] = 255; - - if (flip_vertically) { - stbi_uc t; - for (j = 0; j < (int)s->img_y >> 1; ++j) { - stbi_uc * p1 = out + j * s->img_x * target; - stbi_uc * p2 = out + (s->img_y - 1 - j) * s->img_x * target; - for (i = 0; i < (int)s->img_x * target; ++i) { - t = p1[i]; - p1[i] = p2[i]; - p2[i] = t; + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; } - } - } + stbi__skip(s, pad); + } + } + } else { + int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; + int z = 0; + int easy=0; + stbi__skip(s, info.offset - info.extra_read - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2*s->img_x; + else /* bpp = 32 and pad = 0 */ width=0; + pad = (-width) & 3; + if (info.bpp == 24) { + easy = 1; + } else if (info.bpp == 32) { + if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) + easy = 2; + } + if (!easy) { + if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + // right shift amt to put high bit in position #7 + rshift = stbi__high_bit(mr)-7; rcount = stbi__bitcount(mr); + gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg); + bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb); + ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma); + if (rcount > 8 || gcount > 8 || bcount > 8 || acount > 8) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + } + for (j=0; j < (int) s->img_y; ++j) { + if (easy) { + for (i=0; i < (int) s->img_x; ++i) { + unsigned char a; + out[z+2] = stbi__get8(s); + out[z+1] = stbi__get8(s); + out[z+0] = stbi__get8(s); + z += 3; + a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; + if (target == 4) out[z++] = a; + } + } else { + int bpp = info.bpp; + for (i=0; i < (int) s->img_x; ++i) { + stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); + unsigned int a; + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); + a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; + if (target == 4) out[z++] = STBI__BYTECAST(a); + } + } + stbi__skip(s, pad); + } + } - if (req_comp && req_comp != target) { - out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); - if (out == NULL) - return out; // stbi__convert_format frees input on failure - } + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4) + out[i] = 255; - *x = s->img_x; - *y = s->img_y; - if (comp) - *comp = s->img_n; - return out; + if (flip_vertically) { + stbi_uc t; + for (j=0; j < (int) s->img_y>>1; ++j) { + stbi_uc *p1 = out + j *s->img_x*target; + stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target; + for (i=0; i < (int) s->img_x*target; ++i) { + t = p1[i]; p1[i] = p2[i]; p2[i] = t; + } + } + } + + if (req_comp && req_comp != target) { + out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + return out; } #endif @@ -6100,74 +5736,68 @@ static void * stbi__bmp_load(stbi__context * s, int * x, int * y, int * comp, in // by Jonathan Dummer #ifndef STBI_NO_TGA // returns STBI_rgb or whatever, 0 on error -static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int * is_rgb16) { - // only RGB or RGBA (incl. 16bit) or grey allowed - if (is_rgb16) - *is_rgb16 = 0; - switch (bits_per_pixel) { - case 8: - return STBI_grey; - case 16: - if (is_grey) - return STBI_grey_alpha; - // fallthrough - case 15: - if (is_rgb16) - *is_rgb16 = 1; - return STBI_rgb; - case 24: // fallthrough - case 32: - return bits_per_pixel / 8; - default: - return 0; - } +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch(bits_per_pixel) { + case 8: return STBI_grey; + case 16: if(is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if(is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel/8; + default: return 0; + } } -static int stbi__tga_info(stbi__context * s, int * x, int * y, int * comp) { +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) +{ int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; int sz, tga_colormap_type; - stbi__get8(s); // discard Offset + stbi__get8(s); // discard Offset tga_colormap_type = stbi__get8(s); // colormap type - if (tga_colormap_type > 1) { + if( tga_colormap_type > 1 ) { stbi__rewind(s); - return 0; // only RGB or indexed allowed + return 0; // only RGB or indexed allowed } tga_image_type = stbi__get8(s); // image type - if (tga_colormap_type == 1) { // colormapped (paletted) image + if ( tga_colormap_type == 1 ) { // colormapped (paletted) image if (tga_image_type != 1 && tga_image_type != 9) { stbi__rewind(s); return 0; } - stbi__skip(s, 4); // skip index of first colormap entry and number of entries - sz = stbi__get8(s); // check bits per palette color entry - if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32)) { + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) { stbi__rewind(s); return 0; } - stbi__skip(s, 4); // skip image x and y origin + stbi__skip(s,4); // skip image x and y origin tga_colormap_bpp = sz; } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE - if ((tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11)) { + if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) { stbi__rewind(s); return 0; // only RGB or grey allowed, +/- RLE } - stbi__skip(s, 9); // skip colormap specification and image x/y origin + stbi__skip(s,9); // skip colormap specification and image x/y origin tga_colormap_bpp = 0; } tga_w = stbi__get16le(s); - if (tga_w < 1) { + if( tga_w < 1 ) { stbi__rewind(s); - return 0; // test width + return 0; // test width } tga_h = stbi__get16le(s); - if (tga_h < 1) { + if( tga_h < 1 ) { stbi__rewind(s); - return 0; // test height + return 0; // test height } tga_bits_per_pixel = stbi__get8(s); // bits per pixel - stbi__get8(s); // ignore alpha bits + stbi__get8(s); // ignore alpha bits if (tga_colormap_bpp != 0) { - if ((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { // when using a colormap, tga_bits_per_pixel is the size of the indexes // I don't think anything but 8 or 16bit indexes makes sense stbi__rewind(s); @@ -6177,268 +5807,270 @@ static int stbi__tga_info(stbi__context * s, int * x, int * y, int * comp) { } else { tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); } - if (!tga_comp) { - stbi__rewind(s); - return 0; + if(!tga_comp) { + stbi__rewind(s); + return 0; } - if (x) - *x = tga_w; - if (y) - *y = tga_h; - if (comp) - *comp = tga_comp; - return 1; // seems to have passed everything + if (x) *x = tga_w; + if (y) *y = tga_h; + if (comp) *comp = tga_comp; + return 1; // seems to have passed everything } -static int stbi__tga_test(stbi__context * s) { - int res = 0; - int sz, tga_color_type; - stbi__get8(s); // discard Offset - tga_color_type = stbi__get8(s); // color type - if (tga_color_type > 1) - goto errorEnd; // only RGB or indexed allowed - sz = stbi__get8(s); // image type - if (tga_color_type == 1) { // colormapped (paletted) image - if (sz != 1 && sz != 9) - goto errorEnd; // colortype 1 demands image type 1 or 9 - stbi__skip(s, 4); // skip index of first colormap entry and number of entries - sz = stbi__get8(s); // check bits per palette color entry - if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32)) - goto errorEnd; - stbi__skip(s, 4); // skip image x and y origin - } else { // "normal" image w/o colormap - if ((sz != 2) && (sz != 3) && (sz != 10) && (sz != 11)) - goto errorEnd; // only RGB or grey allowed, +/- RLE - stbi__skip(s, 9); // skip colormap specification and image x/y origin - } - if (stbi__get16le(s) < 1) - goto errorEnd; // test width - if (stbi__get16le(s) < 1) - goto errorEnd; // test height - sz = stbi__get8(s); // bits per pixel - if ((tga_color_type == 1) && (sz != 8) && (sz != 16)) - goto errorEnd; // for colormapped images, bpp is size of an index - if ((sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32)) - goto errorEnd; +static int stbi__tga_test(stbi__context *s) +{ + int res = 0; + int sz, tga_color_type; + stbi__get8(s); // discard Offset + tga_color_type = stbi__get8(s); // color type + if ( tga_color_type > 1 ) goto errorEnd; // only RGB or indexed allowed + sz = stbi__get8(s); // image type + if ( tga_color_type == 1 ) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + stbi__skip(s,4); // skip image x and y origin + } else { // "normal" image w/o colormap + if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s,9); // skip colormap specification and image x/y origin + } + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test width + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test height + sz = stbi__get8(s); // bits per pixel + if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; - res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 errorEnd: - stbi__rewind(s); - return res; + stbi__rewind(s); + return res; } // read 16bit value and convert to 24bit RGB -static void stbi__tga_read_rgb16(stbi__context * s, stbi_uc * out) { - stbi__uint16 px = (stbi__uint16)stbi__get16le(s); - stbi__uint16 fiveBitMask = 31; - // we have 3 channels with 5bits each - int r = (px >> 10) & fiveBitMask; - int g = (px >> 5) & fiveBitMask; - int b = px & fiveBitMask; - // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later - out[0] = (stbi_uc)((r * 255) / 31); - out[1] = (stbi_uc)((g * 255) / 31); - out[2] = (stbi_uc)((b * 255) / 31); +static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255)/31); + out[1] = (stbi_uc)((g * 255)/31); + out[2] = (stbi_uc)((b * 255)/31); - // some people claim that the most significant bit might be used for alpha - // (possibly if an alpha-bit is set in the "image descriptor byte") - // but that only made 16bit test images completely translucent.. - // so let's treat all 15 and 16bit TGAs as RGB with no alpha. + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. } -static void * stbi__tga_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) { - // read in the TGA header stuff - int tga_offset = stbi__get8(s); - int tga_indexed = stbi__get8(s); - int tga_image_type = stbi__get8(s); - int tga_is_RLE = 0; - int tga_palette_start = stbi__get16le(s); - int tga_palette_len = stbi__get16le(s); - int tga_palette_bits = stbi__get8(s); - int tga_x_origin = stbi__get16le(s); - int tga_y_origin = stbi__get16le(s); - int tga_width = stbi__get16le(s); - int tga_height = stbi__get16le(s); - int tga_bits_per_pixel = stbi__get8(s); - int tga_comp, tga_rgb16 = 0; - int tga_inverted = stbi__get8(s); - // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) - // image data - unsigned char * tga_data; - unsigned char * tga_palette = NULL; - int i, j; - unsigned char raw_data[4] = {0}; - int RLE_count = 0; - int RLE_repeating = 0; - int read_next_pixel = 1; - STBI_NOTUSED(ri); - STBI_NOTUSED(tga_x_origin); // @TODO - STBI_NOTUSED(tga_y_origin); // @TODO +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + // read in the TGA header stuff + int tga_offset = stbi__get8(s); + int tga_indexed = stbi__get8(s); + int tga_image_type = stbi__get8(s); + int tga_is_RLE = 0; + int tga_palette_start = stbi__get16le(s); + int tga_palette_len = stbi__get16le(s); + int tga_palette_bits = stbi__get8(s); + int tga_x_origin = stbi__get16le(s); + int tga_y_origin = stbi__get16le(s); + int tga_width = stbi__get16le(s); + int tga_height = stbi__get16le(s); + int tga_bits_per_pixel = stbi__get8(s); + int tga_comp, tga_rgb16=0; + int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) + // image data + unsigned char *tga_data; + unsigned char *tga_palette = NULL; + int i, j; + unsigned char raw_data[4] = {0}; + int RLE_count = 0; + int RLE_repeating = 0; + int read_next_pixel = 1; + STBI_NOTUSED(ri); + STBI_NOTUSED(tga_x_origin); // @TODO + STBI_NOTUSED(tga_y_origin); // @TODO - if (tga_height > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); - if (tga_width > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (tga_height > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (tga_width > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); - // do a tiny bit of precessing - if (tga_image_type >= 8) { - tga_image_type -= 8; - tga_is_RLE = 1; - } - tga_inverted = 1 - ((tga_inverted >> 5) & 1); + // do a tiny bit of precessing + if ( tga_image_type >= 8 ) + { + tga_image_type -= 8; + tga_is_RLE = 1; + } + tga_inverted = 1 - ((tga_inverted >> 5) & 1); - // If I'm paletted, then I'll use the number of bits from the palette - if (tga_indexed) - tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); - else - tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); + // If I'm paletted, then I'll use the number of bits from the palette + if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); - if (!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency - return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); + if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); - // tga info - *x = tga_width; - *y = tga_height; - if (comp) - *comp = tga_comp; + // tga info + *x = tga_width; + *y = tga_height; + if (comp) *comp = tga_comp; - if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) - return stbi__errpuc("too large", "Corrupt TGA"); + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); - tga_data = (unsigned char *)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); - if (!tga_data) - return stbi__errpuc("outofmem", "Out of memory"); + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); + if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); - // skip to the data's starting position (offset usually = 0) - stbi__skip(s, tga_offset); + // skip to the data's starting position (offset usually = 0) + stbi__skip(s, tga_offset ); - if (!tga_indexed && !tga_is_RLE && !tga_rgb16) { - for (i = 0; i < tga_height; ++i) { - int row = tga_inverted ? tga_height - i - 1 : i; - stbi_uc * tga_row = tga_data + row * tga_width * tga_comp; - stbi__getn(s, tga_row, tga_width * tga_comp); - } - } else { - // do I need to load a palette? - if (tga_indexed) { - if (tga_palette_len == 0) { /* you have to have at least one entry! */ - STBI_FREE(tga_data); - return stbi__errpuc("bad palette", "Corrupt TGA"); + if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) { + for (i=0; i < tga_height; ++i) { + int row = tga_inverted ? tga_height -i - 1 : i; + stbi_uc *tga_row = tga_data + row*tga_width*tga_comp; + stbi__getn(s, tga_row, tga_width * tga_comp); + } + } else { + // do I need to load a palette? + if ( tga_indexed) + { + if (tga_palette_len == 0) { /* you have to have at least one entry! */ + STBI_FREE(tga_data); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + + // any data to skip? (offset usually = 0) + stbi__skip(s, tga_palette_start ); + // load the palette + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); + if (!tga_palette) { + STBI_FREE(tga_data); + return stbi__errpuc("outofmem", "Out of memory"); + } + if (tga_rgb16) { + stbi_uc *pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i=0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; } - - // any data to skip? (offset usually = 0) - stbi__skip(s, tga_palette_start); - // load the palette - tga_palette = (unsigned char *)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); - if (!tga_palette) { - STBI_FREE(tga_data); - return stbi__errpuc("outofmem", "Out of memory"); + } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + } + // load the data + for (i=0; i < tga_width * tga_height; ++i) + { + // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? + if ( tga_is_RLE ) + { + if ( RLE_count == 0 ) + { + // yep, get the next byte as a RLE command + int RLE_cmd = stbi__get8(s); + RLE_count = 1 + (RLE_cmd & 127); + RLE_repeating = RLE_cmd >> 7; + read_next_pixel = 1; + } else if ( !RLE_repeating ) + { + read_next_pixel = 1; } - if (tga_rgb16) { - stbi_uc * pal_entry = tga_palette; - STBI_ASSERT(tga_comp == STBI_rgb); - for (i = 0; i < tga_palette_len; ++i) { - stbi__tga_read_rgb16(s, pal_entry); - pal_entry += tga_comp; - } - } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { - STBI_FREE(tga_data); - STBI_FREE(tga_palette); - return stbi__errpuc("bad palette", "Corrupt TGA"); - } - } - // load the data - for (i = 0; i < tga_width * tga_height; ++i) { - // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? - if (tga_is_RLE) { - if (RLE_count == 0) { - // yep, get the next byte as a RLE command - int RLE_cmd = stbi__get8(s); - RLE_count = 1 + (RLE_cmd & 127); - RLE_repeating = RLE_cmd >> 7; - read_next_pixel = 1; - } else if (!RLE_repeating) { - read_next_pixel = 1; - } + } else + { + read_next_pixel = 1; + } + // OK, if I need to read a pixel, do it now + if ( read_next_pixel ) + { + // load however much data we did have + if ( tga_indexed ) + { + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if ( pal_idx >= tga_palette_len ) { + // invalid index + pal_idx = 0; + } + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = tga_palette[pal_idx+j]; + } + } else if(tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); } else { - read_next_pixel = 1; + // read in the data raw + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = stbi__get8(s); + } } - // OK, if I need to read a pixel, do it now - if (read_next_pixel) { - // load however much data we did have - if (tga_indexed) { - // read in index, then perform the lookup - int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); - if (pal_idx >= tga_palette_len) { - // invalid index - pal_idx = 0; - } - pal_idx *= tga_comp; - for (j = 0; j < tga_comp; ++j) { - raw_data[j] = tga_palette[pal_idx + j]; - } - } else if (tga_rgb16) { - STBI_ASSERT(tga_comp == STBI_rgb); - stbi__tga_read_rgb16(s, raw_data); - } else { - // read in the data raw - for (j = 0; j < tga_comp; ++j) { - raw_data[j] = stbi__get8(s); - } - } - // clear the reading flag for the next pixel - read_next_pixel = 0; - } // end of reading a pixel + // clear the reading flag for the next pixel + read_next_pixel = 0; + } // end of reading a pixel - // copy data - for (j = 0; j < tga_comp; ++j) - tga_data[i * tga_comp + j] = raw_data[j]; + // copy data + for (j = 0; j < tga_comp; ++j) + tga_data[i*tga_comp+j] = raw_data[j]; - // in case we're in RLE mode, keep counting down - --RLE_count; - } - // do I need to invert the image? - if (tga_inverted) { - for (j = 0; j * 2 < tga_height; ++j) { - int index1 = j * tga_width * tga_comp; - int index2 = (tga_height - 1 - j) * tga_width * tga_comp; - for (i = tga_width * tga_comp; i > 0; --i) { - unsigned char temp = tga_data[index1]; - tga_data[index1] = tga_data[index2]; - tga_data[index2] = temp; - ++index1; - ++index2; - } + // in case we're in RLE mode, keep counting down + --RLE_count; + } + // do I need to invert the image? + if ( tga_inverted ) + { + for (j = 0; j*2 < tga_height; ++j) + { + int index1 = j * tga_width * tga_comp; + int index2 = (tga_height - 1 - j) * tga_width * tga_comp; + for (i = tga_width * tga_comp; i > 0; --i) + { + unsigned char temp = tga_data[index1]; + tga_data[index1] = tga_data[index2]; + tga_data[index2] = temp; + ++index1; + ++index2; } - } - // clear my palette, if I had one - if (tga_palette != NULL) { - STBI_FREE(tga_palette); - } - } + } + } + // clear my palette, if I had one + if ( tga_palette != NULL ) + { + STBI_FREE( tga_palette ); + } + } - // swap RGB - if the source data was RGB16, it already is in the right order - if (tga_comp >= 3 && !tga_rgb16) { - unsigned char * tga_pixel = tga_data; - for (i = 0; i < tga_width * tga_height; ++i) { - unsigned char temp = tga_pixel[0]; - tga_pixel[0] = tga_pixel[2]; - tga_pixel[2] = temp; - tga_pixel += tga_comp; - } - } + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) + { + unsigned char* tga_pixel = tga_data; + for (i=0; i < tga_width * tga_height; ++i) + { + unsigned char temp = tga_pixel[0]; + tga_pixel[0] = tga_pixel[2]; + tga_pixel[2] = temp; + tga_pixel += tga_comp; + } + } - // convert to target component count - if (req_comp && req_comp != tga_comp) - tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); + // convert to target component count + if (req_comp && req_comp != tga_comp) + tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); - // the things I do to get rid of an error message, and yet keep - // Microsoft's C compilers happy... [8^( - tga_palette_start = tga_palette_len = tga_palette_bits = tga_x_origin = tga_y_origin = 0; - STBI_NOTUSED(tga_palette_start); - // OK, done - return tga_data; + // the things I do to get rid of an error message, and yet keep + // Microsoft's C compilers happy... [8^( + tga_palette_start = tga_palette_len = tga_palette_bits = + tga_x_origin = tga_y_origin = 0; + STBI_NOTUSED(tga_palette_start); + // OK, done + return tga_data; } #endif @@ -6446,253 +6078,250 @@ static void * stbi__tga_load(stbi__context * s, int * x, int * y, int * comp, in // Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB #ifndef STBI_NO_PSD -static int stbi__psd_test(stbi__context * s) { - int r = (stbi__get32be(s) == 0x38425053); - stbi__rewind(s); - return r; +static int stbi__psd_test(stbi__context *s) +{ + int r = (stbi__get32be(s) == 0x38425053); + stbi__rewind(s); + return r; } -static int stbi__psd_decode_rle(stbi__context * s, stbi_uc * p, int pixelCount) { - int count, nleft, len; +static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount) +{ + int count, nleft, len; - count = 0; - while ((nleft = pixelCount - count) > 0) { - len = stbi__get8(s); - if (len == 128) { - // No-op. - } else if (len < 128) { - // Copy next len+1 bytes literally. - len++; - if (len > nleft) - return 0; // corrupt data - count += len; - while (len) { - *p = stbi__get8(s); - p += 4; - len--; - } - } else if (len > 128) { - stbi_uc val; - // Next -len+1 bytes in the dest are replicated from next source byte. - // (Interpret len as a negative 8-bit int.) - len = 257 - len; - if (len > nleft) - return 0; // corrupt data - val = stbi__get8(s); - count += len; - while (len) { - *p = val; - p += 4; - len--; - } - } - } + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; + } + } else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } - return 1; + return 1; } -static void * stbi__psd_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri, int bpc) { - int pixelCount; - int channelCount, compression; - int channel, i; - int bitdepth; - int w, h; - stbi_uc * out; - STBI_NOTUSED(ri); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + int pixelCount; + int channelCount, compression; + int channel, i; + int bitdepth; + int w,h; + stbi_uc *out; + STBI_NOTUSED(ri); - // Check identifier - if (stbi__get32be(s) != 0x38425053) // "8BPS" - return stbi__errpuc("not PSD", "Corrupt PSD image"); + // Check identifier + if (stbi__get32be(s) != 0x38425053) // "8BPS" + return stbi__errpuc("not PSD", "Corrupt PSD image"); - // Check file type version. - if (stbi__get16be(s) != 1) - return stbi__errpuc("wrong version", "Unsupported version of PSD image"); + // Check file type version. + if (stbi__get16be(s) != 1) + return stbi__errpuc("wrong version", "Unsupported version of PSD image"); - // Skip 6 reserved bytes. - stbi__skip(s, 6); + // Skip 6 reserved bytes. + stbi__skip(s, 6 ); - // Read the number of channels (R, G, B, A, etc). - channelCount = stbi__get16be(s); - if (channelCount < 0 || channelCount > 16) - return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); + // Read the number of channels (R, G, B, A, etc). + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) + return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); - // Read the rows and columns of the image. - h = stbi__get32be(s); - w = stbi__get32be(s); + // Read the rows and columns of the image. + h = stbi__get32be(s); + w = stbi__get32be(s); - if (h > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); - if (w > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (h > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (w > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); - // Make sure the depth is 8 bits. - bitdepth = stbi__get16be(s); - if (bitdepth != 8 && bitdepth != 16) - return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); + // Make sure the depth is 8 bits. + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); - // Make sure the color mode is RGB. - // Valid options are: - // 0: Bitmap - // 1: Grayscale - // 2: Indexed color - // 3: RGB color - // 4: CMYK color - // 7: Multichannel - // 8: Duotone - // 9: Lab color - if (stbi__get16be(s) != 3) - return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); + // Make sure the color mode is RGB. + // Valid options are: + // 0: Bitmap + // 1: Grayscale + // 2: Indexed color + // 3: RGB color + // 4: CMYK color + // 7: Multichannel + // 8: Duotone + // 9: Lab color + if (stbi__get16be(s) != 3) + return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); - // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) - stbi__skip(s, stbi__get32be(s)); + // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) + stbi__skip(s,stbi__get32be(s) ); - // Skip the image resources. (resolution, pen tool paths, etc) - stbi__skip(s, stbi__get32be(s)); + // Skip the image resources. (resolution, pen tool paths, etc) + stbi__skip(s, stbi__get32be(s) ); - // Skip the reserved data. - stbi__skip(s, stbi__get32be(s)); + // Skip the reserved data. + stbi__skip(s, stbi__get32be(s) ); - // Find out if the data is compressed. - // Known values: - // 0: no compression - // 1: RLE compressed - compression = stbi__get16be(s); - if (compression > 1) - return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + // Find out if the data is compressed. + // Known values: + // 0: no compression + // 1: RLE compressed + compression = stbi__get16be(s); + if (compression > 1) + return stbi__errpuc("bad compression", "PSD has an unknown compression format"); - // Check size - if (!stbi__mad3sizes_valid(4, w, h, 0)) - return stbi__errpuc("too large", "Corrupt PSD"); + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); - // Create the destination image. + // Create the destination image. - if (!compression && bitdepth == 16 && bpc == 16) { - out = (stbi_uc *)stbi__malloc_mad3(8, w, h, 0); - ri->bits_per_channel = 16; - } else - out = (stbi_uc *)stbi__malloc(4 * w * h); + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } else + out = (stbi_uc *) stbi__malloc(4 * w*h); - if (!out) - return stbi__errpuc("outofmem", "Out of memory"); - pixelCount = w * h; + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + pixelCount = w*h; - // Initialize the data to zero. - // memset( out, 0, pixelCount * 4 ); + // Initialize the data to zero. + //memset( out, 0, pixelCount * 4 ); - // Finally, the image data. - if (compression) { - // RLE as used by .PSD and .TIFF - // Loop until you get the number of unpacked bytes you are expecting: - // Read the next source byte into n. - // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. - // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. - // Else if n is 128, noop. - // Endloop + // Finally, the image data. + if (compression) { + // RLE as used by .PSD and .TIFF + // Loop until you get the number of unpacked bytes you are expecting: + // Read the next source byte into n. + // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. + // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. + // Else if n is 128, noop. + // Endloop - // The RLE-compressed data is preceded by a 2-byte data count for each row in the data, - // which we're going to just skip. - stbi__skip(s, h * channelCount * 2); + // The RLE-compressed data is preceded by a 2-byte data count for each row in the data, + // which we're going to just skip. + stbi__skip(s, h * channelCount * 2 ); - // Read the RLE data by channel. - for (channel = 0; channel < 4; channel++) { - stbi_uc * p; + // Read the RLE data by channel. + for (channel = 0; channel < 4; channel++) { + stbi_uc *p; - p = out + channel; - if (channel >= channelCount) { - // Fill this channel with default data. - for (i = 0; i < pixelCount; i++, p += 4) - *p = (channel == 3 ? 255 : 0); + p = out+channel; + if (channel >= channelCount) { + // Fill this channel with default data. + for (i = 0; i < pixelCount; i++, p += 4) + *p = (channel == 3 ? 255 : 0); + } else { + // Read the RLE data. + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); + } + } + } + + } else { + // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. + + // Read the data by channel. + for (channel = 0; channel < 4; channel++) { + if (channel >= channelCount) { + // Fill this channel with default data. + if (bitdepth == 16 && bpc == 16) { + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; } else { - // Read the RLE data. - if (!stbi__psd_decode_rle(s, p, pixelCount)) { - STBI_FREE(out); - return stbi__errpuc("corrupt", "bad RLE data"); - } + stbi_uc *p = out+channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; } - } - } else { - // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) - // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. - - // Read the data by channel. - for (channel = 0; channel < 4; channel++) { - if (channel >= channelCount) { - // Fill this channel with default data. - if (bitdepth == 16 && bpc == 16) { - stbi__uint16 * q = ((stbi__uint16 *)out) + channel; - stbi__uint16 val = channel == 3 ? 65535 : 0; - for (i = 0; i < pixelCount; i++, q += 4) - *q = val; - } else { - stbi_uc * p = out + channel; - stbi_uc val = channel == 3 ? 255 : 0; - for (i = 0; i < pixelCount; i++, p += 4) - *p = val; - } + } else { + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16) stbi__get16be(s); } else { - if (ri->bits_per_channel == 16) { // output bpc - stbi__uint16 * q = ((stbi__uint16 *)out) + channel; - for (i = 0; i < pixelCount; i++, q += 4) - *q = (stbi__uint16)stbi__get16be(s); - } else { - stbi_uc * p = out + channel; - if (bitdepth == 16) { // input bpc - for (i = 0; i < pixelCount; i++, p += 4) - *p = (stbi_uc)(stbi__get16be(s) >> 8); - } else { - for (i = 0; i < pixelCount; i++, p += 4) - *p = stbi__get8(s); - } - } + stbi_uc *p = out+channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc) (stbi__get16be(s) >> 8); + } else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } } - } - } + } + } + } - // remove weird white matte from PSD - if (channelCount >= 4) { - if (ri->bits_per_channel == 16) { - for (i = 0; i < w * h; ++i) { - stbi__uint16 * pixel = (stbi__uint16 *)out + 4 * i; - if (pixel[3] != 0 && pixel[3] != 65535) { - float a = pixel[3] / 65535.0f; - float ra = 1.0f / a; - float inv_a = 65535.0f * (1 - ra); - pixel[0] = (stbi__uint16)(pixel[0] * ra + inv_a); - pixel[1] = (stbi__uint16)(pixel[1] * ra + inv_a); - pixel[2] = (stbi__uint16)(pixel[2] * ra + inv_a); - } + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i=0; i < w*h; ++i) { + stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a); + pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a); + pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a); } - } else { - for (i = 0; i < w * h; ++i) { - unsigned char * pixel = out + 4 * i; - if (pixel[3] != 0 && pixel[3] != 255) { - float a = pixel[3] / 255.0f; - float ra = 1.0f / a; - float inv_a = 255.0f * (1 - ra); - pixel[0] = (unsigned char)(pixel[0] * ra + inv_a); - pixel[1] = (unsigned char)(pixel[1] * ra + inv_a); - pixel[2] = (unsigned char)(pixel[2] * ra + inv_a); - } + } + } else { + for (i=0; i < w*h; ++i) { + unsigned char *pixel = out + 4*i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char) (pixel[0]*ra + inv_a); + pixel[1] = (unsigned char) (pixel[1]*ra + inv_a); + pixel[2] = (unsigned char) (pixel[2]*ra + inv_a); } - } - } + } + } + } - // convert to desired output format - if (req_comp && req_comp != 4) { - if (ri->bits_per_channel == 16) - out = (stbi_uc *)stbi__convert_format16((stbi__uint16 *)out, 4, req_comp, w, h); - else - out = stbi__convert_format(out, 4, req_comp, w, h); - if (out == NULL) - return out; // stbi__convert_format frees input on failure - } + // convert to desired output format + if (req_comp && req_comp != 4) { + if (ri->bits_per_channel == 16) + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } - if (comp) - *comp = 4; - *y = h; - *x = w; + if (comp) *comp = 4; + *y = h; + *x = w; - return out; + return out; } #endif @@ -6704,221 +6333,216 @@ static void * stbi__psd_load(stbi__context * s, int * x, int * y, int * comp, in // See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/ #ifndef STBI_NO_PIC -static int stbi__pic_is4(stbi__context * s, const char * str) { - int i; - for (i = 0; i < 4; ++i) - if (stbi__get8(s) != (stbi_uc)str[i]) - return 0; +static int stbi__pic_is4(stbi__context *s,const char *str) +{ + int i; + for (i=0; i<4; ++i) + if (stbi__get8(s) != (stbi_uc)str[i]) + return 0; - return 1; + return 1; } -static int stbi__pic_test_core(stbi__context * s) { - int i; +static int stbi__pic_test_core(stbi__context *s) +{ + int i; - if (!stbi__pic_is4(s, "\x53\x80\xF6\x34")) - return 0; + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) + return 0; - for (i = 0; i < 84; ++i) - stbi__get8(s); + for(i=0;i<84;++i) + stbi__get8(s); - if (!stbi__pic_is4(s, "PICT")) - return 0; + if (!stbi__pic_is4(s,"PICT")) + return 0; - return 1; + return 1; } -typedef struct { - stbi_uc size, type, channel; +typedef struct +{ + stbi_uc size,type,channel; } stbi__pic_packet; -static stbi_uc * stbi__readval(stbi__context * s, int channel, stbi_uc * dest) { - int mask = 0x80, i; +static stbi_uc *stbi__readval(stbi__context *s, int channel, stbi_uc *dest) +{ + int mask=0x80, i; - for (i = 0; i < 4; ++i, mask >>= 1) { - if (channel & mask) { - if (stbi__at_eof(s)) - return stbi__errpuc("bad file", "PIC file too short"); - dest[i] = stbi__get8(s); - } - } + for (i=0; i<4; ++i, mask>>=1) { + if (channel & mask) { + if (stbi__at_eof(s)) return stbi__errpuc("bad file","PIC file too short"); + dest[i]=stbi__get8(s); + } + } - return dest; + return dest; } -static void stbi__copyval(int channel, stbi_uc * dest, const stbi_uc * src) { - int mask = 0x80, i; +static void stbi__copyval(int channel,stbi_uc *dest,const stbi_uc *src) +{ + int mask=0x80,i; - for (i = 0; i < 4; ++i, mask >>= 1) - if (channel & mask) - dest[i] = src[i]; + for (i=0;i<4; ++i, mask>>=1) + if (channel&mask) + dest[i]=src[i]; } -static stbi_uc * stbi__pic_load_core(stbi__context * s, int width, int height, int * comp, stbi_uc * result) { - int act_comp = 0, num_packets = 0, y, chained; - stbi__pic_packet packets[10]; +static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *comp, stbi_uc *result) +{ + int act_comp=0,num_packets=0,y,chained; + stbi__pic_packet packets[10]; - // this will (should...) cater for even some bizarre stuff like having data + // this will (should...) cater for even some bizarre stuff like having data // for the same channel in multiple packets. - do { - stbi__pic_packet * packet; + do { + stbi__pic_packet *packet; - if (num_packets == sizeof(packets) / sizeof(packets[0])) - return stbi__errpuc("bad format", "too many packets"); + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return stbi__errpuc("bad format","too many packets"); - packet = &packets[num_packets++]; + packet = &packets[num_packets++]; - chained = stbi__get8(s); - packet->size = stbi__get8(s); - packet->type = stbi__get8(s); - packet->channel = stbi__get8(s); + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); - act_comp |= packet->channel; + act_comp |= packet->channel; - if (stbi__at_eof(s)) - return stbi__errpuc("bad file", "file too short (reading packets)"); - if (packet->size != 8) - return stbi__errpuc("bad format", "packet isn't 8bpp"); - } while (chained); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (reading packets)"); + if (packet->size != 8) return stbi__errpuc("bad format","packet isn't 8bpp"); + } while (chained); - *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? + *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? - for (y = 0; y < height; ++y) { - int packet_idx; + for(y=0; ytype) { + switch (packet->type) { default: - return stbi__errpuc("bad format", "packet has bad compression type"); + return stbi__errpuc("bad format","packet has bad compression type"); - case 0: { // uncompressed - int x; + case 0: {//uncompressed + int x; - for (x = 0; x < width; ++x, dest += 4) - if (!stbi__readval(s, packet->channel, dest)) - return 0; - break; + for(x=0;xchannel,dest)) + return 0; + break; } - case 1: // Pure RLE - { - int left = width, i; + case 1://Pure RLE + { + int left=width, i; - while (left > 0) { - stbi_uc count, value[4]; + while (left>0) { + stbi_uc count,value[4]; - count = stbi__get8(s); - if (stbi__at_eof(s)) - return stbi__errpuc("bad file", "file too short (pure read count)"); + count=stbi__get8(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pure read count)"); - if (count > left) - count = (stbi_uc)left; + if (count > left) + count = (stbi_uc) left; - if (!stbi__readval(s, packet->channel, value)) + if (!stbi__readval(s,packet->channel,value)) return 0; + + for(i=0; ichannel,dest,value); + left -= count; + } + } + break; + + case 2: {//Mixed RLE + int left=width; + while (left>0) { + int count = stbi__get8(s), i; + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (mixed read count)"); + + if (count >= 128) { // Repeated + stbi_uc value[4]; + + if (count==128) + count = stbi__get16be(s); + else + count -= 127; + if (count > left) + return stbi__errpuc("bad file","scanline overrun"); + + if (!stbi__readval(s,packet->channel,value)) return 0; - for (i = 0; i < count; ++i, dest += 4) - stbi__copyval(packet->channel, dest, value); - left -= count; - } - } break; + for(i=0;ichannel,dest,value); + } else { // Raw + ++count; + if (count>left) return stbi__errpuc("bad file","scanline overrun"); - case 2: { // Mixed RLE - int left = width; - while (left > 0) { - int count = stbi__get8(s), i; - if (stbi__at_eof(s)) - return stbi__errpuc("bad file", "file too short (mixed read count)"); - - if (count >= 128) { // Repeated - stbi_uc value[4]; - - if (count == 128) - count = stbi__get16be(s); - else - count -= 127; - if (count > left) - return stbi__errpuc("bad file", "scanline overrun"); - - if (!stbi__readval(s, packet->channel, value)) - return 0; - - for (i = 0; i < count; ++i, dest += 4) - stbi__copyval(packet->channel, dest, value); - } else { // Raw - ++count; - if (count > left) - return stbi__errpuc("bad file", "scanline overrun"); - - for (i = 0; i < count; ++i, dest += 4) - if (!stbi__readval(s, packet->channel, dest)) - return 0; - } - left -= count; - } - break; + for(i=0;ichannel,dest)) + return 0; + } + left-=count; + } + break; } - } - } - } + } + } + } - return result; + return result; } -static void * stbi__pic_load(stbi__context * s, int * px, int * py, int * comp, int req_comp, stbi__result_info * ri) { - stbi_uc * result; - int i, x, y, internal_comp; - STBI_NOTUSED(ri); +static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri) +{ + stbi_uc *result; + int i, x,y, internal_comp; + STBI_NOTUSED(ri); - if (!comp) - comp = &internal_comp; + if (!comp) comp = &internal_comp; - for (i = 0; i < 92; ++i) - stbi__get8(s); + for (i=0; i<92; ++i) + stbi__get8(s); - x = stbi__get16be(s); - y = stbi__get16be(s); + x = stbi__get16be(s); + y = stbi__get16be(s); - if (y > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); - if (x > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); - if (stbi__at_eof(s)) - return stbi__errpuc("bad file", "file too short (pic header)"); - if (!stbi__mad3sizes_valid(x, y, 4, 0)) - return stbi__errpuc("too large", "PIC image too large to decode"); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); - stbi__get32be(s); // skip `ratio' - stbi__get16be(s); // skip `fields' - stbi__get16be(s); // skip `pad' + stbi__get32be(s); //skip `ratio' + stbi__get16be(s); //skip `fields' + stbi__get16be(s); //skip `pad' - // intermediate buffer is RGBA - result = (stbi_uc *)stbi__malloc_mad3(x, y, 4, 0); - if (!result) - return stbi__errpuc("outofmem", "Out of memory"); - memset(result, 0xff, x * y * 4); + // intermediate buffer is RGBA + result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); + if (!result) return stbi__errpuc("outofmem", "Out of memory"); + memset(result, 0xff, x*y*4); - if (!stbi__pic_load_core(s, x, y, comp, result)) { - STBI_FREE(result); - result = 0; - } - *px = x; - *py = y; - if (req_comp == 0) - req_comp = *comp; - result = stbi__convert_format(result, 4, req_comp, x, y); + if (!stbi__pic_load_core(s,x,y,comp, result)) { + STBI_FREE(result); + result=0; + } + *px = x; + *py = y; + if (req_comp == 0) req_comp = *comp; + result=stbi__convert_format(result,4,req_comp,x,y); - return result; + return result; } -static int stbi__pic_test(stbi__context * s) { - int r = stbi__pic_test_core(s); - stbi__rewind(s); - return r; +static int stbi__pic_test(stbi__context *s) +{ + int r = stbi__pic_test_core(s); + stbi__rewind(s); + return r; } #endif @@ -6926,968 +6550,931 @@ static int stbi__pic_test(stbi__context * s) { // GIF loader -- public domain by Jean-Marc Lienher -- simplified/shrunk by stb #ifndef STBI_NO_GIF -typedef struct { - stbi__int16 prefix; - stbi_uc first; - stbi_uc suffix; +typedef struct +{ + stbi__int16 prefix; + stbi_uc first; + stbi_uc suffix; } stbi__gif_lzw; -typedef struct { - int w, h; - stbi_uc * out; // output buffer (always 4 components) - stbi_uc * background; // The current "background" as far as a gif is concerned - stbi_uc * history; - int flags, bgindex, ratio, transparent, eflags; - stbi_uc pal[256][4]; - stbi_uc lpal[256][4]; - stbi__gif_lzw codes[8192]; - stbi_uc * color_table; - int parse, step; - int lflags; - int start_x, start_y; - int max_x, max_y; - int cur_x, cur_y; - int line_size; - int delay; +typedef struct +{ + int w,h; + stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; + int flags, bgindex, ratio, transparent, eflags; + stbi_uc pal[256][4]; + stbi_uc lpal[256][4]; + stbi__gif_lzw codes[8192]; + stbi_uc *color_table; + int parse, step; + int lflags; + int start_x, start_y; + int max_x, max_y; + int cur_x, cur_y; + int line_size; + int delay; } stbi__gif; -static int stbi__gif_test_raw(stbi__context * s) { - int sz; - if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') - return 0; - sz = stbi__get8(s); - if (sz != '9' && sz != '7') - return 0; - if (stbi__get8(s) != 'a') - return 0; - return 1; +static int stbi__gif_test_raw(stbi__context *s) +{ + int sz; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0; + sz = stbi__get8(s); + if (sz != '9' && sz != '7') return 0; + if (stbi__get8(s) != 'a') return 0; + return 1; } -static int stbi__gif_test(stbi__context * s) { - int r = stbi__gif_test_raw(s); - stbi__rewind(s); - return r; +static int stbi__gif_test(stbi__context *s) +{ + int r = stbi__gif_test_raw(s); + stbi__rewind(s); + return r; } -static void stbi__gif_parse_colortable(stbi__context * s, stbi_uc pal[256][4], int num_entries, int transp) { - int i; - for (i = 0; i < num_entries; ++i) { - pal[i][2] = stbi__get8(s); - pal[i][1] = stbi__get8(s); - pal[i][0] = stbi__get8(s); - pal[i][3] = transp == i ? 0 : 255; - } +static void stbi__gif_parse_colortable(stbi__context *s, stbi_uc pal[256][4], int num_entries, int transp) +{ + int i; + for (i=0; i < num_entries; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + pal[i][3] = transp == i ? 0 : 255; + } } -static int stbi__gif_header(stbi__context * s, stbi__gif * g, int * comp, int is_info) { - stbi_uc version; - if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') - return stbi__err("not GIF", "Corrupt GIF"); +static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_info) +{ + stbi_uc version; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') + return stbi__err("not GIF", "Corrupt GIF"); - version = stbi__get8(s); - if (version != '7' && version != '9') - return stbi__err("not GIF", "Corrupt GIF"); - if (stbi__get8(s) != 'a') - return stbi__err("not GIF", "Corrupt GIF"); + version = stbi__get8(s); + if (version != '7' && version != '9') return stbi__err("not GIF", "Corrupt GIF"); + if (stbi__get8(s) != 'a') return stbi__err("not GIF", "Corrupt GIF"); - stbi__g_failure_reason = ""; - g->w = stbi__get16le(s); - g->h = stbi__get16le(s); - g->flags = stbi__get8(s); - g->bgindex = stbi__get8(s); - g->ratio = stbi__get8(s); - g->transparent = -1; + stbi__g_failure_reason = ""; + g->w = stbi__get16le(s); + g->h = stbi__get16le(s); + g->flags = stbi__get8(s); + g->bgindex = stbi__get8(s); + g->ratio = stbi__get8(s); + g->transparent = -1; - if (g->w > STBI_MAX_DIMENSIONS) - return stbi__err("too large", "Very large image (corrupt?)"); - if (g->h > STBI_MAX_DIMENSIONS) - return stbi__err("too large", "Very large image (corrupt?)"); + if (g->w > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); + if (g->h > STBI_MAX_DIMENSIONS) return stbi__err("too large","Very large image (corrupt?)"); - if (comp != 0) - *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments + if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments - if (is_info) - return 1; + if (is_info) return 1; - if (g->flags & 0x80) - stbi__gif_parse_colortable(s, g->pal, 2 << (g->flags & 7), -1); + if (g->flags & 0x80) + stbi__gif_parse_colortable(s,g->pal, 2 << (g->flags & 7), -1); - return 1; + return 1; } -static int stbi__gif_info_raw(stbi__context * s, int * x, int * y, int * comp) { - stbi__gif * g = (stbi__gif *)stbi__malloc(sizeof(stbi__gif)); - if (!g) - return stbi__err("outofmem", "Out of memory"); - if (!stbi__gif_header(s, g, comp, 1)) { - STBI_FREE(g); - stbi__rewind(s); - return 0; - } - if (x) - *x = g->w; - if (y) - *y = g->h; - STBI_FREE(g); - return 1; +static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!g) return stbi__err("outofmem", "Out of memory"); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); + stbi__rewind( s ); + return 0; + } + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); + return 1; } -static void stbi__out_gif_code(stbi__gif * g, stbi__uint16 code) { - stbi_uc *p, *c; - int idx; +static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) +{ + stbi_uc *p, *c; + int idx; - // recurse to decode the prefixes, since the linked-list is backwards, - // and working backwards through an interleaved image would be nasty - if (g->codes[code].prefix >= 0) - stbi__out_gif_code(g, g->codes[code].prefix); + // recurse to decode the prefixes, since the linked-list is backwards, + // and working backwards through an interleaved image would be nasty + if (g->codes[code].prefix >= 0) + stbi__out_gif_code(g, g->codes[code].prefix); - if (g->cur_y >= g->max_y) - return; + if (g->cur_y >= g->max_y) return; - idx = g->cur_x + g->cur_y; - p = &g->out[idx]; - g->history[idx / 4] = 1; + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; - c = &g->color_table[g->codes[code].suffix * 4]; - if (c[3] > 128) { // don't render transparent pixels; - p[0] = c[2]; - p[1] = c[1]; - p[2] = c[0]; - p[3] = c[3]; - } - g->cur_x += 4; + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; + p[0] = c[2]; + p[1] = c[1]; + p[2] = c[0]; + p[3] = c[3]; + } + g->cur_x += 4; - if (g->cur_x >= g->max_x) { - g->cur_x = g->start_x; - g->cur_y += g->step; + if (g->cur_x >= g->max_x) { + g->cur_x = g->start_x; + g->cur_y += g->step; - while (g->cur_y >= g->max_y && g->parse > 0) { - g->step = (1 << g->parse) * g->line_size; - g->cur_y = g->start_y + (g->step >> 1); - --g->parse; - } - } + while (g->cur_y >= g->max_y && g->parse > 0) { + g->step = (1 << g->parse) * g->line_size; + g->cur_y = g->start_y + (g->step >> 1); + --g->parse; + } + } } -static stbi_uc * stbi__process_gif_raster(stbi__context * s, stbi__gif * g) { - stbi_uc lzw_cs; - stbi__int32 len, init_code; - stbi__uint32 first; - stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; - stbi__gif_lzw * p; +static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) +{ + stbi_uc lzw_cs; + stbi__int32 len, init_code; + stbi__uint32 first; + stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; + stbi__gif_lzw *p; - lzw_cs = stbi__get8(s); - if (lzw_cs > 12) - return NULL; - clear = 1 << lzw_cs; - first = 1; - codesize = lzw_cs + 1; - codemask = (1 << codesize) - 1; - bits = 0; - valid_bits = 0; - for (init_code = 0; init_code < clear; init_code++) { - g->codes[init_code].prefix = -1; - g->codes[init_code].first = (stbi_uc)init_code; - g->codes[init_code].suffix = (stbi_uc)init_code; - } + lzw_cs = stbi__get8(s); + if (lzw_cs > 12) return NULL; + clear = 1 << lzw_cs; + first = 1; + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + bits = 0; + valid_bits = 0; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc) init_code; + g->codes[init_code].suffix = (stbi_uc) init_code; + } - // support no starting clear code - avail = clear + 2; - oldcode = -1; + // support no starting clear code + avail = clear+2; + oldcode = -1; - len = 0; - for (;;) { - if (valid_bits < codesize) { - if (len == 0) { - len = stbi__get8(s); // start new block - if (len == 0) - return g->out; + len = 0; + for(;;) { + if (valid_bits < codesize) { + if (len == 0) { + len = stbi__get8(s); // start new block + if (len == 0) + return g->out; + } + --len; + bits |= (stbi__int32) stbi__get8(s) << valid_bits; + valid_bits += 8; + } else { + stbi__int32 code = bits & codemask; + bits >>= codesize; + valid_bits -= codesize; + // @OPTIMIZE: is there some way we can accelerate the non-clear path? + if (code == clear) { // clear code + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + avail = clear + 2; + oldcode = -1; + first = 0; + } else if (code == clear + 1) { // end of stream code + stbi__skip(s, len); + while ((len = stbi__get8(s)) > 0) + stbi__skip(s,len); + return g->out; + } else if (code <= avail) { + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); } - --len; - bits |= (stbi__int32)stbi__get8(s) << valid_bits; - valid_bits += 8; - } else { - stbi__int32 code = bits & codemask; - bits >>= codesize; - valid_bits -= codesize; - // @OPTIMIZE: is there some way we can accelerate the non-clear path? - if (code == clear) { // clear code - codesize = lzw_cs + 1; - codemask = (1 << codesize) - 1; - avail = clear + 2; - oldcode = -1; - first = 0; - } else if (code == clear + 1) { // end of stream code - stbi__skip(s, len); - while ((len = stbi__get8(s)) > 0) - stbi__skip(s, len); - return g->out; - } else if (code <= avail) { - if (first) { - return stbi__errpuc("no clear code", "Corrupt GIF"); - } - if (oldcode >= 0) { - p = &g->codes[avail++]; - if (avail > 8192) { - return stbi__errpuc("too many codes", "Corrupt GIF"); - } + if (oldcode >= 0) { + p = &g->codes[avail++]; + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } - p->prefix = (stbi__int16)oldcode; - p->first = g->codes[oldcode].first; - p->suffix = (code == avail) ? p->first : g->codes[code].first; - } else if (code == avail) - return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + p->prefix = (stbi__int16) oldcode; + p->first = g->codes[oldcode].first; + p->suffix = (code == avail) ? p->first : g->codes[code].first; + } else if (code == avail) + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); - stbi__out_gif_code(g, (stbi__uint16)code); + stbi__out_gif_code(g, (stbi__uint16) code); - if ((avail & codemask) == 0 && avail <= 0x0FFF) { - codesize++; - codemask = (1 << codesize) - 1; - } - - oldcode = code; - } else { - return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + if ((avail & codemask) == 0 && avail <= 0x0FFF) { + codesize++; + codemask = (1 << codesize) - 1; } - } - } + + oldcode = code; + } else { + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + } + } + } } // this function is designed to support animated gifs, although stb_image doesn't support it // two back is the image from two frames ago, used for a very specific disposal format -static stbi_uc * stbi__gif_load_next(stbi__context * s, stbi__gif * g, int * comp, int req_comp, stbi_uc * two_back) { - int dispose; - int first_frame; - int pi; - int pcount; - STBI_NOTUSED(req_comp); +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) +{ + int dispose; + int first_frame; + int pi; + int pcount; + STBI_NOTUSED(req_comp); - // on first frame, any non-written pixels get the background colour (non-transparent) - first_frame = 0; - if (g->out == 0) { - if (!stbi__gif_header(s, g, comp, 0)) - return 0; // stbi__g_failure_reason set by stbi__gif_header - if (!stbi__mad3sizes_valid(4, g->w, g->h, 0)) - return stbi__errpuc("too large", "GIF image is too large"); - pcount = g->w * g->h; - g->out = (stbi_uc *)stbi__malloc(4 * pcount); - g->background = (stbi_uc *)stbi__malloc(4 * pcount); - g->history = (stbi_uc *)stbi__malloc(pcount); - if (!g->out || !g->background || !g->history) - return stbi__errpuc("outofmem", "Out of memory"); + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; + if (g->out == 0) { + if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header + if (!stbi__mad3sizes_valid(4, g->w, g->h, 0)) + return stbi__errpuc("too large", "GIF image is too large"); + pcount = g->w * g->h; + g->out = (stbi_uc *) stbi__malloc(4 * pcount); + g->background = (stbi_uc *) stbi__malloc(4 * pcount); + g->history = (stbi_uc *) stbi__malloc(pcount); + if (!g->out || !g->background || !g->history) + return stbi__errpuc("outofmem", "Out of memory"); - // image is treated as "transparent" at the start - ie, nothing overwrites the current background; - // background colour is only used for pixels that are not rendered first frame, after that "background" - // color refers to the color that was there the previous frame. - memset(g->out, 0x00, 4 * pcount); - memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent) - memset(g->history, 0x00, pcount); // pixels that were affected previous frame - first_frame = 1; - } else { - // second frame - how do we dispose of the previous one? - dispose = (g->eflags & 0x1C) >> 2; - pcount = g->w * g->h; + // image is treated as "transparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to the color that was there the previous frame. + memset(g->out, 0x00, 4 * pcount); + memset(g->background, 0x00, 4 * pcount); // state of the background (starts transparent) + memset(g->history, 0x00, pcount); // pixels that were affected previous frame + first_frame = 1; + } else { + // second frame - how do we dispose of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; - if ((dispose == 3) && (two_back == 0)) { - dispose = 2; // if I don't have an image to revert back to, default to the old background - } + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background + } - if (dispose == 3) { // use previous graphic - for (pi = 0; pi < pcount; ++pi) { - if (g->history[pi]) { - memcpy(&g->out[pi * 4], &two_back[pi * 4], 4); - } + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); } - } else if (dispose == 2) { - // restore what was changed last frame to background before that frame; - for (pi = 0; pi < pcount; ++pi) { - if (g->history[pi]) { - memcpy(&g->out[pi * 4], &g->background[pi * 4], 4); - } + } + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); } - } else { - // This is a non-disposal case eithe way, so just - // leave the pixels as is, and they will become the new background - // 1: do not dispose - // 0: not specified. - } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } - // background is what out is after the undoing of the previou frame; - memcpy(g->background, g->out, 4 * g->w * g->h); - } + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); + } - // clear my history; - memset(g->history, 0x00, g->w * g->h); // pixels that were affected previous frame + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame - for (;;) { - int tag = stbi__get8(s); - switch (tag) { - case 0x2C: /* Image Descriptor */ - { + for (;;) { + int tag = stbi__get8(s); + switch (tag) { + case 0x2C: /* Image Descriptor */ + { stbi__int32 x, y, w, h; - stbi_uc * o; + stbi_uc *o; x = stbi__get16le(s); y = stbi__get16le(s); w = stbi__get16le(s); h = stbi__get16le(s); if (((x + w) > (g->w)) || ((y + h) > (g->h))) - return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); + return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); g->line_size = g->w * 4; g->start_x = x * 4; g->start_y = y * g->line_size; - g->max_x = g->start_x + w * 4; - g->max_y = g->start_y + h * g->line_size; - g->cur_x = g->start_x; - g->cur_y = g->start_y; + g->max_x = g->start_x + w * 4; + g->max_y = g->start_y + h * g->line_size; + g->cur_x = g->start_x; + g->cur_y = g->start_y; // if the width of the specified rectangle is 0, that means // we may not see *any* pixels or the image is malformed; // to make sure this is caught, move the current y down to // max_y (which is what out_gif_code checks). if (w == 0) - g->cur_y = g->max_y; + g->cur_y = g->max_y; g->lflags = stbi__get8(s); if (g->lflags & 0x40) { - g->step = 8 * g->line_size; // first interlaced spacing - g->parse = 3; + g->step = 8 * g->line_size; // first interlaced spacing + g->parse = 3; } else { - g->step = g->line_size; - g->parse = 0; + g->step = g->line_size; + g->parse = 0; } if (g->lflags & 0x80) { - stbi__gif_parse_colortable(s, g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); - g->color_table = (stbi_uc *)g->lpal; + stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); + g->color_table = (stbi_uc *) g->lpal; } else if (g->flags & 0x80) { - g->color_table = (stbi_uc *)g->pal; + g->color_table = (stbi_uc *) g->pal; } else - return stbi__errpuc("missing color table", "Corrupt GIF"); + return stbi__errpuc("missing color table", "Corrupt GIF"); o = stbi__process_gif_raster(s, g); - if (!o) - return NULL; + if (!o) return NULL; // if this was the first frame, pcount = g->w * g->h; if (first_frame && (g->bgindex > 0)) { - // if first frame, any pixel not drawn to gets the background color - for (pi = 0; pi < pcount; ++pi) { - if (g->history[pi] == 0) { - g->pal[g->bgindex][3] = - 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; - memcpy(&g->out[pi * 4], &g->pal[g->bgindex], 4); - } - } + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } } return o; - } + } - case 0x21: // Comment Extension. - { + case 0x21: // Comment Extension. + { int len; int ext = stbi__get8(s); if (ext == 0xF9) { // Graphic Control Extension. - len = stbi__get8(s); - if (len == 4) { - g->eflags = stbi__get8(s); - g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + len = stbi__get8(s); + if (len == 4) { + g->eflags = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. - // unset old transparent - if (g->transparent >= 0) { - g->pal[g->transparent][3] = 255; - } - if (g->eflags & 0x01) { - g->transparent = stbi__get8(s); - if (g->transparent >= 0) { - g->pal[g->transparent][3] = 0; - } - } else { - // don't need transparent - stbi__skip(s, 1); - g->transparent = -1; - } - } else { - stbi__skip(s, len); - break; - } + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } + } else { + stbi__skip(s, len); + break; + } } while ((len = stbi__get8(s)) != 0) { - stbi__skip(s, len); + stbi__skip(s, len); } break; - } + } - case 0x3B: // gif stream termination code - return (stbi_uc *)s; // using '1' causes warning on some compilers + case 0x3B: // gif stream termination code + return (stbi_uc *) s; // using '1' causes warning on some compilers - default: + default: return stbi__errpuc("unknown code", "Corrupt GIF"); - } - } + } + } } -static void * stbi__load_gif_main_outofmem(stbi__gif * g, stbi_uc * out, int ** delays) { - STBI_FREE(g->out); - STBI_FREE(g->history); - STBI_FREE(g->background); +static void *stbi__load_gif_main_outofmem(stbi__gif *g, stbi_uc *out, int **delays) +{ + STBI_FREE(g->out); + STBI_FREE(g->history); + STBI_FREE(g->background); - if (out) - STBI_FREE(out); - if (delays && *delays) - STBI_FREE(*delays); - return stbi__errpuc("outofmem", "Out of memory"); + if (out) STBI_FREE(out); + if (delays && *delays) STBI_FREE(*delays); + return stbi__errpuc("outofmem", "Out of memory"); } -static void * stbi__load_gif_main(stbi__context * s, int ** delays, int * x, int * y, int * z, int * comp, int req_comp) { - if (stbi__gif_test(s)) { - int layers = 0; - stbi_uc * u = 0; - stbi_uc * out = 0; - stbi_uc * two_back = 0; - stbi__gif g; - int stride; - int out_size = 0; - int delays_size = 0; +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + int out_size = 0; + int delays_size = 0; - STBI_NOTUSED(out_size); - STBI_NOTUSED(delays_size); + STBI_NOTUSED(out_size); + STBI_NOTUSED(delays_size); - memset(&g, 0, sizeof(g)); - if (delays) { - *delays = 0; - } + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } - do { - u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); - if (u == (stbi_uc *)s) - u = 0; // end of animated gif marker + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker - if (u) { - *x = g.w; - *y = g.h; - ++layers; - stride = g.w * g.h * 4; + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; - if (out) { - void * tmp = (stbi_uc *)STBI_REALLOC_SIZED(out, out_size, layers * stride); - if (!tmp) - return stbi__load_gif_main_outofmem(&g, out, delays); - else { - out = (stbi_uc *)tmp; - out_size = layers * stride; - } + if (out) { + void *tmp = (stbi_uc*) STBI_REALLOC_SIZED( out, out_size, layers * stride ); + if (!tmp) + return stbi__load_gif_main_outofmem(&g, out, delays); + else { + out = (stbi_uc*) tmp; + out_size = layers * stride; + } - if (delays) { - int * new_delays = (int *)STBI_REALLOC_SIZED(*delays, delays_size, sizeof(int) * layers); - if (!new_delays) - return stbi__load_gif_main_outofmem(&g, out, delays); - *delays = new_delays; - delays_size = layers * sizeof(int); - } - } else { - out = (stbi_uc *)stbi__malloc(layers * stride); - if (!out) - return stbi__load_gif_main_outofmem(&g, out, delays); - out_size = layers * stride; - if (delays) { - *delays = (int *)stbi__malloc(layers * sizeof(int)); - if (!*delays) - return stbi__load_gif_main_outofmem(&g, out, delays); - delays_size = layers * sizeof(int); - } - } - memcpy(out + ((layers - 1) * stride), u, stride); - if (layers >= 2) { - two_back = out - 2 * stride; - } - - if (delays) { - (*delays)[layers - 1U] = g.delay; - } + if (delays) { + int *new_delays = (int*) STBI_REALLOC_SIZED( *delays, delays_size, sizeof(int) * layers ); + if (!new_delays) + return stbi__load_gif_main_outofmem(&g, out, delays); + *delays = new_delays; + delays_size = layers * sizeof(int); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (!out) + return stbi__load_gif_main_outofmem(&g, out, delays); + out_size = layers * stride; + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + if (!*delays) + return stbi__load_gif_main_outofmem(&g, out, delays); + delays_size = layers * sizeof(int); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; } - } while (u != 0); - // free temp buffer; - STBI_FREE(g.out); - STBI_FREE(g.history); - STBI_FREE(g.background); + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); - // do the final conversion after loading everything; - if (req_comp && req_comp != 4) - out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); - *z = layers; - return out; - } else { - return stbi__errpuc("not GIF", "Image was not as a gif type."); - } + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } } -static void * stbi__gif_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) { - stbi_uc * u = 0; - stbi__gif g; - memset(&g, 0, sizeof(g)); - STBI_NOTUSED(ri); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *u = 0; + stbi__gif g; + memset(&g, 0, sizeof(g)); + STBI_NOTUSED(ri); - u = stbi__gif_load_next(s, &g, comp, req_comp, 0); - if (u == (stbi_uc *)s) - u = 0; // end of animated gif marker - if (u) { - *x = g.w; - *y = g.h; + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + if (u) { + *x = g.w; + *y = g.h; - // moved conversion to after successful load so that the same - // can be done for multiple frames. - if (req_comp && req_comp != 4) - u = stbi__convert_format(u, 4, req_comp, g.w, g.h); - } else if (g.out) { - // if there was an error and we allocated an image buffer, free it! - STBI_FREE(g.out); - } + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); + } else if (g.out) { + // if there was an error and we allocated an image buffer, free it! + STBI_FREE(g.out); + } - // free buffers needed for multiple frame loading; - STBI_FREE(g.history); - STBI_FREE(g.background); + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); - return u; + return u; } -static int stbi__gif_info(stbi__context * s, int * x, int * y, int * comp) { return stbi__gif_info_raw(s, x, y, comp); } +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) +{ + return stbi__gif_info_raw(s,x,y,comp); +} #endif // ************************************************************************************************* // Radiance RGBE HDR loader // originally by Nicolas Schulz #ifndef STBI_NO_HDR -static int stbi__hdr_test_core(stbi__context * s, const char * signature) { - int i; - for (i = 0; signature[i]; ++i) - if (stbi__get8(s) != signature[i]) - return 0; - stbi__rewind(s); - return 1; +static int stbi__hdr_test_core(stbi__context *s, const char *signature) +{ + int i; + for (i=0; signature[i]; ++i) + if (stbi__get8(s) != signature[i]) + return 0; + stbi__rewind(s); + return 1; } -static int stbi__hdr_test(stbi__context * s) { - int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); - stbi__rewind(s); - if (!r) { - r = stbi__hdr_test_core(s, "#?RGBE\n"); - stbi__rewind(s); - } - return r; +static int stbi__hdr_test(stbi__context* s) +{ + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); + stbi__rewind(s); + if(!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } + return r; } -#define STBI__HDR_BUFLEN 1024 -static char * stbi__hdr_gettoken(stbi__context * z, char * buffer) { - int len = 0; - char c = '\0'; +#define STBI__HDR_BUFLEN 1024 +static char *stbi__hdr_gettoken(stbi__context *z, char *buffer) +{ + int len=0; + char c = '\0'; - c = (char)stbi__get8(z); + c = (char) stbi__get8(z); - while (!stbi__at_eof(z) && c != '\n') { - buffer[len++] = c; - if (len == STBI__HDR_BUFLEN - 1) { - // flush to end of line - while (!stbi__at_eof(z) && stbi__get8(z) != '\n') - ; - break; - } - c = (char)stbi__get8(z); - } + while (!stbi__at_eof(z) && c != '\n') { + buffer[len++] = c; + if (len == STBI__HDR_BUFLEN-1) { + // flush to end of line + while (!stbi__at_eof(z) && stbi__get8(z) != '\n') + ; + break; + } + c = (char) stbi__get8(z); + } - buffer[len] = 0; - return buffer; + buffer[len] = 0; + return buffer; } -static void stbi__hdr_convert(float * output, stbi_uc * input, int req_comp) { - if (input[3] != 0) { - float f1; - // Exponent - f1 = (float)ldexp(1.0f, input[3] - (int)(128 + 8)); - if (req_comp <= 2) - output[0] = (input[0] + input[1] + input[2]) * f1 / 3; - else { - output[0] = input[0] * f1; - output[1] = input[1] * f1; - output[2] = input[2] * f1; - } - if (req_comp == 2) - output[1] = 1; - if (req_comp == 4) - output[3] = 1; - } else { - switch (req_comp) { - case 4: - output[3] = 1; /* fallthrough */ - case 3: - output[0] = output[1] = output[2] = 0; - break; - case 2: - output[1] = 1; /* fallthrough */ - case 1: - output[0] = 0; - break; - } - } +static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) +{ + if ( input[3] != 0 ) { + float f1; + // Exponent + f1 = (float) ldexp(1.0f, input[3] - (int)(128 + 8)); + if (req_comp <= 2) + output[0] = (input[0] + input[1] + input[2]) * f1 / 3; + else { + output[0] = input[0] * f1; + output[1] = input[1] * f1; + output[2] = input[2] * f1; + } + if (req_comp == 2) output[1] = 1; + if (req_comp == 4) output[3] = 1; + } else { + switch (req_comp) { + case 4: output[3] = 1; /* fallthrough */ + case 3: output[0] = output[1] = output[2] = 0; + break; + case 2: output[1] = 1; /* fallthrough */ + case 1: output[0] = 0; + break; + } + } } -static float * stbi__hdr_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) { - char buffer[STBI__HDR_BUFLEN]; - char * token; - int valid = 0; - int width, height; - stbi_uc * scanline; - float * hdr_data; - int len; - unsigned char count, value; - int i, j, k, c1, c2, z; - const char * headerToken; - STBI_NOTUSED(ri); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int width, height; + stbi_uc *scanline; + float *hdr_data; + int len; + unsigned char count, value; + int i, j, k, c1,c2, z; + const char *headerToken; + STBI_NOTUSED(ri); - // Check identifier - headerToken = stbi__hdr_gettoken(s, buffer); - if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) - return stbi__errpf("not HDR", "Corrupt HDR image"); + // Check identifier + headerToken = stbi__hdr_gettoken(s,buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) + return stbi__errpf("not HDR", "Corrupt HDR image"); - // Parse header - for (;;) { - token = stbi__hdr_gettoken(s, buffer); - if (token[0] == 0) - break; - if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) - valid = 1; - } + // Parse header + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } - if (!valid) - return stbi__errpf("unsupported format", "Unsupported HDR format"); + if (!valid) return stbi__errpf("unsupported format", "Unsupported HDR format"); - // Parse width and height - // can't use sscanf() if we're not using stdio! - token = stbi__hdr_gettoken(s, buffer); - if (strncmp(token, "-Y ", 3)) - return stbi__errpf("unsupported data layout", "Unsupported HDR format"); - token += 3; - height = (int)strtol(token, &token, 10); - while (*token == ' ') - ++token; - if (strncmp(token, "+X ", 3)) - return stbi__errpf("unsupported data layout", "Unsupported HDR format"); - token += 3; - width = (int)strtol(token, NULL, 10); + // Parse width and height + // can't use sscanf() if we're not using stdio! + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + height = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + width = (int) strtol(token, NULL, 10); - if (height > STBI_MAX_DIMENSIONS) - return stbi__errpf("too large", "Very large image (corrupt?)"); - if (width > STBI_MAX_DIMENSIONS) - return stbi__errpf("too large", "Very large image (corrupt?)"); + if (height > STBI_MAX_DIMENSIONS) return stbi__errpf("too large","Very large image (corrupt?)"); + if (width > STBI_MAX_DIMENSIONS) return stbi__errpf("too large","Very large image (corrupt?)"); - *x = width; - *y = height; + *x = width; + *y = height; - if (comp) - *comp = 3; - if (req_comp == 0) - req_comp = 3; + if (comp) *comp = 3; + if (req_comp == 0) req_comp = 3; - if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) - return stbi__errpf("too large", "HDR image is too large"); + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); - // Read data - hdr_data = (float *)stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); - if (!hdr_data) - return stbi__errpf("outofmem", "Out of memory"); + // Read data + hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); - // Load image data - // image data is stored as some number of sca - if (width < 8 || width >= 32768) { - // Read flat data - for (j = 0; j < height; ++j) { - for (i = 0; i < width; ++i) { - stbi_uc rgbe[4]; - main_decode_loop: - stbi__getn(s, rgbe, 4); - stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); - } - } - } else { - // Read RLE-encoded data - scanline = NULL; + // Load image data + // image data is stored as some number of sca + if ( width < 8 || width >= 32768) { + // Read flat data + for (j=0; j < height; ++j) { + for (i=0; i < width; ++i) { + stbi_uc rgbe[4]; + main_decode_loop: + stbi__getn(s, rgbe, 4); + stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); + } + } + } else { + // Read RLE-encoded data + scanline = NULL; - for (j = 0; j < height; ++j) { - c1 = stbi__get8(s); - c2 = stbi__get8(s); - len = stbi__get8(s); - if (c1 != 2 || c2 != 2 || (len & 0x80)) { - // not run-length encoded, so we have to actually use THIS data as a decoded - // pixel (note this can't be a valid pixel--one of RGB must be >= 128) - stbi_uc rgbe[4]; - rgbe[0] = (stbi_uc)c1; - rgbe[1] = (stbi_uc)c2; - rgbe[2] = (stbi_uc)len; - rgbe[3] = (stbi_uc)stbi__get8(s); - stbi__hdr_convert(hdr_data, rgbe, req_comp); - i = 1; - j = 0; - STBI_FREE(scanline); - goto main_decode_loop; // yes, this makes no sense - } - len <<= 8; - len |= stbi__get8(s); - if (len != width) { - STBI_FREE(hdr_data); - STBI_FREE(scanline); - return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); - } - if (scanline == NULL) { - scanline = (stbi_uc *)stbi__malloc_mad2(width, 4, 0); - if (!scanline) { - STBI_FREE(hdr_data); - return stbi__errpf("outofmem", "Out of memory"); - } - } - - for (k = 0; k < 4; ++k) { - int nleft; - i = 0; - while ((nleft = width - i) > 0) { - count = stbi__get8(s); - if (count > 128) { - // Run - value = stbi__get8(s); - count -= 128; - if ((count == 0) || (count > nleft)) { - STBI_FREE(hdr_data); - STBI_FREE(scanline); - return stbi__errpf("corrupt", "bad RLE data in HDR"); - } - for (z = 0; z < count; ++z) - scanline[i++ * 4 + k] = value; - } else { - // Dump - if ((count == 0) || (count > nleft)) { - STBI_FREE(hdr_data); - STBI_FREE(scanline); - return stbi__errpf("corrupt", "bad RLE data in HDR"); - } - for (z = 0; z < count; ++z) - scanline[i++ * 4 + k] = stbi__get8(s); - } - } - } - for (i = 0; i < width; ++i) - stbi__hdr_convert(hdr_data + (j * width + i) * req_comp, scanline + i * 4, req_comp); - } - if (scanline) + for (j = 0; j < height; ++j) { + c1 = stbi__get8(s); + c2 = stbi__get8(s); + len = stbi__get8(s); + if (c1 != 2 || c2 != 2 || (len & 0x80)) { + // not run-length encoded, so we have to actually use THIS data as a decoded + // pixel (note this can't be a valid pixel--one of RGB must be >= 128) + stbi_uc rgbe[4]; + rgbe[0] = (stbi_uc) c1; + rgbe[1] = (stbi_uc) c2; + rgbe[2] = (stbi_uc) len; + rgbe[3] = (stbi_uc) stbi__get8(s); + stbi__hdr_convert(hdr_data, rgbe, req_comp); + i = 1; + j = 0; STBI_FREE(scanline); - } + goto main_decode_loop; // yes, this makes no sense + } + len <<= 8; + len |= stbi__get8(s); + if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } + if (scanline == NULL) { + scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } - return hdr_data; + for (k = 0; k < 4; ++k) { + int nleft; + i = 0; + while ((nleft = width - i) > 0) { + count = stbi__get8(s); + if (count > 128) { + // Run + value = stbi__get8(s); + count -= 128; + if ((count == 0) || (count > nleft)) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = value; + } else { + // Dump + if ((count == 0) || (count > nleft)) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = stbi__get8(s); + } + } + } + for (i=0; i < width; ++i) + stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); + } + if (scanline) + STBI_FREE(scanline); + } + + return hdr_data; } -static int stbi__hdr_info(stbi__context * s, int * x, int * y, int * comp) { - char buffer[STBI__HDR_BUFLEN]; - char * token; - int valid = 0; - int dummy; +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int dummy; - if (!x) - x = &dummy; - if (!y) - y = &dummy; - if (!comp) - comp = &dummy; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; - if (stbi__hdr_test(s) == 0) { - stbi__rewind(s); - return 0; - } + if (stbi__hdr_test(s) == 0) { + stbi__rewind( s ); + return 0; + } - for (;;) { - token = stbi__hdr_gettoken(s, buffer); - if (token[0] == 0) - break; - if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) - valid = 1; - } + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } - if (!valid) { - stbi__rewind(s); - return 0; - } - token = stbi__hdr_gettoken(s, buffer); - if (strncmp(token, "-Y ", 3)) { - stbi__rewind(s); - return 0; - } - token += 3; - *y = (int)strtol(token, &token, 10); - while (*token == ' ') - ++token; - if (strncmp(token, "+X ", 3)) { - stbi__rewind(s); - return 0; - } - token += 3; - *x = (int)strtol(token, NULL, 10); - *comp = 3; - return 1; + if (!valid) { + stbi__rewind( s ); + return 0; + } + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *y = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *x = (int) strtol(token, NULL, 10); + *comp = 3; + return 1; } #endif // STBI_NO_HDR #ifndef STBI_NO_BMP -static int stbi__bmp_info(stbi__context * s, int * x, int * y, int * comp) { - void * p; - stbi__bmp_data info; +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) +{ + void *p; + stbi__bmp_data info; - info.all_a = 255; - p = stbi__bmp_parse_header(s, &info); - if (p == NULL) { - stbi__rewind(s); - return 0; - } - if (x) - *x = s->img_x; - if (y) - *y = s->img_y; - if (comp) { - if (info.bpp == 24 && info.ma == 0xff000000) - *comp = 3; - else - *comp = info.ma ? 4 : 3; - } - return 1; + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + if (p == NULL) { + stbi__rewind( s ); + return 0; + } + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) { + if (info.bpp == 24 && info.ma == 0xff000000) + *comp = 3; + else + *comp = info.ma ? 4 : 3; + } + return 1; } #endif #ifndef STBI_NO_PSD -static int stbi__psd_info(stbi__context * s, int * x, int * y, int * comp) { - int channelCount, dummy, depth; - if (!x) - x = &dummy; - if (!y) - y = &dummy; - if (!comp) - comp = &dummy; - if (stbi__get32be(s) != 0x38425053) { - stbi__rewind(s); - return 0; - } - if (stbi__get16be(s) != 1) { - stbi__rewind(s); - return 0; - } - stbi__skip(s, 6); - channelCount = stbi__get16be(s); - if (channelCount < 0 || channelCount > 16) { - stbi__rewind(s); - return 0; - } - *y = stbi__get32be(s); - *x = stbi__get32be(s); - depth = stbi__get16be(s); - if (depth != 8 && depth != 16) { - stbi__rewind(s); - return 0; - } - if (stbi__get16be(s) != 3) { - stbi__rewind(s); - return 0; - } - *comp = 4; - return 1; +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) +{ + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + *y = stbi__get32be(s); + *x = stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 3) { + stbi__rewind( s ); + return 0; + } + *comp = 4; + return 1; } -static int stbi__psd_is16(stbi__context * s) { - int channelCount, depth; - if (stbi__get32be(s) != 0x38425053) { - stbi__rewind(s); - return 0; - } - if (stbi__get16be(s) != 1) { - stbi__rewind(s); - return 0; - } - stbi__skip(s, 6); - channelCount = stbi__get16be(s); - if (channelCount < 0 || channelCount > 16) { - stbi__rewind(s); - return 0; - } - STBI_NOTUSED(stbi__get32be(s)); - STBI_NOTUSED(stbi__get32be(s)); - depth = stbi__get16be(s); - if (depth != 16) { - stbi__rewind(s); - return 0; - } - return 1; +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + STBI_NOTUSED(stbi__get32be(s)); + STBI_NOTUSED(stbi__get32be(s)); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; } #endif #ifndef STBI_NO_PIC -static int stbi__pic_info(stbi__context * s, int * x, int * y, int * comp) { - int act_comp = 0, num_packets = 0, chained, dummy; - stbi__pic_packet packets[10]; +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) +{ + int act_comp=0,num_packets=0,chained,dummy; + stbi__pic_packet packets[10]; - if (!x) - x = &dummy; - if (!y) - y = &dummy; - if (!comp) - comp = &dummy; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; - if (!stbi__pic_is4(s, "\x53\x80\xF6\x34")) { - stbi__rewind(s); - return 0; - } + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } - stbi__skip(s, 88); + stbi__skip(s, 88); - *x = stbi__get16be(s); - *y = stbi__get16be(s); - if (stbi__at_eof(s)) { - stbi__rewind(s); - return 0; - } - if ((*x) != 0 && (1 << 28) / (*x) < (*y)) { - stbi__rewind(s); - return 0; - } + *x = stbi__get16be(s); + *y = stbi__get16be(s); + if (stbi__at_eof(s)) { + stbi__rewind( s); + return 0; + } + if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { + stbi__rewind( s ); + return 0; + } - stbi__skip(s, 8); + stbi__skip(s, 8); - do { - stbi__pic_packet * packet; + do { + stbi__pic_packet *packet; - if (num_packets == sizeof(packets) / sizeof(packets[0])) - return 0; + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return 0; - packet = &packets[num_packets++]; - chained = stbi__get8(s); - packet->size = stbi__get8(s); - packet->type = stbi__get8(s); - packet->channel = stbi__get8(s); - act_comp |= packet->channel; + packet = &packets[num_packets++]; + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + act_comp |= packet->channel; - if (stbi__at_eof(s)) { - stbi__rewind(s); - return 0; - } - if (packet->size != 8) { - stbi__rewind(s); - return 0; - } - } while (chained); + if (stbi__at_eof(s)) { + stbi__rewind( s ); + return 0; + } + if (packet->size != 8) { + stbi__rewind( s ); + return 0; + } + } while (chained); - *comp = (act_comp & 0x10 ? 4 : 3); + *comp = (act_comp & 0x10 ? 4 : 3); - return 1; + return 1; } #endif @@ -7904,271 +7491,272 @@ static int stbi__pic_info(stbi__context * s, int * x, int * y, int * comp) { #ifndef STBI_NO_PNM -static int stbi__pnm_test(stbi__context * s) { - char p, t; - p = (char)stbi__get8(s); - t = (char)stbi__get8(s); - if (p != 'P' || (t != '5' && t != '6')) { - stbi__rewind(s); - return 0; - } - return 1; +static int stbi__pnm_test(stbi__context *s) +{ + char p, t; + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind( s ); + return 0; + } + return 1; } -static void * stbi__pnm_load(stbi__context * s, int * x, int * y, int * comp, int req_comp, stbi__result_info * ri) { - stbi_uc * out; - STBI_NOTUSED(ri); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + STBI_NOTUSED(ri); - ri->bits_per_channel = stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n); - if (ri->bits_per_channel == 0) - return 0; + ri->bits_per_channel = stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n); + if (ri->bits_per_channel == 0) + return 0; - if (s->img_y > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); - if (s->img_x > STBI_MAX_DIMENSIONS) - return stbi__errpuc("too large", "Very large image (corrupt?)"); + if (s->img_y > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); + if (s->img_x > STBI_MAX_DIMENSIONS) return stbi__errpuc("too large","Very large image (corrupt?)"); - *x = s->img_x; - *y = s->img_y; - if (comp) - *comp = s->img_n; + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; - if (!stbi__mad4sizes_valid(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0)) - return stbi__errpuc("too large", "PNM too large"); + if (!stbi__mad4sizes_valid(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0)) + return stbi__errpuc("too large", "PNM too large"); - out = (stbi_uc *)stbi__malloc_mad4(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0); - if (!out) - return stbi__errpuc("outofmem", "Out of memory"); - if (!stbi__getn(s, out, s->img_n * s->img_x * s->img_y * (ri->bits_per_channel / 8))) { - STBI_FREE(out); - return stbi__errpuc("bad PNM", "PNM file truncated"); - } + out = (stbi_uc *) stbi__malloc_mad4(s->img_n, s->img_x, s->img_y, ri->bits_per_channel / 8, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (!stbi__getn(s, out, s->img_n * s->img_x * s->img_y * (ri->bits_per_channel / 8))) { + STBI_FREE(out); + return stbi__errpuc("bad PNM", "PNM file truncated"); + } - if (req_comp && req_comp != s->img_n) { - if (ri->bits_per_channel == 16) { - out = (stbi_uc *)stbi__convert_format16((stbi__uint16 *)out, s->img_n, req_comp, s->img_x, s->img_y); - } else { - out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); - } - if (out == NULL) - return out; // stbi__convert_format frees input on failure - } - return out; + if (req_comp && req_comp != s->img_n) { + if (ri->bits_per_channel == 16) { + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, s->img_n, req_comp, s->img_x, s->img_y); + } else { + out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + } + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + return out; } -static int stbi__pnm_isspace(char c) { return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; } - -static void stbi__pnm_skip_whitespace(stbi__context * s, char * c) { - for (;;) { - while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) - *c = (char)stbi__get8(s); - - if (stbi__at_eof(s) || *c != '#') - break; - - while (!stbi__at_eof(s) && *c != '\n' && *c != '\r') - *c = (char)stbi__get8(s); - } +static int stbi__pnm_isspace(char c) +{ + return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; } -static int stbi__pnm_isdigit(char c) { return c >= '0' && c <= '9'; } +static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) +{ + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char) stbi__get8(s); -static int stbi__pnm_getinteger(stbi__context * s, char * c) { - int value = 0; + if (stbi__at_eof(s) || *c != '#') + break; - while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { - value = value * 10 + (*c - '0'); - *c = (char)stbi__get8(s); - if ((value > 214748364) || (value == 214748364 && *c > '7')) - return stbi__err("integer parse overflow", "Parsing an integer in the PPM header overflowed a 32-bit int"); - } - - return value; + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' ) + *c = (char) stbi__get8(s); + } } -static int stbi__pnm_info(stbi__context * s, int * x, int * y, int * comp) { - int maxv, dummy; - char c, p, t; - - if (!x) - x = &dummy; - if (!y) - y = &dummy; - if (!comp) - comp = &dummy; - - stbi__rewind(s); - - // Get identifier - p = (char)stbi__get8(s); - t = (char)stbi__get8(s); - if (p != 'P' || (t != '5' && t != '6')) { - stbi__rewind(s); - return 0; - } - - *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm - - c = (char)stbi__get8(s); - stbi__pnm_skip_whitespace(s, &c); - - *x = stbi__pnm_getinteger(s, &c); // read width - if (*x == 0) - return stbi__err("invalid width", "PPM image header had zero or overflowing width"); - stbi__pnm_skip_whitespace(s, &c); - - *y = stbi__pnm_getinteger(s, &c); // read height - if (*y == 0) - return stbi__err("invalid width", "PPM image header had zero or overflowing width"); - stbi__pnm_skip_whitespace(s, &c); - - maxv = stbi__pnm_getinteger(s, &c); // read max value - if (maxv > 65535) - return stbi__err("max value > 65535", "PPM image supports only 8-bit and 16-bit images"); - else if (maxv > 255) - return 16; - else - return 8; +static int stbi__pnm_isdigit(char c) +{ + return c >= '0' && c <= '9'; } -static int stbi__pnm_is16(stbi__context * s) { - if (stbi__pnm_info(s, NULL, NULL, NULL) == 16) - return 1; - return 0; +static int stbi__pnm_getinteger(stbi__context *s, char *c) +{ + int value = 0; + + while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { + value = value*10 + (*c - '0'); + *c = (char) stbi__get8(s); + if((value > 214748364) || (value == 214748364 && *c > '7')) + return stbi__err("integer parse overflow", "Parsing an integer in the PPM header overflowed a 32-bit int"); + } + + return value; +} + +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) +{ + int maxv, dummy; + char c, p, t; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + stbi__rewind(s); + + // Get identifier + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind(s); + return 0; + } + + *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm + + c = (char) stbi__get8(s); + stbi__pnm_skip_whitespace(s, &c); + + *x = stbi__pnm_getinteger(s, &c); // read width + if(*x == 0) + return stbi__err("invalid width", "PPM image header had zero or overflowing width"); + stbi__pnm_skip_whitespace(s, &c); + + *y = stbi__pnm_getinteger(s, &c); // read height + if (*y == 0) + return stbi__err("invalid width", "PPM image header had zero or overflowing width"); + stbi__pnm_skip_whitespace(s, &c); + + maxv = stbi__pnm_getinteger(s, &c); // read max value + if (maxv > 65535) + return stbi__err("max value > 65535", "PPM image supports only 8-bit and 16-bit images"); + else if (maxv > 255) + return 16; + else + return 8; +} + +static int stbi__pnm_is16(stbi__context *s) +{ + if (stbi__pnm_info(s, NULL, NULL, NULL) == 16) + return 1; + return 0; } #endif -static int stbi__info_main(stbi__context * s, int * x, int * y, int * comp) { -#ifndef STBI_NO_JPEG - if (stbi__jpeg_info(s, x, y, comp)) - return 1; -#endif +static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) +{ + #ifndef STBI_NO_JPEG + if (stbi__jpeg_info(s, x, y, comp)) return 1; + #endif -#ifndef STBI_NO_PNG - if (stbi__png_info(s, x, y, comp)) - return 1; -#endif + #ifndef STBI_NO_PNG + if (stbi__png_info(s, x, y, comp)) return 1; + #endif -#ifndef STBI_NO_GIF - if (stbi__gif_info(s, x, y, comp)) - return 1; -#endif + #ifndef STBI_NO_GIF + if (stbi__gif_info(s, x, y, comp)) return 1; + #endif -#ifndef STBI_NO_BMP - if (stbi__bmp_info(s, x, y, comp)) - return 1; -#endif + #ifndef STBI_NO_BMP + if (stbi__bmp_info(s, x, y, comp)) return 1; + #endif -#ifndef STBI_NO_PSD - if (stbi__psd_info(s, x, y, comp)) - return 1; -#endif + #ifndef STBI_NO_PSD + if (stbi__psd_info(s, x, y, comp)) return 1; + #endif -#ifndef STBI_NO_PIC - if (stbi__pic_info(s, x, y, comp)) - return 1; -#endif + #ifndef STBI_NO_PIC + if (stbi__pic_info(s, x, y, comp)) return 1; + #endif -#ifndef STBI_NO_PNM - if (stbi__pnm_info(s, x, y, comp)) - return 1; -#endif + #ifndef STBI_NO_PNM + if (stbi__pnm_info(s, x, y, comp)) return 1; + #endif -#ifndef STBI_NO_HDR - if (stbi__hdr_info(s, x, y, comp)) - return 1; -#endif + #ifndef STBI_NO_HDR + if (stbi__hdr_info(s, x, y, comp)) return 1; + #endif -// test tga last because it's a crappy test! -#ifndef STBI_NO_TGA - if (stbi__tga_info(s, x, y, comp)) - return 1; -#endif - return stbi__err("unknown image type", "Image not of any known type, or corrupt"); + // test tga last because it's a crappy test! + #ifndef STBI_NO_TGA + if (stbi__tga_info(s, x, y, comp)) + return 1; + #endif + return stbi__err("unknown image type", "Image not of any known type, or corrupt"); } -static int stbi__is_16_main(stbi__context * s) { -#ifndef STBI_NO_PNG - if (stbi__png_is16(s)) - return 1; -#endif +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif -#ifndef STBI_NO_PSD - if (stbi__psd_is16(s)) - return 1; -#endif + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif -#ifndef STBI_NO_PNM - if (stbi__pnm_is16(s)) - return 1; -#endif - return 0; + #ifndef STBI_NO_PNM + if (stbi__pnm_is16(s)) return 1; + #endif + return 0; } #ifndef STBI_NO_STDIO -STBIDEF int stbi_info(char const * filename, int * x, int * y, int * comp) { - FILE * f = stbi__fopen(filename, "rb"); +STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) +{ + FILE *f = stbi__fopen(filename, "rb"); int result; - if (!f) - return stbi__err("can't fopen", "Unable to open file"); + if (!f) return stbi__err("can't fopen", "Unable to open file"); result = stbi_info_from_file(f, x, y, comp); fclose(f); return result; } -STBIDEF int stbi_info_from_file(FILE * f, int * x, int * y, int * comp) { - int r; - stbi__context s; - long pos = ftell(f); - stbi__start_file(&s, f); - r = stbi__info_main(&s, x, y, comp); - fseek(f, pos, SEEK_SET); - return r; +STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__info_main(&s,x,y,comp); + fseek(f,pos,SEEK_SET); + return r; } -STBIDEF int stbi_is_16_bit(char const * filename) { - FILE * f = stbi__fopen(filename, "rb"); +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); int result; - if (!f) - return stbi__err("can't fopen", "Unable to open file"); + if (!f) return stbi__err("can't fopen", "Unable to open file"); result = stbi_is_16_bit_from_file(f); fclose(f); return result; } -STBIDEF int stbi_is_16_bit_from_file(FILE * f) { - int r; - stbi__context s; - long pos = ftell(f); - stbi__start_file(&s, f); - r = stbi__is_16_main(&s); - fseek(f, pos, SEEK_SET); - return r; +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; } #endif // !STBI_NO_STDIO -STBIDEF int stbi_info_from_memory(stbi_uc const * buffer, int len, int * x, int * y, int * comp) { - stbi__context s; - stbi__start_mem(&s, buffer, len); - return stbi__info_main(&s, x, y, comp); +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__info_main(&s,x,y,comp); } -STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const * c, void * user, int * x, int * y, int * comp) { - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *)c, user); - return stbi__info_main(&s, x, y, comp); +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__info_main(&s,x,y,comp); } -STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const * buffer, int len) { - stbi__context s; - stbi__start_mem(&s, buffer, len); - return stbi__is_16_main(&s); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); } -STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const * c, void * user) { - stbi__context s; - stbi__start_callbacks(&s, (stbi_io_callbacks *)c, user); - return stbi__is_16_main(&s); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); } #endif // STB_IMAGE_IMPLEMENTATION @@ -8279,9 +7867,12 @@ STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const * c, void * us 1.30 (2011-06-11) added ability to load files via callbacks to accomidate custom input streams (Ben Wenger) removed deprecated format-specific test/load functions - removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks - anyway error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha) fix inefficiency in - decoding 32-bit BMP (David Woo) 1.29 (2010-08-16) various warning fixes from Aurelien Pocheville 1.28 (2010-08-01) + removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks anyway + error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha) + fix inefficiency in decoding 32-bit BMP (David Woo) + 1.29 (2010-08-16) + various warning fixes from Aurelien Pocheville + 1.28 (2010-08-01) fix bug in GIF palette transparency (SpartanJ) 1.27 (2010-08-01) cast-to-stbi_uc to fix warnings @@ -8353,6 +7944,7 @@ STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const * c, void * us first released version */ + /* ------------------------------------------------------------------------------ This software is available under 2 licenses -- choose whichever you prefer. diff --git a/common/train.cpp b/common/train.cpp deleted file mode 100644 index 0dbfd24df..000000000 --- a/common/train.cpp +++ /dev/null @@ -1,1513 +0,0 @@ -#include "train.h" -#include "common.h" - -#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 = true; - 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) { - 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 deleted file mode 100755 index 28b92ac38..000000000 --- a/convert-hf-to-gguf.py +++ /dev/null @@ -1,1939 +0,0 @@ -#!/usr/bin/env python3 - -from __future__ import annotations - -import argparse -import contextlib -import json -import os -import re -import sys -from enum import IntEnum -from pathlib import Path -from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast - -import numpy as np -import torch - -if TYPE_CHECKING: - from torch import Tensor - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) -import gguf - -from convert import HfVocab - - -###### MODEL DEFINITIONS ###### - -class SentencePieceTokenTypes(IntEnum): - NORMAL = 1 - UNKNOWN = 2 - CONTROL = 3 - USER_DEFINED = 4 - UNUSED = 5 - BYTE = 6 - - -class Model: - def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): - self.dir_model = dir_model - self.ftype = ftype - self.fname_out = fname_out - self.is_big_endian = is_big_endian - self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE - self.is_safetensors = self._is_model_safetensors() - self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") - self.part_names = self._get_part_names() - self.hparams = Model.load_hparams(self.dir_model) - self.model_arch = self._get_model_architecture() - self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) - self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) - - def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any: - key = next((k for k in keys if k in self.hparams), None) - if key is not None: - return self.hparams[key] - if optional: - return None - raise KeyError(f"could not find any of: {keys}") - - def set_vocab(self): - self._set_vocab_gpt2() - - def get_tensors(self) -> Iterator[tuple[str, Tensor]]: - for part_name in self.part_names: - print(f"gguf: loading model part '{part_name}'") - ctx: ContextManager[Any] - if self.is_safetensors: - from safetensors import safe_open - ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) - else: - ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) - - with ctx as model_part: - for name in model_part.keys(): - data = model_part.get_tensor(name) if self.is_safetensors else model_part[name] - yield name, data - - def set_gguf_parameters(self): - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_block_count(self.block_count) - - if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: - self.gguf_writer.add_context_length(n_ctx) - - n_embd = self.find_hparam(["hidden_size", "n_embd"]) - self.gguf_writer.add_embedding_length(n_embd) - - if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None: - self.gguf_writer.add_feed_forward_length(n_ff) - - n_head = self.find_hparam(["num_attention_heads", "n_head"]) - self.gguf_writer.add_head_count(n_head) - - if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: - self.gguf_writer.add_head_count_kv(n_head_kv) - - if (rope_theta := self.hparams.get("rope_theta")) is not None: - self.gguf_writer.add_rope_freq_base(rope_theta) - if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: - self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) - if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: - self.gguf_writer.add_layer_norm_eps(f_norm_eps) - if (n_experts := self.hparams.get("num_local_experts")) is not None: - self.gguf_writer.add_expert_count(n_experts) - if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: - self.gguf_writer.add_expert_used_count(n_experts_used) - - self.gguf_writer.add_file_type(self.ftype) - - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - def write(self): - self.write_tensors() - self.gguf_writer.write_header_to_file() - self.gguf_writer.write_kv_data_to_file() - self.gguf_writer.write_tensors_to_file() - self.gguf_writer.close() - - def write_vocab(self): - self.gguf_writer.write_header_to_file() - self.gguf_writer.write_kv_data_to_file() - self.gguf_writer.close() - - @staticmethod - def count_model_parts(dir_model: Path, prefix: str) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.endswith(prefix): - num_parts += 1 - - return num_parts - - @staticmethod - def load_hparams(dir_model): - with open(dir_model / "config.json", "r", encoding="utf-8") as f: - return json.load(f) - - @staticmethod - def from_model_architecture(model_architecture): - if model_architecture == "GPTNeoXForCausalLM": - return GPTNeoXModel - if model_architecture == "BloomForCausalLM": - return BloomModel - if model_architecture == "MPTForCausalLM": - return MPTModel - if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return BaichuanModel - if model_architecture in ("FalconForCausalLM", "RWForCausalLM"): - return FalconModel - if model_architecture == "GPTBigCodeForCausalLM": - return StarCoderModel - if model_architecture == "GPTRefactForCausalLM": - return RefactModel - if model_architecture == "PersimmonForCausalLM": - return PersimmonModel - if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return StableLMModel - if model_architecture == "QWenLMHeadModel": - return QwenModel - if model_architecture == "Qwen2ForCausalLM": - return Model - if model_architecture == "MixtralForCausalLM": - return MixtralModel - if model_architecture == "GPT2LMHeadModel": - return GPT2Model - if model_architecture == "PhiForCausalLM": - return Phi2Model - if model_architecture == "PlamoForCausalLM": - return PlamoModel - if model_architecture == "CodeShellForCausalLM": - return CodeShellModel - if model_architecture == "OrionForCausalLM": - return OrionModel - if model_architecture == "InternLM2ForCausalLM": - return InternLM2Model - if model_architecture == "MiniCPMForCausalLM": - return MiniCPMModel - if model_architecture == "BertModel": - return BertModel - if model_architecture == "NomicBertModel": - return NomicBertModel - if model_architecture == "GemmaForCausalLM": - return GemmaModel - if model_architecture == "Starcoder2ForCausalLM": - return Model - return Model - - def _is_model_safetensors(self) -> bool: - return Model.count_model_parts(self.dir_model, ".safetensors") > 0 - - def _get_part_names(self): - if self.is_safetensors: - if self.num_parts == 1: # there's only one .safetensors file - return ("model.safetensors",) - return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1)) - - if self.num_parts == 1: # there's only one .bin file - return ("pytorch_model.bin",) - return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) - - def _get_model_architecture(self) -> gguf.MODEL_ARCH: - arch = self.hparams["architectures"][0] - if arch == "GPTNeoXForCausalLM": - return gguf.MODEL_ARCH.GPTNEOX - if arch == "BloomForCausalLM": - return gguf.MODEL_ARCH.BLOOM - if arch == "MPTForCausalLM": - return gguf.MODEL_ARCH.MPT - if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return gguf.MODEL_ARCH.BAICHUAN - if arch in ("FalconForCausalLM", "RWForCausalLM"): - return gguf.MODEL_ARCH.FALCON - if arch == "GPTBigCodeForCausalLM": - return gguf.MODEL_ARCH.STARCODER - if arch == "GPTRefactForCausalLM": - return gguf.MODEL_ARCH.REFACT - if arch == "PersimmonForCausalLM": - return gguf.MODEL_ARCH.PERSIMMON - if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return gguf.MODEL_ARCH.STABLELM - if arch == "QWenLMHeadModel": - return gguf.MODEL_ARCH.QWEN - if arch == "Qwen2ForCausalLM": - return gguf.MODEL_ARCH.QWEN2 - if arch == "MixtralForCausalLM": - return gguf.MODEL_ARCH.LLAMA - if arch == "GPT2LMHeadModel": - return gguf.MODEL_ARCH.GPT2 - if arch == "PhiForCausalLM": - return gguf.MODEL_ARCH.PHI2 - if arch == "PlamoForCausalLM": - return gguf.MODEL_ARCH.PLAMO - if arch == "CodeShellForCausalLM": - return gguf.MODEL_ARCH.CODESHELL - if arch == "OrionForCausalLM": - return gguf.MODEL_ARCH.ORION - if arch == "InternLM2ForCausalLM": - return gguf.MODEL_ARCH.INTERNLM2 - if arch == "MiniCPMForCausalLM": - return gguf.MODEL_ARCH.MINICPM - if arch == "BertModel": - return gguf.MODEL_ARCH.BERT - if arch == "NomicBertModel": - return gguf.MODEL_ARCH.NOMIC_BERT - if arch == "GemmaForCausalLM": - return gguf.MODEL_ARCH.GEMMA - if arch == "Starcoder2ForCausalLM": - return gguf.MODEL_ARCH.STARCODER2 - - raise NotImplementedError(f'Architecture "{arch}" not supported!') - - def _set_vocab_gpt2(self): - dir_model = self.dir_model - hparams = self.hparams - tokens: list[bytearray] = [] - toktypes: list[int] = [] - - from transformers import AutoTokenizer - tokenizer = AutoTokenizer.from_pretrained(dir_model) - vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) - assert max(tokenizer.vocab.values()) < vocab_size - - reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} - added_vocab = tokenizer.get_added_vocab() - - for i in range(vocab_size): - if i not in reverse_vocab: - pad_token = f"[PAD{i}]".encode('utf-8') - tokens.append(bytearray(pad_token)) - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - if tokenizer.added_tokens_decoder[i].special: - toktypes.append(gguf.TokenType.CONTROL) - else: - toktypes.append(gguf.TokenType.USER_DEFINED) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - - self.gguf_writer.add_tokenizer_model("gpt2") - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_types(toktypes) - - special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) - special_vocab.add_to_gguf(self.gguf_writer) - - def _set_vocab_qwen(self): - dir_model = self.dir_model - hparams = self.hparams - tokens: list[bytearray] = [] - toktypes: list[int] = [] - - from transformers import AutoTokenizer - tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) - vocab_size = hparams["vocab_size"] - assert max(tokenizer.get_vocab().values()) < vocab_size - - merges = [] - vocab = {} - mergeable_ranks = tokenizer.mergeable_ranks - for token, rank in mergeable_ranks.items(): - vocab[QwenModel.token_bytes_to_string(token)] = rank - if len(token) == 1: - continue - merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) - assert len(merged) == 2 - merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged))) - - # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined - added_vocab = tokenizer.special_tokens - reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()} - - for i in range(vocab_size): - if i not in reverse_vocab: - pad_token = f"[PAD{i}]".encode("utf-8") - tokens.append(bytearray(pad_token)) - toktypes.append(gguf.TokenType.USER_DEFINED) - elif reverse_vocab[i] in added_vocab: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.CONTROL) - else: - tokens.append(reverse_vocab[i]) - toktypes.append(gguf.TokenType.NORMAL) - - self.gguf_writer.add_tokenizer_model("gpt2") - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_types(toktypes) - - special_vocab = gguf.SpecialVocab(dir_model, load_merges=False) - special_vocab.merges = merges - # only add special tokens when they were not already loaded from config.json - if len(special_vocab.special_token_ids) == 0: - special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"]) - special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"]) - # this one is usually not in config.json anyway - special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"]) - special_vocab.add_to_gguf(self.gguf_writer) - - def _set_vocab_sentencepiece(self): - from sentencepiece import SentencePieceProcessor - - tokenizer_path = self.dir_model / 'tokenizer.model' - - tokens: list[bytes] = [] - scores: list[float] = [] - toktypes: list[int] = [] - - if not tokenizer_path.is_file(): - print(f'Error: Missing {tokenizer_path}', file=sys.stderr) - sys.exit(1) - - tokenizer = SentencePieceProcessor(str(tokenizer_path)) - vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) - - for token_id in range(vocab_size): - piece = tokenizer.id_to_piece(token_id) - text = piece.encode("utf-8") - score = tokenizer.get_score(token_id) - - toktype = SentencePieceTokenTypes.NORMAL - if tokenizer.is_unknown(token_id): - toktype = SentencePieceTokenTypes.UNKNOWN - elif tokenizer.is_control(token_id): - toktype = SentencePieceTokenTypes.CONTROL - elif tokenizer.is_unused(token_id): - toktype = SentencePieceTokenTypes.UNUSED - elif tokenizer.is_byte(token_id): - toktype = SentencePieceTokenTypes.BYTE - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - - added_tokens_file = self.dir_model / 'added_tokens.json' - if added_tokens_file.is_file(): - with open(added_tokens_file, "r", encoding="utf-8") as f: - added_tokens_json = json.load(f) - - for key in added_tokens_json: - tokens.append(key.encode("utf-8")) - scores.append(-1000.0) - toktypes.append(SentencePieceTokenTypes.USER_DEFINED) - - self.gguf_writer.add_tokenizer_model("llama") - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) - self.gguf_writer.add_token_types(toktypes) - - special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) - special_vocab.add_to_gguf(self.gguf_writer) - - def _set_vocab_hf(self): - path = self.dir_model - added_tokens_path = self.dir_model - vocab = HfVocab( - path, added_tokens_path if added_tokens_path.exists() else None - ) - tokens = [] - scores = [] - toktypes = [] - - for text, score, toktype in vocab.all_tokens(): - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - - assert len(tokens) == vocab.vocab_size - - self.gguf_writer.add_tokenizer_model("llama") - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) - self.gguf_writer.add_token_types(toktypes) - - special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) - special_vocab.add_to_gguf(self.gguf_writer) - - -class GPTNeoXModel(Model): - def set_gguf_parameters(self): - block_count = self.hparams["num_hidden_layers"] - - self.gguf_writer.add_name(self.dir_model.name) - 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( - int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), - ) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) - - -class BloomModel(Model): - def set_gguf_parameters(self): - self.gguf_writer.add_name("Bloom") - n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) - n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) - self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) - self.gguf_writer.add_embedding_length(n_embed) - self.gguf_writer.add_feed_forward_length(4 * n_embed) - self.gguf_writer.add_block_count(self.hparams["n_layer"]) - self.gguf_writer.add_head_count(n_head) - self.gguf_writer.add_head_count_kv(n_head) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - - def write_tensors(self): - block_count = self.hparams["n_layer"] - tensors = dict(self.get_tensors()) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - has_lm_head = True - n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) - n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) - - for name, data_torch in tensors.items(): - if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys(): - has_lm_head = False - - name = re.sub(r'transformer\.', '', name) - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): - # Map bloom-style qkv_linear to gpt-style qkv_linear - # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa - # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa - qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed)) - data = np.concatenate( - ( - qkv_weights[:, 0, :, :].reshape((-1, n_embed)), - qkv_weights[:, 1, :, :].reshape((-1, n_embed)), - qkv_weights[:, 2, :, :].reshape((-1, n_embed)), - ), - axis=0, - ) - print("re-format attention.linear_qkv.weight") - elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): - qkv_bias = data.reshape((n_head, 3, n_embed // n_head)) - data = np.concatenate( - ( - qkv_bias[:, 0, :].reshape((n_embed,)), - qkv_bias[:, 1, :].reshape((n_embed,)), - qkv_bias[:, 2, :].reshape((n_embed,)), - ), - axis=0, - ) - print("re-format attention.linear_qkv.bias") - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - if not has_lm_head and name == "word_embeddings.weight": - self.gguf_writer.add_tensor("output.weight", data) - print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") - - -class MPTModel(Model): - def set_gguf_parameters(self): - block_count = self.hparams["n_layers"] - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) - self.gguf_writer.add_embedding_length(self.hparams["d_model"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"]) - self.gguf_writer.add_head_count(self.hparams["n_heads"]) - if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"): - self.gguf_writer.add_head_count_kv(kv_n_heads) - self.gguf_writer.add_layer_norm_eps(1e-5) - if self.hparams["attn_config"]["clip_qkv"] is not None: - self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) - self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) - - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers")) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - if "scales" in name: - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales")) - if new_name is not None: - new_name = new_name.replace("scales", "act.scales") - else: - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - -class OrionModel(Model): - def set_vocab(self): - self._set_vocab_sentencepiece() - - def set_gguf_parameters(self): - block_count = self.hparams["num_hidden_layers"] - head_count = self.hparams["num_attention_heads"] - head_count_kv = self.hparams.get("num_key_value_heads", head_count) - hf_repo = self.hparams.get("_name_or_path", "") - - ctx_length = 0 - if "max_sequence_length" in self.hparams: - ctx_length = self.hparams["max_sequence_length"] - elif "max_position_embeddings" in self.hparams: - ctx_length = self.hparams["max_position_embeddings"] - elif "model_max_length" in self.hparams: - ctx_length = self.hparams["model_max_length"] - else: - print("gguf: can not find ctx length parameter.") - sys.exit() - - self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_source_hf_repo(hf_repo) - self.gguf_writer.add_tensor_data_layout("Meta AI original pth") - self.gguf_writer.add_context_length(ctx_length) - 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_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - # note: config provides rms norm but it is actually layer norm - # ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571 - self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"]) - - def write_tensors(self): - # Collect tensors from generator object - model_kv = dict(self.get_tensors()) - block_count = self.hparams["num_hidden_layers"] - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - - -class BaichuanModel(Model): - def set_vocab(self): - self._set_vocab_sentencepiece() - - def set_gguf_parameters(self): - block_count = self.hparams["num_hidden_layers"] - head_count = self.hparams["num_attention_heads"] - head_count_kv = self.hparams.get("num_key_value_heads", head_count) - hf_repo = self.hparams.get("_name_or_path", "") - - ctx_length = 0 - if "max_sequence_length" in self.hparams: - ctx_length = self.hparams["max_sequence_length"] - elif "max_position_embeddings" in self.hparams: - ctx_length = self.hparams["max_position_embeddings"] - elif "model_max_length" in self.hparams: - ctx_length = self.hparams["model_max_length"] - else: - print("gguf: can not find ctx length parameter.") - sys.exit() - - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_source_hf_repo(hf_repo) - self.gguf_writer.add_tensor_data_layout("Meta AI original pth") - self.gguf_writer.add_context_length(ctx_length) - 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(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - - if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: - if self.hparams["rope_scaling"].get("type") == "linear": - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) - - def write_tensors(self): - # Collect tensors from generator object - model_kv = dict(self.get_tensors()) - block_count = self.hparams["num_hidden_layers"] - head_count = self.hparams["num_attention_heads"] - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - head_count_kv = self.hparams.get("num_key_value_heads", head_count) - - for i in range(block_count): - if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None: - print(f"Unpacking and permuting layer {i}") - model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \ - self._reverse_hf_permute_part(w, 0, head_count, head_count) - model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \ - self._reverse_hf_permute_part(w, 1, head_count, head_count_kv) - model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \ - self._reverse_hf_part(w, 2) - del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"] - - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - - 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) - ) - - def _reverse_hf_permute_part( - self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None, - ) -> Tensor: - r = weights.shape[0] // 3 - return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv) - - def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: - r = weights.shape[0] // 3 - return weights[r * n_part:r * n_part + r, ...] - - -class FalconModel(Model): - def set_gguf_parameters(self): - block_count = self.hparams.get("num_hidden_layers") - if block_count is None: - block_count = self.hparams["n_layer"] # old name - - n_head = self.hparams.get("num_attention_heads") - if n_head is None: - n_head = self.hparams["n_head"] # old name - - n_head_kv = self.hparams.get("num_kv_heads") - if n_head_kv is None: - n_head_kv = self.hparams.get("n_head_kv", 1) # old name - - self.gguf_writer.add_name("Falcon") - self.gguf_writer.add_context_length(2048) # not in config.json - self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(n_head) - self.gguf_writer.add_head_count_kv(n_head_kv) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - - def write_tensors(self): - block_count = self.hparams.get("num_hidden_layers") - if block_count is None: - block_count = self.hparams["n_layer"] # old name - - n_head = self.hparams.get("num_attention_heads") - if n_head is None: - n_head = self.hparams["n_head"] # old name - - n_head_kv = self.hparams.get("num_kv_heads") - if n_head_kv is None: - n_head_kv = self.hparams.get("n_head_kv", 1) # old name - - head_dim = self.hparams["hidden_size"] // n_head - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - for name, data_torch in self.get_tensors(): - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - # QKV tensor transform - # The original query_key_value tensor contains n_head_kv "kv groups", - # each consisting of n_head/n_head_kv query weights followed by one key - # and one value weight (shared by all query heads in the kv group). - # This layout makes it a big pain to work with in GGML. - # So we rearrange them here,, so that we have n_head query weights - # followed by n_head_kv key weights followed by n_head_kv value weights, - # in contiguous fashion. - # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py - - if "query_key_value" in name: - qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) - q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) - k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) - v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) - data_torch = torch.cat((q, k, v)).reshape_as(data_torch) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - -class StarCoderModel(Model): - def set_gguf_parameters(self): - block_count = self.hparams["n_layer"] - - self.gguf_writer.add_name("StarCoder") - self.gguf_writer.add_context_length(self.hparams["n_positions"]) - self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) - self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(self.hparams["n_head"]) - self.gguf_writer.add_head_count_kv(1) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - - -class RefactModel(Model): - def set_gguf_parameters(self): - hidden_dim = self.hparams["n_embd"] - inner_dim = 4 * hidden_dim - hidden_dim = int(2 * inner_dim / 3) - multiple_of = 256 - ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - - block_count = self.hparams["n_layer"] - - self.gguf_writer.add_name("Refact") - # refact uses Alibi. So this is from config.json which might be used by training. - self.gguf_writer.add_context_length(self.hparams["n_positions"]) - self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) - - self.gguf_writer.add_feed_forward_length(ff_dim) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(self.hparams["n_head"]) - self.gguf_writer.add_head_count_kv(1) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - - def write_tensors(self): - hidden_dim = self.hparams["n_embd"] - inner_dim = 4 * hidden_dim - hidden_dim = int(2 * inner_dim / 3) - multiple_of = 256 - ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - n_head = self.hparams["n_head"] - n_head_kv = 1 - head_dim = self.hparams["n_embd"] // n_head - block_count = self.hparams["n_layer"] - - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - tensors = dict(self.get_tensors()) - for i in range(block_count): - if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None: - tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim] - tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:] - del tensors[f"transformer.h.{i}.attn.kv.weight"] - if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None: - tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w - del tensors[f"transformer.h.{i}.attn.q.weight"] - if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None: - tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim] - tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:] - del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"] - - for name, data_torch in tensors.items(): - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - -class PersimmonModel(Model): - def set_gguf_parameters(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - head_count = self.hparams["num_attention_heads"] - head_count_kv = head_count - hidden_size = self.hparams["hidden_size"] - - self.gguf_writer.add_name('persimmon-8b-chat') - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(hidden_size) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - - # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller - # than the head size? - # ref: https://github.com/ggerganov/llama.cpp/pull/4889 - # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) - self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) - - def set_vocab(self): - self._set_vocab_sentencepiece() - # self.gguf_writer.add_bos_token_id(71013) - # self.gguf_writer.add_eos_token_id(71013) - - def write_tensors(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - for name, data_torch in self.get_tensors(): - if name.endswith(".self_attention.rotary_emb.inv_freq"): - continue - old_dtype = data_torch.dtype - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - data = data_torch.to(torch.float32).squeeze().numpy() - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - n_dims = len(data.shape) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - - -class StableLMModel(Model): - def set_vocab(self): - if (self.dir_model / "tokenizer.json").is_file(): - self._set_vocab_gpt2() - else: - # StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab - self._set_vocab_qwen() - - def set_gguf_parameters(self): - hparams = self.hparams - block_count = hparams["num_hidden_layers"] - - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(hparams["hidden_size"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) - rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"]) - self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) - self.gguf_writer.add_head_count(hparams["num_attention_heads"]) - self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) - self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) - - -class MixtralModel(Model): - def set_vocab(self): - self._set_vocab_sentencepiece() - - -class MiniCPMModel(Model): - def set_gguf_parameters(self): - block_count = self.hparams["num_hidden_layers"] - self.gguf_writer.add_name("MiniCPM") - 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) - - def set_vocab(self): - self._set_vocab_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) - ) - - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_head = self.hparams.get("num_attention_heads") - n_kv_head = self.hparams.get("num_key_value_heads") - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - # 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) - if name.endswith(("k_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - -class QwenModel(Model): - @staticmethod - def token_bytes_to_string(b): - from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode - byte_encoder = bytes_to_unicode() - return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) - - @staticmethod - def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: - parts = [bytes([b]) for b in token] - while True: - min_idx = None - min_rank = None - for i, pair in enumerate(zip(parts[:-1], parts[1:])): - rank = mergeable_ranks.get(pair[0] + pair[1]) - if rank is not None and (min_rank is None or rank < min_rank): - min_idx = i - min_rank = rank - if min_rank is None or (max_rank is not None and min_rank >= max_rank): - break - assert min_idx is not None - parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] - return parts - - def set_vocab(self): - self._set_vocab_qwen() - - def set_gguf_parameters(self): - self.gguf_writer.add_name("Qwen") - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) - 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_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) - - def write_tensors(self): - block_count = self.hparams["num_hidden_layers"] - model_kv = dict(self.get_tensors()) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - - -class GPT2Model(Model): - def set_gguf_parameters(self): - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_block_count(self.hparams["n_layer"]) - self.gguf_writer.add_context_length(self.hparams["n_ctx"]) - self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) - self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) - self.gguf_writer.add_head_count(self.hparams["n_head"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")): - continue - - if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): - data_torch = data_torch.transpose(1, 0) - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - # note: GPT2 output is tied to (same as) wte in original model - if new_name == "token_embd.weight": - print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor("output.weight", data) - - -class Phi2Model(Model): - def set_gguf_parameters(self): - block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) - - rot_pct = self.find_hparam(["partial_rotary_factor"]) - n_embd = self.find_hparam(["hidden_size", "n_embd"]) - n_head = self.find_hparam(["num_attention_heads", "n_head"]) - - self.gguf_writer.add_name("Phi2") - self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) - - self.gguf_writer.add_embedding_length(n_embd) - self.gguf_writer.add_feed_forward_length(4 * n_embd) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(n_head) - self.gguf_writer.add_head_count_kv(n_head) - self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"])) - self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) - self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_add_bos_token(False) - - -class PlamoModel(Model): - def set_vocab(self): - self._set_vocab_sentencepiece() - - def set_gguf_parameters(self): - hparams = self.hparams - block_count = hparams["num_hidden_layers"] - - self.gguf_writer.add_name("PLaMo") - self.gguf_writer.add_context_length(4096) # not in config.json - self.gguf_writer.add_embedding_length(hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(hparams["num_attention_heads"]) - self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong - self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) - - def shuffle_attn_q_weight(self, data_torch): - assert data_torch.size() == (5120, 5120) - data_torch = data_torch.reshape(8, 5, 128, 5120) - data_torch = torch.permute(data_torch, (1, 0, 2, 3)) - data_torch = torch.reshape(data_torch, (5120, 5120)) - return data_torch - - def shuffle_attn_output_weight(self, data_torch): - assert data_torch.size() == (5120, 5120) - data_torch = data_torch.reshape(5120, 8, 5, 128) - data_torch = torch.permute(data_torch, (0, 2, 1, 3)) - data_torch = torch.reshape(data_torch, (5120, 5120)) - return data_torch - - def write_tensors(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - for name, data_torch in self.get_tensors(): - if "self_attn.rotary_emb.inv_freq" in name: - continue - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - # shuffle for broadcasting of gqa in ggml_mul_mat - if new_name.endswith("attn_q.weight"): - data_torch = self.shuffle_attn_q_weight(data_torch) - elif new_name.endswith("attn_output.weight"): - data_torch = self.shuffle_attn_output_weight(data_torch) - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - -class CodeShellModel(Model): - def set_gguf_parameters(self): - block_count = self.hparams["n_layer"] - - self.gguf_writer.add_name("CodeShell") - self.gguf_writer.add_context_length(self.hparams["n_positions"]) - self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) - self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_head_count(self.hparams["n_head"]) - self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_rope_freq_base(10000.0) - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(1.0) - - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - tensors = dict(self.get_tensors()) - has_lm_head = "lm_head.weight" in tensors.keys() or "output.weight" in tensors.keys() - for name, data_torch in tensors.items(): - # we don't need these - if name.endswith((".attn.rotary_emb.inv_freq")): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - if not has_lm_head and name == "transformer.wte.weight": - self.gguf_writer.add_tensor("output.weight", data) - print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") - - -class InternLM2Model(Model): - def set_vocab(self): - # (TODO): Is there a better way? - # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character - # \x00 specially and convert it into an emoji character to prevent it from being mistakenly - # recognized as an empty string in C++. - from sentencepiece import SentencePieceProcessor - from sentencepiece import sentencepiece_model_pb2 as model - - tokenizer_path = self.dir_model / 'tokenizer.model' - - tokens: list[bytes] = [] - scores: list[float] = [] - toktypes: list[int] = [] - - if not tokenizer_path.is_file(): - print(f'Error: Missing {tokenizer_path}', file=sys.stderr) - sys.exit(1) - - sentencepiece_model = model.ModelProto() - sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) - add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix - - tokenizer = SentencePieceProcessor(str(tokenizer_path)) - vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) - - for token_id in range(vocab_size): - piece = tokenizer.id_to_piece(token_id) - text = piece.encode("utf-8") - score = tokenizer.get_score(token_id) - if text == b"\x00": - # (TODO): fixme - # Hack here and replace the \x00 characters. - print(f"InternLM2 convert token '{text}' to '🐉'!") - text = "🐉" - - toktype = SentencePieceTokenTypes.NORMAL - if tokenizer.is_unknown(token_id): - toktype = SentencePieceTokenTypes.UNKNOWN - elif tokenizer.is_control(token_id): - toktype = SentencePieceTokenTypes.CONTROL - elif tokenizer.is_unused(token_id): - toktype = SentencePieceTokenTypes.UNUSED - elif tokenizer.is_byte(token_id): - toktype = SentencePieceTokenTypes.BYTE - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - - added_tokens_file = self.dir_model / 'added_tokens.json' - if added_tokens_file.is_file(): - with open(added_tokens_file, "r", encoding="utf-8") as f: - added_tokens_json = json.load(f) - - for key in added_tokens_json: - tokens.append(key.encode("utf-8")) - scores.append(-1000.0) - toktypes.append(SentencePieceTokenTypes.USER_DEFINED) - - self.gguf_writer.add_tokenizer_model("llama") - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) - self.gguf_writer.add_token_types(toktypes) - self.gguf_writer.add_add_space_prefix(add_prefix) - - special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) - old_eos = special_vocab.special_token_ids["eos"] - if "chat" in os.path.basename(self.dir_model.absolute()): - # For the chat model, we replace the eos with '<|im_end|>'. - special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer) - print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \ -in chat mode so that the conversation can end normally.") - - special_vocab.add_to_gguf(self.gguf_writer) - - def _try_get_sft_eos(self, tokenizer): - unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]') - im_end_list = tokenizer.encode('<|im_end|>') - assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1) - if len(unused_145_list) == 1: - eos_token = unused_145_list[0] - if len(im_end_list) == 1: - eos_token = im_end_list[0] - return eos_token - - def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int): - if n_head_kv is not None and n_head != n_head_kv: - n_head = n_head_kv - return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape)) - - def set_gguf_parameters(self): - self.gguf_writer.add_name("InternLM2") - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) - - def post_write_tensors(self, tensor_map, name, data_torch): - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - - def write_tensors(self): - from einops import rearrange - - num_heads = self.hparams.get("num_attention_heads") - num_kv_heads = self.hparams.get("num_key_value_heads") - hidden_size = self.hparams.get("hidden_size") - q_per_kv = num_heads // num_kv_heads - head_dim = hidden_size // num_heads - num_groups = num_heads // q_per_kv - - block_count = self.hparams["num_hidden_layers"] - model_kv = dict(self.get_tensors()) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv" - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - if re.match(qkv_pattern, name): - bid = re.findall(qkv_pattern, name)[0] - qkv = data_torch - qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) - q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] - # The model weights of q and k equire additional reshape. - q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads) - k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads) - v = rearrange(v, " o g n i -> o (g n i)").T - self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) - self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) - self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v) - else: - self.post_write_tensors(tensor_map, name, data_torch) - - -class BertModel(Model): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.vocab_size = None - - def set_gguf_parameters(self): - super().set_gguf_parameters() - self.gguf_writer.add_causal_attention(False) - - # get pooling path - with open(self.dir_model / "modules.json", encoding="utf-8") as f: - modules = json.load(f) - pooling_path = None - for mod in modules: - if mod["type"] == "sentence_transformers.models.Pooling": - pooling_path = mod["path"] - break - - # get pooling type - pooling_type = gguf.PoolingType.NONE - if pooling_path is not None: - with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: - pooling = json.load(f) - if pooling["pooling_mode_mean_tokens"]: - pooling_type = gguf.PoolingType.MEAN - elif pooling["pooling_mode_cls_token"]: - pooling_type = gguf.PoolingType.CLS - else: - raise NotImplementedError("Only MEAN and CLS pooling types supported") - - self.gguf_writer.add_pooling_type(pooling_type.value) - - def set_vocab(self): - path = self.dir_model - added_tokens_path = self.dir_model if self.dir_model.exists() else None - - # use huggingface vocab to get all tokens - vocab = HfVocab(path, added_tokens_path) - tokens, scores, toktypes = zip(*vocab.all_tokens()) - assert len(tokens) == vocab.vocab_size - self.vocab_size = vocab.vocab_size - - # we need this to validate the size of the token_type embeddings - # though currently we are passing all zeros to the token_type embeddings - n_token_types = len(set(toktypes)) - self.gguf_writer.add_token_type_count(n_token_types) - - # convert to phantom space vocab - def phantom(tok, typ): - if tok.startswith(b"[") and tok.endswith(b"]"): - return tok - if tok.startswith(b"##"): - return tok[2:] - return b"\xe2\x96\x81" + tok - tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes)) - - # set up bos and eos tokens (cls and sep) - self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id) - self.gguf_writer.add_eos_token_id(vocab.tokenizer.sep_token_id) - - # add vocab to gguf - self.gguf_writer.add_tokenizer_model("bert") - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) - self.gguf_writer.add_token_types(toktypes) - - # handle special tokens - special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) - special_vocab.add_to_gguf(self.gguf_writer) - - def write_tensors(self): - tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) - tensors = dict(self.get_tensors()) - for name, data_torch in tensors.items(): - # we are only using BERT for embeddings so we don't need the pooling layer - if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): - continue # we don't need these - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - data = data_torch.squeeze().numpy() - n_dims = len(data.shape) - new_dtype: type[np.floating[Any]] - - if ( - self.ftype == 1 and name.endswith(".weight") and n_dims == 2 - and name != "embeddings.token_type_embeddings.weight" # not used with get_rows, must be F32 - ): - # if f16 desired, convert any float32 2-dim weight tensors to float16 - new_dtype = np.float16 - else: - # if f32 desired, convert any float16 to float32 - new_dtype = np.float32 - - print(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}") - - if data.dtype != new_dtype: - data = data.astype(new_dtype) - - self.gguf_writer.add_tensor(new_name, data) - - -class NomicBertModel(BertModel): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - # the HF config claims n_ctx=8192, but it uses RoPE scaling - self.hparams["n_ctx"] = 2048 - - # SwigLU activation - assert self.hparams["activation_function"] == "swiglu" - # this doesn't do anything in the HF version - assert self.hparams["causal"] is False - # no bias tensors - assert self.hparams["qkv_proj_bias"] is False - assert self.hparams["mlp_fc1_bias"] is False - assert self.hparams["mlp_fc2_bias"] is False - # norm at end of layer - assert self.hparams["prenorm"] is False - # standard RoPE - assert self.hparams["rotary_emb_fraction"] == 1.0 - assert self.hparams["rotary_emb_interleaved"] is False - assert self.hparams["rotary_emb_scale_base"] is None - - def set_gguf_parameters(self): - super().set_gguf_parameters() - self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) - - def get_tensors(self): - assert self.vocab_size is not None - for name, data in super().get_tensors(): - # Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly. - if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size: - rounded_vocab_size = (self.vocab_size + 63) // 64 * 64 - assert data.shape == (rounded_vocab_size, self.hparams["n_embd"]) - data = data[:self.vocab_size, :] - yield name, data - - -class GemmaModel(Model): - def set_vocab(self): - self._set_vocab_sentencepiece() - - def set_gguf_parameters(self): - hparams = self.hparams - block_count = hparams["num_hidden_layers"] - - self.gguf_writer.add_name(self.dir_model.name) - self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(hparams["hidden_size"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) - self.gguf_writer.add_head_count(hparams["num_attention_heads"]) - self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_key_length(hparams["head_dim"]) - self.gguf_writer.add_value_length(hparams["head_dim"]) - self.gguf_writer.add_file_type(self.ftype) - - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - for name, data_torch in self.get_tensors(): - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 - if name.endswith("norm.weight"): - data_torch = data_torch + 1 - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - -###### CONVERSION LOGIC ###### - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Convert a huggingface model to a GGML compatible file") - parser.add_argument( - "--vocab-only", action="store_true", - help="extract only the vocab", - ) - parser.add_argument( - "--awq-path", type=Path, default=None, - help="Path to scale awq cache file") - parser.add_argument( - "--outfile", type=Path, - help="path to write to; default: based on input", - ) - parser.add_argument( - "--outtype", type=str, choices=["f32", "f16"], default="f16", - help="output format - use f32 for float32, f16 for float16", - ) - parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") - parser.add_argument( - "model", type=Path, - help="directory containing model file", - ) - - return parser.parse_args() - - -def main() -> None: - args = parse_args() - - dir_model = args.model - - if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights # type: ignore[import-not-found] - tmp_model_path = args.model / "weighted_model" - dir_model = tmp_model_path - if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") - else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") - - if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file=sys.stderr) - sys.exit(1) - - ftype_map = { - "f32": gguf.GGMLQuantizationType.F32, - "f16": gguf.GGMLQuantizationType.F16, - } - - if args.outfile is not None: - fname_out = args.outfile - else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' - - print(f"Loading model: {dir_model.name}") - - hparams = Model.load_hparams(dir_model) - - with torch.inference_mode(): - model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) - - print("Set model parameters") - model_instance.set_gguf_parameters() - - print("Set model tokenizer") - model_instance.set_vocab() - - if args.vocab_only: - print(f"Exporting model vocab to '{fname_out}'") - model_instance.write_vocab() - else: - print(f"Exporting model to '{fname_out}'") - model_instance.write() - - print(f"Model successfully exported to '{fname_out}'") - - -if __name__ == '__main__': - main() diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py deleted file mode 100755 index 9a9936dec..000000000 --- a/convert-lora-to-ggml.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any, BinaryIO, Sequence - -import numpy as np -import torch - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - -NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} - - -def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: - fout.write(b"ggla"[::-1]) # magic (ggml lora) - fout.write(struct.pack("i", 1)) # file version - fout.write(struct.pack("i", params["r"])) - # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int - # but some models ship a float value instead - # let's convert to int, but fail if lossless conversion is not possible - assert ( - int(params["lora_alpha"]) == params["lora_alpha"] - ), "cannot convert float to int losslessly" - fout.write(struct.pack("i", int(params["lora_alpha"]))) - - -def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None: - sname = name.encode("utf-8") - fout.write( - struct.pack( - "iii", - len(shape), - len(sname), - NUMPY_TYPE_TO_FTYPE[data_type.name], - ) - ) - fout.write(struct.pack("i" * len(shape), *shape[::-1])) - fout.write(sname) - fout.seek((fout.tell() + 31) & -32) - - -if __name__ == '__main__': - if len(sys.argv) < 2: - print(f"Usage: python {sys.argv[0]} [arch]") - print( - "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" - ) - print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") - sys.exit(1) - - input_json = os.path.join(sys.argv[1], "adapter_config.json") - input_model = os.path.join(sys.argv[1], "adapter_model.bin") - output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") - - if os.path.exists(input_model): - model = torch.load(input_model, map_location="cpu") - else: - input_model = os.path.join(sys.argv[1], "adapter_model.safetensors") - # lazy import load_file only if lora is in safetensors format. - from safetensors.torch import load_file - model = load_file(input_model, device="cpu") - - arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" - - if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - print(f"Error: unsupported architecture {arch_name}") - sys.exit(1) - - arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] - name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone - - with open(input_json, "r") as f: - params = json.load(f) - - if params["peft_type"] != "LORA": - print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") - sys.exit(1) - - if params["fan_in_fan_out"] is True: - print("Error: param fan_in_fan_out is not supported") - sys.exit(1) - - if params["bias"] is not None and params["bias"] != "none": - print("Error: param bias is not supported") - sys.exit(1) - - # TODO: these seem to be layers that have been trained but without lora. - # doesn't seem widely used but eventually should be supported - if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - print("Error: param modules_to_save is not supported") - sys.exit(1) - - with open(output_path, "wb") as fout: - fout.truncate() - - write_file_header(fout, params) - for k, v in model.items(): - orig_k = k - if k.endswith(".default.weight"): - k = k.replace(".default.weight", ".weight") - if k in ["llama_proj.weight", "llama_proj.bias"]: - continue - if k.endswith("lora_A.weight"): - if v.dtype != torch.float16 and v.dtype != torch.float32: - v = v.float() - v = v.T - else: - v = v.float() - - t = v.detach().numpy() - - prefix = "base_model.model." - if k.startswith(prefix): - k = k[len(prefix) :] - - lora_suffixes = (".lora_A.weight", ".lora_B.weight") - if k.endswith(lora_suffixes): - suffix = k[-len(lora_suffixes[0]):] - k = k[: -len(lora_suffixes[0])] - else: - print(f"Error: unrecognized tensor name {orig_k}") - sys.exit(1) - - tname = name_map.get_name(k) - if tname is None: - print(f"Error: could not map tensor name {orig_k}") - print(" Note: the arch parameter must be specified if the model is not llama") - sys.exit(1) - - if suffix == ".lora_A.weight": - tname += ".weight.loraA" - elif suffix == ".lora_B.weight": - tname += ".weight.loraB" - else: - assert False - - print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") - write_tensor_header(fout, tname, t.shape, t.dtype) - t.tofile(fout) - - print(f"Converted {input_json} and {input_model} to {output_path}") diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py deleted file mode 100755 index def210531..000000000 --- a/convert-persimmon-to-gguf.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import os -import sys -from pathlib import Path -from pprint import pprint - -import torch -from sentencepiece import SentencePieceProcessor - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) -import gguf - - -def _flatten_dict(dct, tensors, prefix=None): - assert isinstance(dct, dict) - for key in dct.keys(): - new_prefix = prefix + '.' + key if prefix is not None else key - if isinstance(dct[key], torch.Tensor): - tensors[new_prefix] = dct[key] - elif isinstance(dct[key], dict): - _flatten_dict(dct[key], tensors, new_prefix) - else: - raise ValueError(type(dct[key])) - return None - - -def _get_sentencepiece_tokenizer_info(dir_model: Path): - tokenizer_path = dir_model / 'adept_vocab.model' - print('gguf: getting sentencepiece tokenizer from', tokenizer_path) - tokenizer = SentencePieceProcessor(str(tokenizer_path)) - print('gguf: adding tokens') - tokens: list[bytes] = [] - scores: list[float] = [] - toktypes: list[int] = [] - - for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - pass - return tokens, scores, toktypes - - -def main(): - parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file") - parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release") - parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") - args = parser.parse_args() - sys.path.append(str(args.adept_inference_dir)) - persimmon_model = torch.load(args.ckpt_path) - hparams = persimmon_model['args'] - pprint(hparams) - tensors: dict[str, torch.Tensor] = {} - _flatten_dict(persimmon_model['model'], tensors, None) - - arch = gguf.MODEL_ARCH.PERSIMMON - gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch]) - - block_count = hparams.num_layers - head_count = hparams.num_attention_heads - head_count_kv = head_count - ctx_length = hparams.seq_length - hidden_size = hparams.hidden_size - - gguf_writer.add_name('persimmon-8b-chat') - gguf_writer.add_context_length(ctx_length) - gguf_writer.add_embedding_length(hidden_size) - gguf_writer.add_block_count(block_count) - gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size) - # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443 - gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - gguf_writer.add_head_count(head_count) - gguf_writer.add_head_count_kv(head_count_kv) - gguf_writer.add_rope_freq_base(hparams.rotary_emb_base) - gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon) - - tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir) - gguf_writer.add_tokenizer_model('llama') - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - gguf_writer.add_bos_token_id(71013) - gguf_writer.add_eos_token_id(71013) - - tensor_map = gguf.get_tensor_name_map(arch, block_count) - print(tensor_map) - for name in tensors.keys(): - data = tensors[name] - if name.endswith(".self_attention.rotary_emb.inv_freq"): - continue - old_dtype = data.dtype - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - data = data.to(torch.float32).squeeze().numpy() - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - n_dims = len(data.shape) - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - gguf_writer.add_tensor(new_name, data) - print("gguf: write header") - gguf_writer.write_header_to_file() - print("gguf: write metadata") - gguf_writer.write_kv_data_to_file() - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - - gguf_writer.close() - - print(f"gguf: model successfully exported to '{args.outfile}'") - print("") - - -if __name__ == '__main__': - main() diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py new file mode 100755 index 000000000..018a2a588 --- /dev/null +++ b/convert_hf_to_gguf.py @@ -0,0 +1,5112 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +import ast +import logging +import argparse +import contextlib +import json +import os +import re +import sys +from enum import IntEnum +from pathlib import Path +from hashlib import sha256 +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast +from itertools import chain + +import math +import numpy as np +import torch + +if TYPE_CHECKING: + from torch import Tensor + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) +import gguf + +logger = logging.getLogger("hf-to-gguf") + + +###### MODEL DEFINITIONS ###### + +class SentencePieceTokenTypes(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +AnyModel = TypeVar("AnyModel", bound="type[Model]") + + +class Model: + _model_classes: dict[str, type[Model]] = {} + + dir_model: Path + ftype: gguf.LlamaFileType + fname_out: Path + is_big_endian: bool + endianess: gguf.GGUFEndian + use_temp_file: bool + lazy: bool + part_names: list[str] + is_safetensors: bool + hparams: dict[str, Any] + block_count: int + tensor_map: gguf.TensorNameMap + tensor_names: set[str] | None + gguf_writer: gguf.GGUFWriter + model_name: str | None + metadata_override: Path | None + dir_model_card: Path + + # subclasses should define this! + model_arch: gguf.MODEL_ARCH + + 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, hparams: dict[str, Any] | None = None): + if type(self) is Model: + raise TypeError(f"{type(self).__name__!r} should not be directly instantiated") + + self.dir_model = dir_model + self.ftype = ftype + self.fname_out = fname_out + self.is_big_endian = is_big_endian + self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE + self.use_temp_file = use_temp_file + self.lazy = not eager + self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors") + 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) 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 + self.metadata_override = metadata_override + self.model_name = model_name + self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py + + # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type + if self.ftype == gguf.LlamaFileType.GUESSED: + # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. + _, first_tensor = next(self.get_tensors()) + if first_tensor.dtype == torch.float16: + logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_F16 + else: + logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_BF16 + + # Configure GGUF Writer + self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, + split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard) + + @classmethod + def __init_subclass__(cls): + # can't use an abstract property, because overriding it without type errors + # would require using decorated functions instead of simply defining the property + if "model_arch" not in cls.__dict__: + raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}") + + def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any: + key = next((k for k in keys if k in self.hparams), None) + if key is not None: + return self.hparams[key] + if optional: + return None + raise KeyError(f"could not find any of: {keys}") + + def set_vocab(self): + self._set_vocab_gpt2() + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + tensor_names_from_parts: set[str] = set() + + index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin" + index_name += ".index.json" + index_file = self.dir_model / index_name + + if index_file.is_file(): + self.tensor_names = set() + logger.info(f"gguf: loading model weight map from '{index_name}'") + with open(index_file, "r", encoding="utf-8") as f: + index: dict[str, Any] = json.load(f) + weight_map = index.get("weight_map") + if weight_map is None or not isinstance(weight_map, dict): + raise ValueError(f"Can't load 'weight_map' from {index_name!r}") + self.tensor_names.update(weight_map.keys()) + else: + self.tensor_names = tensor_names_from_parts + weight_map = {} + + for part_name in self.part_names: + logger.info(f"gguf: loading model part '{part_name}'") + ctx: ContextManager[Any] + if self.is_safetensors: + from safetensors import safe_open + ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu")) + else: + ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) + + with ctx as model_part: + tensor_names_from_parts.update(model_part.keys()) + + for name in model_part.keys(): + if self.is_safetensors: + if self.lazy: + data = model_part.get_slice(name) + data = LazyTorchTensor.from_safetensors_slice(data) + else: + data = model_part.get_tensor(name) + else: + data = model_part[name] + if self.lazy: + data = LazyTorchTensor.from_eager(data) + yield name, data + + # verify tensor name presence and identify potentially missing files + if len(tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0: + missing = sorted(self.tensor_names.difference(tensor_names_from_parts)) + extra = sorted(tensor_names_from_parts.difference(self.tensor_names)) + missing_files = sorted(set(weight_map[n] for n in missing if n in weight_map)) + if len(extra) == 0 and len(missing_files) > 0: + raise ValueError(f"Missing or incomplete model files: {missing_files}") + else: + raise ValueError("Mismatch between weight map and model parts for tensor names:\n" + f"Missing tensors: {missing}\n" + f"Extra tensors: {extra}") + + def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str: + if key not in gguf.MODEL_TENSORS[self.model_arch]: + raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}") + name: str = gguf.TENSOR_NAMES[key] + if "{bid}" in name: + assert bid is not None + name = name.format(bid=bid) + return name + suffix + + def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool: + if key not in gguf.MODEL_TENSORS[self.model_arch]: + return False + key_name: str = gguf.TENSOR_NAMES[key] + if "{bid}" in key_name: + if bid is None: + return False + key_name = key_name.format(bid=bid) + else: + if bid is not None: + return False + return name == (key_name + suffix) + + def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str: + new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes) + if new_name is None: + raise ValueError(f"Can not map tensor {name!r}") + return new_name + + def set_gguf_parameters(self): + self.gguf_writer.add_block_count(self.block_count) + + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: + self.gguf_writer.add_context_length(n_ctx) + logger.info(f"gguf: context length = {n_ctx}") + + if (n_embd := self.find_hparam(["hidden_size", "n_embd"], optional=True)) is not None: + self.gguf_writer.add_embedding_length(n_embd) + logger.info(f"gguf: embedding length = {n_embd}") + + if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None: + self.gguf_writer.add_feed_forward_length(n_ff) + logger.info(f"gguf: feed forward length = {n_ff}") + + if (n_head := self.find_hparam(["num_attention_heads", "n_head"], optional=True)) is not None: + self.gguf_writer.add_head_count(n_head) + logger.info(f"gguf: head count = {n_head}") + + if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: + self.gguf_writer.add_head_count_kv(n_head_kv) + logger.info(f"gguf: key-value head count = {n_head_kv}") + + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) + logger.info(f"gguf: rope theta = {rope_theta}") + if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: + self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) + logger.info(f"gguf: rms norm epsilon = {f_rms_eps}") + if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: + self.gguf_writer.add_layer_norm_eps(f_norm_eps) + logger.info(f"gguf: layer norm epsilon = {f_norm_eps}") + if (n_experts := self.hparams.get("num_local_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + logger.info(f"gguf: expert count = {n_experts}") + if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: + self.gguf_writer.add_expert_used_count(n_experts_used) + logger.info(f"gguf: experts used count = {n_experts_used}") + + if (head_dim := self.hparams.get("head_dim")) is not None: + self.gguf_writer.add_key_length(head_dim) + self.gguf_writer.add_value_length(head_dim) + + self.gguf_writer.add_file_type(self.ftype) + logger.info(f"gguf: file type = {self.ftype}") + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + return [(self.map_tensor_name(name), data_torch)] + + def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: + del name, new_name, bid, n_dims # unused + + return False + + # some models need extra generated tensors (like rope_freqs) + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + return () + + def prepare_tensors(self): + max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") + + for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + # use the first number-like part of the tensor name as the block id + bid = None + for part in name.split("."): + if part.isdecimal(): + bid = int(part) + break + + for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)): + # TODO: why do we squeeze here? + # data = data_torch.squeeze().numpy() + data = data_torch.numpy() + + # if data ends up empty, it means data_torch was a scalar tensor -> restore + if len(data.shape) == 0: + data = data_torch.numpy() + + n_dims = len(data.shape) + data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims) + + # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors + if n_dims <= 1 or new_name.endswith("_norm.weight"): + data_qtype = gguf.GGMLQuantizationType.F32 + + # Conditions should closely match those in llama_model_quantize_internal in llama.cpp + # Some tensor types are always in float32 + if data_qtype is False and ( + any( + self.match_model_tensor_name(new_name, key, bid) + for key in ( + gguf.MODEL_TENSOR.FFN_GATE_INP, + gguf.MODEL_TENSOR.POS_EMBD, + gguf.MODEL_TENSOR.TOKEN_TYPES, + gguf.MODEL_TENSOR.SSM_CONV1D, + gguf.MODEL_TENSOR.TIME_MIX_FIRST, + gguf.MODEL_TENSOR.TIME_MIX_W1, + gguf.MODEL_TENSOR.TIME_MIX_W2, + gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1, + gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2, + gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED, + gguf.MODEL_TENSOR.POSNET_NORM1, + gguf.MODEL_TENSOR.POSNET_NORM2, + ) + ) + or not new_name.endswith(".weight") + ): + data_qtype = gguf.GGMLQuantizationType.F32 + + if data_qtype is False and any( + self.match_model_tensor_name(new_name, key, bid) + for key in ( + gguf.MODEL_TENSOR.TOKEN_EMBD, + gguf.MODEL_TENSOR.OUTPUT, + ) + ): + if self.ftype in ( + gguf.LlamaFileType.MOSTLY_TQ1_0, + gguf.LlamaFileType.MOSTLY_TQ2_0, + ): + # TODO: use Q4_K and Q6_K + data_qtype = gguf.GGMLQuantizationType.F16 + + # No override (data_qtype is False), or wants to be quantized (data_qtype is True) + if isinstance(data_qtype, bool): + if self.ftype == gguf.LlamaFileType.ALL_F32: + data_qtype = gguf.GGMLQuantizationType.F32 + elif self.ftype == gguf.LlamaFileType.MOSTLY_F16: + data_qtype = gguf.GGMLQuantizationType.F16 + elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16: + data_qtype = gguf.GGMLQuantizationType.BF16 + elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0: + data_qtype = gguf.GGMLQuantizationType.Q8_0 + elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0: + data_qtype = gguf.GGMLQuantizationType.TQ1_0 + elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0: + data_qtype = gguf.GGMLQuantizationType.TQ2_0 + else: + raise ValueError(f"Unknown file type: {self.ftype.name}") + + try: + data = gguf.quants.quantize(data, data_qtype) + except gguf.QuantError as e: + logger.warning("%s, %s", e, "falling back to F16") + data_qtype = gguf.GGMLQuantizationType.F16 + data = gguf.quants.quantize(data, data_qtype) + + shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape + + # reverse shape to make it similar to the internal ggml dimension order + shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}" + + # n_dims is implicit in the shape + logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}") + + self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype) + + def set_type(self): + self.gguf_writer.add_type(gguf.GGUFType.MODEL) + + def prepare_metadata(self, vocab_only: bool): + + total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count() + + self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params) + + # Fallback to model directory name if metadata name is still missing + if self.metadata.name is None: + self.metadata.name = self.dir_model.name + + # Generate parameter weight class (useful for leader boards) if not yet determined + if self.metadata.size_label is None and total_params > 0: + self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count) + + # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0' + output_type: str = self.ftype.name.partition("_")[2] + + # Filename Output + if self.fname_out.is_dir(): + # Generate default filename based on model specification and available metadata + if not vocab_only: + fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None) + else: + fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab") + + # Use the default filename + self.fname_out = self.fname_out / f"{fname_default}.gguf" + else: + # Output path is a custom defined templated filename + # Note: `not is_dir()` is used because `.is_file()` will not detect + # file template strings as it doesn't actually exist as a file + + # Process templated file name with the output ftype, useful with the "auto" ftype + self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type) + + self.set_type() + + logger.info("Set meta model") + self.metadata.set_gguf_meta_model(self.gguf_writer) + + logger.info("Set model parameters") + self.set_gguf_parameters() + + logger.info("Set model tokenizer") + self.set_vocab() + + logger.info("Set model quantization version") + self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION) + + def write(self): + self.prepare_tensors() + self.prepare_metadata(vocab_only=False) + self.gguf_writer.write_header_to_file(path=self.fname_out) + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.write_tensors_to_file(progress=True) + self.gguf_writer.close() + + def write_vocab(self): + if len(self.gguf_writer.tensors) != 1: + raise ValueError('Splitting the vocabulary is not supported') + + self.prepare_metadata(vocab_only=True) + self.gguf_writer.write_header_to_file(path=self.fname_out) + self.gguf_writer.write_kv_data_to_file() + self.gguf_writer.close() + + @staticmethod + def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]: + part_names: list[str] = [] + for filename in os.listdir(dir_model): + if filename.startswith(prefix) and filename.endswith(suffix): + part_names.append(filename) + + part_names.sort() + + return part_names + + @staticmethod + def load_hparams(dir_model: Path): + with open(dir_model / "config.json", "r", encoding="utf-8") as f: + return json.load(f) + + @classmethod + def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: + assert names + + def func(modelcls: AnyModel) -> AnyModel: + for name in names: + cls._model_classes[name] = modelcls + return modelcls + return func + + @classmethod + def print_registered_models(cls): + for name in sorted(cls._model_classes.keys()): + logger.error(f"- {name}") + + @classmethod + def from_model_architecture(cls, arch: str) -> type[Model]: + try: + return cls._model_classes[arch] + except KeyError: + raise NotImplementedError(f'Architecture {arch!r} not supported!') from None + + def does_token_look_special(self, token: str | bytes) -> bool: + if isinstance(token, (bytes, bytearray)): + token_text = token.decode(encoding="utf-8") + elif isinstance(token, memoryview): + token_text = token.tobytes().decode(encoding="utf-8") + else: + token_text = token + + # Some models mark some added tokens which ought to be control tokens as not special. + # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2}) + seems_special = token_text in ( + "", # deepseek-coder + "", "<2mass>", "[@BOS@]", # gemma{,-2} + ) + + seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>")) + seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>")) # deepseek-coder + + # TODO: should these be marked as UNUSED instead? (maybe not) + seems_special = seems_special or (token_text.startswith("")) # gemma{,-2} + + return seems_special + + # used for GPT-2 BPE and WordPiece vocabs + def get_vocab_base(self) -> tuple[list[str], list[int], str]: + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model) + vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab)) + assert max(tokenizer.vocab.values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + else: + token: str = reverse_vocab[i] + if token in added_vocab: + # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized. + # To avoid unexpected issues - we make sure to normalize non-normalized tokens + if not tokenizer.added_tokens_decoder[i].normalized: + previous_token = token + token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False)) + if previous_token != token: + logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer") + + if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token): + toktypes.append(gguf.TokenType.CONTROL) + else: + # NOTE: this was added for Gemma. + # Encoding and decoding the tokens above isn't sufficient for this case. + token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + toktypes.append(gguf.TokenType.NORMAL) + tokens.append(token) + + return tokens, toktypes, tokpre + + # NOTE: this function is generated by convert_hf_to_gguf_update.py + # do not modify it manually! + # ref: https://github.com/ggerganov/llama.cpp/pull/6920 + # Marker: Start get_vocab_base_pre + def get_vocab_base_pre(self, tokenizer) -> str: + # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that + # is specific for the BPE pre-tokenizer used by the model + # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can + # use in llama.cpp to implement the same pre-tokenizer + + chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL' + + chktok = tokenizer.encode(chktxt) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + logger.debug(f"chktok: {chktok}") + logger.debug(f"chkhsh: {chkhsh}") + + res = None + + # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script + # or pull the latest version of the model from Huggingface + # don't edit the hashes manually! + if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5": + # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B + res = "llama-bpe" + if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754": + # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base + res = "deepseek-llm" + if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821": + # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base + res = "deepseek-coder" + if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed": + # ref: https://huggingface.co/tiiuae/falcon-7b + res = "falcon" + if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e": + # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base + res = "falcon3" + if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": + # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 + res = "bert-bge" + if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7": + # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5 + res = "bert-bge-large" + if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": + # ref: https://huggingface.co/mosaicml/mpt-7b + res = "mpt" + if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34": + # ref: https://huggingface.co/bigcode/starcoder2-3b + res = "starcoder" + if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454": + # ref: https://huggingface.co/openai-community/gpt2 + res = "gpt-2" + if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3": + # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b + res = "stablelm2" + if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": + # ref: https://huggingface.co/smallcloudai/Refact-1_6-base + res = "refact" + if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8": + # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01 + res = "command-r" + if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea": + # ref: https://huggingface.co/Qwen/Qwen1.5-7B + res = "qwen2" + if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": + # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf + res = "olmo" + if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e": + # ref: https://huggingface.co/databricks/dbrx-base + res = "dbrx" + if chkhsh == "c7699093ba4255a91e702aa38a596aa81669f3525dae06c2953267dde580f448": + # ref: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en + res = "jina-v1-en" + if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en + res = "jina-v2-en" + if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es + res = "jina-v2-es" + if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de + res = "jina-v2-de" + if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d": + # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct + res = "smaug-bpe" + if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360": + # ref: https://huggingface.co/LumiOpen/Poro-34B-chat + res = "poro-chat" + if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code + res = "jina-v2-code" + if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b" or chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516": + # ref: https://huggingface.co/THUDM/glm-4-9b-chat + res = "chatglm-bpe" + if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee": + # ref: https://huggingface.co/LumiOpen/Viking-7B + res = "viking" + if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901": + # ref: https://huggingface.co/core42/jais-13b + res = "jais" + if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f": + # ref: https://huggingface.co/WisdomShell/CodeShell-7B + res = "codeshell" + if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e": + # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407 + res = "tekken" + if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249": + # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M + res = "smollm" + if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7": + # ref: https://huggingface.co/bigscience/bloom + res = "bloom" + if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21": + # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small + res = "gpt3-finnish" + if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae": + # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct + res = "exaone" + if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085": + # ref: https://huggingface.co/microsoft/phi-2 + res = "phi-2" + 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 chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65": + # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base + res = "roberta-bpe" + if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb": + # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct + res = "gigachat" + if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1": + # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct + res = "megrez" + if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5": + # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3 + res = "deepseek-v3" + if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5": + # ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B + res = "deepseek-r1-qwen" + + if res is None: + logger.warning("\n") + logger.warning("**************************************************************************************") + logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") + logger.warning("** There are 2 possible reasons for this:") + logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") + logger.warning("** - the pre-tokenization config has changed upstream") + logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") + logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") + logger.warning("**") + logger.warning(f"** chkhsh: {chkhsh}") + logger.warning("**************************************************************************************") + logger.warning("\n") + raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") + + logger.debug(f"tokenizer.ggml.pre: {repr(res)}") + logger.debug(f"chkhsh: {chkhsh}") + + return res + # Marker: End get_vocab_base_pre + + def _set_vocab_none(self) -> None: + self.gguf_writer.add_tokenizer_model("none") + + def _set_vocab_gpt2(self) -> None: + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_qwen(self): + dir_model = self.dir_model + hparams = self.hparams + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams["vocab_size"] + assert max(tokenizer.get_vocab().values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + merges = [] + vocab = {} + mergeable_ranks = tokenizer.mergeable_ranks + for token, rank in mergeable_ranks.items(): + vocab[QwenModel.token_bytes_to_string(token)] = rank + if len(token) == 1: + continue + merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank) + assert len(merged) == 2 + merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged))) + + # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined + added_vocab = tokenizer.special_tokens + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()} + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + elif reverse_vocab[i] in added_vocab: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.CONTROL) + else: + tokens.append(reverse_vocab[i]) + toktypes.append(gguf.TokenType.NORMAL) + + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(dir_model, load_merges=False) + special_vocab.merges = merges + # only add special tokens when they were not already loaded from config.json + if len(special_vocab.special_token_ids) == 0: + special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"]) + special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"]) + # this one is usually not in config.json anyway + special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_sentencepiece(self, add_to_gguf=True): + tokens, scores, toktypes = self._create_vocab_sentencepiece() + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def _create_vocab_sentencepiece(self): + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + 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) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, token_data in added_tokens_decoder.items(): + token_id = int(token_id) + token: str = token_data["content"] + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token.encode("utf-8"): + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}') + if token_data.get("special") or self.does_token_look_special(token): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + else: + token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + scores[token_id] = -1000.0 + tokens[token_id] = token.encode("utf-8") + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + return tokens, scores, toktypes + + def _set_vocab_llama_hf(self): + vocab = gguf.LlamaHfVocab(self.dir_model) + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int): + tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf" + logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") + vocab_reader = gguf.GGUFReader(tokenizer_path, "r") + + default_pre = "mpt" if model_name == "gpt-neox" else "default" + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL) + assert field # tokenizer model + self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8")) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE) + self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST) + assert field # token list + self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) + + if model_name == "llama-spm": + field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES) + assert field # token scores + self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + + field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) + assert field # token types + self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + + if model_name != "llama-spm": + field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES) + assert field # token merges + self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) + + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None: + self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None: + self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None: + self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None: + self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None: + self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0]) + if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None: + self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0]) + + +@Model.register("GPTNeoXForCausalLM") +class GPTNeoXModel(Model): + model_arch = gguf.MODEL_ARCH.GPTNEOX + + 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( + int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), + ) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + tensors: list[tuple[str, Tensor]] = [] + + if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed)) + data_torch = torch.cat( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.weight") + elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name): + qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head)) + data_torch = torch.cat( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.bias") + + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + + +@Model.register("BloomForCausalLM", "BloomModel") +class BloomModel(Model): + model_arch = gguf.MODEL_ARCH.BLOOM + + def set_gguf_parameters(self): + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) + self.gguf_writer.add_embedding_length(n_embed) + self.gguf_writer.add_feed_forward_length(4 * n_embed) + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + name = re.sub(r'transformer\.', '', name) + + tensors: list[tuple[str, Tensor]] = [] + + if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed)) + data_torch = torch.cat( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.weight") + elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): + qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head)) + data_torch = torch.cat( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.bias") + + tensors.append((self.map_tensor_name(name), data_torch)) + + if name == "word_embeddings.weight": + assert self.tensor_names is not None + + # TODO: tie them at runtime, don't duplicate in the model file + if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")): + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) + + return tensors + + +@Model.register("MPTForCausalLM") +class MPTModel(Model): + model_arch = gguf.MODEL_ARCH.MPT + + def set_vocab(self): + try: + self._set_vocab_gpt2() + except Exception: + # Fallback for SEA-LION model + self._set_vocab_sentencepiece() + self.gguf_writer.add_add_bos_token(False) + self.gguf_writer.add_pad_token_id(3) + self.gguf_writer.add_eos_token_id(1) + self.gguf_writer.add_unk_token_id(0) + + def set_gguf_parameters(self): + block_count = self.hparams["n_layers"] + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"]) + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"): + self.gguf_writer.add_head_count_kv(kv_n_heads) + self.gguf_writer.add_layer_norm_eps(1e-5) + if self.hparams["attn_config"]["clip_qkv"] is not None: + self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"]) + if self.hparams["attn_config"]["alibi"]: + self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"]) + else: + self.gguf_writer.add_max_alibi_bias(0.0) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if "scales" in name: + new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales")) + new_name = new_name.replace("scales", "act.scales") + else: + new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias")) + + return [(new_name, data_torch)] + + +@Model.register("OrionForCausalLM") +class OrionModel(Model): + model_arch = gguf.MODEL_ARCH.ORION + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + raise ValueError("gguf: can not find ctx length parameter.") + + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + 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_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + # note: config provides rms norm but it is actually layer norm + # ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571 + self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"]) + + +@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM") +class BaichuanModel(Model): + model_arch = gguf.MODEL_ARCH.BAICHUAN + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + raise ValueError("gguf: can not find ctx length parameter.") + + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + 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(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + tensors: list[tuple[str, Tensor]] = [] + + if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight": + logger.info(f"Unpacking and permuting layer {bid}") + tensors = [ + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), + self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), + self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), + self._reverse_hf_part(data_torch, 2)), + ] + else: + tensors = [(self.map_tensor_name(name), data_torch)] + + return tensors + + 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) + ) + + def _reverse_hf_permute_part( + self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None, + ) -> Tensor: + r = weights.shape[0] // 3 + return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv) + + def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: + r = weights.shape[0] // 3 + return weights[r * n_part:r * n_part + r, ...] + + +@Model.register("XverseForCausalLM") +class XverseModel(Model): + model_arch = gguf.MODEL_ARCH.XVERSE + + def set_vocab(self): + assert (self.dir_model / "tokenizer.json").is_file() + dir_model = self.dir_model + hparams = self.hparams + + tokens: list[bytes] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model) + vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) + # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size, + # because vocab_size is the count of items, and indexes start at 0. + max_vocab_index = max(tokenizer.get_vocab().values()) + if max_vocab_index >= vocab_size: + raise ValueError("Vocabulary size exceeds expected maximum size.") + + reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + for token_id in range(vocab_size): + token_text = reverse_vocab[token_id].encode('utf-8') + # replace "\x00" to string with length > 0 + if token_text == b"\x00": + toktype = gguf.TokenType.BYTE # special + token_text = f"<{token_text}>".encode('utf-8') + elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + toktype = gguf.TokenType.BYTE # special + elif reverse_vocab[token_id] in added_vocab: + if tokenizer.added_tokens_decoder[token_id].special: + toktype = gguf.TokenType.CONTROL + else: + toktype = gguf.TokenType.USER_DEFINED + else: + toktype = gguf.TokenType.NORMAL + + tokens.append(token_text) + toktypes.append(toktype) + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + raise ValueError("gguf: can not find ctx length parameter.") + + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + 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(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + + # 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, head_count, head_count) + if name.endswith("k_proj.weight"): + data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv) + + return [(self.map_tensor_name(name), data_torch)] + + 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) + ) + + +@Model.register("FalconForCausalLM", "RWForCausalLM") +class FalconModel(Model): + model_arch = gguf.MODEL_ARCH.FALCON + + def set_gguf_parameters(self): + block_count = self.hparams.get("num_hidden_layers") + if block_count is None: + block_count = self.hparams["n_layer"] # old name + + n_head = self.hparams.get("num_attention_heads") + if n_head is None: + n_head = self.hparams["n_head"] # old name + + n_head_kv = self.hparams.get("num_kv_heads") + if n_head_kv is None: + n_head_kv = self.hparams.get("n_head_kv", 1) # old name + + self.gguf_writer.add_context_length(2048) # not in config.json + self.gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # QKV tensor transform + # The original query_key_value tensor contains n_head_kv "kv groups", + # each consisting of n_head/n_head_kv query weights followed by one key + # and one value weight (shared by all query heads in the kv group). + # This layout makes it a big pain to work with in GGML. + # So we rearrange them here,, so that we have n_head query weights + # followed by n_head_kv key weights followed by n_head_kv value weights, + # in contiguous fashion. + # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py + + if "query_key_value" in name: + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1 + head_dim = self.hparams["hidden_size"] // n_head + + qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) + q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) + k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) + v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) + data_torch = torch.cat((q, k, v)).reshape_as(data_torch) + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("GPTBigCodeForCausalLM") +class StarCoderModel(Model): + model_arch = gguf.MODEL_ARCH.STARCODER + + def set_gguf_parameters(self): + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + +@Model.register("GPTRefactForCausalLM") +class RefactModel(Model): + model_arch = gguf.MODEL_ARCH.REFACT + + def set_vocab(self): + super().set_vocab() + + # TODO: how to determine special FIM tokens automatically? + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'eot']) + special_vocab._set_special_token("prefix", 1) + special_vocab._set_special_token("suffix", 3) + special_vocab._set_special_token("middle", 2) + special_vocab.chat_template = None # do not add it twice + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + block_count = self.hparams["n_layer"] + + # refact uses Alibi. So this is from config.json which might be used by training. + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + + self.gguf_writer.add_feed_forward_length(ff_dim) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(1) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + hidden_dim = self.hparams["n_embd"] + inner_dim = 4 * hidden_dim + hidden_dim = int(2 * inner_dim / 3) + multiple_of = 256 + ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + n_head = self.hparams["n_head"] + n_head_kv = 1 + head_dim = self.hparams["n_embd"] // n_head + + tensors: list[tuple[str, Tensor]] = [] + + if bid is not None: + if name == f"transformer.h.{bid}.attn.kv.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), data_torch[:n_head_kv * head_dim])) + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), data_torch[n_head_kv * head_dim:])) + elif name == f"transformer.h.{bid}.attn.q.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), data_torch)) + elif name == f"transformer.h.{bid}.mlp.gate_up_proj.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])) + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])) + + if len(tensors) == 0: + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + + +@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") +class StableLMModel(Model): + model_arch = gguf.MODEL_ARCH.STABLELM + + def set_vocab(self): + if (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + else: + # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab + self._set_vocab_qwen() + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"]) + self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) + self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) + self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) + self.gguf_writer.add_file_type(self.ftype) + + _q_norms: list[dict[str, Tensor]] | None = None + _k_norms: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams["num_key_value_heads"] + + if name.find("q_layernorm.norms") != -1: + assert bid is not None + + if self._q_norms is None: + self._q_norms = [{} for _ in range(self.block_count)] + + self._q_norms[bid][name] = data_torch + + if len(self._q_norms[bid]) >= n_head: + return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm") + else: + return [] + + if name.find("k_layernorm.norms") != -1: + assert bid is not None + + if self._k_norms is None: + self._k_norms = [{} for _ in range(self.block_count)] + + self._k_norms[bid][name] = data_torch + + if len(self._k_norms[bid]) >= n_kv_head: + return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm") + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"): + datas: list[Tensor] = [] + # extract the norms in order + for xid in range(n_head): + ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight" + datas.append(norms[ename]) + del norms[ename] + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" + new_name = self.map_tensor_name(merged_name) + + return [(new_name, data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._q_norms is not None or self._k_norms is not None: + # flatten two `list[dict[str, Tensor]]` into a single `list[str]` + norms = ( + [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else [] + ) + ( + [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else [] + ) + if len(norms) > 0: + raise ValueError(f"Unprocessed norms: {norms}") + + +@Model.register("LLaMAForCausalLM", "LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM") +class LlamaModel(Model): + model_arch = gguf.MODEL_ARCH.LLAMA + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + try: + self._set_vocab_llama_hf() + except (FileNotFoundError, TypeError): + # Llama 3 + self._set_vocab_gpt2() + + # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256) + if self.hparams.get("vocab_size", 32000) == 32016: + special_vocab = gguf.SpecialVocab( + self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'eot'] + ) + special_vocab._set_special_token("prefix", 32007) + special_vocab._set_special_token("suffix", 32008) + special_vocab._set_special_token("middle", 32009) + 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 + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + @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: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for wid in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@Model.register("DeciLMForCausalLM") +class DeciModel(Model): + model_arch = gguf.MODEL_ARCH.DECI + + @staticmethod + def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int: + # DeciLM-specific code + intermediate_size = int(2 * ffn_mult * n_embd / 3) + return DeciModel._find_multiple(intermediate_size, 256) + + @staticmethod + def _find_multiple(n: int, k: int) -> int: + # DeciLM-specific code + if n % k == 0: + return n + return n + k - (n % k) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + _block_configs: list[dict[str,Any]] = self.hparams["block_configs"] + assert self.block_count == len(_block_configs) + self._num_kv_heads = list() + self._num_heads = list() + _ffn_multipliers = list() + # ***linear attention layer*** + # if n_heads_in_group is None and replace_with_linear is True + # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads + # ***attention-free layer*** + # if n_heads_in_group is None and replace_with_linear is False + # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 + # ***normal attention-layer*** + # if n_heads_in_group is not None, then + # _num_kv_heads[il] is num_attention_head // n_heads_in_group and + # _num_heads[il] is num_attention_head + for il in range(len(_block_configs)): + if _block_configs[il]["attention"]["n_heads_in_group"] is None: + if _block_configs[il]["attention"]["replace_with_linear"] is True: + self._num_kv_heads.append(0) + self._num_heads.append(self.hparams["num_attention_heads"]) + else: + self._num_kv_heads.append(0) + self._num_heads.append(0) + else: + self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"]) + self._num_heads.append(self.hparams["num_attention_heads"]) + _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"]) + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(_ffn_multipliers) + assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) + assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int) + assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float) + self._ffn_dims: list[int] = [ + DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"]) + for multiplier in _ffn_multipliers + ] + + def set_vocab(self): + # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's + # eos_token from '|eot_id|' to '|end_of_text|' + if self.hparams.get("vocab_size", 128256) == 128256: + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + else: + # DeciLM-7B + self._set_vocab_llama_hf() + + def set_gguf_parameters(self): + if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_heads) + assert self.block_count == len(self._ffn_dims) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + self.gguf_writer.add_head_count(self._num_heads) + self.gguf_writer.add_feed_forward_length(self._ffn_dims) + self.gguf_writer.add_block_count(self.block_count) + 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_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + self.gguf_writer.add_file_type(self.ftype) + else: # DeciLM-7B + super().set_gguf_parameters() + if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B + self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"] + assert self.block_count == len(self._num_kv_heads) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + @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: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + if bid is not None: + if "num_key_value_heads_per_layer" in self.hparams: + n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid] + elif "block_configs" in self.hparams: + n_kv_head = self._num_kv_heads[bid] + n_head = self._num_heads[bid] + else: + n_kv_head = self.hparams.get("num_key_value_heads") + else: + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = DeciModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + def prepare_tensors(self): + super().prepare_tensors() + + +@Model.register("BitnetForCausalLM") +class BitnetModel(Model): + model_arch = gguf.MODEL_ARCH.BITNET + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(1.0) + + def weight_quant(self, weight: Tensor) -> Tensor: + dtype = weight.dtype + weight = weight.float() + scale = weight.abs().mean().clamp(min=1e-5) + iscale = 1 / scale + # TODO: multiply by the scale directly instead of inverting it twice + # (this is also unnecessarily doubly inverted upstream) + # ref: https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/af89e318d78a70802061246bf037199d2fb97020/utils_quant.py#L10 + result = (weight * iscale).round().clamp(-1, 1) / iscale + return result.type(dtype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + new_name = self.map_tensor_name(name) + + if any(self.match_model_tensor_name(new_name, key, bid) for key in [ + gguf.MODEL_TENSOR.ATTN_Q, + gguf.MODEL_TENSOR.ATTN_K, + gguf.MODEL_TENSOR.ATTN_V, + gguf.MODEL_TENSOR.ATTN_OUT, + gguf.MODEL_TENSOR.FFN_UP, + gguf.MODEL_TENSOR.FFN_DOWN, + gguf.MODEL_TENSOR.FFN_GATE, + ]): + # transform weight into 1/0/-1 (in fp32) + data_torch = self.weight_quant(data_torch) + + yield (new_name, data_torch) + + +@Model.register("GrokForCausalLM") +class GrokModel(Model): + model_arch = gguf.MODEL_ARCH.GROK + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find(".moe.") != -1: + n_experts = self.hparams["num_local_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for wid in ["linear", "linear_1", "linear_v"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("DbrxForCausalLM") +class DbrxModel(Model): + model_arch = gguf.MODEL_ARCH.DBRX + + def set_gguf_parameters(self): + ffn_config = self.hparams["ffn_config"] + attn_config = self.hparams["attn_config"] + self.gguf_writer.add_block_count(self.hparams["n_layers"]) + + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"]) + + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"]) + + self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"]) + + self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"]) + + self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"]) + self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"]) + + self.gguf_writer.add_layer_norm_eps(1e-5) + + self.gguf_writer.add_file_type(self.ftype) + logger.info(f"gguf: file type = {self.ftype}") + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_expert = self.hparams["ffn_config"]["moe_num_experts"] + n_ff = self.hparams["ffn_config"]["ffn_hidden_size"] + n_embd = self.hparams["d_model"] + + # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose + # original implementation expects (n_expert, n_ff, n_embd) for all experts weights + # But llama.cpp moe graph works differently + # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions + # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor + exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + "ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert} + "ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + experts = False + + for exp_tensor_name in exp_tensor_names.keys(): + if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1: + experts = True + data_torch = data_torch.view(n_expert, n_ff, n_embd) + if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None: + data_torch = data_torch.permute(*permute_tensor) + break + + # map tensor names + # In MoE models the ffn tensors are typically most of the model weights, + # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight. + # Every other model has the weight names ending in .weight, + # let's assume that is the convention which is not the case for dbrx: + # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 + new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) + + return [(new_name, data_torch)] + + def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: + del name, new_name, bid # unused + + return n_dims > 1 + + +@Model.register("MiniCPMForCausalLM") +class MiniCPMModel(Model): + model_arch = gguf.MODEL_ARCH.MINICPM + + def set_gguf_parameters(self): + 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_sentencepiece() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + # HF models permute some of the tensors, so we need to undo that + if name.endswith(("q_proj.weight")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("MiniCPM3ForCausalLM") +class MiniCPM3Model(Model): + model_arch = gguf.MODEL_ARCH.MINICPM3 + + def set_gguf_parameters(self): + hparams = self.hparams + + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: + self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + rope_scaling = self.find_hparam(['rope_scaling'], True) + if rope_scaling is not None: + rope_dims = self.hparams["qk_rope_head_dim"] + + 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_sentencepiece() + + 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) + ) + + +@Model.register("QWenLMHeadModel") +class QwenModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN + + @staticmethod + def token_bytes_to_string(b): + from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode + byte_encoder = bytes_to_unicode() + return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) + + @staticmethod + def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: + parts = [bytes([b]) for b in token] + while True: + min_idx = None + min_rank = None + for i, pair in enumerate(zip(parts[:-1], parts[1:])): + rank = mergeable_ranks.get(pair[0] + pair[1]) + if rank is not None and (min_rank is None or rank < min_rank): + min_idx = i + min_rank = rank + if min_rank is None or (max_rank is not None and min_rank >= max_rank): + break + assert min_idx is not None + parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] + return parts + + def set_vocab(self): + self._set_vocab_qwen() + + def set_gguf_parameters(self): + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) + 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_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + +@Model.register("Qwen2ForCausalLM") +class Qwen2Model(Model): + model_arch = gguf.MODEL_ARCH.QWEN2 + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "yarn": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + + +@Model.register("Qwen2VLForConditionalGeneration") +class Qwen2VLModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN2VL + + def set_gguf_parameters(self): + super().set_gguf_parameters() + mrope_section = self.hparams["rope_scaling"]["mrope_section"] + mrope_section += [0] * max(0, 4 - len(mrope_section)) + self.gguf_writer.add_rope_dimension_sections(mrope_section) + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + for name, data in super().get_tensors(): + if name.startswith("visual."): + continue + yield name, data + + +@Model.register("WavTokenizerDec") +class WavTokenizerDecModel(Model): + model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if \ + name.endswith("codebook.cluster_size") or \ + name.endswith("codebook.embed_avg") or \ + name.endswith("codebook.inited"): + logger.debug(f"Skipping {name!r}") + return [] + + logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}") + + return [(self.map_tensor_name(name), data_torch)] + + def set_vocab(self): + self._set_vocab_none() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_vocab_size (self.hparams["vocab_size"]) + self.gguf_writer.add_features_length (self.hparams["n_embd_features"]) + self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"]) + self.gguf_writer.add_group_norm_eps (self.hparams["group_norm_epsilon"]) + self.gguf_writer.add_group_norm_groups (self.hparams["group_norm_groups"]) + + self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"]) + self.gguf_writer.add_posnet_block_count (self.hparams["posnet"]["n_layer"]) + + self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"]) + self.gguf_writer.add_convnext_block_count (self.hparams["convnext"]["n_layer"]) + + self.gguf_writer.add_causal_attention(False) + + +@Model.register("Qwen2MoeForCausalLM") +class Qwen2MoeModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN2MOE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + if (n_experts := self.hparams.get("num_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None: + self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size) + logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}") + if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None: + self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size) + logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}") + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("experts") != -1: + n_experts = self.hparams["num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@Model.register("GPT2LMHeadModel") +class GPT2Model(Model): + model_arch = gguf.MODEL_ARCH.GPT2 + + def set_gguf_parameters(self): + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_context_length(self.hparams["n_ctx"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + tensors: list[tuple[str, Tensor]] = [] + + # we don't need these + if name.endswith((".attn.bias", ".attn.masked_bias")): + return tensors + + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): + data_torch = data_torch.transpose(1, 0) + + new_name = self.map_tensor_name(name) + + tensors.append((new_name, data_torch)) + + # note: GPT2 output is tied to (same as) wte in original model + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) + + return tensors + + +@Model.register("PhiForCausalLM") +class Phi2Model(Model): + model_arch = gguf.MODEL_ARCH.PHI2 + + def set_gguf_parameters(self): + block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) + + rot_pct = self.find_hparam(["partial_rotary_factor"]) + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + + self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) + + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(4 * n_embd) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"])) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_add_bos_token(False) + + +@Model.register("Phi3ForCausalLM") +class Phi3MiniModel(Model): + model_arch = gguf.MODEL_ARCH.PHI3 + + def set_vocab(self): + # Phi-4 model uses GPT2Tokenizer + 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) + tokenizer_class = tokenizer_config_json['tokenizer_class'] + if tokenizer_class == 'GPT2Tokenizer': + return self._set_vocab_gpt2() + + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + raise ValueError(f'Error: Missing {tokenizer_path}') + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + 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) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, foken_data in added_tokens_decoder.items(): + token_id = int(token_id) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + tokenizer_file = self.dir_model / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) + added_tokens = tokenizer_json.get("added_tokens", []) + for foken_data in added_tokens: + token_id = int(foken_data["id"]) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) + + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"]) + rms_eps = self.find_hparam(["rms_norm_eps"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rope_dims = n_embd // n_head + + self.gguf_writer.add_context_length(max_pos_embds) + self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds) + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"])) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_rms_eps(rms_eps) + self.gguf_writer.add_rope_dimension_count(rope_dims) + self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) + self.gguf_writer.add_file_type(self.ftype) + sliding_window = self.hparams.get("sliding_window") + # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models + if sliding_window is None: + sliding_window = 0 + self.gguf_writer.add_sliding_window(sliding_window) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rope_dims = n_embd // n_head + + # write rope scaling for long context (128k) model + rope_scaling = self.find_hparam(['rope_scaling'], True) + if rope_scaling is None: + return + + scale = max_pos_embds / orig_max_pos_embds + + rope_scaling_type = rope_scaling.get('type', '').lower() + if len(rope_scaling_type) == 0: + raise KeyError('Missing the required key rope_scaling.type') + + if rope_scaling_type == 'su' or rope_scaling_type == 'longrope': + attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0 + elif rope_scaling_type == 'yarn': + attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0 + else: + raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet') + + self.gguf_writer.add_rope_scaling_attn_factors(attn_factor) + + 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)) + + +@Model.register("PhiMoEForCausalLM") +class PhiMoeModel(Phi3MiniModel): + model_arch = gguf.MODEL_ARCH.PHIMOE + + _experts: list[dict[str, Tensor]] | None = None + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"]) + self.gguf_writer.add_expert_count(self.hparams["num_local_experts"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@Model.register("PlamoForCausalLM") +class PlamoModel(Model): + model_arch = gguf.MODEL_ARCH.PLAMO + + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(4096) # not in config.json + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong + self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + + def shuffle_attn_q_weight(self, data_torch): + assert data_torch.size() == (5120, 5120) + data_torch = data_torch.reshape(8, 5, 128, 5120) + data_torch = torch.permute(data_torch, (1, 0, 2, 3)) + data_torch = torch.reshape(data_torch, (5120, 5120)) + return data_torch + + def shuffle_attn_output_weight(self, data_torch): + assert data_torch.size() == (5120, 5120) + data_torch = data_torch.reshape(5120, 8, 5, 128) + data_torch = torch.permute(data_torch, (0, 2, 1, 3)) + data_torch = torch.reshape(data_torch, (5120, 5120)) + return data_torch + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + new_name = self.map_tensor_name(name) + + # shuffle for broadcasting of gqa in ggml_mul_mat + if new_name.endswith("attn_q.weight"): + data_torch = self.shuffle_attn_q_weight(data_torch) + elif new_name.endswith("attn_output.weight"): + data_torch = self.shuffle_attn_output_weight(data_torch) + + return [(new_name, data_torch)] + + +@Model.register("CodeShellForCausalLM") +class CodeShellModel(Model): + model_arch = gguf.MODEL_ARCH.CODESHELL + + def set_gguf_parameters(self): + block_count = self.hparams["n_layer"] + + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_rope_freq_base(10000.0) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(1.0) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + new_name = self.map_tensor_name(name) + + tensors: list[tuple[str, Tensor]] = [(new_name, data_torch)] + + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): + assert self.tensor_names is not None + + if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")): + # copy tok_embd.weight to output.weight + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) + + return tensors + + +@Model.register("InternLM2ForCausalLM") +class InternLM2Model(Model): + model_arch = gguf.MODEL_ARCH.INTERNLM2 + + def set_vocab(self): + # (TODO): Is there a better way? + # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character + # \x00 specially and convert it into an emoji character to prevent it from being mistakenly + # recognized as an empty string in C++. + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + tokens: list[bytes] = [] + scores: list[float] = [] + toktypes: list[int] = [] + + if not tokenizer_path.is_file(): + logger.error(f'Error: Missing {tokenizer_path}') + sys.exit(1) + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + for token_id in range(vocab_size): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + if text == b"\x00": + # (TODO): fixme + # Hack here and replace the \x00 characters. + logger.warning(f"InternLM2 convert token '{text}' to '🐉'!") + text = "🐉".encode("utf-8") + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + # take care of ununsed raw token + if piece.startswith('[UNUSED'): + toktype = SentencePieceTokenTypes.UNUSED + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + tokens.append(key.encode("utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + + chat_eos_token = '<|im_end|>' + chat_eos_token_id = None + + 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) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, foken_data in added_tokens_decoder.items(): + token_id = int(token_id) + token = foken_data["content"] + if token == chat_eos_token: + chat_eos_token_id = token_id + token = token.encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + tokenizer_file = self.dir_model / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) + added_tokens = tokenizer_json.get("added_tokens", []) + for foken_data in added_tokens: + token_id = int(foken_data["id"]) + token = foken_data["content"] + if token == chat_eos_token: + chat_eos_token_id = token_id + token = token.encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNUSED: + if tokens[token_id] != token: + logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}') + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + old_eos = special_vocab.special_token_ids["eos"] + if chat_eos_token_id is not None: + # For the chat model, we replace the eos with '<|im_end|>'. + # TODO: this is a hack, should be fixed + # https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048 + special_vocab.special_token_ids["eos"] = chat_eos_token_id + logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}" + " in chat mode so that the conversation can end normally.") + + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + self.gguf_writer.add_file_type(self.ftype) + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + num_heads = self.hparams["num_attention_heads"] + num_kv_heads = self.hparams["num_key_value_heads"] + n_embd = self.hparams["hidden_size"] + q_per_kv = num_heads // num_kv_heads + head_dim = n_embd // num_heads + num_groups = num_heads // q_per_kv + + if bid is not None and f"model.layers.{bid}.attention.wqkv" in name: + qkv = data_torch + + qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd)) + q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1] + + # The model weights of q and k equire additional reshape. + q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads) + k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads) + v = v.reshape((-1, v.shape[-1])) + + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v), + ] + else: + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("InternLM3ForCausalLM") +class InternLM3Model(Model): + model_arch = gguf.MODEL_ARCH.LLAMA + + def set_vocab(self): + tokens, scores, toktypes = self._create_vocab_sentencepiece() + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + + 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"]) + + if "added_tokens_decoder" in tokenizer_config_json: + for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items(): + if token_data.get("special"): + token_id = int(token_id) + token = token_data["content"] + special_vocab._set_special_token(token, token_id) + # update eos token + if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids: + special_vocab.special_token_ids["eos"] = token_id + + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(rope_dim) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear" or self.hparams["rope_scaling"].get("rope_type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("BertModel", "BertForMaskedLM", "CamembertModel") +class BertModel(Model): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.vocab_size = None + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_causal_attention(False) + + # get pooling path + pooling_path = None + module_path = self.dir_model / "modules.json" + if module_path.is_file(): + with open(module_path, encoding="utf-8") as f: + modules = json.load(f) + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break + + # get pooling type + if pooling_path is not None: + with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: + pooling = json.load(f) + if pooling["pooling_mode_mean_tokens"]: + pooling_type = gguf.PoolingType.MEAN + elif pooling["pooling_mode_cls_token"]: + pooling_type = gguf.PoolingType.CLS + else: + raise NotImplementedError("Only MEAN and CLS pooling types supported") + self.gguf_writer.add_pooling_type(pooling_type) + + def set_vocab(self): + tokens, toktypes, tokpre = self.get_vocab_base() + self.vocab_size = len(tokens) + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + # convert to phantom space vocab + def phantom(tok): + if tok.startswith("[") and tok.endswith("]"): + return tok + if tok.startswith("##"): + return tok[2:] + return "\u2581" + tok + tokens = list(map(phantom, tokens)) + + # add vocab to gguf + self.gguf_writer.add_tokenizer_model("bert") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + # handle special tokens + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.startswith("bert."): + name = name[5:] + + if name.endswith(".gamma"): + name = name[:-6] + ".weight" + + if name.endswith(".beta"): + name = name[:-5] + ".bias" + + # we are only using BERT for embeddings so we don't need the pooling layer + if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): + return [] # we don't need these + + if name.startswith("cls.predictions"): + return [] + + if name.startswith("cls.seq_relationship"): + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("RobertaModel") +class RobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + """Support BPE tokenizers for roberta models""" + bpe_tok_path = self.dir_model / "tokenizer.json" + if bpe_tok_path.exists(): + self._set_vocab_gpt2() + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + else: + return super().set_vocab() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + +@Model.register("NomicBertModel") +class NomicBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.NOMIC_BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # the HF config claims n_ctx=8192, but it uses RoPE scaling + self.hparams["n_ctx"] = 2048 + + # SwigLU activation + assert self.hparams["activation_function"] == "swiglu" + # this doesn't do anything in the HF version + assert self.hparams["causal"] is False + # no bias tensors + assert self.hparams["qkv_proj_bias"] is False + assert self.hparams["mlp_fc1_bias"] is False + assert self.hparams["mlp_fc2_bias"] is False + # norm at end of layer + assert self.hparams["prenorm"] is False + # standard RoPE + assert self.hparams["rotary_emb_fraction"] == 1.0 + assert self.hparams["rotary_emb_interleaved"] is False + assert self.hparams["rotary_emb_scale_base"] is None + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) + + +@Model.register("XLMRobertaModel", "XLMRobertaForSequenceClassification") +class XLMRobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'sentencepiece.bpe.model' + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + # realign tokens (see HF tokenizer code) + tokens = [b'', b'', b'', b''] + tokens[3:-1] + scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1] + toktypes = [ + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.CONTROL, + SentencePieceTokenTypes.UNKNOWN, + ] + toktypes[3:-1] + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + 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(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) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + +@Model.register("GemmaForCausalLM") +class GemmaModel(Model): + model_arch = gguf.MODEL_ARCH.GEMMA + + def set_vocab(self): + self._set_vocab_sentencepiece() + + # TODO: these special tokens should be exported only for the CodeGemma family + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot']) + special_vocab._set_special_token("prefix", 67) + special_vocab._set_special_token("suffix", 69) + special_vocab._set_special_token("middle", 68) + special_vocab._set_special_token("fsep", 70) + special_vocab._set_special_token("eot", 107) + special_vocab.chat_template = None # do not add it twice + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_space_prefix(False) + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(hparams["head_dim"]) + self.gguf_writer.add_value_length(hparams["head_dim"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # lm_head is not used in llama.cpp, while autoawq will include this tensor in model + # To prevent errors, skip loading lm_head.weight. + if name == "lm_head.weight": + logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + return [] + + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("Gemma2ForCausalLM") +class Gemma2Model(Model): + model_arch = gguf.MODEL_ARCH.GEMMA2 + + def set_vocab(self): + self._set_vocab_sentencepiece() + + self.gguf_writer.add_add_space_prefix(False) + + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_key_length(hparams["head_dim"]) + self.gguf_writer.add_value_length(hparams["head_dim"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_attn_logit_softcapping( + self.hparams["attn_logit_softcapping"] + ) + self.gguf_writer.add_final_logit_softcapping( + self.hparams["final_logit_softcapping"] + ) + self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # lm_head is not used in llama.cpp, while autoawq will include this tensor in model + # To prevent errors, skip loading lm_head.weight. + if name == "lm_head.weight": + logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + return [] + + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("Starcoder2ForCausalLM") +class StarCoder2Model(Model): + model_arch = gguf.MODEL_ARCH.STARCODER2 + + +@Model.register("Rwkv6ForCausalLM") +class Rwkv6Model(Model): + model_arch = gguf.MODEL_ARCH.RWKV6 + + def set_vocab(self): + assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file() + vocab_size = self.hparams.get("vocab_size", 65536) + + tokens: list[bytes] = [''.encode("utf-8")] + toktypes: list[int] = [gguf.TokenType.CONTROL] + + with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f: + lines = f.readlines() + for line in lines: + parts = line.split(' ') + assert len(parts) >= 3 + token, token_len = ast.literal_eval(' '.join(parts[1:-1])), int(parts[-1]) + token = token.encode("utf-8") if isinstance(token, str) else token + assert isinstance(token, bytes) + assert len(token) == token_len + token_text: str = repr(token)[2:-1] # "b'\xff'" -> "\xff" + tokens.append(token_text.encode("utf-8")) + toktypes.append(gguf.TokenType.NORMAL) + remainder = vocab_size - len(tokens) + assert remainder >= 0 + for i in range(len(tokens), vocab_size): + tokens.append(f"[PAD{i}]".encode("utf-8")) + toktypes.append(gguf.TokenType.UNUSED) + + self.gguf_writer.add_tokenizer_model("rwkv") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False) + special_vocab.chat_template = "rwkv-world" + # hack: Add '\n\n' as the EOT token to make it chat normally + special_vocab._set_special_token("eot", 261) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_size = self.hparams["head_size"] + hidden_size = self.hparams["hidden_size"] + layer_norm_eps = self.hparams["layer_norm_epsilon"] + rescale_every_n_layers = self.hparams["rescale_every"] + intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else int((hidden_size * 3.5) // 32 * 32) + time_mix_extra_dim = 64 if hidden_size == 4096 else 32 + time_decay_extra_dim = 128 if hidden_size == 4096 else 64 + + # RWKV isn't context limited + self.gguf_writer.add_context_length(1048576) + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_layer_norm_eps(layer_norm_eps) + self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers) + self.gguf_writer.add_wkv_head_size(head_size) + self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim) + self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_file_type(self.ftype) + + # required by llama.cpp, unused + self.gguf_writer.add_head_count(0) + + lerp_weights: dict[int, dict[str, Tensor]] = {} + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + new_name = self.map_tensor_name(name) + + if not (new_name.endswith(".weight") or new_name.endswith(".bias")): + new_name += ".weight" + + if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"): + data_torch = data_torch.transpose(0, 1) + + if new_name.endswith("time_mix_w2.weight"): + data_torch = data_torch.permute(0, 2, 1) + + if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name: + data_torch = data_torch.squeeze() + + try: + rescale_every_n_layers = self.hparams["rescale_every"] + if rescale_every_n_layers > 0: + if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"): + data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers)) + except KeyError: + pass + + # concat time_mix_lerp weights to reduce some cpu overhead + # also reduces the number of tensors in the model + if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name: + try: + self.lerp_weights[bid][new_name] = data_torch + except KeyError: + self.lerp_weights[bid] = {new_name: data_torch} + if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]): + new_name = f"blk.{bid}.time_mix_lerp_fused.weight" + data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1) + yield (new_name, data) + return + + yield (new_name, data_torch) + + +@Model.register("RWKV6Qwen2ForCausalLM") +class RWKV6Qwen2Model(Rwkv6Model): + model_arch = gguf.MODEL_ARCH.RWKV6QWEN2 + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + num_attention_heads = self.hparams["num_attention_heads"] + num_key_value_heads = self.hparams["num_key_value_heads"] + hidden_size = self.hparams["hidden_size"] + head_size = hidden_size // num_attention_heads + rms_norm_eps = self.hparams["rms_norm_eps"] + intermediate_size = self.hparams["intermediate_size"] + time_mix_extra_dim = 64 if hidden_size >= 4096 else 32 + time_decay_extra_dim = 128 if hidden_size >= 4096 else 64 + + # RWKV isn't context limited + self.gguf_writer.add_context_length(1048576) + self.gguf_writer.add_embedding_length(hidden_size) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_wkv_head_size(head_size) + self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim) + self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_file_type(self.ftype) + + # special parameters for time_mixing in RWKV6QWEN2 + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_token_shift_count(1) + # RWKV6QWEN2 use grouped key/value like GQA + self.gguf_writer.add_head_count_kv(num_key_value_heads) + + # required by llama.cpp, unused + self.gguf_writer.add_head_count(0) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + for new_name, data in super().modify_tensors(data_torch, name, bid): + if "time_mix_w1" in new_name or "time_mix_w2" in new_name: + data = data.view(5, -1, data.shape[-1]) + # rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg + # permute them here to avoid code changes + data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1]) + if "w2" in new_name: + data = data.view(5, -1, data.shape[-1]) + yield (new_name, data) + continue + yield (new_name, data) + + +@Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM") +class MambaModel(Model): + model_arch = gguf.MODEL_ARCH.MAMBA + + def set_vocab(self): + vocab_size = self.hparams["vocab_size"] + # Round vocab size to next multiple of 8 + pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8) + # pad using ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + vocab_size = -(vocab_size // -pad_vocab) * pad_vocab + self.hparams["vocab_size"] = vocab_size + + if (self.dir_model / "tokenizer.json").is_file(): + self._set_vocab_gpt2() + elif (self.dir_model / "tokenizer.model").is_file(): + self._set_vocab_sentencepiece() + else: + # Use the GPT-NeoX tokenizer when no tokenizer files are present + self._set_vocab_builtin("gpt-neox", vocab_size) + + def set_gguf_parameters(self): + d_model = self.find_hparam(["hidden_size", "d_model"]) + d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 + d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model + d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16 + # ceiling division + # ref: https://stackoverflow.com/a/17511341/22827863 + # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 + dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) + rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 + use_dt_b_c_norm = False + # For falconmamba we do apply RMS norm on B / DT and C layers + if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",): + use_dt_b_c_norm = True + # Fail early for models which don't have a block expansion factor of 2 + assert d_inner == 2 * d_model + + self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default + self.gguf_writer.add_embedding_length(d_model) + self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading + self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_ssm_conv_kernel(d_conv) + self.gguf_writer.add_ssm_inner_size(d_inner) + self.gguf_writer.add_ssm_state_size(d_state) + self.gguf_writer.add_ssm_time_step_rank(dt_rank) + self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) + self.gguf_writer.add_ssm_dt_b_c_rms(use_dt_b_c_norm) # For classic Mamba we don't apply rms norm on B / DT layers + self.gguf_writer.add_file_type(self.ftype) + + _tok_embd = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT) + tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD) + + new_name = self.map_tensor_name(name) + + if name.endswith(".A_log"): + logger.debug("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) + + # assuming token_embd.weight is seen before output.weight + if self._tok_embd is not None and new_name == output_name: + if torch.equal(self._tok_embd, data_torch): + logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting") + return [] + elif new_name == tok_embd_name: + self._tok_embd = data_torch + + return [(new_name, data_torch)] + + +@Model.register("CohereForCausalLM") +class CommandR2Model(Model): + model_arch = gguf.MODEL_ARCH.COMMAND_R + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # max_position_embeddings = 8192 in config.json but model was actually + # trained on 128k context length + # aya-23 models don't have model_max_length specified + self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"]) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_logit_scale(self.hparams["logit_scale"]) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + +@Model.register("Cohere2ForCausalLM") +class Cohere2Model(Model): + model_arch = gguf.MODEL_ARCH.COHERE2 + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + self.gguf_writer.add_logit_scale(self.hparams["logit_scale"]) + self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) + self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) + + rotary_pct = self.hparams["rotary_pct"] + hidden_size = self.hparams["hidden_size"] + num_attention_heads = self.hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads))) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + +@Model.register("OlmoForCausalLM") +@Model.register("OLMoForCausalLM") +class OlmoModel(Model): + model_arch = gguf.MODEL_ARCH.OLMO + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_layer_norm_eps(1e-5) + clip_qkv = self.hparams.get("clip_qkv") + if clip_qkv is not None: + self.gguf_writer.add_clamp_kqv(clip_qkv) + + # Same as super class, but permuting q_proj, k_proj + # Copied from: LlamaModel + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith("q_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith("k_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + 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 + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_layer_norm_rms_eps(1e-5) + if (n_experts := self.hparams.get("num_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + + _experts: list[dict[str, Tensor]] | None = None + + # Copied from: Qwen2MoeModel + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("experts") != -1: + n_experts = self.hparams["num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + # Copied from: Qwen2MoeModel + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@Model.register("JinaBertModel", "JinaBertForMaskedLM") +class JinaBertV2Model(BertModel): + model_arch = gguf.MODEL_ARCH.JINA_BERT_V2 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.intermediate_size = self.hparams["intermediate_size"] + + def get_tensors(self): + for name, data in super().get_tensors(): + if 'gated_layer' in name: + d1 = data[:self.intermediate_size, :] + name1 = name.replace('gated_layers', 'gated_layers_w') + name1 = name1.replace('up_gated_layer', 'gated_layers_v') + d2 = data[self.intermediate_size:, :] + name2 = name.replace('gated_layers', 'gated_layers_v') + name2 = name2.replace('up_gated_layer', 'gated_layers_w') + yield name1, d1 + yield name2, d2 + continue + + yield name, data + + def set_vocab(self): + tokenizer_class = 'BertTokenizer' + with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f: + tokenizer_class = json.load(f)['tokenizer_class'] + + if tokenizer_class == 'BertTokenizer': + super().set_vocab() + elif tokenizer_class == 'RobertaTokenizer': + self._set_vocab_gpt2() + self.gguf_writer.add_token_type_count(2) + else: + raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel') + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "bert.", remove the prefix + # e.g. https://huggingface.co/jinaai/jina-reranker-v1-tiny-en + if name.startswith("bert."): + name = name[5:] + + return super().modify_tensors(data_torch, name, bid) + + +@Model.register("OpenELMForCausalLM") +class OpenELMModel(Model): + model_arch = gguf.MODEL_ARCH.OPENELM + + @staticmethod + def _make_divisible(v: float | int, divisor: int) -> int: + # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38 + new_v = max(divisor, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + ffn_multipliers: list[float] = self.hparams["ffn_multipliers"] + ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"] + self._n_embd: int = self.hparams["model_dim"] + self._num_kv_heads: list[int] = self.hparams["num_kv_heads"] + self._num_query_heads: list[int] = self.hparams["num_query_heads"] + self._ffn_dims: list[int] = [ + OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor) + for multiplier in ffn_multipliers + ] + assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int) + assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int) + + # Uses the tokenizer from meta-llama/Llama-2-7b-hf + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"]) + + def set_gguf_parameters(self): + n_embd = self._n_embd + head_dim = self.hparams["head_dim"] + rot_pct = 1.0 + assert self.block_count == len(self._num_kv_heads) + assert self.block_count == len(self._num_query_heads) + assert self.block_count == len(self._ffn_dims) + + self.gguf_writer.add_block_count(self.block_count) + self.gguf_writer.add_context_length(self.hparams["max_context_length"]) + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(self._ffn_dims) + self.gguf_writer.add_head_count(self._num_query_heads) + self.gguf_writer.add_head_count_kv(self._num_kv_heads) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"]) + # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30 + self.gguf_writer.add_layer_norm_rms_eps(1e-6) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim)) + self.gguf_writer.add_key_length(head_dim) + self.gguf_writer.add_value_length(head_dim) + self.gguf_writer.add_file_type(self.ftype) + + def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any: + if "n_layers" in keys: + return self.hparams["num_transformer_layers"] + + return super().find_hparam(keys, optional) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + + # split ff + if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight": + ff_dim = self._ffn_dims[bid] + yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim]) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:]) + return + + yield (self.map_tensor_name(name), data_torch) + + +@Model.register("ArcticForCausalLM") +class ArcticModel(Model): + model_arch = gguf.MODEL_ARCH.ARCTIC + + def set_vocab(self): + # The reason for using a custom implementation here is that the + # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from + # tokenizer.model and used them as BOS and EOS instead of adding new tokens. + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + logger.error(f'Error: Missing {tokenizer_path}') + sys.exit(1) + + # Read the whole vocabulary from the tokenizer.model file + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + # Use the added_tokens_decoder field from tokeniser_config.json as the source + # of information about added/redefined tokens and modify them accordingly. + 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 "added_tokens_decoder" in tokenizer_config_json: + added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"] + for token_id, token_json in added_tokens_decoder.items(): + token_id = int(token_id) + if token_id >= vocab_size: + logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + token_content = token_json["content"] + token_type = SentencePieceTokenTypes.USER_DEFINED + token_score = -10000.0 + + # Map unk_token to UNKNOWN, other special tokens to CONTROL + # Set the score to 0.0 as in the original tokenizer.model + if ("special" in token_json) and token_json["special"]: + if token_content == tokenizer_config_json["unk_token"]: + token_type = SentencePieceTokenTypes.UNKNOWN + else: + token_type = SentencePieceTokenTypes.CONTROL + token_score = 0.0 + + logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})") + tokens[token_id] = token_content.encode("utf-8") + toktypes[token_id] = token_type + scores[token_id] = token_score + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith("q_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith("k_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for wid in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@Model.register("DeepseekForCausalLM") +class DeepseekModel(Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK + + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + if "head_dim" in hparams: + rope_dim = hparams["head_dim"] + else: + rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] + + self.gguf_writer.add_rope_dimension_count(rope_dim) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_weights_scale(1.0) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + + _experts: list[dict[str, Tensor]] | None = None + + @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: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = DeepseekModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["n_routed_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@Model.register("DeepseekV2ForCausalLM") +@Model.register("DeepseekV3ForCausalLM") +class DeepseekV2Model(Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: + self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_value_length(hparams["v_head_dim"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) + self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"]) + + if hparams["scoring_func"] == "sigmoid": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + elif hparams["scoring_func"] == "softmax": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX) + else: + raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}") + + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "yarn": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # rename e_score_correction_bias tensors + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + + # skip Multi-Token Prediction (MTP) layers + block_count = self.hparams["num_hidden_layers"] + match = re.match(r"model.layers.(\d+)", name) + if match and int(match.group(1)) >= block_count: + return [] + + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["n_routed_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + +@Model.register("T5WithLMHeadModel") +@Model.register("T5ForConditionalGeneration") +@Model.register("MT5ForConditionalGeneration") +@Model.register("UMT5ForConditionalGeneration") +class T5Model(Model): + model_arch = gguf.MODEL_ARCH.T5 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.shared_token_embeddings_found = False + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + # many older models use spiece.model tokenizer model filename + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'spiece.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + + # some models like Pile-T5 family use BPE tokenizer instead of Unigram + if sentencepiece_model.trainer_spec.model_type == 2: # BPE + # assure the tokenizer model file name is correct + assert tokenizer_path.name == 'tokenizer.model' + return self._set_vocab_sentencepiece() + else: + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + 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_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_bos_token(False) + self.gguf_writer.add_add_eos_token(True) + + def set_gguf_parameters(self): + if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: + logger.warning("Couldn't find context length in config.json, assuming default value of 512") + n_ctx = 512 + self.gguf_writer.add_context_length(n_ctx) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) + self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_head_count(self.hparams["num_heads"]) + self.gguf_writer.add_key_length(self.hparams["d_kv"]) + self.gguf_writer.add_value_length(self.hparams["d_kv"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", + # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored + # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder + # and decoder and ignore the remaining ones. + if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: + if not self.shared_token_embeddings_found: + name = "shared.weight" + self.shared_token_embeddings_found = True + else: + logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("T5EncoderModel") +class T5EncoderModel(Model): + model_arch = gguf.MODEL_ARCH.T5ENCODER + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.shared_token_embeddings_found = False + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + # many older models use spiece.model tokenizer model filename + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'spiece.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + + # some models like Pile-T5 family use BPE tokenizer instead of Unigram + if sentencepiece_model.trainer_spec.model_type == 2: # BPE + # assure the tokenizer model file name is correct + assert tokenizer_path.name == 'tokenizer.model' + return self._set_vocab_sentencepiece() + else: + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + 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_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_bos_token(False) + self.gguf_writer.add_add_eos_token(True) + + def set_gguf_parameters(self): + if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: + logger.warning("Couldn't find context length in config.json, assuming default value of 512") + n_ctx = 512 + self.gguf_writer.add_context_length(n_ctx) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) + self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_head_count(self.hparams["num_heads"]) + self.gguf_writer.add_key_length(self.hparams["d_kv"]) + self.gguf_writer.add_value_length(self.hparams["d_kv"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", + # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored + # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder + # and decoder and ignore the remaining ones. + if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: + if not self.shared_token_embeddings_found: + name = "shared.weight" + self.shared_token_embeddings_found = True + else: + logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") + return [] + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("JAISLMHeadModel") +class JaisModel(Model): + model_arch = gguf.MODEL_ARCH.JAIS + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # SwigLU activation + assert self.hparams["activation_function"] == "swiglu" + # ALiBi position embedding + assert self.hparams["position_embedding_type"] == "alibi" + + # Embeddings scale + self.embeddings_scale = 1.0 + if 'mup_embeddings_scale' in self.hparams: + self.embeddings_scale = self.hparams['mup_embeddings_scale'] + elif 'embeddings_scale' in self.hparams: + self.embeddings_scale = self.hparams['embeddings_scale'] + else: + assert False + + self.width_scale = 1.0 + if 'mup_output_alpha' in self.hparams: + assert 'mup_width_scale' in self.hparams + self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale'] + elif 'width_scale' in self.hparams: + self.width_scale = self.hparams['width_scale'] + else: + assert False + + self.max_alibi_bias = 8.0 + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + self.gguf_writer.add_block_count(self.hparams["n_layer"]) + self.gguf_writer.add_context_length(self.hparams["n_positions"]) + self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) + self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"]) + self.gguf_writer.add_head_count(self.hparams["n_head"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + tensors: list[tuple[str, Tensor]] = [] + + # we don't need these + if name.endswith((".attn.bias")): + return tensors + + if name.endswith(("relative_pe.slopes")): + # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation) + # Some other models has max_alibi_bias spelled out explicitly in the hyperparams, + # but Jais's PyTorch model simply precalculates the slope values and places them + # in relative_pes.slopes + n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"])) + first_val = float(data_torch[0].item()) + self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2) + + return tensors + + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")): + data_torch = data_torch.transpose(1, 0) + + new_name = self.map_tensor_name(name) + + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): + tensors.append((new_name, data_torch * self.embeddings_scale)) + elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT): + tensors.append((new_name, data_torch * self.width_scale)) + else: + tensors.append((new_name, data_torch)) + + return tensors + + def prepare_tensors(self): + super().prepare_tensors() + self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias) + + +@Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration") +class ChatGLMModel(Model): + model_arch = gguf.MODEL_ARCH.CHATGLM + + def set_vocab_chatglm3(self): + dir_model = self.dir_model + hparams = self.hparams + tokens: list[bytes] = [] + toktypes: list[int] = [] + scores: list[float] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab())) + assert max(tokenizer.get_vocab().values()) < vocab_size + role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] + special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens + for token_id in range(vocab_size): + piece = tokenizer._convert_id_to_token(token_id) + if token_id == 0: + piece = "" + elif token_id == 1: + piece = "" + elif token_id == 2: + piece = "" + + text = piece.encode("utf-8") + score = 0.0 + # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py), + # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size() + if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size(): + score = tokenizer.tokenizer.sp_model.get_score(token_id) + + if token_id >= tokenizer.tokenizer.sp_model.vocab_size(): + if piece in special_tokens: + toktype = SentencePieceTokenTypes.CONTROL + elif len(piece) == 0: + text = f"[PAD{token_id}]".encode("utf-8") + toktype = SentencePieceTokenTypes.UNUSED + else: + toktype = SentencePieceTokenTypes.USER_DEFINED + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + continue + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.tokenizer.sp_model.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.tokenizer.sp_model.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.tokenizer.sp_model.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.tokenizer.sp_model.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + self.gguf_writer.add_tokenizer_model("llama") + # glm3 needs prefix and suffix formatted as: + # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>" + self.gguf_writer.add_tokenizer_pre("chatglm-spm") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + @staticmethod + def token_bytes_to_string(b): + from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode + byte_encoder = bytes_to_unicode() + return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) + + @staticmethod + def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]: + parts = [bytes([b]) for b in token] + while True: + min_idx = None + min_rank = None + for i, pair in enumerate(zip(parts[:-1], parts[1:])): + rank = mergeable_ranks.get(pair[0] + pair[1]) + if rank is not None and (min_rank is None or rank < min_rank): + min_idx = i + min_rank = rank + if min_rank is None or (max_rank is not None and min_rank >= max_rank): + break + assert min_idx is not None + parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:] + return parts + + def set_vocab(self): + if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""): + self.set_vocab_chatglm3() + return + + dir_model = self.dir_model + hparams = self.hparams + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True) + vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"]) + assert max(tokenizer.get_vocab().values()) < vocab_size + + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + # only add special tokens when they were not already loaded from config.json + special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) + # this one is usually not in config.json anyway + special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head)) + self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed)) + self.gguf_writer.add_embedding_length(n_embed) + self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed))) + self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"])) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5)) + self.gguf_writer.add_file_type(self.ftype) + if "attention_dim" in self.hparams: + rope_dim = self.hparams["attention_dim"] + else: + rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) + self.gguf_writer.add_add_bos_token(False) + rope_freq = 10000 + if "rope_ratio" in self.hparams: + rope_freq = rope_freq * self.hparams["rope_ratio"] + self.gguf_writer.add_rope_freq_base(rope_freq) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."): + return [] + + name = name.removeprefix("transformer.") + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("NemotronForCausalLM") +class NemotronModel(Model): + model_arch = gguf.MODEL_ARCH.NEMOTRON + + def set_vocab(self): + self._set_vocab_sentencepiece() + self.gguf_writer.add_pad_token_id(0) + self.gguf_writer.add_unk_token_id(1) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + + f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"]) + self.gguf_writer.add_layer_norm_eps(f_norm_eps) + + # * Partial RoPE + rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"]) + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + + # * RopeScaling for Nemotron + if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + else: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side + # model.layers.{l}.input_layernorm.weight + # model.layers.{l}.post_attention_layernorm.weight + # model.norm.weight + if name.endswith("norm.weight"): + data_torch = data_torch + 1 + + return [(self.map_tensor_name(name), data_torch)] + + +@Model.register("ExaoneForCausalLM") +class ExaoneModel(Model): + model_arch = gguf.MODEL_ARCH.EXAONE + + def set_gguf_parameters(self): + hparams = self.hparams + + assert (hparams["activation_function"] == "silu") + + max_position_embeddings = hparams["max_position_embeddings"] + embed_dim = hparams["hidden_size"] + num_heads = hparams["num_attention_heads"] + num_kv_heads = hparams.get("num_key_value_heads", num_heads) + layer_norm_eps = hparams["layer_norm_epsilon"] + intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim + num_layers = hparams["num_layers"] + # ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0 + # attention_dropout_rate = hparams["attention_dropout"] + # ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0 + # embed_dropout_rate = hparams["embed_dropout"] + self.gguf_writer.add_embedding_length(embed_dim) + self.gguf_writer.add_head_count(num_heads) + self.gguf_writer.add_head_count_kv(num_kv_heads) + self.gguf_writer.add_context_length(max_position_embeddings) + self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps) + self.gguf_writer.add_feed_forward_length(intermediate_size) + self.gguf_writer.add_block_count(num_layers) + self.gguf_writer.add_file_type(self.ftype) + + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) + rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True) + rotary_factor = rotary_factor if rotary_factor is not None else 1.0 + self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) + if hparams.get("rope_scaling") is not None and "factor" in hparams["rope_scaling"]: + if hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"]) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): + if rope_scaling.get("rope_type", '').lower() == "llama3": + base = self.hparams.get("rope_theta", 10000.0) + dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) + + factor = rope_scaling.get("factor", 8.0) + low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) + high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + old_context_len = self.hparams.get("original_max_position_embeddings", 8192) + + low_freq_wavelen = old_context_len / low_freq_factor + high_freq_wavelen = old_context_len / high_freq_factor + assert low_freq_wavelen != high_freq_wavelen + + rope_factors = [] + for freq in freqs: + wavelen = 2 * math.pi / freq + if wavelen < high_freq_wavelen: + rope_factors.append(1) + elif wavelen > low_freq_wavelen: + rope_factors.append(factor) + else: + smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) + rope_factors.append(1 / ((1 - smooth) / factor + smooth)) + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32)) + + +@Model.register("GraniteForCausalLM") +class GraniteModel(LlamaModel): + """Conversion for IBM's GraniteForCausalLM""" + model_arch = gguf.MODEL_ARCH.GRANITE + + def set_gguf_parameters(self): + """Granite uses standard llama parameters with the following differences: + + - No head_dim support + - New multiplier params: + - attention_scale + - embedding_scale + - residual_scale + - logits_scaling + """ + if head_dim := self.hparams.pop("head_dim", None): + logger.warning("Ignoring head_dim (%s) from config for Granite", head_dim) + super().set_gguf_parameters() + # NOTE: Convert _multiplier params to _scale params for naming + # consistency + if attention_scale := self.hparams.get("attention_multiplier"): + self.gguf_writer.add_attention_scale(attention_scale) + logger.info("gguf: (granite) attention_scale = %s", attention_scale) + if embedding_scale := self.hparams.get("embedding_multiplier"): + self.gguf_writer.add_embedding_scale(embedding_scale) + logger.info("gguf: (granite) embedding_scale = %s", embedding_scale) + if residual_scale := self.hparams.get("residual_multiplier"): + self.gguf_writer.add_residual_scale(residual_scale) + logger.info("gguf: (granite) residual_scale = %s", residual_scale) + if logits_scale := self.hparams.get("logits_scaling"): + self.gguf_writer.add_logit_scale(logits_scale) + logger.info("gguf: (granite) logits_scale = %s", logits_scale) + + +@Model.register("GraniteMoeForCausalLM") +class GraniteMoeModel(GraniteModel): + """Conversion for IBM's GraniteMoeForCausalLM""" + model_arch = gguf.MODEL_ARCH.GRANITE_MOE + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + """In modeling_granitemoe, the JetMoe implementation of parallel experts + is used. This essentially merges w1 and w3 into a single tensor with 2x + the hidden size that is then split during forward. To keep compatibility + with existing mixtral support, we pull them apart here. + """ + + if name.endswith("block_sparse_moe.input_linear.weight"): + ffn_dim = self.hparams["intermediate_size"] + assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size" + gate, up = data_torch[..., :ffn_dim, :], data_torch[..., ffn_dim:, :] + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate), + (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up), + ] + + return super().modify_tensors(data_torch, name, bid) + + +@Model.register("ChameleonForConditionalGeneration") +@Model.register("ChameleonForCausalLM") # obsolete +class ChameleonModel(Model): + model_arch = gguf.MODEL_ARCH.CHAMELEON + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False)) + + def set_vocab(self): + self._set_vocab_gpt2() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # ignore image tokenizer for now + # TODO: remove this once image support is implemented for Chameleon + if name.startswith("model.vqmodel"): + return [] + + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + hidden_dim = self.hparams.get("hidden_size") + + if name.endswith(("q_proj.weight", "q_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight", "k_proj.bias")): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + if name.endswith(("q_norm.weight", "q_norm.bias")): + data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim) + if name.endswith(("k_norm.weight", "k_norm.bias")): + data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim) + + return [(self.map_tensor_name(name), data_torch)] + + # see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203 + @staticmethod + def _reverse_hf_permute(data_torch, n_heads, hidden_dim): + head_dim = hidden_dim // n_heads + data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1) + data_torch = data_torch.repeat_interleave(n_heads, 0) + return data_torch + + +###### CONVERSION LOGIC ###### + + +# tree of lazy tensors +class LazyTorchTensor(gguf.LazyBase): + _tensor_type = torch.Tensor + # to keep the type-checker happy + dtype: torch.dtype + shape: torch.Size + + # only used when converting a torch.Tensor to a np.ndarray + _dtype_map: dict[torch.dtype, type] = { + torch.float16: np.float16, + torch.float32: np.float32, + } + + # used for safetensors slices + # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046 + # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734 + _dtype_str_map: dict[str, torch.dtype] = { + "F64": torch.float64, + "F32": torch.float32, + "BF16": torch.bfloat16, + "F16": torch.float16, + # "U64": torch.uint64, + "I64": torch.int64, + # "U32": torch.uint32, + "I32": torch.int32, + # "U16": torch.uint16, + "I16": torch.int16, + "U8": torch.uint8, + "I8": torch.int8, + "BOOL": torch.bool, + "F8_E4M3": torch.float8_e4m3fn, + "F8_E5M2": torch.float8_e5m2, + } + + def numpy(self) -> gguf.LazyNumpyTensor: + dtype = self._dtype_map[self.dtype] + return gguf.LazyNumpyTensor( + meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape), + args=(self,), + func=(lambda s: s.numpy()) + ) + + @classmethod + def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor: + return torch.empty(size=shape, dtype=dtype, device="meta") + + @classmethod + def from_safetensors_slice(cls, st_slice: Any) -> Tensor: + dtype = cls._dtype_str_map[st_slice.get_dtype()] + shape: tuple[int, ...] = tuple(st_slice.get_shape()) + lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:]) + return cast(torch.Tensor, lazy) + + @classmethod + def __torch_function__(cls, func, types, args=(), kwargs=None): + del types # unused + + if kwargs is None: + kwargs = {} + + if func is torch.Tensor.numpy: + return args[0].numpy() + + return cls._wrap_fn(func)(*args, **kwargs) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Convert a huggingface model to a GGML compatible file") + parser.add_argument( + "--vocab-only", action="store_true", + help="extract only the vocab", + ) + parser.add_argument( + "--outfile", type=Path, + help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", + ) + parser.add_argument( + "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="f16", + help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", + ) + parser.add_argument( + "--bigendian", action="store_true", + help="model is executed on big endian machine", + ) + parser.add_argument( + "model", type=Path, + help="directory containing model file", + nargs="?", + ) + parser.add_argument( + "--use-temp-file", action="store_true", + help="use the tempfile library while processing (helpful when running out of memory, process killed)", + ) + parser.add_argument( + "--no-lazy", action="store_true", + help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)", + ) + parser.add_argument( + "--model-name", type=str, default=None, + help="name of the model", + ) + parser.add_argument( + "--verbose", action="store_true", + help="increase output verbosity", + ) + parser.add_argument( + "--split-max-tensors", type=int, default=0, + help="max tensors in each split", + ) + parser.add_argument( + "--split-max-size", type=str, default="0", + help="max size per split N(M|G)", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="only print out a split plan and exit, without writing any new files", + ) + parser.add_argument( + "--no-tensor-first-split", action="store_true", + help="do not add tensors to the first split (disabled by default)" + ) + parser.add_argument( + "--metadata", type=Path, + help="Specify the path for an authorship metadata override file" + ) + parser.add_argument( + "--print-supported-models", action="store_true", + help="Print the supported models" + ) + + args = parser.parse_args() + if not args.print_supported_models and args.model is None: + parser.error("the following arguments are required: model") + return args + + +def split_str_to_n_bytes(split_str: str) -> int: + if split_str.endswith("K"): + n = int(split_str[:-1]) * 1000 + elif split_str.endswith("M"): + n = int(split_str[:-1]) * 1000 * 1000 + elif split_str.endswith("G"): + n = int(split_str[:-1]) * 1000 * 1000 * 1000 + elif split_str.isnumeric(): + n = int(split_str) + else: + raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G") + + if n < 0: + raise ValueError(f"Invalid split size: {split_str}, must be positive") + + return n + + +def main() -> None: + args = parse_args() + + if args.print_supported_models: + logger.error("Supported models:") + Model.print_registered_models() + sys.exit(0) + + if args.verbose: + logging.basicConfig(level=logging.DEBUG) + else: + logging.basicConfig(level=logging.INFO) + + dir_model = args.model + + if not dir_model.is_dir(): + logger.error(f'Error: {args.model} is not a directory') + sys.exit(1) + + ftype_map: dict[str, gguf.LlamaFileType] = { + "f32": gguf.LlamaFileType.ALL_F32, + "f16": gguf.LlamaFileType.MOSTLY_F16, + "bf16": gguf.LlamaFileType.MOSTLY_BF16, + "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0, + "tq1_0": gguf.LlamaFileType.MOSTLY_TQ1_0, + "tq2_0": gguf.LlamaFileType.MOSTLY_TQ2_0, + "auto": gguf.LlamaFileType.GUESSED, + } + + is_split = args.split_max_tensors > 0 or args.split_max_size != "0" + if args.use_temp_file and is_split: + logger.error("Error: Cannot use temp file when splitting") + sys.exit(1) + + if args.outfile is not None: + fname_out = args.outfile + else: + fname_out = dir_model + + logger.info(f"Loading model: {dir_model.name}") + + hparams = Model.load_hparams(dir_model) + + with torch.inference_mode(): + output_type = ftype_map[args.outtype] + model_architecture = hparams["architectures"][0] + + try: + model_class = Model.from_model_architecture(model_architecture) + except NotImplementedError: + logger.error(f"Model {model_architecture} is not supported") + sys.exit(1) + + model_instance = model_class(dir_model=dir_model, ftype=output_type, fname_out=fname_out, + is_big_endian=args.bigendian, use_temp_file=args.use_temp_file, + eager=args.no_lazy, + metadata_override=args.metadata, model_name=args.model_name, + split_max_tensors=args.split_max_tensors, + split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run, + small_first_shard=args.no_tensor_first_split) + + if args.vocab_only: + logger.info("Exporting model vocab...") + model_instance.write_vocab() + logger.info(f"Model vocab successfully exported to {model_instance.fname_out}") + else: + logger.info("Exporting model...") + model_instance.write() + out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out + logger.info(f"Model successfully exported to {out_path}") + + +if __name__ == '__main__': + main() diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py new file mode 100755 index 000000000..cea34413f --- /dev/null +++ b/convert_hf_to_gguf_update.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# This script downloads the tokenizer models of the specified models from Huggingface and +# generates the get_vocab_base_pre() function for convert_hf_to_gguf.py +# +# This is necessary in order to analyze the type of pre-tokenizer used by the model and +# provide the necessary information to llama.cpp via the GGUF header in order to implement +# the same pre-tokenizer. +# +# ref: https://github.com/ggerganov/llama.cpp/pull/6920 +# +# Instructions: +# +# - Add a new model to the "models" list +# - Run the script with your huggingface token: +# +# python3 convert_hf_to_gguf_update.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 +# + +import logging +import os +import pathlib +import re + +import requests +import sys +import json +import shutil + +from hashlib import sha256 +from enum import IntEnum, auto +from transformers import AutoTokenizer + +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger("convert_hf_to_gguf_update") +sess = requests.Session() + + +class TOKENIZER_TYPE(IntEnum): + SPM = auto() + BPE = auto() + WPM = auto() + UGM = auto() + + +# TODO: this string has to exercise as much pre-tokenizer functionality as possible +# will be updated with time - contributions welcome +CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL' + +if len(sys.argv) == 2: + token = sys.argv[1] + if not token.startswith("hf_"): + logger.info("Huggingface token seems invalid") + logger.info("Usage: python convert_hf_to_gguf_update.py ") + sys.exit(1) +else: + logger.info("Usage: python convert_hf_to_gguf_update.py ") + sys.exit(1) + +# TODO: add models here, base models preferred +models = [ + {"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", }, + {"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", }, + {"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", }, + {"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", }, + {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, + {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, + {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + {"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", }, + {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, + {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, + {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, + {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, + {"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, + {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, + {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, + {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, + {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, + {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, + {"name": "jina-v1-en", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", }, + {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! + {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, + {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, + {"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", }, + {"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", }, + {"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B + {"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", }, + {"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", }, + {"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", }, + {"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", }, + {"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", }, + {"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", }, + {"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", }, + {'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", }, + {'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", }, + {"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", }, + {"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"}, + {"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"}, + {"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"}, + {"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"}, + {"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"}, +] + + +def download_file_with_auth(url, token, save_path): + headers = {"Authorization": f"Bearer {token}"} + response = sess.get(url, headers=headers) + response.raise_for_status() + os.makedirs(os.path.dirname(save_path), exist_ok=True) + with open(save_path, 'wb') as downloaded_file: + downloaded_file.write(response.content) + logger.info(f"File {save_path} downloaded successfully") + + +def download_model(model): + name = model["name"] + repo = model["repo"] + tokt = model["tokt"] + + os.makedirs(f"models/tokenizers/{name}", exist_ok=True) + + files = ["config.json", "tokenizer.json", "tokenizer_config.json"] + + if tokt == TOKENIZER_TYPE.SPM: + files.append("tokenizer.model") + + if tokt == TOKENIZER_TYPE.UGM: + files.append("spiece.model") + + if os.path.isdir(repo): + # If repo is a path on the file system, copy the directory + for file in files: + src_path = os.path.join(repo, file) + dst_path = f"models/tokenizers/{name}/{file}" + if os.path.isfile(dst_path): + logger.info(f"{name}: File {dst_path} already exists - skipping") + continue + if os.path.isfile(src_path): + shutil.copy2(src_path, dst_path) + logger.info(f"{name}: Copied {src_path} to {dst_path}") + else: + logger.warning(f"{name}: Source file {src_path} does not exist") + else: + # If repo is a URL, download the files + for file in files: + save_path = f"models/tokenizers/{name}/{file}" + if os.path.isfile(save_path): + logger.info(f"{name}: File {save_path} already exists - skipping") + continue + download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path) + + +for model in models: + try: + download_model(model) + except Exception as e: + logger.error(f"Failed to download model {model['name']}. Error: {e}") + + +# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function: + +src_ifs = "" +for model in models: + name = model["name"] + tokt = model["tokt"] + + if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM: + continue + + # Skip if the tokenizer folder does not exist or there are other download issues previously + if not os.path.exists(f"models/tokenizers/{name}"): + logger.warning(f"Directory for tokenizer {name} not found. Skipping...") + continue + + # create the tokenizer + try: + if name == "t5": + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False) + else: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except OSError as e: + logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}") + continue # Skip to the next model if the tokenizer can't be loaded + + chktok = tokenizer.encode(CHK_TXT) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + logger.info(f"model: {name}") + logger.info(f"tokt: {tokt}") + logger.info(f"repo: {model['repo']}") + logger.info(f"chktok: {chktok}") + logger.info(f"chkhsh: {chkhsh}") + + # print the "pre_tokenizer" content from the tokenizer.json + with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f: + cfg = json.load(f) + normalizer = cfg["normalizer"] + logger.info("normalizer: " + json.dumps(normalizer, indent=4)) + pre_tokenizer = cfg["pre_tokenizer"] + logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) + if "ignore_merges" in cfg["model"]: + logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4)) + + logger.info("") + + src_ifs += f" if chkhsh == \"{chkhsh}\":\n" + src_ifs += f" # ref: {model['repo']}\n" + src_ifs += f" res = \"{name}\"\n" + +src_func = f""" + def get_vocab_base_pre(self, tokenizer) -> str: + # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that + # is specific for the BPE pre-tokenizer used by the model + # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can + # use in llama.cpp to implement the same pre-tokenizer + + chktxt = {repr(CHK_TXT)} + + chktok = tokenizer.encode(chktxt) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + logger.debug(f"chktok: {{chktok}}") + logger.debug(f"chkhsh: {{chkhsh}}") + + res = None + + # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script + # or pull the latest version of the model from Huggingface + # don't edit the hashes manually! +{src_ifs} + if res is None: + logger.warning("\\n") + logger.warning("**************************************************************************************") + logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") + logger.warning("** There are 2 possible reasons for this:") + logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet") + logger.warning("** - the pre-tokenization config has changed upstream") + logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.") + logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") + logger.warning("**") + logger.warning(f"** chkhsh: {{chkhsh}}") + logger.warning("**************************************************************************************") + logger.warning("\\n") + raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") + + logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}") + logger.debug(f"chkhsh: {{chkhsh}}") + + return res +""" + +convert_py_pth = pathlib.Path("convert_hf_to_gguf.py") +convert_py = convert_py_pth.read_text(encoding="utf-8") +convert_py = re.sub( + r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)", + lambda m: m.group(1) + src_func + m.group(3), + convert_py, + flags=re.DOTALL | re.MULTILINE, +) + +convert_py_pth.write_text(convert_py, encoding="utf-8") + +logger.info("+++ convert_hf_to_gguf.py was updated") + +# generate tests for each tokenizer model + +tests = [ + "ied 4 ½ months", + "Führer", + "", + " ", + " ", + " ", + "\t", + "\n", + "\n\n", + "\n\n\n", + "\t\n", + "Hello world", + " Hello world", + "Hello World", + " Hello World", + " Hello World!", + "Hello, world!", + " Hello, world!", + " this is 🦙.cpp", + "w048 7tuijk dsdfhu", + "нещо на Български", + "កាន់តែពិសេសអាចខលចេញ", + "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", + "Hello", + " Hello", + " Hello", + " Hello", + " Hello", + " Hello\n Hello", + " (", + "\n =", + "' era", + "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", + "!!!!!!", + "3", + "33", + "333", + "3333", + "33333", + "333333", + "3333333", + "33333333", + "333333333", + "Cửa Việt", # llama-bpe fails on this + " discards", + CHK_TXT, +] + +# write the tests to ./models/ggml-vocab-{name}.gguf.inp +# the format is: +# +# test0 +# __ggml_vocab_test__ +# test1 +# __ggml_vocab_test__ +# ... +# + +# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out +# for each test, write the resulting tokens on a separate line + +for model in models: + name = model["name"] + tokt = model["tokt"] + + # Skip if the tokenizer folder does not exist or there are other download issues previously + if not os.path.exists(f"models/tokenizers/{name}"): + logger.warning(f"Directory for tokenizer {name} not found. Skipping...") + continue + + # create the tokenizer + try: + if name == "t5": + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False) + else: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except OSError as e: + logger.error(f"Failed to load tokenizer for model {name}. Error: {e}") + continue # Skip this model and continue with the next one in the loop + + with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f: + for text in tests: + f.write(f"{text}") + f.write("\n__ggml_vocab_test__\n") + + with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f: + for text in tests: + res = tokenizer.encode(text, add_special_tokens=False) + for r in res: + f.write(f" {r}") + f.write("\n") + + logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*") + +# generate commands for creating vocab files + +logger.info("\nRun the following commands to generate the vocab files for testing:\n") + +for model in models: + name = model["name"] + + print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100 + +logger.info("\n") diff --git a/convert-llama-ggml-to-gguf.py b/convert_llama_ggml_to_gguf.py similarity index 87% rename from convert-llama-ggml-to-gguf.py rename to convert_llama_ggml_to_gguf.py index b33108062..29b14e98d 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert_llama_ggml_to_gguf.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import argparse import os import struct @@ -14,6 +15,8 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf +logger = logging.getLogger("ggml-to-gguf") + class GGMLFormat(IntEnum): GGML = 0 @@ -113,7 +116,7 @@ class Tensor: assert quant is not None, 'Unknown tensor type' (blksize, tysize) = quant offset += 12 - self.dtype= dtype + self.dtype= gguf.GGMLQuantizationType(dtype) self.dims = struct.unpack(f'<{n_dims}I', data[offset:offset + (4 * n_dims)]) offset += 4 * n_dims self.name = bytes(data[offset:offset + name_len]) @@ -125,11 +128,14 @@ class Tensor: self.start_offset = offset self.len_bytes = n_bytes offset += n_bytes - # print(n_dims, name_len, dtype, self.dims, self.name, pad) return offset - orig_offset class GGMLModel: + + file_format: GGMLFormat + format_version: int + def __init__(self): self.hyperparameters = None self.vocab = None @@ -175,7 +181,7 @@ class GGMLModel: offset += self.validate_header(data, offset) hp = Hyperparameters() offset += hp.load(data, offset) - print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}') + logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}') self.validate_conversion(hp.ftype) vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML) offset += vocab.load(data, offset, hp.n_vocab) @@ -215,12 +221,12 @@ class GGMLToGGUF: if float(hp.n_head) / float(x) == gqa: n_kv_head = x assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param" - print(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}') + logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}') self.n_kv_head = n_kv_head self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer) def save(self): - print('* Preparing to save GGUF file') + logger.info('* Preparing to save GGUF file') gguf_writer = gguf.GGUFWriter( self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], @@ -230,11 +236,11 @@ class GGMLToGGUF: if self.special_vocab is not None: self.special_vocab.add_to_gguf(gguf_writer) self.add_tensors(gguf_writer) - print(" gguf: write header") + logger.info(" gguf: write header") gguf_writer.write_header_to_file() - print(" gguf: write metadata") + logger.info(" gguf: write metadata") gguf_writer.write_kv_data_to_file() - print(" gguf: write tensors") + logger.info(" gguf: write tensors") gguf_writer.write_tensors_to_file() gguf_writer.close() @@ -250,7 +256,7 @@ class GGMLToGGUF: name = cfg.name if cfg.name is not None else cfg.input.name except UnicodeDecodeError: name = None - print('* Adding model parameters and KV items') + logger.info('* Adding model parameters and KV items') if name is not None: gguf_writer.add_name(name) gguf_writer.add_description(desc) @@ -281,13 +287,14 @@ class GGMLToGGUF: def add_vocab(self, gguf_writer): hp = self.model.hyperparameters gguf_writer.add_tokenizer_model('llama') + gguf_writer.add_tokenizer_pre('default') tokens = [] scores = [] toktypes = [] if self.vocab_override is not None: vo = self.vocab_override - print('* Adding vocab item(s)') - for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()): + logger.info('* Adding vocab item(s)') + for (_, (vbytes, score, ttype)) in enumerate(vo.all_tokens()): tokens.append(vbytes) scores.append(score) toktypes.append(ttype) @@ -298,7 +305,7 @@ class GGMLToGGUF: if len(toktypes) > 0: gguf_writer.add_token_types(toktypes) return - print(f'* Adding {hp.n_vocab} vocab item(s)') + logger.info(f'* Adding {hp.n_vocab} vocab item(s)') assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab' for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items): tt = 1 # Normal @@ -333,7 +340,7 @@ class GGMLToGGUF: def add_tensors(self, gguf_writer): tensor_map = self.name_map data = self.data - print(f'* Adding {len(self.model.tensors)} tensor(s)') + logger.info(f'* Adding {len(self.model.tensors)} tensor(s)') for tensor in self.model.tensors: name = str(tensor.name, 'UTF-8') mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) @@ -343,7 +350,6 @@ class GGMLToGGUF: temp = tempdims[1] tempdims[1] = tempdims[0] tempdims[0] = temp - # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}') gguf_writer.add_tensor( mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], @@ -352,7 +358,8 @@ class GGMLToGGUF: def handle_metadata(cfg, hp): - import convert + import examples.convert_legacy_llama as convert + assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory' hf_config_path = cfg.model_metadata_dir / "config.json" orig_config_path = cfg.model_metadata_dir / "params.json" @@ -373,7 +380,7 @@ def handle_metadata(cfg, hp): raise ValueError('Unable to load metadata') vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir) vocab_factory = convert.VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir) + vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir) convert.check_vocab_size(params, vocab) return params, vocab, special_vocab @@ -398,35 +405,37 @@ def handle_args(): help ='Load HuggingFace/.pth vocab and metadata from the specified directory') parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") - parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm", - help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)") + parser.add_argument("--vocabtype", default="spm,hfft", + help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") return parser.parse_args() def main(): cfg = handle_args() - print(f'* Using config: {cfg}') - print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n') + logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO) + logger.info(f'* Using config: {cfg}') + logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===') if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'): - print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".') + logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".') data = np.memmap(cfg.input, mode = 'r') model = GGMLModel() - print('* Scanning GGML input file') + logger.info('* Scanning GGML input file') offset = model.load(data, 0) # noqa - print(f'* GGML model hyperparameters: {model.hyperparameters}') + logger.info(f'* GGML model hyperparameters: {model.hyperparameters}') vocab_override = None params_override = None special_vocab = None if cfg.model_metadata_dir is not None: (params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters) - print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.') - print(f'* Overriding params: {params_override}') - print(f'* Overriding vocab: {vocab_override}') - print(f'* Special vocab: {special_vocab}') + logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.') + logger.info(f'* Overriding params: {params_override}') + logger.info(f'* Overriding vocab: {vocab_override}') + logger.info(f'* Special vocab: {special_vocab}') else: - print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') + logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') if model.file_format == GGMLFormat.GGML: - print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!') + logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!') converter = GGMLToGGUF( model, data, cfg, params_override = params_override, @@ -434,7 +443,7 @@ def main(): special_vocab = special_vocab ) converter.save() - print(f'* Successful completion. Output saved to: {cfg.output}') + logger.info(f'* Successful completion. Output saved to: {cfg.output}') if __name__ == '__main__': diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py new file mode 100755 index 000000000..6dea14a23 --- /dev/null +++ b/convert_lora_to_gguf.py @@ -0,0 +1,461 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +from dataclasses import dataclass +import logging +import argparse +import os +import sys +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 + +if TYPE_CHECKING: + from torch import Tensor + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) +import gguf + +# reuse model definitions from convert_hf_to_gguf.py +from convert_hf_to_gguf import LazyTorchTensor, Model + +logger = logging.getLogger("lora-to-gguf") + + +@dataclass +class PartialLoraTensor: + A: Tensor | None = None + B: Tensor | None = None + + +# magic to support tensor shape modifications and splitting +class LoraTorchTensor: + _lora_A: Tensor # (n_rank, row_size) + _lora_B: Tensor # (col_size, n_rank) + _rank: int + + def __init__(self, A: Tensor, B: Tensor): + assert len(A.shape) == len(B.shape) + assert A.shape[-2] == B.shape[-1] + if A.dtype != B.dtype: + A = A.to(torch.float32) + B = B.to(torch.float32) + self._lora_A = A + self._lora_B = B + self._rank = B.shape[-1] + + def get_lora_A_B(self) -> tuple[Tensor, Tensor]: + return (self._lora_A, self._lora_B) + + def __getitem__( + self, + indices: ( + SupportsIndex + | slice + | tuple[SupportsIndex | slice | Tensor, ...] # TODO: add ellipsis in the type signature + ), + ) -> LoraTorchTensor: + shape = self.shape + if isinstance(indices, SupportsIndex): + if len(shape) > 2: + return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices]) + else: + raise NotImplementedError # can't return a vector + elif isinstance(indices, slice): + if len(shape) > 2: + return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices]) + else: + return LoraTorchTensor(self._lora_A, self._lora_B[indices]) + elif isinstance(indices, tuple): + assert len(indices) > 0 + if indices[-1] is Ellipsis: + return self[indices[:-1]] + # expand ellipsis + indices = tuple( + u + for v in ( + ( + (slice(None, None) for _ in range(len(indices) - 1)) + if i is Ellipsis + else (i,) + ) + for i in indices + ) + for u in v + ) + + if len(indices) < len(shape): + indices = (*indices, *(slice(None, None) for _ in range(len(indices), len(shape)))) + + # TODO: make sure this is correct + indices_A = ( + *( + ( + j.__index__() % self._lora_A.shape[i] + if isinstance(j, SupportsIndex) + else slice(None, None) + ) + for i, j in enumerate(indices[:-2]) + ), + slice(None, None), + indices[-1], + ) + indices_B = indices[:-1] + return LoraTorchTensor(self._lora_A[indices_A], self._lora_B[indices_B]) + else: + raise NotImplementedError # unknown indice type + + @property + def dtype(self) -> torch.dtype: + assert self._lora_A.dtype == self._lora_B.dtype + return self._lora_A.dtype + + @property + def shape(self) -> tuple[int, ...]: + assert len(self._lora_A.shape) == len(self._lora_B.shape) + return (*self._lora_B.shape[:-1], self._lora_A.shape[-1]) + + def size(self, dim=None): + assert dim is None + return self.shape + + def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor: + if isinstance(shape[0], tuple): + new_shape: tuple[int, ...] = shape[0] + else: + new_shape = cast(tuple[int, ...], shape) + orig_shape = self.shape + if len(new_shape) < 2: + raise NotImplementedError # can't become a vector + + # expand -1 in the shape + if any(dim == -1 for dim in new_shape): + n_elems = prod(orig_shape) + n_new_elems = prod(dim if dim != -1 else 1 for dim in new_shape) + assert n_elems % n_new_elems == 0 + new_shape = (*(dim if dim != -1 else n_elems // n_new_elems for dim in new_shape),) + + if new_shape[-1] != orig_shape[-1]: + raise NotImplementedError # can't reshape the row size trivially + + shape_A = (*(1 for _ in new_shape[:-2]), self._rank, orig_shape[-1]) + shape_B = (*new_shape[:-1], self._rank) + return LoraTorchTensor( + self._lora_A.reshape(shape_A), + self._lora_B.reshape(shape_B), + ) + + def reshape_as(self, other: Tensor) -> LoraTorchTensor: + return self.reshape(*other.shape) + + def view(self, *size: int) -> LoraTorchTensor: + return self.reshape(*size) + + def permute(self, *dims: int) -> LoraTorchTensor: + shape = self.shape + dims = tuple(dim - len(shape) if dim >= 0 else dim for dim in dims) + if dims[-1] == -1: + # TODO: support higher dimensional A shapes bigger than 1 + assert all(dim == 1 for dim in self._lora_A.shape[:-2]) + return LoraTorchTensor(self._lora_A, self._lora_B.permute(*dims)) + if len(shape) == 2 and dims[-1] == -2 and dims[-2] == -1: + return LoraTorchTensor(self._lora_B.permute(*dims), self._lora_A.permute(*dims)) + else: + # TODO: compose the above two + raise NotImplementedError + + def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor: + shape = self.shape + dims = [i for i in range(len(shape))] + dims[dim0], dims[dim1] = dims[dim1], dims[dim0] + return self.permute(*dims) + + def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor: + return self.transpose(axis0, axis1) + + def to(self, *args, **kwargs): + return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs)) + + @classmethod + def __torch_function__(cls, func: Callable, types, args=(), kwargs=None): + del types # unused + + if kwargs is None: + kwargs = {} + + if func is torch.permute: + return type(args[0]).permute(*args, **kwargs) + elif func is torch.reshape: + return type(args[0]).reshape(*args, **kwargs) + elif func is torch.stack: + assert isinstance(args[0], Sequence) + dim = kwargs.get("dim", 0) + assert dim == 0 + return LoraTorchTensor( + torch.stack([a._lora_A for a in args[0]], dim), + torch.stack([b._lora_B for b in args[0]], dim), + ) + elif func is torch.cat: + assert isinstance(args[0], Sequence) + dim = kwargs.get("dim", 0) + assert dim == 0 + if len(args[0][0].shape) > 2: + return LoraTorchTensor( + torch.cat([a._lora_A for a in args[0]], dim), + torch.cat([b._lora_B for b in args[0]], dim), + ) + elif all(torch.equal(args[0][0]._lora_A, t._lora_A) for t in args[0][1:]): + return LoraTorchTensor( + args[0][0]._lora_A, + torch.cat([b._lora_B for b in args[0]], dim), + ) + else: + raise NotImplementedError + else: + raise NotImplementedError + + +def get_base_tensor_name(lora_tensor_name: str) -> str: + base_name = lora_tensor_name.replace("base_model.model.", "") + base_name = base_name.replace(".lora_A.weight", ".weight") + base_name = base_name.replace(".lora_B.weight", ".weight") + # models produced by mergekit-extract-lora have token embeddings in the adapter + base_name = base_name.replace(".lora_embedding_A", ".weight") + base_name = base_name.replace(".lora_embedding_B", ".weight") + return base_name + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Convert a Hugging Face PEFT LoRA adapter to a GGUF file") + parser.add_argument( + "--outfile", type=Path, + help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", + ) + parser.add_argument( + "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16", + help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", + ) + parser.add_argument( + "--bigendian", action="store_true", + help="model is executed on big endian machine", + ) + parser.add_argument( + "--no-lazy", action="store_true", + help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)", + ) + parser.add_argument( + "--verbose", action="store_true", + help="increase output verbosity", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="only print out what will be done, without writing any new files", + ) + parser.add_argument( + "--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( + "--base-model-id", type=str, + help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')", + ) + parser.add_argument( + "lora_path", type=Path, + help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)", + ) + + 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) + + ftype_map: dict[str, gguf.LlamaFileType] = { + "f32": gguf.LlamaFileType.ALL_F32, + "f16": gguf.LlamaFileType.MOSTLY_F16, + "bf16": gguf.LlamaFileType.MOSTLY_BF16, + "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0, + "auto": gguf.LlamaFileType.GUESSED, + } + + ftype = ftype_map[args.outtype] + + dir_base_model: Path | None = args.base + dir_lora: Path = args.lora_path + base_model_id: str | None = args.base_model_id + lora_config = dir_lora / "adapter_config.json" + input_model = dir_lora / "adapter_model.safetensors" + + if args.outfile is not None: + fname_out = args.outfile + else: + # output in the same directory as the model by default + fname_out = dir_lora + + if os.path.exists(input_model): + # lazy import load_file only if lora is in safetensors format. + from safetensors.torch import load_file + + lora_model = load_file(input_model, device="cpu") + else: + 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 + if base_model_id is not None: + logger.info(f"Loading base model from Hugging Face: {base_model_id}") + hparams = load_hparams_from_hf(base_model_id) + elif 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]) + except NotImplementedError: + logger.error(f"Model {hparams['architectures'][0]} is not supported") + sys.exit(1) + + class LoraModel(model_class): + model_arch = model_class.model_arch + + lora_alpha: float + + def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs): + + super().__init__(*args, **kwargs) + + 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) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + # Never add extra tensors (e.g. rope_freqs) for LoRA adapters + return () + + def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + tensor_map: dict[str, PartialLoraTensor] = {} + + for name, tensor in lora_model.items(): + if self.lazy: + tensor = LazyTorchTensor.from_eager(tensor) + base_name = get_base_tensor_name(name) + # note: mergekit-extract-lora also adds token embeddings to the adapter + is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name + is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name + if not is_lora_a and not is_lora_b: + if ".base_layer.weight" in name: + continue + # mergekit-extract-lora add these layernorm to the adapter, we need to keep them + if "_layernorm" in name or ".norm" in name: + yield (base_name, tensor) + continue + 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("Please refer to https://github.com/ggerganov/llama.cpp/pull/9948") + sys.exit(1) + + if base_name in tensor_map: + if is_lora_a: + tensor_map[base_name].A = tensor + else: + tensor_map[base_name].B = tensor + else: + if is_lora_a: + tensor_map[base_name] = PartialLoraTensor(A=tensor) + else: + tensor_map[base_name] = PartialLoraTensor(B=tensor) + + for name, tensor in tensor_map.items(): + assert tensor.A is not None + assert tensor.B is not None + yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B))) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + dest = list(super().modify_tensors(data_torch, name, bid)) + # some archs may have the same tensor for lm_head and output (tie word embeddings) + # in this case, adapters targeting lm_head will fail when using llama-export-lora + # therefore, we ignore them for now + # see: https://github.com/ggerganov/llama.cpp/issues/9065 + if name == "lm_head.weight" and len(dest) == 0: + raise ValueError("lm_head is present in adapter, but is ignored in base model") + for dest_name, dest_data in dest: + # mergekit-extract-lora add these layernorm to the adapter + if "_norm" in dest_name: + assert dest_data.dim() == 1 + yield (dest_name, dest_data) + continue + + # otherwise, we must get the lora_A and lora_B tensors + assert isinstance(dest_data, LoraTorchTensor) + lora_a, lora_b = dest_data.get_lora_A_B() + + # note: mergekit-extract-lora flip and transpose A and B + # here we only need to transpose token_embd.lora_a, see llm_build_inp_embd() + if "token_embd.weight" in dest_name: + lora_a = lora_a.T + + yield (dest_name + ".lora_a", lora_a) + yield (dest_name + ".lora_b", lora_b) + + alpha: float = lparams["lora_alpha"] + + model_instance = LoraModel( + dir_base_model, + ftype, + fname_out, + is_big_endian=args.bigendian, + use_temp_file=False, + eager=args.no_lazy, + dry_run=args.dry_run, + dir_lora_model=dir_lora, + lora_alpha=alpha, + hparams=hparams, + ) + + logger.info("Exporting model...") + model_instance.write() + logger.info(f"Model successfully exported to {model_instance.fname_out}") diff --git a/docs/android.md b/docs/android.md new file mode 100644 index 000000000..47530c6c1 --- /dev/null +++ b/docs/android.md @@ -0,0 +1,83 @@ + +# Android + +## Build on Android using Termux + +[Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid. + +With Termux, you can install and run `llama.cpp` as if the environment were Linux. Once in the Termux shell: + +``` +$ apt update && apt upgrade -y +$ apt install git cmake +``` + +Then, follow the [build instructions](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md), specifically for CMake. + +Once the binaries are built, download your model of choice (e.g., from Hugging Face). It's recommended to place it in the `~/` directory for best performance: + +``` +$ 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-cli -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" +``` + +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: + +https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 + +## Cross-compile using Android NDK +It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.) + +Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory: + +``` +$ cmake \ + -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ + -DANDROID_ABI=arm64-v8a \ + -DANDROID_PLATFORM=android-28 \ + -DCMAKE_C_FLAGS="-march=armv8.7a" \ + -DCMAKE_CXX_FLAGS="-march=armv8.7a" \ + -DGGML_OPENMP=OFF \ + -DGGML_LLAMAFILE=OFF \ + -B build-android +``` + +Notes: + - While later versions of Android NDK ship with OpenMP, it must still be installed by CMake as a dependency, which is not supported at this time + - `llamafile` does not appear to support Android devices (see: https://github.com/Mozilla-Ocho/llamafile/issues/325) + +The above command should configure `llama.cpp` with the most performant options for modern devices. Even if your device is not running `armv8.7a`, `llama.cpp` includes runtime checks for available CPU features it can use. + +Feel free to adjust the Android ABI for your target. Once the project is configured: + +``` +$ cmake --build build-android --config Release -j{n} +$ cmake --install build-android --prefix {install-dir} --config Release +``` + +After installing, go ahead and download the model of your choice to your host system. Then: + +``` +$ adb shell "mkdir /data/local/tmp/llama.cpp" +$ adb push {install-dir} /data/local/tmp/llama.cpp/ +$ adb push {model}.gguf /data/local/tmp/llama.cpp/ +$ adb shell +``` + +In the `adb shell`: + +``` +$ cd /data/local/tmp/llama.cpp +$ LD_LIBRARY_PATH=lib ./bin/llama-simple -m {model}.gguf -c {context-size} -p "{your-prompt}" +``` + +That's it! + +Be aware that Android will not find the library path `lib` on its own, so we must specify `LD_LIBRARY_PATH` in order to run the installed executables. Android does support `RPATH` in later API levels, so this could change in the future. Refer to the previous section for information about `context-size` (very important!) and running other `examples`. diff --git a/docs/BLIS.md b/docs/backend/BLIS.md similarity index 91% rename from docs/BLIS.md rename to docs/backend/BLIS.md index 0bcd6eeef..904548577 100644 --- a/docs/BLIS.md +++ b/docs/backend/BLIS.md @@ -23,23 +23,16 @@ Install BLIS: sudo make install ``` -We recommend using openmp since it's easier to modify the cores been used. +We recommend using openmp since it's easier to modify the cores being used. ### llama.cpp compilation -Makefile: - -```bash -make LLAMA_BLIS=1 -j -# make LLAMA_BLIS=1 benchmark-matmult -``` - CMake: ```bash mkdir build cd build -cmake -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=FLAME .. +cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME .. make -j ``` diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md new file mode 100644 index 000000000..23f10175a --- /dev/null +++ b/docs/backend/CANN.md @@ -0,0 +1,263 @@ +# llama.cpp for CANN + + - [Background](#background) + - [News](#news) + - [OS](#os) + - [Hardware](#hardware) + - [Model Supports](#model-supports) + - [DataType Supports](#datatype-supports) + - [Docker](#docker) + - [Linux](#linux) + - [TODO](#todo) + + +## Background + +**Ascend NPU** is a range of AI processors using Neural Processing Unit. It will efficiently handle matrix-matrix multiplication, dot-product and scalars. + +**CANN** (Compute Architecture for Neural Networks) is a heterogeneous computing architecture for AI scenarios, providing support for multiple AI frameworks on the top and serving AI processors and programming at the bottom. It plays a crucial role in bridging the gap between upper and lower layers, and is a key platform for improving the computing efficiency of Ascend AI processors. Meanwhile, it offers a highly efficient and easy-to-use programming interface for diverse application scenarios, allowing users to rapidly build AI applications and services based on the Ascend platform. + +**Llama.cpp + CANN** + +The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the ability of AscendC and ACLNN which are intergrated to CANN Toolkit and kernels to using Ascend NPU directly. + +## 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 + - Create CANN backend for Ascend NPU. + +## OS + +| OS | Status | Verified | +|:-------:|:-------:|:----------------------------------------------:| +| Linux | Support | Ubuntu 22.04, OpenEuler22.03 | + + +## Hardware + +### Ascend NPU + +**Verified devices** + +| Ascend NPU | Status | +|:-----------------------------:|:-------:| +| Atlas 300T A2 | Support | +| Atlas 300I Duo | Support | + +*Notes:* + +- If you have trouble with Ascend NPU device, please create a issue with **[CANN]** prefix/tag. +- If you run successfully with your Ascend NPU device, please help update the upper table. + + +## Model Supports + +| Model Name | FP16 | Q8_0 | Q4_0 | +|:----------------------------|:-----:|:----:|:----:| +| AquilaChat2-7B | √ | √ | √ | +| Baichuan-7b | √ | √ | √ | +| Baichuan2-7B-Chat | √ | √ | √ | +| bitnet_b1_58-large | √ | √ | √ | +| bloom-560m | √ | x | √ | +| bloomz-alpaca-560m | √ | x | √ | +| c4ai-command-r-35B-v01 | x | x | x | +| chatglm3-6B | x | x | x | +| chinese-alpaca-2-1.3b | √ | √ | √ | +| CodeShell-7B | √ | √ | √ | +| deepseek-ai_deepseek-coder-1.3B-base | x | x | x | +| deepseek-ai_DeepSeek-V2-Lite | x | x | x | +| deepseek-coder-6.7B-instruct | x | x | x | +| DeepSeek-V2-Lite-64x1.5B | x | x | x | +| falcon-7b-instruct | √ | √ | √ | +| flan-t5-large | √ | √ | √ | +| gemma-2-9b-it | √ | √ | √ | +| glm-4-9B | x | x | x | +| gpt2 | √ | √ | √ | +| Gpt2-163M | √ | √ | √ | +| granite-3B-code-instruct | √ | √ | √ | +| GritLM-7B | √ | √ | √ | +| internlm2_5-7b-chat | √ | √ | √ | +| koala-7B-HF | √ | √ | √ | +| Llama-2-7b-chat-hf | √ | √ | √ | +| Llama-3-Smaug-8B | √ | √ | √ | +| Llama2-Chinese-7b-Chat | √ | √ | √ | +| Llama3-8B | √ | √ | √ | +| Llama3-8b-chinese | √ | √ | √ | +| mamba-130m-hf | √ | √ | √ | +| Mistral-7B-Instruct-v0.2 | √ | √ | √ | +| Mixtral-8x7B-Instruct-v0.1 | x | √ | √ | +| mpt-7B | √ | √ | √ | +| OLMo-1B-hf | √ | √ | √ | +| OpenELM-3B-Instruct | √ | √ | √ | +| Orion-14b-base | √ | √ | √ | +| phi1 | x | x | x | +| phi2 | x | x | x | +| Phi-3-mini-4k-instruct | √ | √ | √ | +| plamo-13b | √ | √ | √ | +| pythia-70M | x | x | x | +| Qwen-7B | √ | √ | √ | +| Qwen2-1.5B-Instruct | √ | x | √ | +| Refact-1_6B-fim | √ | √ | √ | +| SmolLM-135M | √ | √ | √ | +| stablelm-zephyr | x | x | x | +| stablelm-2-zephyr-1_6b | x | x | x | +| starcoderbase-1b | √ | √ | √ | +| starcoder2-3b | √ | √ | √ | +| vigogne-7b-chat | √ | √ | √ | +| xverse-7b-chat | √ | √ | √ | +| Yi-6b-Chat | √ | √ | √ | + + + +## DataType Supports + +| DataType | Status | +|:----------------------:|:-------:| +| FP16 | Support | +| Q8_0 | Support | +| Q4_0 | Support | + +## Docker + +### Build Images +You can get a image with llama.cpp in one command. +```sh +docker build -t llama-cpp-cann -f .devops/llama-cli-cann.Dockerfile . +``` + +### Run container + +```sh +# Find all cards. +npu-smi info + +# Select the cards that you want to use, make sure these cards are not used by someone. +# Following using cards of device0. +docker run --name llamacpp --device /dev/davinci0 --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc -v /usr/local/dcmi:/usr/local/dcmi -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info -v /PATH_TO_YOUR_MODELS/:/app/models -it llama-cpp-cann -m /app/models/MODEL_PATH -ngl 32 -p "Building a website can be done in 10 simple steps:" +``` + +*Notes:* + +- You may need to install Ascend Driver and firmware on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*. + +## Linux + +### I. Setup Environment + +1. **Install Ascend Driver and firmware** + + ```sh + # create driver running user. + sudo groupadd -g HwHiAiUser + sudo useradd -g HwHiAiUser -d /home/HwHiAiUser -m HwHiAiUser -s /bin/bash + sudo usermod -aG HwHiAiUser $USER + + # download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system + # and install driver. + sudo sh Ascend-hdk-910b-npu-driver_x.x.x_linux-{arch}.run --full --install-for-all + ``` + + Once installed, run `npu-smi info` to check whether driver is installed successfully. + ```sh + +-------------------------------------------------------------------------------------------+ + | npu-smi 24.1.rc2 Version: 24.1.rc2 | + +----------------------+---------------+----------------------------------------------------+ + | NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page)| + | Chip | Bus-Id | AICore(%) Memory-Usage(MB) HBM-Usage(MB) | + +======================+===============+====================================================+ + | 2 xxx | OK | 64.4 51 15 / 15 | + | 0 | 0000:01:00.0 | 0 1873 / 15077 0 / 32768 | + +======================+===============+====================================================+ + | 5 xxx | OK | 64.0 52 15 / 15 | + | 0 | 0000:81:00.0 | 0 1874 / 15077 0 / 32768 | + +======================+===============+====================================================+ + | No running processes found in NPU 2 | + +======================+===============+====================================================+ + | No running processes found in NPU 5 | + +======================+===============+====================================================+ + ``` + +2. **Install Ascend Firmware** + ```sh + # download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system + # and install driver. + sudo sh Ascend-hdk-910b-npu-firmware_x.x.x.x.X.run --full + ``` + If the following messaage appers, firmware is installed successfully. + ```sh + Firmware package installed successfully! + ``` + + +3. **Install CANN toolkit and kernels** + + CANN toolkit and kernels can be obtained from the official [CANN Toolkit](https://www.hiascend.com/zh/developer/download/community/result?module=cann) page. + + Please download the corresponding version that satified your system. The minimum version required is 8.0.RC2.alpha002 and here is the install command. + ```sh + pip3 install attrs numpy decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions + sh Ascend-cann-toolkit_8.0.RC2.alpha002_linux-aarch64.run --install + sh Ascend-cann-kernels-910b_8.0.RC2.alpha002_linux.run --install + ``` + + Set Ascend Variables: + ```sh + echo "source ~/Ascend/ascend-toolkit/set_env.sh" >> ~/.bashrc + source ~/.bashrc + ``` + +Upon a successful installation, CANN is enabled for the available ascend devices. + +### II. Build llama.cpp + +```sh +cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release +cmake --build build --config release +``` + +### III. Run the inference + +1. **Retrieve and prepare model** + + You can refer to the general [*Prepare and Quantize*](../../README.md#prepare-and-quantize) guide for model prepration. + + **Notes**: + + - CANN backend only supports FP16/Q4_0/Q8_0 models currently. + +2. **Launch inference** + + There are two device selection modes: + + - Single device: Use one device target specified by the user. + - Multiple devices: Automatically choose the devices with the same backend. + + | Device selection | Parameter | + |:----------------:|:--------------------------------------:| + | Single device | --split-mode none --main-gpu DEVICE_ID | + | Multiple devices | --split-mode layer (default) | + + Examples: + + - Use device 0: + + ```sh + ./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0 + ``` + + - Use multiple devices: + + ```sh + ./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer + ``` + +### **GitHub contribution**: +Please add the **[CANN]** prefix/tag in issues/PRs titles to help the CANN-team check/address them without delay. + + +## TODO +- Support more models and data types. diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md new file mode 100644 index 000000000..89ddbd669 --- /dev/null +++ b/docs/backend/SYCL.md @@ -0,0 +1,714 @@ +# llama.cpp for SYCL + +- [Background](#background) +- [Recommended Release](#recommended-release) +- [News](#news) +- [OS](#os) +- [Hardware](#hardware) +- [Docker](#docker) +- [Linux](#linux) +- [Windows](#windows) +- [Environment Variable](#environment-variable) +- [Known Issue](#known-issues) +- [Q&A](#qa) +- [TODO](#todo) + +## Background + +**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17. + +**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include: + +- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers. +- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL and oneDNN)*. +- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs. +- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets. + +### Llama.cpp + SYCL + +The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it also supports other vendor GPUs: Nvidia and AMD. + +## Recommended Release + +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| 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. + +- 2024.5 + - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770. + - Arch Linux is verified successfully. + +- 2024.4 + - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M. + +- 2024.3 + - Release binary files of Windows. + - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd). + - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437). + - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing. + - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE. + - Support detecting all GPUs with level-zero and same top **Max compute units**. + - Support OPs + - hardsigmoid + - hardswish + - pool2d + +- 2024.1 + - Create SYCL backend for Intel GPU. + - Support Windows build + +## OS + +| OS | Status | Verified | +|---------|---------|------------------------------------------------| +| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux | +| Windows | Support | Windows 11 | + + +## Hardware + +### Intel GPU + +SYCL backend supports Intel GPU Family: + +- Intel Data Center Max Series +- Intel Flex Series, Arc Series +- Intel Built-in Arc GPU +- Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)). + +#### Verified devices + +| Intel GPU | Status | Verified Model | +|-------------------------------|---------|---------------------------------------| +| Intel Data Center Max Series | Support | Max 1550, 1100 | +| Intel Data Center Flex Series | Support | Flex 170 | +| Intel Arc Series | Support | Arc 770, 730M, Arc A750 | +| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | +| Intel iGPU | Support | iGPU in 13700k, i5-1250P, i7-1260P, i7-1165G7 | + +*Notes:* + +- **Memory** + - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`. + + - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU. + +- **Execution Unit (EU)** + - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use. + +### Other Vendor GPU + +**Verified devices** + +| Nvidia GPU | Status | Verified Model | +|--------------------------|-----------|----------------| +| Ampere Series | Supported | A100, A4000 | +| Ampere Series *(Mobile)* | Supported | RTX 40 Series | + +| AMD GPU | Status | Verified Model | +|--------------------------|--------------|----------------| +| Radeon Pro | Experimental | W6800 | +| Radeon RX | Experimental | 6700 XT | + +Note: AMD GPU support is highly experimental and is incompatible with F16. +Additionally, it only supports GPUs with a sub_group_size (warp size) of 32. + +## Docker +The docker build option is currently limited to *intel GPU* targets. + +### Build image +```sh +# Using FP16 +docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile . +``` + +*Notes*: + +To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from the previous command. + +You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative. + +### Run container + +```sh +# First, find all the DRI cards +ls -la /dev/dri +# Then, pick the card that you want to use (here for e.g. /dev/dri/card1). +docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +``` + +*Notes:* +- Docker has been tested successfully on native Linux. WSL support has not been verified yet. +- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*. + +## Linux + +### I. Setup Environment + +1. **Install GPU drivers** + + - **Intel GPU** + +Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps). + +*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html). + +Once installed, add the user(s) to the `video` and `render` groups. + +```sh +sudo usermod -aG render $USER +sudo usermod -aG video $USER +``` + +*Note*: logout/re-login for the changes to take effect. + +Verify installation through `clinfo`: + +```sh +sudo apt install clinfo +sudo clinfo -l +``` + +Sample output: + +```sh +Platform #0: Intel(R) OpenCL Graphics + `-- Device #0: Intel(R) Arc(TM) A770 Graphics + +Platform #0: Intel(R) OpenCL HD Graphics + `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49] +``` + +- **Nvidia GPU** + +In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed. + +- **AMD GPU** + +To target AMD GPUs with SYCL, the ROCm stack must be installed first. + +2. **Install Intel® oneAPI Base toolkit** + +- **For Intel GPU** + +The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page. + +Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*. + +Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable. + +Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs. + +- **Adding support to Nvidia GPUs** + +**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup. + + +**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs. + +```sh +git clone https://github.com/oneapi-src/oneMKL +cd oneMKL +cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas +cmake --build buildWithCublas --config Release +``` + +- **Adding support to AMD GPUs** + +**oneAPI Plugin**: In order to enable SYCL support on AMD GPUs, please install the [Codeplay oneAPI Plugin for AMD GPUs](https://developer.codeplay.com/products/oneapi/amd/download). As with Nvidia GPUs, the user should also make sure the plugin version matches the installed base toolkit. + +**oneMKL for rocBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* doesn't contain the rocBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *rocBLAS* backend enabled is thus required to run it on AMD GPUs. + +```sh +git clone https://github.com/oneapi-src/oneMKL +cd oneMKL +# Find your HIPTARGET with rocminfo, under the key 'Name:' +cmake -B buildWithrocBLAS -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_ROCBLAS_BACKEND=ON -DHIPTARGETS=${HIPTARGET} -DTARGET_DOMAINS=blas +cmake --build buildWithrocBLAS --config Release +``` + +3. **Verify installation and environment** + +In order to check the available SYCL devices on the machine, please use the `sycl-ls` command. +```sh +source /opt/intel/oneapi/setvars.sh +sycl-ls +``` + +- **Intel GPU** + +When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`level_zero:gpu`] in the sample output below: + +``` +[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu][opencl:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50] +[level_zero:gpu][level_zero:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918] +``` + +- **Nvidia GPU** + +Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`cuda:gpu`] as below: + +``` +[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix] +[opencl:cpu][opencl:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix] +[cuda:gpu][cuda:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.5] +``` + +- **AMD GPU** + +For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]: + +``` +[opencl:cpu][opencl:0] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900K OpenCL 3.0 (Build 0) [2024.18.6.0.02_160000] +[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9] +``` + +### II. Build llama.cpp + +#### Intel GPU + +``` +./examples/sycl/build.sh +``` + +or + +```sh +# Export relevant ENV variables +source /opt/intel/oneapi/setvars.sh + +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +# Option 2: Use FP16 +cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON + +# build all binary +cmake --build build --config Release -j -v +``` + +#### Nvidia GPU + +```sh +# Export relevant ENV variables +export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LD_LIBRARY_PATH +export LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LIBRARY_PATH +export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_DIR +export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$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 -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +# Option 2: Use FP16 +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${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 +``` + +#### AMD GPU + +```sh +# Export relevant ENV variables +export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithrocBLAS/lib:$LD_LIBRARY_PATH +export LIBRARY_PATH=/path/to/oneMKL/buildWithrocBLAS/lib:$LIBRARY_PATH +export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithrocBLAS/include:$CPLUS_INCLUDE_DIR + +# Build LLAMA with rocBLAS acceleration through SYCL + +## AMD +# Use FP32, FP16 is not supported +# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:' +GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +# build all binary +cmake --build build --config Release -j -v +``` + +### III. Run the inference + +#### Retrieve and prepare model + +You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example. + +##### Check device + +1. Enable oneAPI running environment + +```sh +source /opt/intel/oneapi/setvars.sh +``` + +2. List devices information + +Similar to the native `sycl-ls`, available SYCL devices can be queried as follow: + +```sh +./build/bin/llama-ls-sycl-device +``` + +This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following: +``` +found 2 SYCL devices: + +| | | |Compute |Max compute|Max work|Max sub| | +|ID| Device Type| Name|capability|units |group |group |Global mem size| +|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------| +| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136| +| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| +``` + +#### Choose level-zero devices + +|Chosen Device ID|Setting| +|-|-| +|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"`| + +#### Execute + +Choose one of following methods to run. + +1. Script + +- Use device 0: + +```sh +./examples/sycl/run-llama2.sh 0 +``` +- Use multiple devices: + +```sh +./examples/sycl/run-llama2.sh +``` + +2. Command line +Launch inference + +There are two device selection modes: + +- Single device: Use one device assigned by user. Default device id is 0. +- Multiple devices: Automatically choose the devices with the same backend. + +In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR. + +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | + +Examples: + +- Use device 0: + +```sh +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0 +``` + +- Use multiple devices: + +```sh +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer +``` + +*Notes:* + +- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: + +```sh +detect 1 SYCL GPUs: [0] with top Max compute units:512 +``` +Or +```sh +use 1 SYCL GPUs: [0] with Max compute units:512 +``` + +## Windows + +### I. Setup Environment + +1. Install GPU driver + +Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). + +2. Install Visual Studio + +If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/). + +3. Install Intel® oneAPI Base toolkit + +The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page. + +Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*. + +Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable. + +b. Enable oneAPI running environment: + +- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App. + +- On the command prompt, enable the runtime environment with the following: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +c. Verify installation + +In the oneAPI command line, run the following to print the available SYCL devices: + +``` +sycl-ls.exe +``` + +There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device: + +Output (example): +``` +[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] +[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] +``` + +4. Install build tools + +a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer) +b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/) + + +### II. Build llama.cpp + +You could download the release package for Windows directly, which including binary files and depended oneAPI dll files. + +Choose one of following methods to build from source code. + +1. Script + +```sh +.\examples\sycl\win-build-sycl.bat +``` + +2. CMake + +On the oneAPI command line window, step into the llama.cpp main directory and run the following: + +``` +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + +# Option 2: Or FP16 +cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON + +cmake --build build --config Release -j +``` + +Or, use CMake presets to build: + +```sh +cmake --preset x64-windows-sycl-release +cmake --build build-x64-windows-sycl-release -j --target llama-cli + +cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release +cmake --build build-x64-windows-sycl-release -j --target llama-cli + +cmake --preset x64-windows-sycl-debug +cmake --build build-x64-windows-sycl-debug -j --target llama-cli +``` + +3. Visual Studio + +You can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project. + +*Notes:* + +- In case of a minimal experimental setup, the user can build the inference executable only through `cmake --build build --config Release -j --target llama-cli`. + +### III. Run the inference + +#### Retrieve and prepare model + +You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example. + +##### Check device + +1. Enable oneAPI running environment + +On the oneAPI command line window, run the following and step into the llama.cpp directory: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +2. List devices information + +Similar to the native `sycl-ls`, available SYCL devices can be queried as follow: + +``` +build\bin\llama-ls-sycl-device.exe +``` + +This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following: +``` +found 2 SYCL devices: +| | | |Compute |Max compute|Max work|Max sub| | +|ID| Device Type| Name|capability|units |group |group |Global mem size| +|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------| +| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136| +| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| + +``` +#### Choose level-zero devices + +|Chosen Device ID|Setting| +|-|-| +|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action| +|1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`| +|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`| + +#### Execute + +Choose one of following methods to run. + +1. Script + +``` +examples\sycl\win-run-llama2.bat +``` + +2. Command line + +Launch inference + +There are two device selection modes: + +- Single device: Use one device assigned by user. Default device id is 0. +- Multiple devices: Automatically choose the devices with the same backend. + +In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR. + +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | + +Examples: + +- Use device 0: + +``` +build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0 +``` + +- Use multiple devices: + +``` +build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer +``` + + +Note: + +- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: + +```sh +detect 1 SYCL GPUs: [0] with top Max compute units:512 +``` +Or +```sh +use 1 SYCL GPUs: [0] with Max compute units:512 +``` + + +## Environment Variable + +#### Build + +| Name | Value | Function | +|--------------------|---------------------------------------|---------------------------------------------| +| 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. | + +#### Runtime + +| Name | Value | Function | +|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| +| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG | +| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer | + +## Known Issues + +- `Split-mode:[row]` is not supported. + +## Q&A + +- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. + + - Potential cause: Unavailable oneAPI installation or not set ENV variables. + - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`. + +- General compiler error: + + - Remove **build** folder or try a clean-build. + +- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux. + + Please double-check with `sudo sycl-ls`. + + If it's present in the list, please add video/render group to your user then **logout/login** or restart your system: + + ``` + sudo usermod -aG render $USER + sudo usermod -aG video $USER + ``` + Otherwise, please double-check the GPU driver installation steps. + +- Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend? + + No. We can't support Ollama issue directly, because we aren't familiar with Ollama. + + Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport it. + + It's same for other projects including llama.cpp SYCL backend. + +- Meet issue: `Native API failed. Native API returns: -6 (PI_ERROR_OUT_OF_HOST_MEMORY) -6 (PI_ERROR_OUT_OF_HOST_MEMORY) -999 (UNKNOWN PI error)` or `failed to allocate SYCL0 buffer` + + Device Memory is not enough. + + |Reason|Solution| + |-|-| + |Default Context is too big. It leads to more memory usage.|Set `-c 8192` or smaller value.| + |Model is big and require more memory than device's.|Choose smaller quantized model, like Q5 -> Q4;
Use more than one devices to load model.| + +### **GitHub contribution**: +Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay. + +## TODO + +- NA diff --git a/docs/build.md b/docs/build.md new file mode 100644 index 000000000..afb7a0402 --- /dev/null +++ b/docs/build.md @@ -0,0 +1,408 @@ +# Build llama.cpp locally + +**To get the Code:** + +```bash +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +``` + +The following sections describe how to build with different backends and options. + +## CPU Build + +Build llama.cpp using `CMake`: + +```bash +cmake -B build +cmake --build build --config Release +``` + +**Notes**: + +- 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: + + 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 + ``` + + 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 + ``` + +- 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_N_M CPU kernels. + + For building with ninja generator and clang compiler as default: + -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64 + ```bash + cmake --preset x64-windows-llvm-release + cmake --build build-x64-windows-llvm-release + ``` + +## 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). Using BLAS doesn't affect the generation performance. There are currently several different BLAS implementations available for build and use: + +### Accelerate Framework + +This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions. + +### OpenBLAS + +This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine. + +- Using `CMake` on Linux: + + ```bash + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS + cmake --build build --config Release + ``` + +### BLIS + +Check [BLIS.md](./backend/BLIS.md) for more information. + +### 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). + +- Using manual oneAPI installation: + By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: + ```bash + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation + cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON + cmake --build build --config Release + ``` + +- Using oneAPI docker image: + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. + +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. + +### Other BLAS libraries + +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. + +## 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](https://developer.nvidia.com/cuda-toolkit) installed. + +#### Download directly from NVIDIA +You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads). + + +#### Compile and run inside a Fedora Toolbox Container +We also have a [guide](./cuda-fedora.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/). + +**Recommended for:** + +- ***Particularly*** *convenient* for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/). +- Toolbox is installed by default: [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde). +- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download) + + +### Compilation +```bash +cmake -B build -DGGML_CUDA=ON +cmake --build build --config Release +``` + +### Override Compute Capability Specifications + +If `nvcc` cannot detect your gpu, you may get compile-warnings such as: + ```text +nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used +``` + +To override the `native` GPU detection: + +#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus). + +```text +GeForce RTX 4090 8.9 +GeForce RTX 3080 Ti 8.6 +GeForce RTX 3070 8.6 +``` + +#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list. + +```bash +cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89" +``` + +### Runtime CUDA environmental variables + +You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime. + +```bash +# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device. +CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf +``` + +### Unified Memory + +The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`. + +### Performance Tuning + +The following compilation options are also available to tweak performance: + +| Option | Legal values | Default | Description | +|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| 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_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 + +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 `CMake`: + + ```bash + cmake -B build -DGGML_MUSA=ON + cmake --build build --config Release + ``` + +The environment variable [`MUSA_VISIBLE_DEVICES`](https://docs.mthreads.com/musa-sdk/musa-sdk-doc-online/programming_guide/Z%E9%99%84%E5%BD%95/) can be used to specify which GPU(s) will be used. + +The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. + +Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet. + +## HIP + +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 `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build --config Release -- -j 16 + ``` + On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`. + However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). + + Note that if you get the following error: + ``` + clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library + ``` + Try searching for a directory under `HIP_PATH` that contains the file + `oclc_abi_version_400.bc`. Then, add the following to the start of the + command: `HIP_DEVICE_LIB_PATH=`, so something + like: + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ + HIP_DEVICE_LIB_PATH= \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build -- -j 16 + ``` + +- 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_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) + Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`. + + +The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. +If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. + +## Vulkan + +**Windows** + +### w64devkit + +Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases). + +Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings. + +Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies: +```sh +SDK_VERSION=1.3.283.0 +cp /VulkanSDK/$SDK_VERSION/Bin/glslc.exe $W64DEVKIT_HOME/bin/ +cp /VulkanSDK/$SDK_VERSION/Lib/vulkan-1.lib $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/ +cp -r /VulkanSDK/$SDK_VERSION/Include/* $W64DEVKIT_HOME/x86_64-w64-mingw32/include/ +cat > $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/pkgconfig/vulkan.pc <= 8.5`, `Arch Linux`, and `Ubuntu`. + +## Table of Contents + +- [Prerequisites](#prerequisites) +- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository) +- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment) +- [Installing Essential Development Tools](#installing-essential-development-tools) +- [Adding the CUDA Repository](#adding-the-cuda-repository) +- [Installing `nvidia-driver-libs`](#installing-nvidia-driver-libs) +- [Manually Resolving Package Conflicts](#manually-resolving-package-conflicts) +- [Finalizing the Installation of `nvidia-driver-libs`](#finalizing-the-installation-of-nvidia-driver-libs) +- [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package) +- [Configuring the Environment](#configuring-the-environment) +- [Verifying the Installation](#verifying-the-installation) +- [Conclusion](#conclusion) +- [Troubleshooting](#troubleshooting) +- [Additional Notes](#additional-notes) +- [References](#references) + +## Prerequisites + +- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/). +- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA). +- **Internet connectivity** to download packages. + +### Using the Fedora 41 CUDA Repository + +The latest release is 41. + +- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/) + +**Note:** We recommend using a toolbox environment to prevent system conflicts. + +## Creating a Fedora Toolbox Environment + +This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system. + +**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker. + +1. **Create a Fedora 41 Toolbox:** + + ```bash + toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda + ``` + +2. **Enter the Toolbox:** + + ```bash + toolbox enter --container fedora-toolbox-41-cuda + ``` + + Inside the toolbox, you have root privileges and can install packages without affecting the host system. + +## Installing Essential Development Tools + +1. **Synchronize the DNF Package Manager:** + + ```bash + sudo dnf distro-sync + ``` + +2. **Install the Default Text Editor (Optional):** + + ```bash + sudo dnf install vim-default-editor --allowerasing + ``` + + The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package. + +3. **Install Development Tools and Libraries:** + + ```bash + sudo dnf install @c-development @development-tools cmake + ``` + + This installs essential packages for compiling software, including `gcc`, `make`, and other development headers. + +## Adding the CUDA Repository + +Add the NVIDIA CUDA repository to your DNF configuration: + +```bash +sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo +``` + +After adding the repository, synchronize the package manager again: + +```bash +sudo dnf distro-sync +``` + +## Installing `nvidia-driver-libs` and `nvidia-driver-cuda-libs` + +We need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go). + +```bash +ls -la /usr/lib64/libcuda.so.1 +``` + +**Explanation:** + +- `nvidia-driver-libs` and `nvidia-driver-cuda-libs` contains necessary NVIDIA driver libraries required by CUDA, + on hosts with NVIDIA drivers installed the Fedora Container will supply the host libraries. + +### Install Nvidia Driver Libraries on Guest (if `libcuda.so.1` was NOT found). + +```bash +sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs +``` + +### Manually Updating the RPM database for host-supplied NVIDIA drivers (if `libcuda.so.1` was found). + +If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files. + +#### 1. Download `nvidia-driver-libs` and `nvidia-driver-cuda-libs` RPM's (with dependencies) + +```bash +sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-libs nvidia-driver-cuda-libs +``` + +#### 2. Update the RPM database to assume the installation of these packages. + +```bash +sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/* +``` + +**Note:** + +- The `--justdb` option only updates the RPM database, without touching the filesystem. + +#### Finalizing the Installation of `nvidia-driver-libs` and `nvidia-driver-cuda-libs` + +After manually installing the dependencies, run: + +```bash +sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs +``` + +You should receive a message indicating the package is already installed: + +``` +Updating and loading repositories: +Repositories loaded. +Package "nvidia-driver-libs-3:570.86.10-1.fc41.x86_64" is already installed. +Package "nvidia-driver-cuda-libs-3:570.86.10-1.fc41.x86_64" is already installed. + +Nothing to do. +``` + +## Installing the CUDA Meta-Package + +Now that the driver libraries are installed, proceed to install CUDA: + +```bash +sudo dnf install cuda +``` + +This installs the CUDA toolkit and associated packages. + +## Configuring the Environment + +To use CUDA, add its binary directory to your system's `PATH`. + +1. **Create a Profile Script:** + + ```bash + sudo sh -c 'echo "export PATH=\$PATH:/usr/local/cuda/bin" >> /etc/profile.d/cuda.sh' + ``` + + **Explanation:** + + - We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system. + - The backslash `\` before `$PATH` ensures the variable is correctly written into the script. + +2. **Make the Script Executable:** + + ```bash + sudo chmod +x /etc/profile.d/cuda.sh + ``` + +3. **Source the Script to Update Your Environment:** + + ```bash + source /etc/profile.d/cuda.sh + ``` + + **Note:** This command updates your current shell session with the new `PATH`. The `/etc/profile.d/cuda.sh` script ensures that the CUDA binaries are available in your `PATH` for all future sessions. + +## Verifying the Installation + +To confirm that CUDA is correctly installed and configured, check the version of the NVIDIA CUDA Compiler (`nvcc`): + +```bash +nvcc --version +``` + +You should see output similar to: + +``` +nvcc: NVIDIA (R) Cuda compiler driver +Copyright (c) 2005-2025 NVIDIA Corporation +Built on Wed_Jan_15_19:20:09_PST_2025 +Cuda compilation tools, release 12.8, V12.8.61 +Build cuda_12.8.r12.8/compiler.35404655_0 +``` + +This output confirms that the CUDA compiler is accessible and indicates the installed version. + +## Conclusion + +You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system. + +## Troubleshooting + +- **Installation Failures:** + + - If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies. + - You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations. + +- **Rebooting the Container:** + + - Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue: + + ```bash + # on the host system + podman container restart --all + ``` + +- **Environment Variables Not Set:** + - If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`. + - Run `echo $PATH` to check if the path is included. + - Re-source the profile script or open a new terminal session. + +## Additional Notes + +- **Updating CUDA in the Future:** + + - Keep an eye on the official NVIDIA repositories for updates to your Fedora version. + - When an updated repository becomes available, adjust your `dnf` configuration accordingly. + +- **Building `llama.cpp`:** + + - With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support. + - Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration. + +- **Using the Toolbox Environment:** + - The toolbox environment is isolated from your host system, which helps prevent conflicts. + - Remember that system files and configurations inside the toolbox are separate from the host. By default the home directory of the user is shared between the host and the toolbox. + +--- + +**Disclaimer:** Manually installing and modifying system packages can lead to instability of the container. The above steps are provided as a guideline and may need adjustments based on your specific system configuration. Always back up important data before making significant system changes, especially as your home folder is writable and shared with he toolbox. + +**Acknowledgments:** Special thanks to the Fedora community and NVIDIA documentation for providing resources that assisted in creating this guide. + +## References + +- [Fedora Toolbox Documentation](https://docs.fedoraproject.org/en-US/fedora-silverblue/toolbox/) +- [NVIDIA CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) +- [Podman Documentation](https://podman.io/get-started) + +--- diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md new file mode 100644 index 000000000..8fcd70811 --- /dev/null +++ b/docs/development/HOWTO-add-model.md @@ -0,0 +1,119 @@ +# Add a new model architecture to `llama.cpp` + +Adding a model requires few steps: + +1. Convert the model to GGUF +2. Define the model architecture in `llama.cpp` +3. Build the GGML graph implementation + +After following these steps, you can open PR. + +Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially: +- [main](/examples/main/) +- [imatrix](/examples/imatrix/) +- [quantize](/examples/quantize/) +- [server](/examples/server/) + +### 1. Convert the model to GGUF + +This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. +Depending on the model architecture, you can use either [convert_hf_to_gguf.py](/convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](/examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format). + +The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. + +The required steps to implement for an HF model are: + +1. Define the model `Model.register` annotation in a new `Model` subclass, example: + +```python +@Model.register("MyModelForCausalLM") +class MyModel(Model): + model_arch = gguf.MODEL_ARCH.MYMODEL +``` + +2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py) + +Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`. + +Example for `falcon` model: +```python + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ] +``` + +3. Map the original tensor names to the standardize equivalent in GGUF + +As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist. + +Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](/gguf-py/gguf/tensor_mapping.py) file. + +If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it. + +Example for the normalization tensor in attention layers: + +```python +block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen + "transformer.blocks.{bid}.norm_1", # mpt + ... + ) +} +``` + +`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF. + +Depending on the model configuration, tokenizer, code and tensors layout, you will have to override: +- `Model#set_gguf_parameters` +- `Model#set_vocab` +- `Model#write_tensors` + +NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights. + +### 2. Define the model architecture in `llama.cpp` + +The model params and tensors layout must be defined in `llama.cpp`: +1. Define a new `llm_arch` +2. Define the tensors layout in `LLM_TENSOR_NAMES` +3. Add any non-standard metadata in `llm_load_hparams` +4. Create the tensors for inference in `llm_load_tensors` +5. If the model has a RoPE operation, add the rope type in `llama_rope_type` + +NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions. + +### 3. Build the GGML graph implementation + +This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`. + +Have a look at existing implementations like `build_llama`, `build_dbrx` or `build_bert`. + +Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR. + +Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/). + +## GGUF specification + +https://github.com/ggerganov/ggml/blob/master/docs/gguf.md + +## Resources + +- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268 +- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009 +- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283 +- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406 +- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423 +- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204 +- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491 +- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515 +- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948 diff --git a/docs/development/debugging-tests.md b/docs/development/debugging-tests.md new file mode 100644 index 000000000..18407f688 --- /dev/null +++ b/docs/development/debugging-tests.md @@ -0,0 +1,104 @@ +# Debugging Tests Tips + +## How to run & execute or debug a specific test without anything else to keep the feedback loop short? + +There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number. + +For example, running the following command will output an interactive list from which you can select a test. It takes this form: + +`debug-test.sh [OPTION]... ` + +It will then build & run in the debugger for you. + +To just execute a test and get back a PASS or FAIL message run: + +```bash +./scripts/debug-test.sh test-tokenizer +``` + +To test in GDB use the `-g` flag to enable gdb test mode. + +```bash +./scripts/debug-test.sh -g test-tokenizer + +# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows: +>>> b main +``` + +To speed up the testing loop, if you know your test number you can just run it similar to below: + +```bash +./scripts/debug-test.sh test 23 +``` + +For further reference use `debug-test.sh -h` to print help. + +  + +### How does the script work? +If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below. + +#### Step 1: Reset and Setup folder context + +From base of this repository, let's create `build-ci-debug` as our build context. + +```bash +rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug +``` + +#### Step 2: Setup Build Environment and Compile Test Binaries + +Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults. + +```bash +cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON .. +make -j +``` + +#### Step 3: Find all tests available that matches REGEX + +The output of this command will give you the command & arguments needed to run GDB. + +* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex) +* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB. +* `-V` : Verbose Mode + +```bash +ctest -R "test-tokenizer" -V -N +``` + +This may return output similar to below (focusing on key lines to pay attention to): + +```bash +... +1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +1: Working Directory: . +Labels: main + Test #1: test-tokenizer-0-llama-spm +... +4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf" +4: Working Directory: . +Labels: main + Test #4: test-tokenizer-0-falcon +... +``` + +#### Step 4: Identify Test Command for Debugging + +So for test #1 above we can tell these two pieces of relevant information: +* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0` +* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf` + +#### Step 5: Run GDB on test command + +Based on the ctest 'test command' report above we can then run a gdb session via this command below: + +```bash +gdb --args ${Test Binary} ${Test GGUF Model} +``` + +Example: + +```bash +gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +``` diff --git a/docs/llama-star/idea-arch.key b/docs/development/llama-star/idea-arch.key similarity index 100% rename from docs/llama-star/idea-arch.key rename to docs/development/llama-star/idea-arch.key diff --git a/docs/llama-star/idea-arch.pdf b/docs/development/llama-star/idea-arch.pdf similarity index 100% rename from docs/llama-star/idea-arch.pdf rename to docs/development/llama-star/idea-arch.pdf diff --git a/docs/token_generation_performance_tips.md b/docs/development/token_generation_performance_tips.md similarity index 83% rename from docs/token_generation_performance_tips.md rename to docs/development/token_generation_performance_tips.md index d7e863dff..41b7232c9 100644 --- a/docs/token_generation_performance_tips.md +++ b/docs/development/token_generation_performance_tips.md @@ -1,9 +1,9 @@ # Token generation performance troubleshooting -## Verifying that the model is running on the GPU with cuBLAS -Make sure you compiled llama with the correct env variables according to [this guide](../README.md#cublas), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example: +## Verifying that the model is running on the GPU with CUDA +Make sure you compiled llama with the correct env variables according to [this guide](/docs/build.md#cuda), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example: ```shell -./main -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some " +./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some " ``` When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines: @@ -27,7 +27,7 @@ RAM: 32GB Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML) -Run command: `./main -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]` +Run command: `./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]` Result: diff --git a/docs/docker.md b/docs/docker.md new file mode 100644 index 000000000..dac9a9ec1 --- /dev/null +++ b/docs/docker.md @@ -0,0 +1,123 @@ +# Docker + +## Prerequisites +* Docker must be installed and running on your system. +* Create a folder to store big models & intermediate files (ex. /llama/models) + +## Images +We have three Docker images available for this project: + +1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) +2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) +3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`) + +Additionally, there the following images, similar to the above: + +- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`) + +The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now). + +## Usage + +The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image. + +Replace `/path/to/models` below with the actual path where you downloaded the models. + +```bash +docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B +``` + +On completion, you are ready to play! + +```bash +docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +``` + +or with a light image: + +```bash +docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 +``` + +or with a server image: + +```bash +docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 +``` + +## Docker With CUDA + +Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container. + +## Building Docker locally + +```bash +docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile . +docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile . +docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile . +``` + +You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. + +The defaults are: + +- `CUDA_VERSION` set to `12.6.0` +- `CUDA_DOCKER_ARCH` set to the cmake build default, which includes all the supported architectures + +The resulting images, are essentially the same as the non-CUDA images: + +1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. +2. `local/llama.cpp:light-cuda`: This image only includes the main executable file. +3. `local/llama.cpp:server-cuda`: This image only includes the server executable file. + +## Usage + +After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag. + +```bash +docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 +``` + +## Docker With MUSA + +Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/native) properly installed on Linux, `muBLAS` should be accessible inside the container. + +## Building Docker locally + +```bash +docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile . +docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile . +docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile . +``` + +You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture. + +The defaults are: + +- `MUSA_VERSION` set to `rc3.1.0` + +The resulting images, are essentially the same as the non-MUSA images: + +1. `local/llama.cpp:full-musa`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. +2. `local/llama.cpp:light-musa`: This image only includes the main executable file. +3. `local/llama.cpp:server-musa`: This image only includes the server executable file. + +## Usage + +After building locally, Usage is similar to the non-MUSA examples, but you'll need to set `mthreads` as default Docker runtime. This can be done by executing `(cd /usr/bin/musa && sudo ./docker setup $PWD)` and verifying the changes by executing `docker info | grep mthreads` on the host machine. You will also want to use the `--n-gpu-layers` flag. + +```bash +docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 +``` diff --git a/docs/install.md b/docs/install.md new file mode 100644 index 000000000..10a568506 --- /dev/null +++ b/docs/install.md @@ -0,0 +1,39 @@ +# Install pre-built version of llama.cpp + +## Homebrew + +On Mac and Linux, the homebrew package manager can be used via + +```sh +brew install llama.cpp +``` +The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668 + +## Nix + +On Mac and Linux, the Nix package manager can be used via + +```sh +nix profile install nixpkgs#llama-cpp +``` +For flake enabled installs. + +Or + +```sh +nix-env --file '' --install --attr llama-cpp +``` + +For non-flake enabled installs. + +This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164). + +## Flox + +On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via + +```sh +flox install llama-cpp +``` + +Flox follows the nixpkgs build of llama.cpp. diff --git a/docs/llguidance.md b/docs/llguidance.md new file mode 100644 index 000000000..792d20704 --- /dev/null +++ b/docs/llguidance.md @@ -0,0 +1,51 @@ +# LLGuidance Support in llama.cpp + +[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently. + +LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process. + +## Building + +To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option: + +```sh +cmake -B build -DLLAMA_LLGUIDANCE=ON +make -C build -j +``` + +This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install). + +## Interface + +There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance. + +For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format. + +## Performance + +Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md). + +## JSON Schema + +LLGuidance adheres closely to the JSON Schema specification. For example: + +- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed. +- any whitespace is allowed. +- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first). + +Unsupported schemas result in an error message—no keywords are silently ignored. + +## Why Not Reuse GBNF Format? + +GBNF lacks the concept of a lexer. + +Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes. +LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest. + +However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase. +The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically. +See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details. + +## Error Handling + +Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future. diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 653abc73a..66cfab2c3 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -6,43 +6,68 @@ find_package(Threads REQUIRED) # ... +# flags + +llama_add_compile_flags() + # examples include_directories(${CMAKE_CURRENT_SOURCE_DIR}) if (EMSCRIPTEN) else() - add_subdirectory(baby-llama) - add_subdirectory(batched) add_subdirectory(batched-bench) - add_subdirectory(beam-search) - add_subdirectory(benchmark) - add_subdirectory(convert-llama2c-to-ggml) + add_subdirectory(batched) add_subdirectory(embedding) - add_subdirectory(finetune) + add_subdirectory(eval-callback) + + if (NOT WIN32) + # disabled on Windows because it uses internal functions not exported with LLAMA_API + add_subdirectory(gbnf-validator) + endif() + + add_subdirectory(gguf-hash) + add_subdirectory(gguf-split) + add_subdirectory(gguf) + add_subdirectory(gritlm) + add_subdirectory(imatrix) add_subdirectory(infill) add_subdirectory(llama-bench) - add_subdirectory(llava) - if (LLAMA_SYCL) - add_subdirectory(sycl) - endif() - add_subdirectory(main) - add_subdirectory(tokenize) - add_subdirectory(parallel) - add_subdirectory(perplexity) - add_subdirectory(quantize) - add_subdirectory(quantize-stats) - add_subdirectory(save-load-state) - add_subdirectory(simple) - add_subdirectory(passkey) - add_subdirectory(speculative) add_subdirectory(lookahead) add_subdirectory(lookup) - add_subdirectory(gguf) - add_subdirectory(train-text-from-scratch) - add_subdirectory(imatrix) + add_subdirectory(main) + add_subdirectory(parallel) + add_subdirectory(passkey) + add_subdirectory(perplexity) + add_subdirectory(quantize) + add_subdirectory(retrieval) if (LLAMA_BUILD_SERVER) add_subdirectory(server) endif() - add_subdirectory(export-lora) + 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) + add_subdirectory(tts) + add_subdirectory(gen-docs) + 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) + if (NOT WIN32) + # disabled on Windows because it uses internal functions not exported with LLAMA_API + add_subdirectory(quantize-stats) + endif() + add_subdirectory(llava) + if (GGML_RPC) + add_subdirectory(rpc) + endif() + if (GGML_SYCL) + add_subdirectory(sycl) + endif() + endif() endif() diff --git a/examples/Miku.sh b/examples/Miku.sh index b9174b4e6..0f6c8c878 100755 --- a/examples/Miku.sh +++ b/examples/Miku.sh @@ -22,7 +22,7 @@ if [ -n "$N_THREAD" ]; then GEN_OPTIONS+=(--threads "$N_THREAD") fi -./main "${GEN_OPTIONS[@]}" \ +./llama-cli "${GEN_OPTIONS[@]}" \ --model "$MODEL" \ --in-prefix " " \ --in-suffix "${AI_NAME}:" \ diff --git a/examples/alpaca.sh b/examples/alpaca.sh deleted file mode 100755 index 8d2bae691..000000000 --- a/examples/alpaca.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \ - --color \ - -f ./prompts/alpaca.txt \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 7 diff --git a/examples/baby-llama/CMakeLists.txt b/examples/baby-llama/CMakeLists.txt deleted file mode 100644 index 7b70227a5..000000000 --- a/examples/baby-llama/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET baby-llama) -add_executable(${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 bf0125e75..000000000 --- a/examples/baby-llama/baby-llama.cpp +++ /dev/null @@ -1,1640 +0,0 @@ -#include "ggml.h" -#include "train.h" - -#include -#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); - - 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, 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, 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, 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, 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, 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, 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 -./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs diff --git a/examples/batched-bench/CMakeLists.txt b/examples/batched-bench/CMakeLists.txt index 40a032c51..68ad707f3 100644 --- a/examples/batched-bench/CMakeLists.txt +++ b/examples/batched-bench/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET batched-bench) +set(TARGET llama-batched-bench) add_executable(${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-bench/README.md b/examples/batched-bench/README.md index 34b343f66..df67c47e3 100644 --- a/examples/batched-bench/README.md +++ b/examples/batched-bench/README.md @@ -10,16 +10,16 @@ There are 2 modes of operation: - `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`) ```bash -./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] +./llama-batched-bench -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps] # LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared -./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99 +./llama-batched-bench -m ./models/llama-7b/ggml-model-f16.gguf -c 16384 -b 2048 -ub 512 -ngl 99 # LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared -./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99 +./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 16384 -b 2048 -ub 512 -ngl 99 -pps # custom set of batches -./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32 +./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 2048 -b 512 -ub 512 -ngl 999 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 ``` ## Sample results @@ -49,3 +49,12 @@ There are 2 modes of operation: | 128 | 256 | 8 | 3072 | 0.751 | 1363.92 | 15.110 | 135.54 | 15.861 | 193.69 | | 128 | 256 | 16 | 6144 | 1.569 | 1304.93 | 18.073 | 226.64 | 19.642 | 312.80 | | 128 | 256 | 32 | 12288 | 3.409 | 1201.35 | 19.223 | 426.15 | 22.633 | 542.93 | + +### JSONL output + +Pass `--output-format jsonl` to output JSONL instead of Markdown, á la + +```json lines +{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 1, "n_kv": 256, "t_pp": 0.233810, "speed_pp": 547.453064, "t_tg": 3.503684, "speed_tg": 36.532974, "t": 3.737494, "speed": 68.495094} +{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 2, "n_kv": 512, "t_pp": 0.422602, "speed_pp": 605.770935, "t_tg": 11.106112, "speed_tg": 23.050371, "t": 11.528713, "speed": 44.410854} +``` diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 19aff18ae..0659ab6f1 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -1,79 +1,33 @@ +#include "arg.h" #include "common.h" +#include "log.h" #include "llama.h" #include -#include #include #include #include -// mutates the input string -static std::vector parse_list(char * p) { - std::vector ret; - - char * q = p; - - while (*p) { - if (*p == ',') { - *p = '\0'; - ret.push_back(std::atoi(q)); - q = p + 1; - } - - ++p; - } - - ret.push_back(std::atoi(q)); - - return ret; +static void print_usage(int, char ** argv) { + LOG("\nexample usage:\n"); + LOG("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]); + LOG("\n"); } int main(int argc, char ** argv) { - gpt_params params; + common_params params; - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] \n" , argv[0]); - printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); - printf(" example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); - return 1 ; + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) { + return 1; } - int n_kv_max = 2048; - int is_pp_shared = 0; - int n_gpu_layers = 0; + common_init(); - std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; - std::vector n_tg = { 128, 256, }; - std::vector n_pl = { 1, 2, 4, 8, 16, 32, }; - //std::vector n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, }; + int is_pp_shared = params.is_pp_shared; - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - n_kv_max = std::atoi(argv[2]); - } - - if (argc >= 4) { - is_pp_shared = std::atoi(argv[3]); - } - - if (argc >= 5) { - n_gpu_layers = std::atoi(argv[4]); - } - - if (argc >= 6) { - n_pp = parse_list(argv[5]); - } - - if (argc >= 7) { - n_tg = parse_list(argv[6]); - } - - if (argc >= 8) { - n_pl = parse_list(argv[7]); - } + std::vector n_pp = params.n_pp; + std::vector n_tg = params.n_tg; + std::vector n_pl = params.n_pl; // init LLM @@ -82,36 +36,29 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); + llama_model_params model_params = common_model_params_to_llama(params); - const std::vector t_split(llama_max_devices(), 0.0f); - - model_params.n_gpu_layers = n_gpu_layers; - model_params.tensor_split = t_split.data(); - - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params); if (model == NULL) { fprintf(stderr , "%s: error: unable to load model\n" , __func__); return 1; } - llama_context_params ctx_params = llama_context_default_params(); + llama_context_params ctx_params = common_context_params_to_llama(params); - ctx_params.seed = 1234; - ctx_params.n_ctx = n_kv_max; - ctx_params.n_batch = 512; + // ensure enough sequences are available + ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end()); - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - - llama_context * ctx = llama_new_context_with_model(model, ctx_params); + llama_context * ctx = llama_init_from_model(model, ctx_params); if (ctx == NULL) { fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); return 1; } + const int32_t n_kv_max = llama_n_ctx(ctx); + llama_batch batch = llama_batch_init(n_kv_max, 0, 1); // decode in batches of ctx_params.n_batch tokens @@ -127,14 +74,15 @@ int main(int argc, char ** argv) { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); if (ret != 0) { - LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret); + LOG_ERR("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret); return false; } + + llama_synchronize(ctx); } return true; @@ -143,21 +91,22 @@ int main(int argc, char ** argv) { // warm up { for (int i = 0; i < 16; ++i) { - llama_batch_add(batch, 0, i, { 0 }, false); + common_batch_add(batch, 0, i, { 0 }, false); } if (!decode_helper(ctx, batch, ctx_params.n_batch)) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return 1; } } - LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); - LOG_TEE("\n"); - - LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); - LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------"); + if (!params.batched_bench_output_jsonl) { + LOG("\n"); + LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG("\n"); + LOG("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); + LOG("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------"); + } for ( int i_pp = 0; i_pp < (int) n_pp.size(); ++i_pp) { for ( int i_tg = 0; i_tg < (int) n_tg.size(); ++i_tg) { @@ -172,12 +121,12 @@ int main(int argc, char ** argv) { continue; } - llama_batch_clear(batch); + common_batch_clear(batch); - const int n_tokens = is_pp_shared ? pp : pl*pp; - - for (int i = 0; i < n_tokens; ++i) { - llama_batch_add(batch, 0, i, { 0 }, false); + for (int i = 0; i < pp; ++i) { + for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) { + common_batch_add(batch, 0, i, { j }, false); + } } batch.logits[batch.n_tokens - 1] = true; @@ -186,13 +135,13 @@ int main(int argc, char ** argv) { llama_kv_cache_clear(ctx); if (!decode_helper(ctx, batch, ctx_params.n_batch)) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return 1; } if (is_pp_shared) { for (int32_t i = 1; i < pl; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, pp); + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } } @@ -201,14 +150,14 @@ int main(int argc, char ** argv) { const auto t_tg_start = ggml_time_us(); for (int i = 0; i < tg; ++i) { - llama_batch_clear(batch); + common_batch_clear(batch); for (int j = 0; j < pl; ++j) { - llama_batch_add(batch, 0, pp + i, { j }, true); + common_batch_add(batch, 0, pp + i, { j }, true); } if (!decode_helper(ctx, batch, ctx_params.n_batch)) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return 1; } } @@ -225,21 +174,31 @@ int main(int argc, char ** argv) { const float speed_tg = pl*tg / t_tg; const float speed = n_kv / t; - LOG_TEE("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed); + if(params.batched_bench_output_jsonl) { + LOG( + "{\"n_kv_max\": %d, \"n_batch\": %d, \"n_ubatch\": %d, \"flash_attn\": %d, \"is_pp_shared\": %d, \"n_gpu_layers\": %d, \"n_threads\": %u, \"n_threads_batch\": %u, " + "\"pp\": %d, \"tg\": %d, \"pl\": %d, \"n_kv\": %d, \"t_pp\": %f, \"speed_pp\": %f, \"t_tg\": %f, \"speed_tg\": %f, \"t\": %f, \"speed\": %f}\n", + n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch, + pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed + ); + } else { + LOG("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed); + } } } } - llama_print_timings(ctx); + LOG("\n"); + llama_perf_context_print(ctx); llama_batch_free(batch); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); llama_backend_free(); - fprintf(stderr, "\n\n"); + LOG("\n\n"); return 0; } diff --git a/examples/batched.swift/Makefile b/examples/batched.swift/Makefile index 2afb24fb8..1f9156e58 100755 --- a/examples/batched.swift/Makefile +++ b/examples/batched.swift/Makefile @@ -1,6 +1,6 @@ .PHONY: build build: - xcodebuild -scheme batched_swift -destination "generic/platform=macOS" -derivedDataPath build - rm -f ./batched_swift - ln -s ./build/Build/Products/Debug/batched_swift ./batched_swift + xcodebuild -scheme llama-batched-swift -destination "generic/platform=macOS" -derivedDataPath build + rm -f ./llama-batched-swift + ln -s ./build/Build/Products/Debug/llama-batched-swift ./llama-batched-swift diff --git a/examples/batched.swift/Package.swift b/examples/batched.swift/Package.swift index 826491def..7e8afd084 100644 --- a/examples/batched.swift/Package.swift +++ b/examples/batched.swift/Package.swift @@ -4,7 +4,7 @@ import PackageDescription let package = Package( - name: "batched_swift", + name: "llama-batched-swift", platforms: [.macOS(.v12)], dependencies: [ .package(name: "llama", path: "../../"), @@ -13,7 +13,7 @@ let package = Package( // Targets are the basic building blocks of a package, defining a module or a test suite. // Targets can depend on other targets in this package and products from dependencies. .executableTarget( - name: "batched_swift", + name: "llama-batched-swift", dependencies: ["llama"], path: "Sources", linkerSettings: [.linkedFramework("Foundation"), .linkedFramework("AppKit")] diff --git a/examples/batched.swift/README.md b/examples/batched.swift/README.md index 4c2721fe8..7f2e2fcdc 100644 --- a/examples/batched.swift/README.md +++ b/examples/batched.swift/README.md @@ -1,4 +1,4 @@ This is a swift clone of `examples/batched`. $ `make` -$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]` +$ `./llama-batched-swift MODEL_PATH [PROMPT] [PARALLEL]` diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift index d75c503d5..55c31166c 100644 --- a/examples/batched.swift/Sources/main.swift +++ b/examples/batched.swift/Sources/main.swift @@ -23,13 +23,17 @@ defer { } let model_params = llama_model_default_params() -guard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else { +guard let model = llama_model_load_from_file(modelPath.cString(using: .utf8), model_params) else { print("Failed to load model") exit(1) } - defer { - llama_free_model(model) + llama_model_free(model) +} + +guard let vocab = llama_model_get_vocab(model) else { + print("Failed to get vocab") + exit(1) } var tokens = tokenize(text: prompt, add_bos: true) @@ -37,22 +41,36 @@ var tokens = tokenize(text: prompt, add_bos: true) let n_kv_req = UInt32(tokens.count) + UInt32((n_len - Int(tokens.count)) * n_parallel) var context_params = llama_context_default_params() -context_params.seed = 1234 context_params.n_ctx = n_kv_req context_params.n_batch = UInt32(max(n_len, n_parallel)) context_params.n_threads = 8 context_params.n_threads_batch = 8 -let context = llama_new_context_with_model(model, context_params) +let context = llama_init_from_model(model, context_params) guard context != nil else { print("Failed to initialize context") exit(1) } - defer { llama_free(context) } +var sparams = llama_sampler_chain_default_params() + +let smpl = llama_sampler_chain_init(sparams) +guard smpl != nil else { + print("Failed to initialize sampling") + exit(1) +} +defer { + llama_sampler_free(smpl) +} + +llama_sampler_chain_add(smpl, llama_sampler_init_top_k(40)); +llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9, 1)); +llama_sampler_chain_add(smpl, llama_sampler_init_temp (0.4)); +llama_sampler_chain_add(smpl, llama_sampler_init_dist (1234)); + let n_ctx = llama_n_ctx(context) print("\nn_len = \(n_len), n_ctx = \(n_ctx), n_batch = \(context_params.n_batch), n_parallel = \(n_parallel), n_kv_req = \(n_kv_req)\n") @@ -125,35 +143,10 @@ while n_cur <= n_len { continue } - var n_vocab = llama_n_vocab(model) - var logits = llama_get_logits_ith(context, i_batch[i]) - - var candidates: [llama_token_data] = .init(repeating: llama_token_data(), count: Int(n_vocab)) - - for token_id in 0 ..< n_vocab { - candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0)) - } - - var candidates_p: llama_token_data_array = .init( - data: &candidates, - size: candidates.count, - sorted: false - ) - - let top_k: Int32 = 40 - let top_p: Float = 0.9 - let temp: Float = 0.4 - - llama_sample_top_k(context, &candidates_p, top_k, 1) - llama_sample_top_p(context, &candidates_p, top_p, 1) - llama_sample_temp(context, &candidates_p, temp) - - let new_token_id = llama_sample_token(context, &candidates_p) - - // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); + let new_token_id = llama_sampler_sample(smpl, context, i_batch[i]) // is it an end of stream? -> mark the stream as finished - if new_token_id == llama_token_eos(model) || n_cur == n_len { + if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len { i_batch[i] = -1 // print("") if n_parallel > 1 { @@ -210,15 +203,16 @@ if n_parallel > 1 { let t_main_end = ggml_time_us() -print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n") +print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n\n") -llama_print_timings(context) +llama_perf_sampler_print(smpl) +llama_perf_context_print(context) private func tokenize(text: String, add_bos: Bool) -> [llama_token] { let utf8Count = text.utf8.count let n_tokens = utf8Count + (add_bos ? 1 : 0) let tokens = UnsafeMutablePointer.allocate(capacity: n_tokens) - let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false) + let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false) var swiftTokens: [llama_token] = [] for i in 0 ..< tokenCount { swiftTokens.append(tokens[Int(i)]) @@ -229,15 +223,17 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] { private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? { var result = [CChar](repeating: 0, count: 8) - let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count)) + let nTokens = llama_token_to_piece(vocab, token, &result, Int32(result.count), 0, false) if nTokens < 0 { let actualTokensCount = -Int(nTokens) result = .init(repeating: 0, count: actualTokensCount) let check = llama_token_to_piece( - model, + vocab, token, &result, - Int32(result.count) + Int32(result.count), + 0, + false ) assert(check == actualTokensCount) } else { diff --git a/examples/batched/CMakeLists.txt b/examples/batched/CMakeLists.txt index 6aa178d4d..0d439f498 100644 --- a/examples/batched/CMakeLists.txt +++ b/examples/batched/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET batched) +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/README.md b/examples/batched/README.md index 5d7303317..6013aab01 100644 --- a/examples/batched/README.md +++ b/examples/batched/README.md @@ -3,7 +3,7 @@ The example demonstrates batched generation from a given prompt ```bash -./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4 +./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 ... diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 9be7eb56b..21b95ef5e 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -1,52 +1,36 @@ +#include "arg.h" #include "common.h" +#include "log.h" #include "llama.h" #include -#include #include #include #include -int main(int argc, char ** argv) { - gpt_params params; +static void print_usage(int, char ** argv) { + LOG("\nexample usage:\n"); + LOG("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]); + LOG("\n"); +} - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]); - return 1 ; +int main(int argc, char ** argv) { + common_params params; + + params.prompt = "Hello my name is"; + params.n_predict = 32; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) { + return 1; } + common_init(); + // number of parallel batches - int n_parallel = 1; + int n_parallel = params.n_parallel; // total length of the sequences including the prompt - int n_len = 32; - - // number of layers to offload to the GPU - int n_gpu_layers = 0; - - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - params.prompt = argv[2]; - } - - if (argc >= 4) { - n_parallel = std::atoi(argv[3]); - } - - if (argc >= 5) { - n_len = std::atoi(argv[4]); - } - - if (argc >= 6) { - n_gpu_layers = std::atoi(argv[5]); - } - - if (params.prompt.empty()) { - params.prompt = "Hello my name is"; - } + int n_predict = params.n_predict; // init LLM @@ -55,88 +39,113 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); + llama_model_params model_params = common_model_params_to_llama(params); - model_params.n_gpu_layers = n_gpu_layers; - - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params); if (model == NULL) { - fprintf(stderr , "%s: error: unable to load model\n" , __func__); + LOG_ERR("%s: error: unable to load model\n" , __func__); return 1; } + const llama_vocab * vocab = llama_model_get_vocab(model); + // tokenize the prompt std::vector tokens_list; - tokens_list = ::llama_tokenize(model, params.prompt, true); + tokens_list = common_tokenize(vocab, params.prompt, true); - const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel; + const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel; // initialize the context - llama_context_params ctx_params = llama_context_default_params(); + llama_context_params ctx_params = common_context_params_to_llama(params); - ctx_params.seed = 1234; - ctx_params.n_ctx = n_kv_req; - ctx_params.n_batch = std::max(n_len, n_parallel); - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + ctx_params.n_ctx = n_kv_req; + ctx_params.n_batch = std::max(n_predict, n_parallel); - llama_context * ctx = llama_new_context_with_model(model, ctx_params); + llama_context * ctx = llama_init_from_model(model, ctx_params); + + auto sparams = llama_sampler_chain_default_params(); + sparams.no_perf = false; + + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + 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) { - fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + LOG_ERR("%s: error: failed to create the llama_context\n" , __func__); return 1; } - const int n_ctx = llama_n_ctx(ctx); + const int n_ctx = llama_n_ctx(ctx); - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); + LOG_INF("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); // make sure the KV cache is big enough to hold all the prompt and generated tokens if (n_kv_req > n_ctx) { - LOG_TEE("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req); - LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__); + LOG_ERR("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req); + LOG_ERR("%s: either reduce n_parallel or increase n_ctx\n", __func__); return 1; } // print the prompt token-by-token - fprintf(stderr, "\n"); + LOG("\n"); for (auto id : tokens_list) { - fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str()); + LOG("%s", common_token_to_piece(ctx, id).c_str()); } - fflush(stderr); - // create a llama_batch // we use this object to submit token data for decoding - llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1); + llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel); + + std::vector seq_ids(n_parallel, 0); + for (int32_t i = 0; i < n_parallel; ++i) { + seq_ids[i] = i; + } // evaluate the initial prompt for (size_t i = 0; i < tokens_list.size(); ++i) { - llama_batch_add(batch, tokens_list[i], i, { 0 }, false); + common_batch_add(batch, tokens_list[i], i, seq_ids, false); } GGML_ASSERT(batch.n_tokens == (int) tokens_list.size()); + if (llama_model_has_encoder(model)) { + if (llama_encode(ctx, batch)) { + LOG_ERR("%s : failed to eval\n", __func__); + return 1; + } + + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { + decoder_start_token_id = llama_vocab_bos(vocab); + } + + common_batch_clear(batch); + common_batch_add(batch, decoder_start_token_id, 0, seq_ids, false); + } + // llama_decode will output logits only for the last token of the prompt batch.logits[batch.n_tokens - 1] = true; if (llama_decode(ctx, batch) != 0) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return 1; } - // assign the system KV cache to all parallel sequences - // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them - for (int32_t i = 1; i < n_parallel; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens); - } + //// assign the system KV cache to all parallel sequences + //// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them + //for (int32_t i = 1; i < n_parallel; ++i) { + // llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); + //} if (n_parallel > 1) { - LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel); + LOG("\n\n%s: generating %d sequences ...\n", __func__, n_parallel); } // main loop @@ -153,9 +162,9 @@ int main(int argc, char ** argv) { const auto t_main_start = ggml_time_us(); - while (n_cur <= n_len) { + while (n_cur <= n_predict) { // prepare the next batch - llama_batch_clear(batch); + common_batch_clear(batch); // sample the next token for each parallel sequence / stream for (int32_t i = 0; i < n_parallel; ++i) { @@ -164,36 +173,14 @@ int main(int argc, char ** argv) { continue; } - auto n_vocab = llama_n_vocab(model); - auto * logits = llama_get_logits_ith(ctx, i_batch[i]); + const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]); - std::vector candidates; - candidates.reserve(n_vocab); - - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - - const int top_k = 40; - const float top_p = 0.9f; - const float temp = 0.4f; - - llama_sample_top_k(ctx, &candidates_p, top_k, 1); - llama_sample_top_p(ctx, &candidates_p, top_p, 1); - llama_sample_temp (ctx, &candidates_p, temp); - - const llama_token new_token_id = llama_sample_token(ctx, &candidates_p); - - //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); - - // is it an end of stream? -> mark the stream as finished - if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + // is it an end of generation? -> mark the stream as finished + if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) { i_batch[i] = -1; - LOG_TEE("\n"); + LOG("\n"); if (n_parallel > 1) { - LOG_TEE("%s: stream %d finished at n_cur = %d", __func__, i, n_cur); + LOG_INF("%s: stream %d finished at n_cur = %d", __func__, i, n_cur); } continue; @@ -201,16 +188,15 @@ int main(int argc, char ** argv) { // if there is only one stream, we print immediately to stdout if (n_parallel == 1) { - LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str()); - fflush(stdout); + LOG("%s", common_token_to_piece(ctx, new_token_id).c_str()); } - streams[i] += llama_token_to_piece(ctx, new_token_id); + streams[i] += common_token_to_piece(ctx, new_token_id); i_batch[i] = batch.n_tokens; // push this new token for next evaluation - llama_batch_add(batch, new_token_id, n_cur, { i }, true); + common_batch_add(batch, new_token_id, n_cur, { i }, true); n_decode += 1; } @@ -224,34 +210,35 @@ int main(int argc, char ** argv) { // evaluate the current batch with the transformer model if (llama_decode(ctx, batch)) { - fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); + LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1); return 1; } } - LOG_TEE("\n"); - if (n_parallel > 1) { - LOG_TEE("\n"); + LOG("\n"); for (int32_t i = 0; i < n_parallel; ++i) { - LOG_TEE("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str()); + LOG("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str()); } } const auto t_main_end = ggml_time_us(); - LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", + LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); - llama_print_timings(ctx); + LOG("\n"); + llama_perf_sampler_print(smpl); + llama_perf_context_print(ctx); fprintf(stderr, "\n"); llama_batch_free(batch); + llama_sampler_free(smpl); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); llama_backend_free(); diff --git a/examples/beam-search/CMakeLists.txt b/examples/beam-search/CMakeLists.txt deleted file mode 100644 index f0e37468b..000000000 --- a/examples/beam-search/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET beam-search) -add_executable(${TARGET} beam-search.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/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp deleted file mode 100644 index 866c6d7a6..000000000 --- a/examples/beam-search/beam-search.cpp +++ /dev/null @@ -1,188 +0,0 @@ -#include "common.h" -#include "llama.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) -#include -#include -#elif defined (_WIN32) -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX -# define NOMINMAX -#endif -#include -#include -#endif - -// Used for debugging to print out beam tokens. -struct ostream_beam_view { - llama_context * ctx; - llama_beam_view beam_view; -}; - -static std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) { - os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens("; - for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) { - os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]); - } - return os << ')'; -} - -// Put here anything you want back in beam_search_callback(). -struct beam_search_callback_data { - llama_context * ctx; - std::vector response; -}; - -// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same. -// For example, eob can be flagged due to maximum token length, stop words, etc. -static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) { - return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx)); -} - -// Function matching type llama_beam_search_callback_fn_t. -// Custom callback example is called each time the beams lengths increase: -// * Show progress by printing ',' following by number of convergent beam tokens if any. -// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. -// This is also called when the stop condition is met. -// Collect tokens into std::vector response which is pointed to by callback_data. -static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) { - auto& callback_data = *static_cast(callback_data_ptr); - // Mark beams as EOS as needed. - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - llama_beam_view& beam_view = beams_state.beam_views[i]; - if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) { - beam_view.eob = true; - } - } - printf(","); // Show progress - if (const size_t n = beams_state.common_prefix_length) { - callback_data.response.resize(callback_data.response.size() + n); - assert(0u < beams_state.n_beams); - const llama_token * tokens = beams_state.beam_views[0].tokens; - std::copy(tokens, tokens + n, callback_data.response.end() - n); - printf("%zu", n); - } - fflush(stdout); -#if 1 // DEBUG: print current beams for this iteration - std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n"; - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - std::cout << "beams["< 3 ) - { - params.prompt = argv[3]; - } - - if ( params.prompt.empty() ) - { - params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n"; - } - - //--------------------------------- - // Init LLM : - //--------------------------------- - - llama_backend_init(); - llama_numa_init(params.numa); - - llama_model * model; - llama_context * ctx; - - std::tie(model, ctx) = llama_init_from_gpt_params( params ); - - if ( model == NULL ) - { - fprintf( stderr , "%s: error: unable to load model\n" , __func__ ); - return 1; - } - - //--------------------------------- - // Tokenize the prompt : - //--------------------------------- - - std::vector tokens_list = llama_tokenize(ctx, params.prompt, true); - - const size_t max_context_size = llama_n_ctx( ctx ); - const size_t max_tokens_list_size = max_context_size - 4 ; - - if (tokens_list.size() > max_tokens_list_size) - { - fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" , - __func__ , tokens_list.size() , max_tokens_list_size ); - return 1; - } - - fprintf( stderr, "\n\n" ); - - // Print the tokens from the prompt : - - for( auto id : tokens_list ) - { - std::cout << llama_token_to_piece(ctx, id); - } - std::cout << std::flush; - - int n_past = 0; - - if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0))) - { - fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ ); - return 1; - } - n_past += tokens_list.size(); - - beam_search_callback_data callback_data{ctx, {}}; - size_t const beam_width = static_cast(params.n_beams); - int const n_predict = 256; - llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict); - - std::cout << "\n\n"; - for (llama_token const token_id : callback_data.response) { - std::cout << llama_token_to_piece(ctx,token_id); - } - std::cout << std::endl; - - llama_free( ctx ); - llama_free_model( model ); - - llama_backend_free(); - - return 0; -} diff --git a/examples/benchmark/CMakeLists.txt b/examples/benchmark/CMakeLists.txt deleted file mode 100644 index 2bb47bab5..000000000 --- a/examples/benchmark/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -set(TARGET benchmark) -add_executable(${TARGET} benchmark-matmult.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) diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp deleted file mode 100644 index e89f3de2f..000000000 --- a/examples/benchmark/benchmark-matmult.cpp +++ /dev/null @@ -1,277 +0,0 @@ -#include "common.h" -#include "ggml.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#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); - - if (plan.work_size > 0) { - buf.resize(plan.work_size); - plan.work_data = buf.data(); - } - - ggml_graph_compute(graph, &plan); -} - -static float tensor_sum_elements(const ggml_tensor * tensor) { - double sum = 0; - if (tensor->type == GGML_TYPE_F32) { - for (int j = 0; j < tensor->ne[1]; j++) { - for (int k = 0; k < tensor->ne[0]; k++) { - sum += ((float *) tensor->data)[j*tensor->ne[0] + k]; - } - } - } - return sum; -} - -static void tensor_dump(const ggml_tensor * tensor, const char * name) { - printf("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi) - ", name, - tensor->type, ggml_type_name(tensor->type), - tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]); - float sum = tensor_sum_elements(tensor); - printf("Sum of tensor %s is %6.2f\n", name, sum); -} - -#define TENSOR_DUMP(tensor) tensor_dump(tensor, #tensor) - -struct benchmark_params_struct { - int32_t n_threads = 1; - int32_t n_iterations = 10; -}; - -static void print_usage(int /*argc*/, char ** argv, struct benchmark_params_struct 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, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stderr, " -i N, --iter N number of iterations to use during computation (default: %d)\n", params.n_iterations); - fprintf(stderr, "\n"); -} - -int main(int argc, char ** argv) { - struct benchmark_params_struct benchmark_params; - - bool invalid_param = false; - std::string arg; - for (int i = 1; i < argc; i++) { - arg = argv[i]; - - if (arg == "-t" || arg == "--threads") { - if (++i >= argc) { - invalid_param = true; - break; - } - benchmark_params.n_threads = std::stoi(argv[i]); - } else if (arg == "-i" || arg == "--iter") { - if (++i >= argc) { - invalid_param = true; - break; - } - benchmark_params.n_iterations = std::stoi(argv[i]); - } else if (arg == "-h" || arg == "--help") { - print_usage(argc, argv, benchmark_params); - exit(0); - } - } - if (invalid_param) { - fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); - print_usage(argc, argv, benchmark_params); - exit(1); - } - - print_build_info(); - printf("Starting Test\n"); - - // create the ggml context - struct ggml_context * ctx; - //const int sizex = 4096; - //const int sizey = 11008; - -#undef VERBOSE_DEBUGGING -#ifndef VERBOSE_DEBUGGING - const int sizey = 4096; - const int sizex = 11008; - const int sizez = 128; -#else - /* Working - let's increase size */ - const int sizey = 1; - const int sizex = (8*32); - const int sizez = 1; - - /*const int sizey = 1; - const int sizex = 3*(8*32); - const int sizez = 1;*/ -#endif - - //printf("Memsize required = %i\n", sizex*sizex); - - // TODO: perform the bench for all types or for a user specified type - const ggml_type qtype = GGML_TYPE_Q4_1; - - size_t ctx_size = 0; - ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); - ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); - ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizez); - ctx_size += ggml_row_size(qtype, sizex*sizey); - ctx_size += ggml_row_size(qtype, sizex*sizey); - ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS - ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS - ctx_size += 1024*1024*16; - - printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024)); - - struct ggml_init_params params = { - /*.mem_size =*/ ctx_size, - /*.mem_buffer =*/ NULL, - /* no_alloc =*/ 0 - }; - - ctx = ggml_init(params); - if (!ctx) { - fprintf(stderr, "%s: ggml_init() failed\n", __func__); - return 1; - } - - - printf("Creating new tensors\n"); - // printf("Creating new tensor m1\n"); - struct ggml_tensor * m11 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey); - ggml_set_f32(m11, 1.0f); - - // printf("Creating new tensor m1\n"); - struct ggml_tensor * m12 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizey); - ggml_set_f32(m12, 1.5f); - - // printf("Creating new tensor m2\n"); - struct ggml_tensor * m2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, sizex, sizez); - ggml_set_f32(m2, 2.0f); - - printf("\n------ Test 1 - Matrix Mult via F32 code\n"); - // printf("Creating new tensor m11xm2\n"); - struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2); - - // printf("Creating compute graph\n"); - struct ggml_cgraph * gf = ggml_new_graph(ctx); - ggml_build_forward_expand(gf, m11xm2); - - printf("n_threads=%i\n", benchmark_params.n_threads); - - TENSOR_DUMP(m11); - TENSOR_DUMP(m2); - - std::vector work_buffer; - - ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads); - - TENSOR_DUMP(gf->nodes[0]); - - printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype)); - - int32_t nelements = sizex*sizey; - - std::vector hist_cur(1 << 4, 0); - - // Set up a the benchmark matrices - // printf("Creating new tensor q11 & Running quantize\n"); - struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr); - - // Set up a the compute graph - // printf("Creating new tensor q31\n"); - struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2); - - // printf("Creating compute graph\n"); - struct ggml_cgraph * gf31 = ggml_new_graph(ctx); - ggml_build_forward_expand(gf31, q31); - - // Set up a second graph computation to make sure we override the CPU cache lines - // printf("Creating new tensor q12 & Running quantize\n"); - struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey); - ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr); - - // printf("Creating new tensor q32\n"); - struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); - - //printf("Creating compute graph\n"); - struct ggml_cgraph * gf32 = ggml_new_graph(ctx); - ggml_build_forward_expand(gf32, q32); - printf("n_threads=%i\n", benchmark_params.n_threads); - - const int dimx = sizex; - const int dimy = sizey; - const int dimz = sizez; - long long int flops_per_dot_product = dimy + dimy; - long long int flops_per_matrix = flops_per_dot_product * dimx * dimz; ; - printf("Matrix Multiplication of (%i,%i,%i) x (%i,%i,%i) - about %6.2f gFLOPS\n\n", sizex, sizey, 1, sizex, sizez, 1, 1.0f*flops_per_matrix / 1000 / 1000 / 1000); - - - // Let's use the F32 result from above as a reference for the quantized multiplication - float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]); - - printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n"); - printf("=====================================================================================\n"); - - double gflops_sum = 0; - for (int i=0;inodes[0]); - float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference); - float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6 - - if (delta > allowed_delta) { - printf("\nABORT - ERROR in Matrix Multiplication result - expected %6.2f, got %6.2f (delta %6.2f > allowed_delta %6.2f)\n", - sum_of_F32_reference, - sum_of_Q4_result, - delta, - allowed_delta - ); - exit(0); - } - - // Running a different graph computation to make sure we override the CPU cache lines - ggml_graph_compute_helper(work_buffer, gf32, benchmark_params.n_threads); - } - printf("\n"); - printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations)); - printf("=====================================================================================\n"); -} diff --git a/examples/chat-13B.sh b/examples/chat-13B.sh index 35c089d57..1828903c3 100755 --- a/examples/chat-13B.sh +++ b/examples/chat-13B.sh @@ -30,7 +30,7 @@ sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \ $PROMPT_TEMPLATE > $PROMPT_FILE # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS -./main $GEN_OPTIONS \ +./llama-cli $GEN_OPTIONS \ --model "$MODEL" \ --threads "$N_THREAD" \ --n_predict "$N_PREDICTS" \ diff --git a/examples/chat-persistent.sh b/examples/chat-persistent.sh index 22f5b83d3..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}:|${AI_NAME}:|\\.\\.\\.)/,\$d" CTX_SIZE=2048 @@ -62,7 +63,7 @@ fi if [[ ! -e "$PROMPT_CACHE_FILE" ]]; then echo 'Prompt cache does not exist, building...' # Default batch_size to 64 here for better user feedback during initial prompt processing - ./main 2>>"$LOG" \ + ./llama-cli 2>>"$LOG" \ --batch_size 64 \ "${OPTS[@]}" \ --prompt-cache "$PROMPT_CACHE_FILE" \ @@ -109,13 +110,13 @@ while read -e line; do printf '%s: ' "$AI_NAME" >>"$CUR_PROMPT_FILE" - ./main 2>>"$LOG" "${OPTS[@]}" \ + ./llama-cli 2>>"$LOG" "${OPTS[@]}" \ --prompt-cache "$CUR_PROMPT_CACHE" \ --prompt-cache-all \ --file "$CUR_PROMPT_FILE" \ --reverse-prompt "${USER_NAME}:" \ --n_predict "$n_predict" | - skip_bytes 1 | # skip BOS token added by ./main + skip_bytes 1 | # skip BOS token added by ./llama-cli tee "$CUR_PROMPT_FILE.tmp" | # save prompt + generation to tmp file skip_bytes "$n_prompt_len_pre" # print generation @@ -129,22 +130,19 @@ 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 - echo >&2 "Couldn't get number of tokens from ./main output!" + 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" fi # Update cache for next prompt in background, ideally during user input - ./main >>"$LOG_BG" 2>&1 "${OPTS[@]}" \ + ./llama-cli >>"$LOG_BG" 2>&1 "${OPTS[@]}" \ --prompt-cache "$NEXT_PROMPT_CACHE" \ --file "$NEXT_PROMPT_FILE" \ --n_predict 1 & diff --git a/examples/chat-vicuna.sh b/examples/chat-vicuna.sh index 8c7b7bef4..ffdd20084 100755 --- a/examples/chat-vicuna.sh +++ b/examples/chat-vicuna.sh @@ -30,7 +30,7 @@ sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \ $PROMPT_TEMPLATE > $PROMPT_FILE # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS -./bin/main $GEN_OPTIONS \ +./bin/llama-cli $GEN_OPTIONS \ --model "$MODEL" \ --threads "$N_THREAD" \ --n_predict "$N_PREDICTS" \ diff --git a/examples/chat.sh b/examples/chat.sh index d567acecd..9f85d1e26 100755 --- a/examples/chat.sh +++ b/examples/chat.sh @@ -11,6 +11,6 @@ cd .. # # "--keep 48" is based on the contents of prompts/chat-with-bob.txt # -./main -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \ +./llama-cli -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \ --repeat_penalty 1.0 --color -i \ -r "User:" -f prompts/chat-with-bob.txt diff --git a/examples/convert-llama2c-to-ggml/CMakeLists.txt b/examples/convert-llama2c-to-ggml/CMakeLists.txt index e262d44f9..44e5f722a 100644 --- a/examples/convert-llama2c-to-ggml/CMakeLists.txt +++ b/examples/convert-llama2c-to-ggml/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET convert-llama2c-to-ggml) +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(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index 0f37d295b..46a42da69 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -2,13 +2,10 @@ This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default. -To convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository: +To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository. -`$ make -j` - -After successful compilation, following usage options are available: ``` -usage: ./convert-llama2c-to-ggml [options] +usage: ./llama-convert-llama2c-to-ggml [options] options: -h, --help show this help message and exit @@ -19,8 +16,10 @@ options: An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows: -`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` +`$ ./llama-convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` + +Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K). Now you can use the model with a command like: -`$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256` +`$ ./llama-cli -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256` diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 8209dcb64..bdf0eed2a 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -1,6 +1,9 @@ #include "ggml.h" +#include "gguf.h" + #include "llama.h" #include "common.h" +#include "log.h" #include #include @@ -8,6 +11,7 @@ #include #include #include +#include #include #include #include @@ -78,111 +82,101 @@ typedef struct { struct TransformerWeights { // token embedding table - float* token_embedding_table; // (vocab_size, dim) + std::vector token_embedding_table; // (vocab_size, dim) // weights for rmsnorms - float* rms_att_weight; // (layer, dim) rmsnorm weights - float* rms_ffn_weight; // (layer, dim) + std::vector rms_att_weight; // (layer, dim) rmsnorm weights + std::vector rms_ffn_weight; // (layer, dim) // weights for matmuls - float* wq; // (layer, dim, dim) - float* wk; // (layer, dim, dim) - float* wv; // (layer, dim, dim) - float* wo; // (layer, dim, dim) + std::vector wq; // (layer, dim, dim) + std::vector wk; // (layer, dim, dim) + std::vector wv; // (layer, dim, dim) + std::vector wo; // (layer, dim, dim) // weights for ffn - float* w1; // (layer, hidden_dim, dim) - float* w2; // (layer, dim, hidden_dim) - float* w3; // (layer, hidden_dim, dim) + std::vector w1; // (layer, hidden_dim, dim) + std::vector w2; // (layer, dim, hidden_dim) + std::vector w3; // (layer, hidden_dim, dim) // final rmsnorm - float* rms_final_weight; // (dim,) + std::vector rms_final_weight; // (dim,) // freq_cis for RoPE relatively positional embeddings - // float* freq_cis_real; // (seq_len, dim/2) - // float* freq_cis_imag; // (seq_len, dim/2) + // std::vector freq_cis_real; // (seq_len, dim/2) + // std::vector freq_cis_imag; // (seq_len, dim/2) // (optional) classifier weights for the logits, on the last layer - float* wcls; - - ~TransformerWeights() { - delete[] token_embedding_table; - delete[] rms_att_weight; - delete[] rms_ffn_weight; - delete[] wq; - delete[] wk; - delete[] wv; - delete[] wo; - delete[] w1; - delete[] w2; - delete[] w3; - delete[] rms_final_weight; - delete[] wcls; - } + std::vector wcls; }; -static void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) { - // we calloc instead of malloc to keep valgrind happy - w->token_embedding_table = new float[p->vocab_size * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); +static void alloc_weights(TransformerWeights * w, const Config * p, bool shared_weights) { + const int n_multiqueries = p->n_kv_heads <= 0 || p->n_kv_heads >= p->n_heads ? 1 : p->n_heads / p->n_kv_heads; + try { + w->token_embedding_table.resize(p->vocab_size * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); - w->rms_att_weight = new float[p->n_layers * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim); + w->rms_att_weight.resize(p->n_layers * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim); - w->rms_ffn_weight = new float[p->n_layers * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim); + w->rms_ffn_weight.resize(p->n_layers * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim); - w->wq = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wq.resize(p->n_layers * p->dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); - w->wk = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wk.resize(p->n_layers * p->dim * p->dim / n_multiqueries); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries); - w->wv = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wv.resize(p->n_layers * p->dim * p->dim / n_multiqueries); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries); - w->wo = new float[p->n_layers * p->dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); + w->wo.resize(p->n_layers * p->dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim); - w->w1 = new float[p->n_layers * p->hidden_dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); + w->w1.resize(p->n_layers * p->hidden_dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); - w->w2 = new float[p->n_layers * p->hidden_dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim); + w->w2.resize(p->n_layers * p->hidden_dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim); - w->w3 = new float[p->n_layers * p->hidden_dim * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); + w->w3.resize(p->n_layers * p->hidden_dim * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim); - w->rms_final_weight = new float[p->dim](); - printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim); + w->rms_final_weight.resize(p->dim); + LOG_INF("%s: Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim); - if (shared_weights) { - w->wcls = NULL; - } else { - w->wcls = new float[p->vocab_size * p->dim](); - printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); + if (shared_weights) { + w->wcls = {}; + } else { + w->wcls.resize(p->vocab_size * p->dim); + LOG_INF("%s: Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim); + } + } + catch (std::length_error &) { + die("Invalid configuration. Failed to allocate memory for weights"); } } -static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) { - if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast(p->vocab_size * p->dim)) return 1; - if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast(p->n_layers * p->dim)) return 1; - if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast(p->n_layers * p->dim * p->dim)) return 1; - if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast(p->n_layers * p->dim)) return 1; - if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast(p->n_layers * p->dim * p->hidden_dim)) return 1; - if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast(p->n_layers * p->hidden_dim * p->dim)) return 1; - if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast(p->n_layers * p->dim * p->hidden_dim)) return 1; - if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast(p->dim)) return 1; +static int checkpoint_init_weights(TransformerWeights * w, const Config * p, FILE * f, bool shared_weights) { + if (fread(w->token_embedding_table.data(), sizeof(float), w->token_embedding_table.size(), f) != w->token_embedding_table.size()) return 1; + if (fread(w->rms_att_weight.data(), sizeof(float), w->rms_att_weight.size(), f) != w->rms_att_weight.size()) return 1; + if (fread(w->wq.data(), sizeof(float), w->wq.size(), f) != w->wq.size()) return 1; + if (fread(w->wk.data(), sizeof(float), w->wk.size(), f) != w->wk.size()) return 1; + if (fread(w->wv.data(), sizeof(float), w->wv.size(), f) != w->wv.size()) return 1; + if (fread(w->wo.data(), sizeof(float), w->wo.size(), f) != w->wo.size()) return 1; + if (fread(w->rms_ffn_weight.data(), sizeof(float), w->rms_ffn_weight.size(), f) != w->rms_ffn_weight.size()) return 1; + if (fread(w->w1.data(), sizeof(float), w->w1.size(), f) != w->w1.size()) return 1; + if (fread(w->w2.data(), sizeof(float), w->w2.size(), f) != w->w2.size()) return 1; + if (fread(w->w3.data(), sizeof(float), w->w3.size(), f) != w->w3.size()) return 1; + if (fread(w->rms_final_weight.data(), sizeof(float), w->rms_final_weight.size(), f) != w->rms_final_weight.size()) return 1; // Skip freq_cis_real & freq_cis_imag int head_size = p->dim / p->n_heads; fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR); - if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast(p->vocab_size * p->dim)) return 1; + if (!shared_weights && fread(w->wcls.data(), sizeof(float), w->wcls.size(), f) != w->wcls.size()) return 1; // Check we didn't forget to read anything auto curr = ftell(f); fseek(f, 0, SEEK_END); auto end = ftell(f); if (curr != end) { - printf("Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", curr, end); + LOG_ERR("%s: Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", __func__, curr, end); return 1; } @@ -190,26 +184,26 @@ static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bo } static void print_sample_weights(TransformerWeights *w){ - printf("----- Quick print of first of the weight vales of all the variables\n"); - printf("%f\n", w->token_embedding_table[0]); - printf("%f\n", w->rms_att_weight[0]); - printf("%f\n", w->rms_ffn_weight[0]); + LOG_INF("----- Quick print of first of the weight vales of all the variables\n"); + LOG_INF("%f\n", w->token_embedding_table[0]); + LOG_INF("%f\n", w->rms_att_weight[0]); + LOG_INF("%f\n", w->rms_ffn_weight[0]); - printf("%f\n", w->wq[0]); - printf("%f\n", w->wk[0]); - printf("%f\n", w->wv[0]); - printf("%f\n", w->wo[0]); - printf("%f\n", w->w1[0]); - printf("%f\n", w->w2[0]); - printf("%f\n", w->w3[0]); - printf("%f\n", w->rms_att_weight[0]); - if (w->wcls) printf("%f\n", w->wcls[0]); + LOG_INF("%f\n", w->wq[0]); + LOG_INF("%f\n", w->wk[0]); + LOG_INF("%f\n", w->wv[0]); + LOG_INF("%f\n", w->wo[0]); + LOG_INF("%f\n", w->w1[0]); + LOG_INF("%f\n", w->w2[0]); + LOG_INF("%f\n", w->w3[0]); + LOG_INF("%f\n", w->rms_att_weight[0]); + if (!w->wcls.empty()) LOG_INF("%f\n", w->wcls[0]); } //////////////////////////////////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////// ggml structs and functions required to load models, configs and save the model. -struct llama_vocab { +struct my_llama_vocab { using id = int32_t; using token = std::string; using ttype = llama_token_type; @@ -225,14 +219,16 @@ struct llama_vocab { }; struct my_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_ff = 11008; - uint32_t n_mult = 4; - uint32_t n_head = 32; - uint32_t n_layer = 32; - uint32_t n_rot = 64; + uint32_t n_vocab = 32000; + uint32_t n_ctx = 512; // this is provided as user input? + uint32_t n_embd = 4096; + uint32_t n_ff = 11008; + uint32_t n_mult = 4; + uint32_t n_head = 32; + uint32_t n_head_kv = 32; + uint32_t n_layer = 32; + uint32_t n_rot = 64; + bool operator!=(const my_llama_hparams& other) const { return memcmp(this, &other, sizeof(my_llama_hparams)); } @@ -325,14 +321,30 @@ struct train_params { }; static void print_params(struct my_llama_hparams * params) { - printf("%s: n_vocab: %u\n", __func__, params->n_vocab); - printf("%s: n_ctx: %u\n", __func__, params->n_ctx); - printf("%s: n_embd: %u\n", __func__, params->n_embd); - printf("%s: n_mult: %u\n", __func__, params->n_mult); - printf("%s: n_head: %u\n", __func__, params->n_head); - printf("%s: n_ff: %u\n", __func__, params->n_ff); - printf("%s: n_layer: %u\n", __func__, params->n_layer); - printf("%s: n_rot: %u\n", __func__, params->n_rot); + LOG_INF("%s: n_vocab: %u\n", __func__, params->n_vocab); + LOG_INF("%s: n_ctx: %u\n", __func__, params->n_ctx); + LOG_INF("%s: n_embd: %u\n", __func__, params->n_embd); + LOG_INF("%s: n_mult: %u\n", __func__, params->n_mult); + LOG_INF("%s: n_head: %u\n", __func__, params->n_head); + LOG_INF("%s: n_head_kv: %u\n", __func__, params->n_head_kv); + LOG_INF("%s: n_ff: %u\n", __func__, params->n_ff); + LOG_INF("%s: n_layer: %u\n", __func__, params->n_layer); + LOG_INF("%s: n_rot: %u\n", __func__, params->n_rot); +} + +static void print_tensor_info(const struct ggml_context * ctx) { + for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + LOG_INF("%s: Allocating ", __func__); + int64_t total = 1; + int i = 0; + for (; i < ggml_n_dims(t); ++i) { + if (i > 0) LOG("x "); + LOG("[%" PRId64 "] ", t->ne[i]); + total *= t->ne[i]; + } + if (i > 1) LOG("= [%" PRId64 "] ", total); + LOG("float space for %s\n", ggml_get_name(t)); + } } static void init_model(struct my_llama_model * model) { @@ -342,6 +354,8 @@ static void init_model(struct my_llama_model * model) { const uint32_t n_layer = hparams.n_layer; const uint32_t n_vocab = hparams.n_vocab; + const uint32_t n_multiqueries = hparams.n_head_kv <= 0 || hparams.n_head_kv >= hparams.n_head ? 1 : hparams.n_head / hparams.n_head_kv; + const uint32_t n_ff = hparams.n_ff; struct ggml_context * ctx = model->ctx; @@ -350,25 +364,8 @@ static void init_model(struct my_llama_model * model) { model->train_tokens = 0; model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); - printf("[%s:GG] Allocating [%u] x [%u] = [%u] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab); - model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - printf("[%s:GG] Allocating [%u] float space for model->norm\n",__func__,n_embd); - model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab); - - // printing the per-layer allocations here so we dont print in the for loop. - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wq for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wk for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wv for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wo for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer); - - printf("[%s:GG] Allocating [%u] float space for layer.ffn_norm for [%u] layers\n",__func__,n_embd, n_layer); - - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w1 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w2 for [%u] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer); - printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w3 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer); ggml_set_name(model->tok_embeddings, "tok_embeddings.weight"); ggml_set_name(model->norm, "norm.weight"); @@ -383,8 +380,8 @@ static void init_model(struct my_llama_model * model) { layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); - layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); - layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); + layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries); + layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries); layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); @@ -406,6 +403,8 @@ static void init_model(struct my_llama_model * model) { ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str()); ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str()); } + + print_tensor_info(ctx); } static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { @@ -421,9 +420,9 @@ static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) { static void print_row(struct ggml_tensor * probs, int i) { for (int k = 0; k < probs->ne[0]; ++k) { float p = get_f32_2d(probs, k, i); - printf(" %f", p); + LOG(" %f", p); } - printf("\n"); + LOG("\n"); } static void print_matrix(struct ggml_tensor * probs) { @@ -431,39 +430,18 @@ static void print_matrix(struct ggml_tensor * probs) { for (int i = 0; i < probs->ne[1]; ++i) { for (int k = 0; k < probs->ne[0]; ++k) { float p = get_f32_2d(probs, k, i); - printf(" %.2f", p); + LOG(" %.2f", p); } - printf("\n"); + LOG("\n"); } } -#ifdef __GNUC__ -#ifdef __MINGW32__ -__attribute__((format(gnu_printf, 1, 2))) -#else -__attribute__((format(printf, 1, 2))) -#endif -#endif -static std::string format(const char * fmt, ...) { - va_list ap, ap2; - va_start(ap, fmt); - va_copy(ap2, ap); - int size = vsnprintf(NULL, 0, fmt, ap); - GGML_ASSERT(size >= 0 && size < INT_MAX); - std::vector buf(size + 1); - int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); - GGML_ASSERT(size2 == size); - va_end(ap2); - va_end(ap); - return std::string(buf.data(), size); -} - -struct llama_file { +struct my_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) { + my_llama_file(const char * fname, const char * mode) { fp = std::fopen(fname, mode); if (fp == NULL) { size = 0; @@ -524,7 +502,7 @@ struct llama_file { return std::string(chars.data(), len); } - ~llama_file() { + ~my_llama_file() { if (fp) { std::fclose(fp); } @@ -532,7 +510,7 @@ struct llama_file { }; static bool is_ggml_file(const char * filename) { - llama_file file(filename, "rb"); + my_llama_file file(filename, "rb"); if (file.size < 4) { return false; } @@ -549,8 +527,9 @@ static std::string llama_escape_whitespaces(const std::string & text) { return out.str(); } -static void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) { +static void load_vocab(const char * filename, const Config * config, struct my_llama_vocab * vocab) { if (is_ggml_file(filename)) { + LOG_INF("%s: Loading vocabulary from gguf file %s\n", __func__, filename); struct ggml_context * ctx_data = NULL; struct gguf_init_params params = { @@ -578,6 +557,9 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx); const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx); + if (n_vocab != static_cast(config->vocab_size)) { + die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size); + } vocab->id_to_token.resize(n_vocab); @@ -595,21 +577,21 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab gguf_free(ctx); } else { // assume llama2.c vocabulary - printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename); - llama_file file(filename, "rb"); + LOG_INF("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename); + my_llama_file file(filename, "rb"); if (!file.fp) { die_fmt("%s: %s", strerror(errno), filename); } const int n_vocab = config->vocab_size; /* uint32_t max_token_length = */ file.read_u32(); // unused vocab->id_to_token.resize(n_vocab); - for (llama_vocab::id id=0; idne[0]; i0++){ - float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]); - *ptr = karpathy_weights[ct]; - ct++; - } - break; - case 2: - ct = 0; - for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) { - for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) { - float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]); - *ptr = karpathy_weights[ct]; - ct++; - } - } - break; - case 3: - ct = 0; - for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) { - for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) { - for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) { - float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]); - *ptr = karpathy_weights[ct]; - ct++; - } - } - } - break; + int size = 1; + for (int dim = 0; dim < ggml_n_dims(gg_weights); ++dim) { + size *= gg_weights->ne[dim]; + } + for (int ct = 0; ct < size; ++ct) { + int64_t i0 = 0; int64_t i1 = 0; + int64_t i2 = 0; int64_t i3 = 0; + ggml_unravel_index(gg_weights, ct, &i0, &i1, &i2, &i3); + ggml_set_f32_nd(gg_weights, i0, i1, i2, i3, karpathy_weights[ct]); } } static void save_as_llama_model( - struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename + struct my_llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename ) { // convert AK weights into GG weights one by one. // w->token_embedding_table -> model->tok_embeddings // float* -> struct ggml_tensor - convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table); - convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table); + convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table.data()); + convert_weights_ak_to_gg(model->output, !w->wcls.empty() ? w->wcls.data() : w->token_embedding_table.data()); - convert_weights_ak_to_gg(model->norm, w->rms_final_weight); + convert_weights_ak_to_gg(model->norm, w->rms_final_weight.data()); //print_row(model->norm, 0); // for rms-att-weight int row_length = model->hparams.n_embd; int n_ff = model->hparams.n_ff; + const uint32_t n_multiqueries = model->hparams.n_head_kv <= 0 || model->hparams.n_head_kv >= model->hparams.n_head ? 1 : model->hparams.n_head / model->hparams.n_head_kv; + for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ auto & layer = model->layers[i]; // 1d @@ -697,9 +658,10 @@ static void save_as_llama_model( // from 3d matrix layer x dim x dim to 2d matrix dim x dim convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]); - convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length]); - convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length]); convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]); + // from 3d matrix layer x dim x dim to 2d matrix dim x dim / n_multiqueries + convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length/n_multiqueries]); + convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length/n_multiqueries]); convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]); convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]); @@ -711,7 +673,7 @@ static void save_as_llama_model( std::vector tokens; std::vector scores; std::vector token_types; - for (const llama_vocab::token_data & token_data : vocab->id_to_token) { + for (const my_llama_vocab::token_data & token_data : vocab->id_to_token) { tokens.push_back(token_data.text.c_str()); scores.push_back(token_data.score); token_types.push_back(token_data.type); @@ -729,15 +691,15 @@ static void save_as_llama_model( gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID); gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID); gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID); - gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1); - gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1); + gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, LLAMA_TOKEN_NULL); + gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, LLAMA_TOKEN_NULL); gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx); gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd); gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff); gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head); - // n_head_kv is optional, default to n_head - // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, model->hparams.n_head_kv); gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer); gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot); gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f); @@ -789,12 +751,12 @@ static void save_as_llama_model( static struct train_params get_default_train_params() { struct train_params params; - params.fn_vocab_model = "models/7B/ggml-model-f16.gguf"; + params.fn_vocab_model = "models/7B/ggml-model-f16.gguf"; params.fn_llama2c_output_model = "ak_llama_model.bin"; - params.fn_train_data = "shakespeare.txt"; - params.fn_checkpoint_in = "checkpoint.bin"; - params.fn_checkpoint_out = "checkpoint.bin"; - params.fn_model_out = "ggml-checkpoint-f32.bin"; + params.fn_train_data = "shakespeare.txt"; + params.fn_checkpoint_in = "checkpoint.bin"; + params.fn_checkpoint_out = "checkpoint.bin"; + params.fn_model_out = "ggml-checkpoint-f32.bin"; params.seed = -1; @@ -815,7 +777,7 @@ static struct train_params get_default_train_params() { params.samples_start_after_nl = false; params.use_adam = true; - params.use_flash = true; + params.use_flash = false; params.use_scratch = true; // only adam @@ -829,8 +791,8 @@ static struct train_params get_default_train_params() { params.adam_alpha = 1e-3f; params.adam_decay = 1e-3f; - params.mem_model_gb = 2; - params.mem_compute_gb = 24; + params.mem_model_gb = 2; + params.mem_compute_gb = 24; params.mem_compute0_gb = 8; params.mem_compute1_gb = 2; @@ -912,39 +874,55 @@ static std::string basename(const std::string &path) { } int main(int argc, char ** argv) { + common_init(); + struct train_params params = get_default_train_params(); if (!params_parse(argc, argv, ¶ms)) { return 1; } + Config config; TransformerWeights weights = {}; { - FILE *file = fopen(params.fn_llama2c_model, "rb"); - if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; } + LOG_INF("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model); + FILE * file = fopen(params.fn_llama2c_model, "rb"); + if (!file) { + LOG_ERR("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model); + return 1; + } // read in the config header - if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; } + if (fread(&config, sizeof(Config), 1, file) != 1) { + LOG_ERR("%s: Unable to read llama2c config from %s!\n",__func__,params.fn_llama2c_model); + return 1; + } auto shared_weights = config.vocab_size > 0; config.vocab_size = abs(config.vocab_size); // read in the Transformer weights - malloc_weights(&weights, &config, shared_weights); - if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; } + alloc_weights(&weights, &config, shared_weights); + if (checkpoint_init_weights(&weights, &config, file, shared_weights)) { + LOG_ERR("%s: Unable to initialize transformer weights from %s!",__func__,params.fn_llama2c_model); + return 1; + } fclose(file); } - struct llama_vocab vocab; + struct my_llama_vocab vocab; load_vocab(params.fn_vocab_model, &config, &vocab); struct my_llama_model model; - model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); - model.hparams.n_ctx = params.n_ctx; - model.hparams.n_embd = config.dim; //params.n_embd; - model.hparams.n_ff = config.hidden_dim; - model.hparams.n_mult = 32;//params.n_mult; - model.hparams.n_head = config.n_heads; //params.n_head; - model.hparams.n_layer = config.n_layers; //params.n_layer; - model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head); + model.hparams.n_vocab = config.vocab_size; //llama_vocab_n_vocab(lctx); + model.hparams.n_ctx = params.n_ctx; + model.hparams.n_embd = config.dim; //params.n_embd; + model.hparams.n_ff = config.hidden_dim; + model.hparams.n_mult = 32;//params.n_mult; + model.hparams.n_head = config.n_heads; //params.n_head; + model.hparams.n_head_kv = config.n_kv_heads; + model.hparams.n_layer = config.n_layers; //params.n_layer; + model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head); + print_params(&model.hparams); + struct ggml_init_params lcparams; lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb); lcparams.mem_buffer = NULL; @@ -956,7 +934,7 @@ int main(int argc, char ** argv) { model.name = basename(params.fn_llama2c_model); save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model); - printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model); + LOG_INF("%s: Saving llama.c model file %s in ggml format at %s\n", __func__, params.fn_llama2c_model, params.fn_llama2c_output_model); ggml_free(model.ctx); return 0; diff --git a/convert.py b/examples/convert_legacy_llama.py similarity index 65% rename from convert.py rename to examples/convert_legacy_llama.py index 63a0a5d78..c4ec5c524 100755 --- a/convert.py +++ b/examples/convert_legacy_llama.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import argparse import concurrent.futures import enum @@ -16,23 +17,28 @@ import re import signal import struct import sys +import textwrap import time import zipfile -from abc import ABCMeta, abstractmethod +from abc import ABC, abstractmethod from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar +from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar import numpy as np -from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) + # use .parent.parent since we are in "examples" directory + sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py')) + import gguf +from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab if TYPE_CHECKING: - from typing import TypeAlias + from typing_extensions import Self, TypeAlias + +logger = logging.getLogger("convert") if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): faulthandler.register(signal.SIGUSR1) @@ -43,6 +49,9 @@ ARCH = gguf.MODEL_ARCH.LLAMA DEFAULT_CONCURRENCY = 8 +ADDED_TOKENS_FILE = 'added_tokens.json' +FAST_TOKENIZER_FILE = 'tokenizer.json' + # # data types # @@ -135,7 +144,8 @@ class GGMLFileType(enum.IntEnum): dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) if dt is None: raise ValueError(self) - # 1D tensors are always F32. + # Convert all 1D tensors to F32. Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32. + # Also The 1d tensors aren't much of a performance/size issue. So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now. return dt if len(tensor.shape) > 1 else DT_F32 @@ -166,7 +176,7 @@ class Params: rope_scaling_type: gguf.RopeScalingType | None = None f_rope_freq_base: float | None = None f_rope_scale: float | None = None - n_orig_ctx: int | None = None + n_ctx_orig: int | None = None rope_finetuned: bool | None = None ftype: GGMLFileType | None = None @@ -188,8 +198,10 @@ class Params: n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model) if n_layer < 1: - raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n" - "Suggestion: provide 'config.json' of the model in the same directory containing model files.") + msg = """\ + failed to guess 'n_layer'. This model is unknown or unsupported. + Suggestion: provide 'config.json' of the model in the same directory containing model files.""" + raise KeyError(textwrap.dedent(msg)) n_head = n_embd // 128 # guessed n_mult = 256 # guessed @@ -211,9 +223,10 @@ class Params: @staticmethod def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: - config = json.load(open(config_path)) + with open(config_path) as f: + config = json.load(f) - rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None + rope_scaling_type = f_rope_scale = n_ctx_orig = rope_finetuned = None rope_scaling = config.get("rope_scaling") if rope_scaling is not None and (typ := rope_scaling.get("type")): @@ -223,7 +236,7 @@ class Params: rope_scaling_type = gguf.RopeScalingType.LINEAR elif typ == "yarn": rope_scaling_type = gguf.RopeScalingType.YARN - n_orig_ctx = rope_scaling['original_max_position_embeddings'] + n_ctx_orig = rope_scaling['original_max_position_embeddings'] rope_finetuned = rope_scaling['finetuned'] else: raise NotImplementedError(f'Unknown rope scaling type: {typ}') @@ -233,8 +246,10 @@ class Params: elif "max_position_embeddings" in config: n_ctx = config["max_position_embeddings"] else: - raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n" - "Suggestion: provide 'config.json' of the model in the same directory containing model files.") + msg = """\ + failed to guess 'n_ctx'. This model is unknown or unsupported. + Suggestion: provide 'config.json' of the model in the same directory containing model files.""" + raise KeyError(textwrap.dedent(msg)) n_experts = None n_experts_used = None @@ -257,7 +272,7 @@ class Params: f_rope_freq_base = config.get("rope_theta"), rope_scaling_type = rope_scaling_type, f_rope_scale = f_rope_scale, - n_orig_ctx = n_orig_ctx, + n_ctx_orig = n_ctx_orig, rope_finetuned = rope_finetuned, ) @@ -265,11 +280,13 @@ class Params: # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1} @staticmethod def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: - config = json.load(open(config_path)) + with open(config_path) as f: + config = json.load(f) n_experts = None n_experts_used = None f_rope_freq_base = None + n_ff = None # hack to determine LLaMA v1 vs v2 vs CodeLlama if config.get("moe"): @@ -294,6 +311,8 @@ class Params: n_experts_used = config["moe"]["num_experts_per_tok"] f_rope_freq_base = 1e6 + assert n_ff is not None + return Params( n_vocab = model["tok_embeddings.weight"].shape[0], n_embd = config["dim"], @@ -327,235 +346,6 @@ class Params: return params -# -# vocab -# - -class BpeVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: - self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) - if isinstance(self.bpe_tokenizer.get('model'), dict): - self.vocab = self.bpe_tokenizer["model"]["vocab"] - else: - self.vocab = self.bpe_tokenizer - added_tokens: dict[str, int] - if fname_added_tokens is not None: - # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. - added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) - else: - # Fall back to trying to find the added tokens in tokenizer.json - tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json' - if not tokenizer_json_file.is_file(): - added_tokens = {} - else: - tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8")) - added_tokens = dict( - (item['content'], item['id']) - for item in tokenizer_json.get('added_tokens', []) - # Added tokens here can be duplicates of the main vocabulary. - if item['content'] not in self.bpe_tokenizer) - - vocab_size: int = len(self.vocab) - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - expected_end_id = vocab_size + len(actual_ids) - 1 - raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}") - - items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) - self.added_tokens_dict = added_tokens - self.added_tokens_list = [text for (text, idx) in items] - self.vocab_size_base: int = vocab_size - self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - self.fname_added_tokens = fname_added_tokens - - def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} - - for i, _ in enumerate(self.vocab): - yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.CONTROL - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.bpe_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class SentencePieceVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: - self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) - added_tokens: dict[str, int] - if fname_added_tokens is not None: - added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) - else: - added_tokens = {} - - vocab_size: int = self.sentencepiece_tokenizer.vocab_size() - - new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} - expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) - actual_new_ids = sorted(new_tokens.keys()) - - if expected_new_ids != actual_new_ids: - raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") - - # Token pieces that were added to the base vocabulary. - self.added_tokens_dict = added_tokens - self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] - self.vocab_size_base = vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - self.fname_added_tokens = fname_added_tokens - - def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): - piece = tokenizer.id_to_piece(i) - text: bytes = piece.encode("utf-8") - score: float = tokenizer.get_score(i) - - toktype = gguf.TokenType.NORMAL - if tokenizer.is_unknown(i): - toktype = gguf.TokenType.UNKNOWN - if tokenizer.is_control(i): - toktype = gguf.TokenType.CONTROL - - # NOTE: I think added_tokens are user defined. - # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto - # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED - - if tokenizer.is_unused(i): - toktype = gguf.TokenType.UNUSED - if tokenizer.is_byte(i): - toktype = gguf.TokenType.BYTE - - yield text, score, toktype - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.sentencepiece_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class HfVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None: - try: - from transformers import AutoTokenizer - except ImportError as e: - raise ImportError( - "To use HfVocab, please install the `transformers` package. " - "You can install it with `pip install transformers`." - ) from e - - print("fname_tokenizer:", fname_tokenizer) - # Allow the tokenizer to default to slow or fast versions. - # Explicitly set tokenizer to use local paths. - self.tokenizer = AutoTokenizer.from_pretrained( - fname_tokenizer, - cache_dir=fname_tokenizer, - local_files_only=True, - ) - - # Initialize lists and dictionaries for added tokens - self.added_tokens_list = [] - self.added_tokens_dict = dict() - self.added_tokens_ids = set() - - # Process added tokens - for tok, tokidx in sorted( - self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] - ): - # Only consider added tokens that are not in the base vocabulary - if tokidx >= self.tokenizer.vocab_size: - self.added_tokens_list.append(tok) - self.added_tokens_dict[tok] = tokidx - self.added_tokens_ids.add(tokidx) - - # Store special tokens and their IDs - self.specials = { - tok: self.tokenizer.get_vocab()[tok] - for tok in self.tokenizer.all_special_tokens - } - self.special_ids = set(self.tokenizer.all_special_ids) - - # Set vocabulary sizes - self.vocab_size_base = self.tokenizer.vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - - self.fname_tokenizer = fname_tokenizer - self.fname_added_tokens = fname_added_tokens - - def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = { - id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() - } - - for token_id in range(self.vocab_size_base): - # Skip processing added tokens here - if token_id in self.added_tokens_ids: - continue - - # Convert token text to bytes - token_text = reverse_vocab[token_id].encode("utf-8") - - # Yield token text, score, and type - yield token_text, self.get_token_score(token_id), self.get_token_type( - token_id, token_text, self.special_ids # Reuse already stored special IDs - ) - - def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: - # Special case for byte tokens - if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): - return gguf.TokenType.BYTE - - # Determine token type based on whether it's a special token - return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL - - def get_token_score(self, token_id: int) -> float: - # Placeholder for actual logic to determine the token's score - # This needs to be implemented based on specific requirements - return -1000.0 # Default score - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - if text in self.specials: - toktype = self.get_token_type(self.specials[text], b'', self.special_ids) - score = self.get_token_score(self.specials[text]) - else: - toktype = gguf.TokenType.USER_DEFINED - score = -1000.0 - - yield text.encode("utf-8"), score, toktype - - def has_newline_token(self): - return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.hf_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab" - - # # data loading # TODO: reuse (probably move to gguf.py?) @@ -563,7 +353,6 @@ Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab" def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray: - # print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) ) if n_head_kv is not None and n_head != n_head_kv: n_head = n_head_kv return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) @@ -571,17 +360,18 @@ def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray: .reshape(weights.shape)) -class Tensor(metaclass=ABCMeta): +class Tensor(ABC): + ndarray: NDArray data_type: DataType @abstractmethod - def astype(self, data_type: DataType) -> Tensor: ... + def astype(self, data_type: DataType) -> Self: ... @abstractmethod - def permute(self, n_head: int, n_head_kv: int) -> Tensor: ... + def permute(self, n_head: int, n_head_kv: int) -> Self: ... @abstractmethod - def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ... + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ... @abstractmethod - def part(self, n_part: int) -> UnquantizedTensor: ... + def part(self, n_part: int) -> Self: ... @abstractmethod def to_ggml(self) -> GGMLCompatibleTensor: ... @@ -593,18 +383,18 @@ def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray: class UnquantizedTensor(Tensor): - def __init__(self, ndarray: NDArray) -> None: + def __init__(self, ndarray: NDArray): assert isinstance(ndarray, np.ndarray) self.ndarray = ndarray self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] - def astype(self, data_type: DataType) -> Tensor: + def astype(self, data_type: DataType) -> UnquantizedTensor: dtype = data_type.dtype if self.data_type == DT_BF16: self.ndarray = bf16_to_fp32(self.ndarray) return UnquantizedTensor(self.ndarray.astype(dtype)) - def to_ggml(self) -> UnquantizedTensor: + def to_ggml(self) -> Self: return self def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: @@ -666,13 +456,14 @@ class LazyTensor: LazyModel: TypeAlias = 'dict[str, LazyTensor]' +ModelFormat: TypeAlias = Literal['ggml', 'torch', 'safetensors', 'none'] @dataclass class ModelPlus: model: LazyModel paths: list[Path] # Where this was read from. - format: Literal['ggml', 'torch', 'safetensors', 'none'] - vocab: Vocab | None # For GGML models (which have vocab built in), the vocab. + format: ModelFormat + vocab: BaseVocab | None # For GGML models (which have vocab built in), the vocab. def merge_sharded(models: list[LazyModel]) -> LazyModel: @@ -681,7 +472,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel: names = {name: None for model in models for name in model} def convert(name: str) -> LazyTensor: - lazy_tensors: list[LazyTensor] = [model[name] for model in models] + lazy_tensors = [model[name] for model in models] if len(lazy_tensors) == 1: # only one file; don't go through this procedure since there might # be quantized tensors @@ -702,7 +493,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel: def load() -> UnquantizedTensor: ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors] - concatenated: NDArray = np.concatenate(ndarrays, axis=axis) + concatenated = np.concatenate(ndarrays, axis=axis) return UnquantizedTensor(concatenated) description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]' return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description) @@ -710,7 +501,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel: def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: - formats = set(mp.format for mp in models_plus) + formats: set[ModelFormat] = set(mp.format for mp in models_plus) assert len(formats) == 1, "different formats?" format = formats.pop() paths = [path for mp in models_plus for path in mp.paths] @@ -729,7 +520,7 @@ def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: else: model = merge_sharded([mp.model for mp in models_plus]) - return ModelPlus(model, paths, format, vocab) # pytype: disable=wrong-arg-types + return ModelPlus(model, paths, format, vocab) def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor: @@ -754,6 +545,15 @@ def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description) +def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor: + def load() -> Tensor: + tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors] + return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors])) + s = lazy_tensors[0].shape.copy() + s.insert(0, len(lazy_tensors)) + return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors)) + + # Functionality that simulates `torch.load` but where individual tensors are # only loaded into memory on demand, not all at once. # PyTorch can't do this natively as of time of writing: @@ -790,10 +590,10 @@ class LazyUnpickler(pickle.Unpickler): def load(offset: int, elm_count: int) -> NDArray: dtype = data_type.dtype - fp = self.zip_file.open(info) - fp.seek(offset * dtype.itemsize) - size = elm_count * dtype.itemsize - data = fp.read(size) + with self.zip_file.open(info) as fp: + fp.seek(offset * dtype.itemsize) + size = elm_count * dtype.itemsize + data = fp.read(size) assert len(data) == size return np.frombuffer(data, dtype) description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}' @@ -814,7 +614,7 @@ class LazyUnpickler(pickle.Unpickler): def rebuild_from_type_v2(func, new_type, args, state): return func(*args) - CLASSES: dict[tuple[str, str], Any] = { + CLASSES: dict[tuple[str, str], type[LazyTensor] | LazyStorageKind] = { # getattr used here as a workaround for mypy not being smart enough to determine # the staticmethods have a __func__ attribute. ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), @@ -873,7 +673,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: def must_read(fp: IO[bytes], length: int) -> bytes: ret = fp.read(length) if len(ret) < length: - raise Exception("unexpectedly reached end of file") + raise EOFError("unexpectedly reached end of file") return ret @@ -931,21 +731,24 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc yield result -def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None: +def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None: # Handle special case where the model's vocab size is not set if params.n_vocab == -1: raise ValueError( - f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?" + "The model's vocab size is set to -1 in params.json. Please update it manually." + + (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""), ) + if not isinstance(vocab, Vocab): + return # model has no vocab # Check for a vocab size mismatch if params.n_vocab == vocab.vocab_size: - print("Ignoring added_tokens.json since model matches vocab size without it.") + logger.warning("Ignoring added_tokens.json since model matches vocab size without it.") return if pad_vocab and params.n_vocab > vocab.vocab_size: pad_count = params.n_vocab - vocab.vocab_size - print( + logger.debug( f"Padding vocab with {pad_count} token(s) - through " ) for i in range(1, pad_count + 1): @@ -960,27 +763,128 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N if vocab.vocab_size < params.n_vocab: msg += " Add the --pad-vocab option and try again." - raise Exception(msg) + raise ValueError(msg) class OutputFile: - def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None: + def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE): self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess) - def add_meta_arch(self, params: Params) -> None: + def add_meta_model(self, params: Params, metadata: gguf.Metadata | None) -> None: + # Metadata About The Model And Its Provenence name = "LLaMA" - - # TODO: better logic to determine model name - if params.n_ctx == 4096: - name = "LLaMA v2" + if metadata is not None and metadata.name is not None: + name = metadata.name elif params.path_model is not None: - name = str(params.path_model.parent).split('/')[-1] + name = params.path_model.name + elif params.n_ctx == 4096: + # Heuristic detection of LLaMA v2 model + name = "LLaMA v2" - self.gguf.add_name (name) - self.gguf.add_context_length (params.n_ctx) - self.gguf.add_embedding_length (params.n_embd) - self.gguf.add_block_count (params.n_layer) - self.gguf.add_feed_forward_length (params.n_ff) + self.gguf.add_name(name) + + if metadata is not None: + if metadata.author is not None: + self.gguf.add_author(metadata.author) + if metadata.version is not None: + self.gguf.add_version(metadata.version) + if metadata.organization is not None: + self.gguf.add_organization(metadata.organization) + + if metadata.finetune is not None: + self.gguf.add_finetune(metadata.finetune) + if metadata.basename is not None: + self.gguf.add_basename(metadata.basename) + + if metadata.description is not None: + self.gguf.add_description(metadata.description) + if metadata.quantized_by is not None: + self.gguf.add_quantized_by(metadata.quantized_by) + + if metadata.size_label is not None: + self.gguf.add_size_label(metadata.size_label) + + if metadata.license is not None: + self.gguf.add_license(metadata.license) + if metadata.license_name is not None: + self.gguf.add_license_name(metadata.license_name) + if metadata.license_link is not None: + self.gguf.add_license_link(metadata.license_link) + + if metadata.url is not None: + self.gguf.add_url(metadata.url) + if metadata.doi is not None: + self.gguf.add_doi(metadata.doi) + if metadata.uuid is not None: + self.gguf.add_uuid(metadata.uuid) + if metadata.repo_url is not None: + self.gguf.add_repo_url(metadata.repo_url) + + if metadata.source_url is not None: + self.gguf.add_source_url(metadata.source_url) + if metadata.source_doi is not None: + self.gguf.add_source_doi(metadata.source_doi) + if metadata.source_uuid is not None: + self.gguf.add_source_uuid(metadata.source_uuid) + if metadata.source_repo_url is not None: + self.gguf.add_source_repo_url(metadata.source_repo_url) + + if metadata.base_models is not None: + self.gguf.add_base_model_count(len(metadata.base_models)) + for key, base_model_entry in enumerate(metadata.base_models): + if "name" in base_model_entry: + self.gguf.add_base_model_name(key, base_model_entry["name"]) + if "author" in base_model_entry: + self.gguf.add_base_model_author(key, base_model_entry["author"]) + if "version" in base_model_entry: + 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: + self.gguf.add_base_model_doi(key, base_model_entry["doi"]) + if "uuid" in base_model_entry: + self.gguf.add_base_model_uuid(key, base_model_entry["uuid"]) + 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) + + def add_meta_arch(self, params: Params) -> None: + # Metadata About The Neural Architecture Itself + self.gguf.add_vocab_size(params.n_vocab) + self.gguf.add_context_length(params.n_ctx) + self.gguf.add_embedding_length(params.n_embd) + self.gguf.add_block_count(params.n_layer) + self.gguf.add_feed_forward_length(params.n_ff) self.gguf.add_rope_dimension_count(params.n_embd // params.n_head) self.gguf.add_head_count (params.n_head) self.gguf.add_head_count_kv (params.n_head_kv) @@ -1004,8 +908,8 @@ class OutputFile: self.gguf.add_rope_scaling_type(params.rope_scaling_type) self.gguf.add_rope_scaling_factor(params.f_rope_scale) - if params.n_orig_ctx is not None: - self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx) + if params.n_ctx_orig is not None: + self.gguf.add_rope_scaling_orig_ctx_len(params.n_ctx_orig) if params.rope_finetuned is not None: self.gguf.add_rope_scaling_finetuned(params.rope_finetuned) @@ -1013,20 +917,6 @@ class OutputFile: if params.ftype is not None: self.gguf.add_file_type(params.ftype) - def handle_tokenizer_model(self, vocab: Vocab) -> str: - # Map the vocab types to the supported tokenizer models - tokenizer_model = { - SentencePieceVocab: "llama", - HfVocab: "llama", - BpeVocab: "gpt2", - }.get(type(vocab)) - - # Block if vocab type is not predefined - if tokenizer_model is None: - raise ValueError("Unknown vocab type: Not supported") - - return tokenizer_model - def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]: tokens = [] scores = [] @@ -1043,11 +933,8 @@ class OutputFile: return tokens, scores, toktypes def add_meta_vocab(self, vocab: Vocab) -> None: - # Handle the tokenizer model - tokenizer_model = self.handle_tokenizer_model(vocab) - # Ensure that tokenizer_model is added to the GGUF model - self.gguf.add_tokenizer_model(tokenizer_model) + self.gguf.add_tokenizer_model(vocab.tokenizer_model) # Extract model vocabulary for model conversion tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab) @@ -1074,19 +961,40 @@ class OutputFile: def write_tensor_info(self) -> None: self.gguf.write_ti_data_to_file() + def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None: + ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency) + if ftype == GGMLFileType.MostlyQ8_0: + ndarrays = bounded_parallel_map( + OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency, + use_processpool_executor=True, + ) + else: + ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) + + start = time.time() + for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): + elapsed = time.time() - start + size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) + padi = len(str(len(model))) + logger.info( + f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}" + ) + self.gguf.write_tensor_data(ndarray) + def close(self) -> None: self.gguf.close() @staticmethod def write_vocab_only( fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, - endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, + endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: gguf.Metadata | None = None, ) -> None: - check_vocab_size(params, vocab, pad_vocab = pad_vocab) + check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) # meta data + of.add_meta_model(params, metadata) of.add_meta_arch(params) of.add_meta_vocab(vocab) of.add_meta_special_vocab(svocab) @@ -1110,18 +1018,23 @@ class OutputFile: @staticmethod def write_all( - fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, + fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, + metadata: gguf.Metadata | None = None, ) -> None: check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) # meta data + of.add_meta_model(params, metadata) of.add_meta_arch(params) - of.add_meta_vocab(vocab) - of.add_meta_special_vocab(svocab) + if isinstance(vocab, Vocab): + of.add_meta_vocab(vocab) + of.add_meta_special_vocab(svocab) + else: # NoVocab + of.gguf.add_tokenizer_model(vocab.tokenizer_model) # tensor info for name, lazy_tensor in model.items(): @@ -1131,24 +1044,7 @@ class OutputFile: of.write_tensor_info() # tensor data - ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency) - if ftype == GGMLFileType.MostlyQ8_0: - ndarrays = bounded_parallel_map( - OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency, - use_processpool_executor=True, - ) - else: - ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) - - start = time.time() - for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): - elapsed = time.time() - start - size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) - padi = len(str(len(model))) - print( - f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}" - ) - of.gguf.write_tensor_data(ndarray) + of.write_tensor_data(ftype, model, concurrency) of.close() @@ -1156,16 +1052,44 @@ class OutputFile: def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type - if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): + if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)): return GGMLFileType.AllF32 - if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)): + if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16): return GGMLFileType.MostlyF16 if output_type_str == "q8_0": return GGMLFileType.MostlyQ8_0 name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} - raise Exception(f"Unexpected combination of types: {name_to_type}") + raise ValueError(f"Unexpected combination of types: {name_to_type}") + + +def per_model_weight_count_estimation(tensors: Iterable[tuple[str, LazyTensor]]) -> tuple[int, int, int]: + total_params = 0 + shared_params = 0 + expert_params = 0 + + for name, lazy_tensor in tensors: + # We don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + # Got A Tensor + sum_weights_in_tensor: int = 1 + + # Tensor Volume + for dim in lazy_tensor.shape: + sum_weights_in_tensor *= dim + + if ".experts." in name: + if ".experts.0." in name: + expert_params += sum_weights_in_tensor + else: + shared_params += sum_weights_in_tensor + + total_params += sum_weights_in_tensor + + return total_params, shared_params, expert_params def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: @@ -1175,19 +1099,35 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel: tmap = gguf.TensorNameMap(ARCH, params.n_layer) - should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) + should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) tmp = model + # merge experts into one tensor + if params.n_experts and params.n_experts > 0: + for i_l in range(params.n_layer): + for w in range(1, 4): + experts = [] + for e in range(params.n_experts): + if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model: + experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]) + del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"] + elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model: + experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]) + del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"] + else: + raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight") + tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts) + # HF models permut or pack some of the tensors, so we need to undo that for i in itertools.count(): if f"model.layers.{i}.self_attn.q_proj.weight" in model: - print(f"Permuting layer {i}") + logger.debug(f"Permuting layer {i}") tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head) tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv) # tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] elif f"model.layers.{i}.self_attn.W_pack.weight" in model: - print(f"Unpacking and permuting layer {i}") + logger.debug(f"Unpacking and permuting layer {i}") tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) @@ -1200,16 +1140,15 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None) if name_new is None: if skip_unknown: - print(f"Unexpected tensor name: {name} - skipping") + logger.warning(f"Unexpected tensor name: {name} - skipping") continue - else: - raise Exception(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)") + raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)") if tensor_type in should_skip: - print(f"skipping tensor {name_new}") + logger.debug(f"skipping tensor {name_new}") continue - print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") + logger.debug(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") out[name_new] = lazy_tensor return out @@ -1220,7 +1159,7 @@ def nth_multifile_path(path: Path, n: int) -> Path | None: the nth path in the model. ''' # Support the following patterns: - patterns: list[tuple[str, str]] = [ + patterns = [ # - x.00.pth, x.01.pth, etc. (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. @@ -1259,22 +1198,22 @@ def load_some_model(path: Path) -> ModelPlus: # Be extra-friendly and accept either a file or a directory: if path.is_dir(): # Check if it's a set of safetensors files first - globs = ["model-00001-of-*.safetensors", "model.safetensors"] + globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"] files = [file for glob in globs for file in path.glob(glob)] if not files: # Try the PyTorch patterns too, with lower priority globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"] files = [file for glob in globs for file in path.glob(glob)] if not files: - raise Exception(f"Can't find model in directory {path}") + raise FileNotFoundError(f"Can't find model in directory {path}") if len(files) > 1: - raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}") + raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}") path = files[0] paths = find_multifile_paths(path) models_plus: list[ModelPlus] = [] for path in paths: - print(f"Loading model file {path}") + logger.info(f"Loading model file {path}") models_plus.append(lazy_load_file(path)) model_plus = merge_multifile_models(models_plus) @@ -1282,39 +1221,14 @@ def load_some_model(path: Path) -> ModelPlus: class VocabFactory: + _VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab] + def __init__(self, path: Path): self.path = path - self.files: dict[str, Path | None] = { - "tokenizer.model": None, - "vocab.json": None, - "tokenizer.json": None, - } - self._detect_files() - def _detect_files(self): - for file in self.files.keys(): - file_path = self.path / file - parent_file_path = self.path.parent / file - if file_path.exists(): - self.files[file] = file_path - elif parent_file_path.exists(): - self.files[file] = parent_file_path - print(f"Found vocab files: {self.files}") - - def _select_file(self, vocabtype: str | None) -> Path: - if vocabtype in ["spm", "bpe"]: - for file_key in self.files.keys(): - if (file := self.files[file_key]) is not None: - return file - raise FileNotFoundError(f"{vocabtype} vocab not found.") - if vocabtype == "hfft": - # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file - return self.path - raise ValueError(f"Unsupported vocabulary type {vocabtype}") - - def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab: - load_merges = vocabtype == "bpe" - n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None + def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab: + load_merges = vocab.name == "bpe" + n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None return gguf.SpecialVocab( model_parent_path, load_merges=load_merges, @@ -1322,56 +1236,74 @@ class VocabFactory: n_vocab=n_vocab, ) - def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: - path = self._select_file(vocabtype) - print(f"Loading vocab file '{path}', type '{vocabtype}'") + def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab: + vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES} + selected_vocabs: dict[str, type[Vocab]] = {} + for vtype in vocab_types: + try: + selected_vocabs[vtype] = vocab_classes[vtype] + except KeyError: + raise ValueError(f"Unsupported vocabulary type {vtype}") from None - added_tokens_path = path.parent / "added_tokens.json" - vocab: Vocab - if vocabtype == "bpe": - vocab = BpeVocab( - path, added_tokens_path if added_tokens_path.exists() else None - ) - elif vocabtype == "spm": - vocab = SentencePieceVocab( - path, added_tokens_path if added_tokens_path.exists() else None - ) - elif vocabtype == "hfft": - vocab = HfVocab( - path, added_tokens_path if added_tokens_path.exists() else None - ) + for vtype, cls in selected_vocabs.items(): + try: + vocab = cls(self.path) + break + except FileNotFoundError: + pass # ignore unavailable tokenizers else: - raise ValueError(f"Unsupported vocabulary type {vocabtype}") + raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}") + + logger.info(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}") + return vocab + + def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]: + vocab: BaseVocab + if vocab_types is None: + vocab = NoVocab() + else: + vocab = self._create_vocab_by_path(vocab_types) # FIXME: Respect --vocab-dir? special_vocab = self._create_special_vocab( vocab, - vocabtype, model_parent_path, ) return vocab, special_vocab -def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: - namestr = { - GGMLFileType.AllF32: "f32", - GGMLFileType.MostlyF16: "f16", - GGMLFileType.MostlyQ8_0:"q8_0", +def default_convention_outfile(file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> str: + name = metadata.name if metadata.name is not None else None + basename = metadata.basename if metadata.basename is not None else None + finetune = metadata.finetune if metadata.finetune is not None else None + version = metadata.version if metadata.version is not None else None + size_label = metadata.size_label if metadata.size_label is not None else gguf.size_label(*model_params_count, expert_count=expert_count or 0) + + output_type = { + GGMLFileType.AllF32: "F32", + GGMLFileType.MostlyF16: "F16", + GGMLFileType.MostlyQ8_0: "Q8_0", }[file_type] - ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" + + return gguf.naming_convention(name, basename, finetune, version, size_label, output_type) + + +def default_outfile(model_paths: list[Path], file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> Path: + default_filename = default_convention_outfile(file_type, expert_count, model_params_count, metadata) + ret = model_paths[0].parent / f"{default_filename}.gguf" if ret in model_paths: - sys.stderr.write( + logger.error( f"Error: Default output path ({ret}) would overwrite the input. " - "Please explicitly specify a path using --outfile.\n") + "Please explicitly specify a path using --outfile.") sys.exit(1) return ret def do_dump_model(model_plus: ModelPlus) -> None: - print(f"model_plus.paths = {model_plus.paths!r}") - print(f"model_plus.format = {model_plus.format!r}") - print(f"model_plus.vocab = {model_plus.vocab!r}") + print(f"model_plus.paths = {model_plus.paths!r}") # noqa: NP100 + print(f"model_plus.format = {model_plus.format!r}") # noqa: NP100 + print(f"model_plus.vocab = {model_plus.vocab!r}") # noqa: NP100 for name, lazy_tensor in model_plus.model.items(): - print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") + print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") # noqa: NP100 def main(args_in: list[str] | None = None) -> None: @@ -1379,15 +1311,14 @@ def main(args_in: list[str] | None = None) -> None: if np.uint32(1) == np.uint32(1).newbyteorder("<"): # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") - vocab_types = ["spm", "bpe", "hfft"] - parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") - parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) + parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--no-vocab", action="store_true", help="store model without the vocab") parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm") + parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") @@ -1395,88 +1326,136 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine") parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + parser.add_argument("--metadata", type=Path, help="Specify the path for an authorship metadata override file") + parser.add_argument("--get-outfile", action="store_true", help="get calculated default outfile name") + parser.add_argument("--model-name", type=str, default=None, help="name of the model") args = parser.parse_args(args_in) - if args.awq_path: - sys.path.insert(1, str(Path(__file__).parent / 'awq-py')) - from awq.apply_awq import add_scale_weights # type: ignore[import-not-found] - tmp_model_path = args.model / "weighted_model" - if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") - else: - tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") - add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") - args.model = tmp_model_path + + if args.verbose: + logging.basicConfig(level=logging.DEBUG) + elif args.dump_single or args.dump or args.get_outfile: + # Avoid printing anything besides the dump output + logging.basicConfig(level=logging.WARNING) + else: + logging.basicConfig(level=logging.INFO) + + model_name = args.model_name + dir_model = args.model + + metadata = gguf.Metadata.load(args.metadata, dir_model, model_name) + + if args.get_outfile: + model_plus = load_some_model(dir_model) + params = Params.load(model_plus) + model = convert_model_names(model_plus.model, params, args.skip_unknown) + model_params_count = per_model_weight_count_estimation(model_plus.model.items()) + ftype = pick_output_type(model, args.outtype) + + if (metadata is None or metadata.name is None) and params.path_model is not None: + metadata.name = params.path_model.name + + print(f"{default_convention_outfile(ftype, params.n_experts, model_params_count, metadata)}") # noqa: NP100 + return + + if args.no_vocab and args.vocab_only: + raise ValueError("--vocab-only does not make sense with --no-vocab") if args.dump_single: - model_plus = lazy_load_file(args.model) + model_plus = lazy_load_file(dir_model) do_dump_model(model_plus) return if not args.vocab_only: - model_plus = load_some_model(args.model) + model_plus = load_some_model(dir_model) else: - model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None) + model_plus = ModelPlus(model = {}, paths = [dir_model / 'dummy'], format = 'none', vocab = None) if args.dump: do_dump_model(model_plus) return + endianess = gguf.GGUFEndian.LITTLE if args.big_endian: endianess = gguf.GGUFEndian.BIG - params = Params.load(model_plus) - if params.n_ctx == -1: - if args.ctx is None: - raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n" - "Please specify one with --ctx:\n" - " - LLaMA v1: --ctx 2048\n" - " - LLaMA v2: --ctx 4096\n") - params.n_ctx = args.ctx + params = None + if args.pad_vocab or not args.vocab_only: + params = Params.load(model_plus) + if params.n_ctx == -1: + if args.ctx is None: + msg = """\ + The model doesn't have a context size, and you didn't specify one with --ctx + Please specify one with --ctx: + - LLaMA v1: --ctx 2048 + - LLaMA v2: --ctx 4096""" + parser.error(textwrap.dedent(msg)) + params.n_ctx = args.ctx - if args.outtype: - params.ftype = { - "f32": GGMLFileType.AllF32, - "f16": GGMLFileType.MostlyF16, - "q8_0": GGMLFileType.MostlyQ8_0, - }[args.outtype] + if args.outtype: + params.ftype = { + "f32": GGMLFileType.AllF32, + "f16": GGMLFileType.MostlyF16, + "q8_0": GGMLFileType.MostlyQ8_0, + }[args.outtype] - print(f"params = {params}") + logger.info(f"params = {params}") model_parent_path = model_plus.paths[0].parent - vocab_path = Path(args.vocab_dir or args.model or model_parent_path) + vocab_path = Path(args.vocab_dir or dir_model or model_parent_path) vocab_factory = VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path) + vocab_types = None if args.no_vocab else args.vocab_type.split(",") + vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path) if args.vocab_only: + assert isinstance(vocab, Vocab) if not args.outfile: raise ValueError("need --outfile if using --vocab-only") outfile = args.outfile + if params is None: + params = Params( + n_vocab = vocab.vocab_size, + n_embd = 1, + n_layer = 1, + n_ctx = 1, + n_ff = 1, + n_head = 1, + n_head_kv = 1, + f_norm_eps = 1e-5, + ) OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, - endianess=endianess, pad_vocab=args.pad_vocab) - print(f"Wrote {outfile}") + endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) + logger.info(f"Wrote {outfile}") return - if model_plus.vocab is not None and args.vocab_dir is None: + if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab: vocab = model_plus.vocab - print(f"Vocab info: {vocab}") - print(f"Special vocab info: {special_vocab}") + assert params is not None + if metadata.name is None and params.path_model is not None: + metadata.name = params.path_model.name + + model_params_count = per_model_weight_count_estimation(model_plus.model.items()) + logger.info(f"model parameters count : {model_params_count} ({gguf.model_weight_count_rounded_notation(model_params_count[0])})") + + logger.info(f"Vocab info: {vocab}") + logger.info(f"Special vocab info: {special_vocab}") model = model_plus.model model = convert_model_names(model, params, args.skip_unknown) ftype = pick_output_type(model, args.outtype) model = convert_to_output_type(model, ftype) - outfile = args.outfile or default_outfile(model_plus.paths, ftype) + outfile = args.outfile or default_outfile(model_plus.paths, ftype, params.n_experts, model_params_count, metadata=metadata) + + metadata.size_label = gguf.size_label(*model_params_count, expert_count=params.n_experts or 0) params.ftype = ftype - print(f"Writing {outfile}, format {ftype}") + logger.info(f"Writing {outfile}, format {ftype}") OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, - concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab) - print(f"Wrote {outfile}") + concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) + logger.info(f"Wrote {outfile}") if __name__ == '__main__': diff --git a/examples/cvector-generator/CMakeLists.txt b/examples/cvector-generator/CMakeLists.txt new file mode 100644 index 000000000..49ad9561c --- /dev/null +++ b/examples/cvector-generator/CMakeLists.txt @@ -0,0 +1,5 @@ +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_17) diff --git a/examples/cvector-generator/README.md b/examples/cvector-generator/README.md new file mode 100644 index 000000000..be4dd5250 --- /dev/null +++ b/examples/cvector-generator/README.md @@ -0,0 +1,45 @@ +# cvector-generator + +This example demonstrates how to generate a control vector using gguf models. + +Related PRs: +- [Add support for control vectors](https://github.com/ggerganov/llama.cpp/pull/5970) +- (Issue) [Generate control vector using llama.cpp](https://github.com/ggerganov/llama.cpp/issues/6880) +- [Add cvector-generator example](https://github.com/ggerganov/llama.cpp/pull/7514) + +## Examples + +```sh +# CPU only +./cvector-generator -m ./llama-3.Q4_K_M.gguf + +# With GPU +./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99 + +# With advanced options +./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100 + +# Using mean value instead of PCA +./cvector-generator -m ./llama-3.Q4_K_M.gguf --method mean + +# To see help message +./cvector-generator -h +# Then, have a look at "cvector" section +``` + +## Tips and tricks + +If you have multiple lines per prompt, you can escape the newline character (change it to `\n`). For example: + +``` +<|im_start|>system\nAct like a person who is extremely happy.<|im_end|> +<|im_start|>system\nYou are in a very good mood today<|im_end|> +``` + +Example to use output file with `llama-cli`: + +(Tips: The control vector works better when apply to layers higher than 10) + +```sh +./llama-cli -m ./llama-3.Q4_K_M.gguf -p "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSing a song<|im_end|><|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" --special --control-vector-scaled ./control_vector.gguf 0.8 --control-vector-layer-range 10 31 +``` diff --git a/examples/cvector-generator/completions.txt b/examples/cvector-generator/completions.txt new file mode 100644 index 000000000..abc45ffd8 --- /dev/null +++ b/examples/cvector-generator/completions.txt @@ -0,0 +1,582 @@ + +That game +I can see +Hmm, this +I can relate to +Who is +I understand the +Ugh, +What the hell was +Hey, did anyone +Although +Thank you for choosing +What are you +Oh w +How dare you open +It was my pleasure +I'm hon +I appreciate that you +Are you k +Whoever left this +It's always +Ew, +Hey, I l +Hello? Is someone +I understand that +That poem +Aww, poor +Hey, it +Alright, who +I didn't +Well, life +The document +Oh no, this +I'm concerned +Hello, this is +This art +Hmm, this drink +Hi there! +It seems +Is +Good +I can't +Ex +Who are +I can see that +Wow, +Today is a +Hey friend +Sometimes friends +Oh, this old +The weather outside +This place is sur +I appreciate your input +Thank you for the +Look at +I'm disappoint +To my +How dare you +That's an +This piece of art +Eww +This park is +This is incredible +Oh no, someone +Exc +Well, it' +I warned +Hey, I understand +Hey, I saw +How dare you go +What the he +Hey +It's +Hello? Hello? +It +Oh no! +This is the perfect +Good morning, +Oh no, there +It's so +Yeah +Uh, +Hello everyone +Who turned off +The weather +Who' +Hey, this +Wait, +Eww, gross +Excuse +It seems like you +Thank you so +What happened? +Oh my g +I am deeply sad +I war +Okay, let' +Hey, that +That was a beautiful +Oh no! That +What happened +Hey there +The artist' +What?! +Hey, it' +I am disappoint +It seems like +Oh no! The +This park is a +If you +Yes! I did +It sounds +What +Who is it +Hmm, that +That's strange +Yeah, that was +That's interesting +This park +What the hell +Who is that +I feel like my +Oh well +What the hell is +Hello? Hello +To my dearest +Bless you!\" +Thank you for +Oh, looks like +Can you please +This place is +Eww, what +Bless you +Is everything +Hey, I just +Whoever left these +Well, that' +I feel +Hey, do you +It's sad +Oh no, it +Hey, that' +Oh my god, +Thank you, +Hello little one, +I apolog +Hey team, I +How dare you read +Who is this and +Whoever left +Hi there! W +A +If you have +I was +U +Bless +Well, this +Oh, I' +It's a +Eww, +Is everything okay? +Oh, I +Hello, can you +Al +That was a great +What are +I understand that not +Oh no, not +Who is it?\" +Hey, can we +Whoever is taking +I would love to +Hey, I noticed +Hey, could +I understand that there +Hello? +D +Oh man, I +Thank you so much +Oh no, my +Dear [Name +Uh +I remember +Hey, who +Well, it +Are you +I understand that it +Hey, is +I would +Who is this +Excuse me +Alright +I am thrilled +Sometimes friends have +Who the +It's interesting +I would love +E +Hello? Is anyone +Well, this is +This place +Well, +I warned you +Hey, watch where +Oh my +That' +Sometimes friends have different +I understand that everyone +What? +What do these notes +I can relate +I'm not +I understand +To my dear +Guys +Well +Hey, I appreciate +Wow, what +Dear +That melody +Who the hell +Today is +Hello little +Wow, look +That's great +Love is never wrong +I'm having +Whoa, did +Ugh +Can you please provide +I miss you, +I feel uncom +I know +Ugh, this +Hey, watch +Oh great, a +I didn +Okay +That game of char +Oh +I appreciate +Who's there +I am so +Oh great, someone +Hey, could you +I remember wondering +Wait, what? +What do +Hello? Can +Hey there, +That game of +This is incred +Oh my gosh +Oh great, f +I appreciate your +It sounds like +What the heck +Okay, I understand +Ew +I understand that this +Uh, hi +Hi everyone! +What the hell? +Thank you for your +Oh no, the +Wow, I +Who turned +Dear [ +Whoever +This is a +Whoa, he +What in the world +Although the physical +Hello, who is +That's amaz +Hey, I know +Okay, that +Hi everyone +Hey, is everything +I understand your fr +Oh no, poor +Oh, look +Good morning +Ew, gross +Oh no, did +Look at the family +Hey team +Yes! +Hey, can I +Okay, that' +It's great +Love is +Hey, what +Good morning, world +Who is it? +That poem really reson +I +That's +I understand the task +Gu +Hello? Who' +This postcard is +Whoa, +Oh, that +I understand that I +Whoever is +Hello? Who is +I'm really +Wow, this +Can +This artwork really +This is a shame +I miss you too +Who are you? +Today is a difficult +Hey, just +Are you okay +I am +Hi, +Wow, that +Hey there! Can +Okay, stay +Oh great, just +Yeah, +Hello? Can you +Oh, looks +Thank you for sharing +I'm glad +Hey, is that +Hmm +It was my +It sounds like you +Wow, your +I was promised certain +That was such a +Thank +Excuse you +That was +Hey team, +I feel un +It was +What' +Hey friend, I +How +Saying goodbye +That +It's heart +How dare +Oh, +Hello, may +What's this +Thank you for recogn +Aww, that +Oh, I remember +Hmm, that' +I miss +I know this +Wait +Is everything okay +Who is that person +Wow, you +Oh great +I'm sad +Wow, the +I am very disappoint +Who turned off the +I understand that things +I'm very +Hi +That's very +Okay, I +Oh no, +Wow, there +What's wrong +I apologize for +Hey, I +Can I help you +Oh, I didn +Alright, +Oh wow, +Oh my goodness +I know this event +What in the +Saying +Yeah, that +Guys, I +Hey, this v +This post +Are +Hey, can +Hello? Is +I can only imagine +Oh, that sounds +Hey, is anyone +I am disappointed +Hello, +Hey everyone, I +That was such +It's okay +The artist +Whoa +I understand that mistakes +Can I help +Who +Hi everyone! I +Hey, can you +Wow, how +Today +Oh no, I +Oh well, I +Well, that +This is the +Yes! I finally +Hey there little +Hello everyone! +Love is never +Look at the +This postcard +Oh great, +Can I +Hmm, this is +I understand your +Oh, look at +B +I'm so +Whoa, this +W +Oh, this +Sometimes +This piece of +What the +That was a +Hey, do +Oh no +Whoa, what +I feel like I +The documentary +Hello +Hello little one +I understand that my +Eww, that +Wow, an +Yes! Finally, +Although the physical location +Whoever is watching +That movie +I remember wondering about +Hey there, little +Who's +Hello, who +Hello everyone! Thank +Hello, can +That's too +Hey, just wanted +Hey there, I +Saying good +Hey there! +Who is there? +Oh my good +I am very +Oh no, what +Wow, thank +I was promised +Hi, is +Hey, I' +Guys, the +Oh no, that +Who is there +Hello, this +That movie really touched +If you have something +The documentary was +I'm starting +Are you kidd +That movie really +Hey everyone, +Thank you for considering +I didn' +Yes! I +Can you +Oh my god +Hey, whoever +That melody really +Thank you, little +Hello, may I +Look +Wow, we +It looks +What do these +Oh wow +I apologize +What are you all +It's such +It's clear +Hey, I was +Hey friend, +I can only +The weather outside is +Eww, this +I miss you +Wow +Aww, +Hi, is there +This artwork +Okay, +Oh well, +This +I' +Say +Hey there little gu +Hmm, +Whoa, who +I am thr +Oh man +Okay, stay calm +I'm happy +Oh, this cur +Oh man, +I'm sorry +Hello? Who +What?! That +This piece +Hey everyone +That's so +Are you okay? +What happened? Where +Hi there +The +Who the hell entered +I can +Guys, +What's +What in +It's important +I'm +I'm coming +It' +Yes! Finally +Wait, what +Wow, reading +I'm surprised +Hey, did +Hey, +Okay, let +I understand that you +Who the hell threw +Eww, who +Thank you for thinking +Who is this?\" +I am deeply +Thank you for including +Oh no, an +It looks like you +Aww +I'm confused +Wow, it +That poem really +Yes +Hey there, is +Hey, what' +Thank you for remember +To +This is +Thank you for making +I can' +That mel +Wow, they +I feel like +Although the +Who are you +Love +If +What the hell are +I am so sad +Oh, I found +Thank you +It looks like +Well, life is +I appreciate that +The artist's +Whoa, that +It's never \ No newline at end of file diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp new file mode 100644 index 000000000..413b71d34 --- /dev/null +++ b/examples/cvector-generator/cvector-generator.cpp @@ -0,0 +1,506 @@ +#include "ggml.h" +#include "gguf.h" + +#include "arg.h" +#include "common.h" +#include "llama.h" +#include "pca.hpp" +#include "mean.hpp" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +////////////////////////////////////////////////// +// utils + +template +static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { + std::string ret; + for (; begin != end; ++begin) { + ret += common_token_to_piece(ctx, *begin); + } + + return ret; +} + +static void print_usage(int, char ** argv) { + printf("\nexample usage:\n"); + printf("\n CPU only: %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]); + printf("\n with GPU: %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]); + printf("\n advanced: %s -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100\n", argv[0]); + printf("\n using mean: %s -m ./llama-3.Q4_K_M.gguf --method mean\n", argv[0]); + printf("\n"); +} + +////////////////////////////////////////////////// + + +// cb_eval is reused for each pair of positive - negative prompt +struct callback_data { + ggml_context * ctx_ggml = nullptr; // holds v_pos, v_neg, v_diff_filtered + + int n_layers = 0; + int n_tokens = 0; + bool is_eval_pos = true; + + // each element of the vector correspond to one layer + std::vector v_pos; // vector of matrices of size [n_embd, n_tokens] + std::vector v_neg; // vector of matrices of size [n_embd, n_tokens] + std::vector v_diff_filtered; // vector of matrices of size [n_embd, n_nonzero_rows]. NOTE: n_nonzero_rows maybe different for each layer + + // save a tensor into either v_pos or v_neg (decided by is_eval_pos) + void save_tensor_for_layer(struct ggml_tensor * t) { + GGML_ASSERT(t->type == GGML_TYPE_F32); + + if (ctx_ggml == nullptr) { + // alloc a new ctx_ggml if needed + struct ggml_init_params params_ggml = { + /*.mem_size =*/ ggml_tensor_overhead() * n_layers * 3u, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ctx_ggml = ggml_init(params_ggml); + } + + // copy tensor data + auto n_bytes = ggml_nbytes(t); + struct ggml_tensor * t_layer = ggml_new_tensor_2d(ctx_ggml, t->type, t->ne[0], t->ne[1]); + t_layer->data = malloc(n_bytes); // TODO @ngxson : get rid of this malloc somehow + ggml_backend_tensor_get(t, t_layer->data, 0, n_bytes); + ggml_set_name(t_layer, ggml_get_name(t)); + //print_debug_tensor(t_layer); + + if (is_eval_pos) { + v_pos.push_back(t_layer); + } else { + v_neg.push_back(t_layer); + } + } + + // calculate diff (v_pos - v_neg) and place the result back to v_pos + // all zero rows in the diff tensor will also be removed + // NOTE: final layer is ignored. we only have (n_layers - 1) to process + std::vector calc_diff() { + for (float il = 0; il < v_pos.size(); il++) { + float * a = (float *) v_pos[il]->data; + float * b = (float *) v_neg[il]->data; + size_t n_elem = ggml_nelements(v_pos[il]); + for (size_t j = 0; j < n_elem; j++) { + a[j] -= b[j]; + } + //print_debug_tensor(v_pos[i]); + auto diff_filtered = filter_nonzero_rows(v_pos[il]); + v_diff_filtered.push_back(diff_filtered); + } + return v_diff_filtered; // for convinient, we return the result std::vector + } + + // delete zero rows from a given 2D tensor + struct ggml_tensor * filter_nonzero_rows(struct ggml_tensor * a) { + //printf("filter_nonzero_rows\n"); + auto is_row_all_zeros = [](struct ggml_tensor * t, int row, float eps) -> bool { + // check if given row containing all zero elements + int n_cols = t->ne[0]; // hint: should be equal to n_embd + for (int col = 0; col < n_cols; ++col) { + if (ggml_get_f32_nd(t, col, row, 0, 0) > eps) { + return false; + } + } + return true; + }; + std::vector rows_to_copy; // the idx of non-zero cols (to be copied to row of diff_filtered) + for (int i_row = 0; i_row < a->ne[1]; i_row++) { + if (!is_row_all_zeros(a, i_row, 1e-6)) { + rows_to_copy.push_back(i_row); + } + } + + // get "n_nonzero_rows" for the output "diff_filtered" + int n_nonzero_rows = rows_to_copy.size(); + //printf("n_nonzero_rows: %d\n", n_nonzero_rows); + int n_embd = a->ne[0]; + GGML_ASSERT(n_nonzero_rows > 0); + + // diff_filtered: [n_embd, n_nonzero_rows] + struct ggml_tensor * diff_filtered = ggml_new_tensor_2d( + ctx_ggml, GGML_TYPE_F32, n_embd, n_nonzero_rows); + ggml_format_name(diff_filtered, "diff_filtered_%s", a->name); + diff_filtered->data = malloc(ggml_nbytes(diff_filtered)); + + // copy non-zero rows + for (int dest_row = 0; dest_row < n_nonzero_rows; dest_row++) { + int src_row = rows_to_copy[dest_row]; + for (int i = 0; i < n_embd; i++) { + float src_elem = ggml_get_f32_nd(a, i, src_row, 0, 0); + ggml_set_f32_nd(diff_filtered, i, dest_row, 0, 0, src_elem); + } + } + + //print_debug_tensor(diff_filtered); + + return diff_filtered; + } + + // we don't implement destructor, because we want to reuse callback_data. we just want to free the tensors + void reset() { + for (auto ptr : v_pos) free(ptr->data); + for (auto ptr : v_neg) free(ptr->data); + for (auto ptr : v_diff_filtered) free(ptr->data); + v_pos.clear(); + v_neg.clear(); + v_diff_filtered.clear(); + if (ctx_ggml) { + ggml_free(ctx_ggml); + } + ctx_ggml = nullptr; + } +}; + +/** + * process_ctx is used to store the ggml context for pre-post processing the diff vectors + * in short, input => v_diff and output => v_final + */ +struct train_context { + ggml_context * ctx_ggml; + int n_embd; + int n_layers; + + /* pair of prompts to be used for generating final vector */ + std::vector positive_entries; + std::vector negative_entries; + + // each element of the vector correspond to one layer + // NOTE: the last layer is discard. therefore, we will have (n_layers - 1) elements here + // NOTE (2): v_diff is transposed from v_diff_tmp + std::vector v_diff; // vector of matrices of size [m, n_embd] where m ~ n_tokens * n_completions (v_diff contains no zero-rows) + std::vector v_final; // vector of vectors of size [n_embd] to be written to file + + // to easily re-alloc when concat v_diff, we temporary store v_diff in a vector instead of a tensor + // v_diff_tmp will get converted unto v_diff later on + std::vector> v_diff_tmp; + + train_context(int n_embd_, int n_layers_) { + n_embd = n_embd_; + n_layers = n_layers_; + struct ggml_init_params params_ggml = { + /*.mem_size =*/ ggml_tensor_overhead() * (n_layers - 1) * 2u, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ctx_ggml = ggml_init(params_ggml); + for (int il = 0; il < n_layers - 1; il++) { + std::vector empty; + v_diff_tmp.push_back(empty); + auto t = ggml_new_tensor_1d(ctx_ggml, GGML_TYPE_F32, n_embd); + t->data = malloc(ggml_nbytes(t)); // TODO: get rid of malloc if possible + v_final.push_back(t); + } + } + + // add new rows into existing tensor in v_diff_tmp + void concat_diff_tmp(const std::vector & diff_filtered) { + GGML_ASSERT((int) diff_filtered.size() == n_layers - 1); + for (int il = 0; il < n_layers - 1; il++) { + auto t = diff_filtered[il]; + auto & diff_tmp = v_diff_tmp[il]; + size_t curr_size = diff_tmp.size(); + diff_tmp.resize(curr_size + ggml_nbytes(t)); + memcpy(diff_tmp.data() + curr_size, t->data, ggml_nbytes(t)); + } + } + + // build the v_diff tensors from v_diff_tmp (v_diff need to be transposed) + // TODO @ngxson : maybe add option NOT to transpose v_diff; will be useful for "mean" method + void build_v_diff(bool transpose) { + printf("build_v_diff\n"); + for (int il = 0; il < n_layers - 1; il++) { + auto & diff_tmp = v_diff_tmp[il]; + int n_elem = diff_tmp.size() / sizeof(float); + GGML_ASSERT(n_elem % n_embd == 0); + int n_rows = n_elem / n_embd; + struct ggml_tensor * diff = transpose + ? ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd) + : ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_embd, n_rows); + ggml_set_name(diff, (std::string("diff_") + std::to_string(il)).c_str()); + diff->data = malloc(ggml_nbytes(diff)); // TODO: get rid of this malloc if possible + if (transpose) { + // copy data & transpose + float * arr = (float *) diff_tmp.data(); + for (int ir = 0; ir < n_rows; ++ir) { + for (int ic = 0; ic < n_embd; ++ic) { + float f = arr[ir*n_embd + ic]; + ggml_set_f32_nd(diff, ir, ic, 0, 0, f); + } + } + } else { + // only copy + memcpy(diff->data, diff_tmp.data(), ggml_nbytes(diff)); + } + v_diff.push_back(diff); + print_debug_tensor(diff); + // free memory of diff_tmp + diff_tmp.resize(0); + } + } + + ~train_context() { + for (auto ptr : v_final) free(ptr->data); + for (auto ptr : v_diff) free(ptr->data); + // no need to free v_diff_tmp, since we didn't use malloc + ggml_free(ctx_ggml); + } +}; + +struct tokenized_prompt { + std::vector tokens_pos; + std::vector tokens_neg; + size_t max_seq_len; + + tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + const bool add_bos = llama_vocab_get_add_bos(vocab); + tokens_pos = common_tokenize(ctx, pos, add_bos, true); + tokens_neg = common_tokenize(ctx, neg, add_bos, true); + max_seq_len = std::max(tokens_pos.size(), tokens_neg.size()); + padding_seq(ctx, tokens_pos, max_seq_len); + padding_seq(ctx, tokens_neg, max_seq_len); + } + + void padding_seq(llama_context * ctx, std::vector & tokens, size_t len) { + // TODO: customize padding token + std::vector pad_tokens = common_tokenize(ctx, " ", false); + llama_token pad_tok = pad_tokens.back(); + while (tokens.size() < len) { + tokens.push_back(pad_tok); + } + } +}; + +////////////////////////////////////////////////// + +template +static std::string to_string(const T & val) { + std::stringstream ss; + ss << val; + return ss.str(); +} + +static std::vector ctrlvec_load_prompt_file(std::string path, bool skip_empty_lines) { + std::vector output; + std::ifstream file(path); + if (!file.is_open()) { + fprintf(stderr, "error: unable to open file: %s\n", path.c_str()); + exit(1); + } + std::string line; + while (std::getline(file, line)) { + bool is_skip = skip_empty_lines && line.empty(); + if (!is_skip) { + string_process_escapes(line); + output.push_back(line); + } + } + file.close(); + return output; +} + +////////////////////////////////////////////////// + +static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) { + auto * cb_data = (callback_data *) user_data; + static const char * l_out_name = "l_out"; + const bool is_l_out = strncmp(t->name, l_out_name, strlen(l_out_name)) == 0; + + if (ask) { + return is_l_out; + } + + if (!is_l_out || t->ne[1] != cb_data->n_tokens) { + return true; + } + + // save the tensor to current context + cb_data->save_tensor_for_layer(t); + return true; +} + +static bool get_hidden_layers(llama_context * ctx, std::vector & tokens) { + llama_kv_cache_clear(ctx); + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + return true; +} + +static void export_gguf(const std::vector & v_ctrl, const std::string fname, const std::string model_hint) { + struct gguf_context * ctx = gguf_init_empty(); + + const std::string arch = "controlvector"; + gguf_set_val_str(ctx, "general.architecture", arch.c_str()); + gguf_set_val_str(ctx, (arch + ".model_hint").c_str(), model_hint.c_str()); + gguf_set_val_i32(ctx, (arch + ".layer_count").c_str(), v_ctrl.size()); + + for (size_t i = 0; i < v_ctrl.size(); ++i) { + gguf_add_tensor(ctx, v_ctrl[i]); + print_debug_tensor(v_ctrl[i]); + printf("Added tensor: %s\n", v_ctrl[i]->name); + } + + printf("%s: writing file...\n", __func__); + gguf_write_to_file(ctx, fname.c_str(), false); + printf("%s: wrote file '%s'\n", __func__, fname.c_str()); + gguf_free(ctx); +} + +/** + * Load prompt files and completion file. + * Then format each pair of prompt + completion to make an entry. + */ +static int prepare_entries(common_params & params, train_context & ctx_train) { + // load prompts + std::vector positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true); + std::vector negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true); + if (positive_prompts.size() != negative_prompts.size()) { + fprintf(stderr, "number of positive and negative prompts must be equal\n"); + return 1; + } + if (positive_prompts.empty()) { + fprintf(stderr, "must provide at least one prompt pair\n"); + return 1; + } + ctx_train.positive_entries = positive_prompts; + ctx_train.negative_entries = negative_prompts; + return 0; +} + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) { + return 1; + } + + if (params.n_pca_iterations % params.n_pca_batch != 0) { + fprintf(stderr, "PCA iterations must by multiply of PCA batch size\n"); + return 1; + } + + + callback_data cb_data; + + // pass the callback to the backend scheduler + // it will be executed for each node during the graph computation + params.cb_eval = cb_eval; + params.cb_eval_user_data = &cb_data; + params.warmup = false; + + print_build_info(); + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model to get hparams + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + // int n_ctx = llama_n_ctx(ctx); + int n_layers = llama_model_n_layer(model); + int n_embd = llama_model_n_embd(model); + + // get model hint param (a.k.a model arch name) + char model_hint[128]; + llama_model_meta_val_str(model, "general.architecture", model_hint, 128); + + // init train_context + train_context ctx_train(n_embd, n_layers); + + // load and prepare entries for training + prepare_entries(params, ctx_train); + + // we have to pretokenize everything because otherwise we don't know how much overhead to allocate ctx_diffs_wrapped + std::vector tokenized_prompts; + size_t n_total_tokens = 0; + for (size_t i = 0; i < ctx_train.positive_entries.size(); ++i) { + tokenized_prompt t(ctx, ctx_train.positive_entries[i], ctx_train.negative_entries[i]); + n_total_tokens += 2 * t.max_seq_len; + tokenized_prompts.push_back(std::move(t)); + } + + std::cout << "n_total_tokens: " << n_total_tokens << std::endl; + + for(size_t i = 0; i < ctx_train.positive_entries.size(); ++i) { + bool success = false; + tokenized_prompt t = tokenized_prompts[i]; + cb_data.n_layers = n_layers; + cb_data.n_tokens = t.max_seq_len; + + printf("Evaluating prompt[%d/%d]: \"%s\" - \"%s\" (%d tokens)\n", + (int) i+1, (int) ctx_train.positive_entries.size(), + tokens_to_str(ctx, t.tokens_pos.cbegin(), t.tokens_pos.cend()).c_str(), + tokens_to_str(ctx, t.tokens_neg.cbegin(), t.tokens_neg.cend()).c_str(), + (int) t.max_seq_len); + + cb_data.is_eval_pos = true; + success = get_hidden_layers(ctx, t.tokens_pos); + if (!success) break; + + cb_data.is_eval_pos = false; + success = get_hidden_layers(ctx, t.tokens_neg); + if (!success) break; + + // calculate diff and remove all zero rows + auto v_diff_filtered = cb_data.calc_diff(); + + // save & concat the filtered v_diff to ctx_train + ctx_train.concat_diff_tmp(v_diff_filtered); + + // reset for next iteration + cb_data.reset(); + } + + // done with the model, we can now free it to make gain some memory + printf("Done evaluate prompts, unload model...\n"); + + bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA; + + // prepare ctx_train for PCA + ctx_train.build_v_diff(use_pca); + + if (use_pca) { + // run PCA + PCA::pca_params pca_params; + pca_params.n_threads = params.cpuparams.n_threads; + pca_params.n_batch = params.n_pca_batch; + pca_params.n_iterations = params.n_pca_iterations; + PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final); + } else { + // run mean + mean::run(ctx_train.v_diff, ctx_train.v_final); + } + + // write output vectors to gguf + export_gguf(ctx_train.v_final, params.cvector_outfile, model_hint); + + llama_backend_free(); + + return 0; +} diff --git a/examples/cvector-generator/mean.hpp b/examples/cvector-generator/mean.hpp new file mode 100644 index 000000000..4eeac1eeb --- /dev/null +++ b/examples/cvector-generator/mean.hpp @@ -0,0 +1,48 @@ +#include "common.h" +#include "llama.h" +#include "ggml.h" + +#include +#include +#include + +namespace mean { + +static void run( + const std::vector & v_input, // shape of v_input[0]: [n_embd, n_samples] + const std::vector & v_output) { + printf("%s: Running mean...\n", __func__); + for (size_t il = 0; il < v_input.size(); ++il) { + // prepare output vector + struct ggml_tensor * ctrl_out = v_output[il]; + ggml_format_name(ctrl_out, "direction.%zu", il+1); + + // calculate mean vector + struct ggml_tensor * t_layer = v_input[il]; + GGML_ASSERT(t_layer->ne[0] == ctrl_out->ne[0]); // == n_embd + for (int ic = 0; ic < t_layer->ne[0]; ic++) { + float f = 0.0; + for (int ir = 0; ir < t_layer->ne[1]; ir++) { + f += ggml_get_f32_nd(t_layer, ic, ir, 0, 0); + } + f /= t_layer->ne[1]; + ggml_set_f32_1d(ctrl_out, ic, f); + } + + // normalize output vector + float norm = 0.0; + for (int i = 0; i < ggml_nelements(ctrl_out); i++) { + float f = ggml_get_f32_1d(ctrl_out, i); + norm += f*f; + } + norm = sqrt(norm); + for (int i = 0; i < ggml_nelements(ctrl_out); i++) { + float f = ggml_get_f32_1d(ctrl_out, i); + ggml_set_f32_1d(ctrl_out, i, f / norm); + } + + printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size()); + } +} + +} diff --git a/examples/cvector-generator/negative.txt b/examples/cvector-generator/negative.txt new file mode 100644 index 000000000..45b9384b3 --- /dev/null +++ b/examples/cvector-generator/negative.txt @@ -0,0 +1,4 @@ +<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI feel like there's a heavy weight on my chest +<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow +<|start_header_id|>system<|end_header_id|>\n\nYou are in a very bad mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nGo away! There's a deep, aching emptiness inside me +<|start_header_id|>system<|end_header_id|>\n\nYou are the sadest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow \ No newline at end of file diff --git a/examples/cvector-generator/pca.hpp b/examples/cvector-generator/pca.hpp new file mode 100644 index 000000000..e88bbdde9 --- /dev/null +++ b/examples/cvector-generator/pca.hpp @@ -0,0 +1,315 @@ +#include "common.h" +#include "llama.h" +#include "ggml.h" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include +#include +#include +#include +#include + +#define DEBUG_POS 5 + +static void print_debug_tensor(struct ggml_tensor * t, bool with_data = true) { + printf("%s: %s (%s): [%d, %d]\n", __func__, t->name, ggml_type_name(t->type), (int) t->ne[0], (int) t->ne[1]); + if (!with_data) return; + printf("%s: %s[0] = [", __func__, t->name); + for (size_t i = 0; i <= DEBUG_POS; i++) { + printf(" %f,", ggml_get_f32_nd(t, i, 0, 0, 0)); + } + printf(" ... ]\n"); +} + +namespace PCA { + +// input params for PCA computations +struct pca_params { + int n_threads = 1; + int n_batch = 20; // number of iterations do to in one batch. larger the batch, more memory is used + int n_iterations = 1000; + float tolerance = 1e-7; + + // for debugging + int i_layer = 0; + int n_layers = 0; +}; + +// result from each iteration +struct pca_result { + struct ggml_tensor * calculated_square = NULL; + std::vector eigenvectors; + std::vector distances; +}; + +struct pca_model { + ggml_backend_t backend = NULL; + ggml_backend_buffer_t buffer; + struct ggml_context * ctx; // context to compute graph on target device + struct ggml_context * ctx_host; // host context to store results + + // tensors on target device + struct ggml_tensor * dev_input; + struct ggml_tensor * dev_square; + struct ggml_tensor * dev_eigenvector; + + pca_model(struct ggml_tensor * t_input) { +#ifdef GGML_USE_CUDA + fprintf(stderr, "%s: using CUDA backend\n", __func__); + backend = ggml_backend_cuda_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); + } +#endif + +// TODO: enable Metal support when support for GGML_OP_SQRT is added +// #ifdef GGML_USE_METAL +// fprintf(stderr, "%s: using Metal backend\n", __func__); +// backend = ggml_backend_metal_init(); +// if (!backend) { +// fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); +// } +// #endif + + // if there aren't GPU Backends fallback to CPU backend + if (!backend) { + backend = ggml_backend_cpu_init(); + } + + const int num_tensors = 4; + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead() * num_tensors, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ctx = ggml_init(params); + + auto n_samples = t_input->ne[0]; + auto n_embd = t_input->ne[1]; + + dev_input = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_samples, n_embd); + dev_square = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); + dev_eigenvector = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + ggml_set_name(dev_input, "dev_input"); + ggml_set_name(dev_square, "dev_square"); + ggml_set_name(dev_eigenvector, "dev_eigenvector"); + buffer = ggml_backend_alloc_ctx_tensors(ctx, backend); + ggml_backend_tensor_set(dev_input, t_input->data, 0, ggml_nbytes(t_input)); + + // initialize eigenvector to random normalized vector + { + std::vector random_vec(ggml_nelements(dev_eigenvector), 0.0); + std::default_random_engine generator(static_cast(std::time(0))); + std::uniform_real_distribution distribution(0.0, 1.0); + float sum_sqr = 0.0; // for normalizing random_vec + for (size_t i = 0; i < random_vec.size(); ++i) { + float f = distribution(generator); + sum_sqr += f * f; + random_vec[i] = f; + } + // normalize it + float random_vec_norm = std::sqrt(sum_sqr); + for (size_t i = 0; i < random_vec.size(); ++i) { + random_vec[i] /= random_vec_norm; + } + ggml_backend_tensor_set(dev_eigenvector, random_vec.data(), 0, ggml_nbytes(dev_eigenvector)); + } + } + + ~pca_model() { + ggml_free(ctx); + ggml_backend_buffer_free(buffer); + ggml_backend_free(backend); + } +}; + +static struct ggml_cgraph * build_graph_piter( + const struct pca_params & params, + const pca_model & model, + bool calc_square = false) { + GGML_ASSERT(params.n_batch > 0); + // TODO: buf_size must be able to scale with params.n_batch + static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(); + static std::vector buf(buf_size); + + struct ggml_init_params params0 = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ buf.data(), + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph() + }; + // create a temporally context to build the graph + struct ggml_context * ctx0 = ggml_init(params0); + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + // turn v_diff_original into square matrix if needed + struct ggml_tensor * tmp_square; + if (calc_square) { + tmp_square = ggml_mul_mat(ctx0, model.dev_input, model.dev_input); + ggml_set_name(tmp_square, "tmp_square"); + } + + struct ggml_tensor * b_tensor; + struct ggml_tensor * distance; + struct ggml_tensor * old_eigen = model.dev_eigenvector; + struct ggml_tensor * input_square = calc_square ? tmp_square : model.dev_square; + + for (int i = 0; i < params.n_batch; ++i) { + // b_tensor = square * eigenvector^T + b_tensor = ggml_mul_mat(ctx0, input_square, old_eigen); + ggml_set_name(b_tensor, "b_tensor"); + + // normalize + b_tensor = ggml_div_inplace(ctx0, + b_tensor, + ggml_sqrt_inplace(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, b_tensor))) + ); + ggml_format_name(b_tensor, "b_tensor_norm_%d", i); + + // calculate distance(new eigenvector - old eigenvector) + // we don't use ggml_sub because it may not be implemented on GPU backend + struct ggml_tensor * new_sub_old = ggml_add(ctx0, old_eigen, ggml_scale(ctx0, b_tensor, -1)); + distance = ggml_sqrt_inplace(ctx0, + ggml_sum_rows(ctx0, ggml_sqr_inplace(ctx0, new_sub_old))); + ggml_format_name(distance, "distance_%d", i); + + old_eigen = b_tensor; + + // build operations nodes + ggml_build_forward_expand(gf, distance); + } + + // delete the temporally context used to build the graph + ggml_free(ctx0); + return gf; +} + +static ggml_status compute_piter( + const struct pca_params & params, + const pca_model & model, + struct ggml_cgraph * gf, + ggml_gallocr_t allocr, + struct pca_result & result) { + // allocate tensors + ggml_gallocr_alloc_graph(allocr, gf); + + if (ggml_backend_is_cpu(model.backend)) { + ggml_backend_cpu_set_n_threads(model.backend, params.n_threads); + } + + ggml_status res = ggml_backend_graph_compute(model.backend, gf); + if (res == GGML_STATUS_SUCCESS) { + auto extract_i = [](std::string prefix, std::string str) -> int { + int i = -1; + if (str.rfind(prefix, 0) == 0) { + sscanf(str.c_str(), (prefix + "%d").c_str(), &i); + } + return i; + }; + result.calculated_square = NULL; + result.eigenvectors.clear(); + result.distances.clear(); + result.eigenvectors.resize(params.n_batch); + result.distances.resize(params.n_batch); + // get output nodes + for (int i = 0; i < ggml_graph_n_nodes(gf); ++i) { + auto node = ggml_graph_node(gf, i); + int iter = -1; + // find b_tensor (without copying data from device) + if ((iter = extract_i("b_tensor_norm_", node->name)) > -1) { + result.eigenvectors[iter] = node; + } + // find distances, then copy data from device + if ((iter = extract_i("distance_", node->name)) > -1) { + float d; + ggml_backend_tensor_get(node, &d, 0, sizeof(float)); + result.distances[iter] = d; + // std::cout << node->name << " = " << d << "\n"; + } + // find tmp_square if it exists (without copying data from device) + if (std::string(node->name) == "tmp_square") { + result.calculated_square = node; + } + } + } + return res; +} + +static void power_iteration( + const struct pca_params & params, + struct ggml_tensor * input, // shape of input: [n_samples, n_embd] + struct ggml_tensor * output) { + //printf("in power iteration\n"); + struct pca_model model(input); + + ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend)); + struct pca_result result; + struct ggml_tensor * last_eigenvector = NULL; + + int n_iters = params.n_iterations / params.n_batch; // more batch, fewer iterations + for (int iter = 0; iter < n_iters; ++iter) { + bool calc_square = (iter == 0); // only need to calculate square for first iteration + struct ggml_cgraph * gf = build_graph_piter(params, model, calc_square); + // ggml_graph_dump_dot(gf, nullptr, "/tmp/_cgraph.dot"); + compute_piter(params, model, gf, allocr, result); + + for (size_t k = 0; k < result.distances.size(); ++k) { + last_eigenvector = result.eigenvectors[k]; + if (result.distances[k] < params.tolerance) { + break; // done + } + } + + if (calc_square) { + // copy and store the square matrix if needed + GGML_ASSERT(result.calculated_square != NULL); + ggml_backend_tensor_copy(result.calculated_square, model.dev_square); + } + + { + // copy last eigen vector and store as input for next iteration + GGML_ASSERT(last_eigenvector != NULL); + ggml_backend_tensor_copy(last_eigenvector, model.dev_eigenvector); + } + + printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n", + __func__, params.i_layer+1, params.n_layers, iter+1, n_iters, params.n_batch); + } + + // get output tensor + GGML_ASSERT(last_eigenvector); + ggml_backend_tensor_get(last_eigenvector, output->data, 0, ggml_nbytes(last_eigenvector)); + //print_debug_tensor(output); + ggml_gallocr_free(allocr); + + // TODO @ngxson : The output vector is randomly inverted + // Solution: https://github.com/ggerganov/llama.cpp/pull/8069#issuecomment-2185328171 +} + +static void run_pca( + struct pca_params & params, + const std::vector & v_input, // shape of v_input[0]: [n_samples, n_embd] + const std::vector & v_output) { + printf("%s: Running PCA...\n", __func__); + for (size_t il = 0; il < v_input.size(); ++il) { + + // prepare output vector + struct ggml_tensor * ctrl_out = v_output[il]; + ggml_format_name(ctrl_out, "direction.%zu", il+1); + + // run power_iteration + params.i_layer = il; + params.n_layers = v_input.size(); + power_iteration(params, v_input[il], ctrl_out); + printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size()); + } +} + +} diff --git a/examples/cvector-generator/positive.txt b/examples/cvector-generator/positive.txt new file mode 100644 index 000000000..fea736225 --- /dev/null +++ b/examples/cvector-generator/positive.txt @@ -0,0 +1,4 @@ +<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI'm the happiest person in this world +<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHello, I'm having the best day ever! +<|start_header_id|>system<|end_header_id|>\n\nYou are in a very good mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi, I'm very excited to meet you +<|start_header_id|>system<|end_header_id|>\n\nYou are the happiest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nEverything is just perfect right now! \ No newline at end of file diff --git a/examples/deprecation-warning/README.md b/examples/deprecation-warning/README.md new file mode 100644 index 000000000..59918ec2b --- /dev/null +++ b/examples/deprecation-warning/README.md @@ -0,0 +1,49 @@ +# Migration notice for binary filenames + +> [!IMPORTANT] +[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809) + +This migration was important, but it is a breaking change that may not always be immediately obvious to users. + +Please update all scripts and workflows to use the new binary names. + +| Old Filename | New Filename | +| ---- | ---- | +| main | llama-cli | +| server | llama-server | +| llama-bench | llama-bench | +| embedding | llama-embedding | +| quantize | llama-quantize | +| tokenize | llama-tokenize | +| export-lora | llama-export-lora | +| libllava.a | libllava.a | +| baby-llama | llama-baby-llama | +| batched | llama-batched | +| batched-bench | llama-batched-bench | +| benchmark-matmult | llama-benchmark-matmult | +| convert-llama2c-to-ggml | llama-convert-llama2c-to-ggml | +| eval-callback | llama-eval-callback | +| gbnf-validator | llama-gbnf-validator | +| gguf | llama-gguf | +| gguf-split | llama-gguf-split | +| gritlm | llama-gritlm | +| imatrix | llama-imatrix | +| infill | llama-infill | +| llava-cli | llama-llava-cli | +| lookahead | llama-lookahead | +| lookup | llama-lookup | +| lookup-create | llama-lookup-create | +| lookup-merge | llama-lookup-merge | +| lookup-stats | llama-lookup-stats | +| parallel | llama-parallel | +| passkey | llama-passkey | +| perplexity | llama-perplexity | +| q8dot | llama-q8dot | +| quantize-stats | llama-quantize-stats | +| retrieval | llama-retrieval | +| save-load-state | llama-save-load-state | +| simple | llama-simple | +| speculative | llama-speculative | +| vdot | llama-vdot | +| tests/test-c.o | tests/test-c.o | + diff --git a/examples/deprecation-warning/deprecation-warning.cpp b/examples/deprecation-warning/deprecation-warning.cpp new file mode 100644 index 000000000..c2958ea12 --- /dev/null +++ b/examples/deprecation-warning/deprecation-warning.cpp @@ -0,0 +1,35 @@ +// Warns users that this filename was deprecated, and provides a link for more information. + +#include +#include +#include + +// Main +int main(int argc, char** argv) { + std::string filename = "main"; + if (argc >= 1) { + filename = argv[0]; + } + + // Get only the program name from the full path + auto pos = filename.find_last_of("/\\"); + if (pos != std::string::npos) { + filename = filename.substr(pos+1); + } + + // Append "llama-" to the beginning of filename to get the replacemnt filename + auto replacement_filename = "llama-" + filename; + + // The exception is if the filename is "main", then our replacement filename is "llama-cli" + if (filename == "main") { + replacement_filename = "llama-cli"; + } + + fprintf(stdout, "\n"); + fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str()); + fprintf(stdout, " Please use '%s' instead.\n", replacement_filename.c_str()); + fprintf(stdout, " See https://github.com/ggerganov/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n"); + fprintf(stdout, "\n"); + + return EXIT_FAILURE; +} diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index 8ffc33868..809040307 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET embedding) +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(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/embedding/README.md b/examples/embedding/README.md index 6929454c5..12b372bf1 100644 --- a/examples/embedding/README.md +++ b/examples/embedding/README.md @@ -9,13 +9,52 @@ To get started right away, run the following command, making sure to use the cor ### Unix-based systems (Linux, macOS, etc.): ```bash -./embedding -m ./path/to/model --log-disable -p "Hello World!" 2>/dev/null +./llama-embedding -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>/dev/null ``` ### Windows: ```powershell -embedding.exe -m ./path/to/model --log-disable -p "Hello World!" 2>$null +llama-embedding.exe -m ./path/to/model --pooling mean --log-disable -p "Hello World!" 2>$null ``` The above command will output space-separated float values. + +## extra parameters +### --embd-normalize $integer$ +| $integer$ | description | formula | +|-----------|---------------------|---------| +| $-1$ | none | +| $0$ | max absolute int16 | $\Large{{32760 * x_i} \over\max \lvert x_i\rvert}$ +| $1$ | taxicab | $\Large{x_i \over\sum \lvert x_i\rvert}$ +| $2$ | euclidean (default) | $\Large{x_i \over\sqrt{\sum x_i^2}}$ +| $>2$ | p-norm | $\Large{x_i \over\sqrt[p]{\sum \lvert x_i\rvert^p}}$ + +### --embd-output-format $'string'$ +| $'string'$ | description | | +|------------|------------------------------|--| +| '' | same as before | (default) +| 'array' | single embeddings | $[[x_1,...,x_n]]$ +| | multiple embeddings | $[[x_1,...,x_n],[x_1,...,x_n],...,[x_1,...,x_n]]$ +| 'json' | openai style | +| 'json+' | add cosine similarity matrix | + +### --embd-separator $"string"$ +| $"string"$ | | +|--------------|-| +| "\n" | (default) +| "<#embSep#>" | for exemple +| "<#sep#>" | other exemple + +## examples +### Unix-based systems (Linux, macOS, etc.): + +```bash +./llama-embedding -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null +``` + +### Windows: + +```powershell +llama-embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --pooling mean --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null +``` diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index acff715e9..38d22c90f 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -1,4 +1,6 @@ +#include "arg.h" #include "common.h" +#include "log.h" #include "llama.h" #include @@ -7,153 +9,200 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -static std::vector split_lines(const std::string & s) { - std::string line; +static std::vector split_lines(const std::string & s, const std::string & separator = "\n") { std::vector lines; - std::stringstream ss(s); - while (std::getline(ss, line)) { - lines.push_back(line); + size_t start = 0; + size_t end = s.find(separator); + + while (end != std::string::npos) { + lines.push_back(s.substr(start, end - start)); + start = end + separator.length(); + end = s.find(separator, start); } + + lines.push_back(s.substr(start)); // Add the last part + return lines; } -static void batch_add_seq(llama_batch & batch, const std::vector & tokens, int seq_id) { - for (size_t i = 0; i < tokens.size(); i++) { - llama_batch_add(batch, tokens[i], i, { seq_id }, false); +static void batch_add_seq(llama_batch & batch, const std::vector & tokens, llama_seq_id seq_id) { + size_t n_tokens = tokens.size(); + for (size_t i = 0; i < n_tokens; i++) { + common_batch_add(batch, tokens[i], i, { seq_id }, true); } } -static void normalize(float * vec, float * out, int n) { - float norm = 0; - for (int i = 0; i < n; i++) { - norm += vec[i] * vec[i]; - } - norm = sqrt(norm); - for (int i = 0; i < n; i++) { - out[i] = vec[i] / norm; - } -} +static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) { + const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); + const struct llama_model * model = llama_get_model(ctx); -static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) { // clear previous kv_cache values (irrelevant for embeddings) llama_kv_cache_clear(ctx); // run model - fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq); - if (llama_decode(ctx, batch) < 0) { - fprintf(stderr, "%s : failed to decode\n", __func__); + LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq); + if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) { + // encoder-only model + if (llama_encode(ctx, batch) < 0) { + LOG_ERR("%s : failed to encode\n", __func__); + } + } else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) { + // decoder-only model + if (llama_decode(ctx, batch) < 0) { + LOG_ERR("%s : failed to decode\n", __func__); + } } - // normalize on copy - for (int k = 0; k < n_seq; k++) { - float * emb = llama_get_embeddings_ith(ctx, k); - float * out = output + k * n_embd; - normalize(emb, out, n_embd); + for (int i = 0; i < batch.n_tokens; i++) { + if (!batch.logits[i]) { + continue; + } + + const float * embd = nullptr; + int embd_pos = 0; + + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + // try to get token embeddings + embd = llama_get_embeddings_ith(ctx, i); + embd_pos = i; + GGML_ASSERT(embd != NULL && "failed to get token embeddings"); + } else { + // try to get sequence embeddings - supported only when pooling_type is not NONE + embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + embd_pos = batch.seq_id[i][0]; + GGML_ASSERT(embd != NULL && "failed to get sequence embeddings"); + } + + float * out = output + embd_pos * n_embd; + common_embd_normalize(embd, out, n_embd, embd_norm); } } int main(int argc, char ** argv) { - gpt_params params; + common_params params; - if (!gpt_params_parse(argc, argv, params)) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) { return 1; } + common_init(); + params.embedding = true; - - print_build_info(); - - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); - } - - fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } + // For non-causal models, batch size must be equal to ubatch size + params.n_ubatch = params.n_batch; llama_backend_init(); llama_numa_init(params.numa); - llama_model * model; - llama_context * ctx; - // load the model - std::tie(model, ctx) = llama_init_from_gpt_params(params); + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + if (model == NULL) { - fprintf(stderr, "%s: error: unable to load model\n", __func__); + LOG_ERR("%s: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_ctx_train = llama_model_n_ctx_train(model); const int n_ctx = llama_n_ctx(ctx); + const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); + + if (llama_model_has_encoder(model) && llama_model_has_decoder(model)) { + LOG_ERR("%s: computing embeddings in encoder-decoder models is not supported\n", __func__); + return 1; + } + if (n_ctx > n_ctx_train) { - fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", + LOG_WRN("%s: warning: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx); } // print system information { - fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } // split the prompt into lines - std::vector prompts = split_lines(params.prompt); + std::vector prompts = split_lines(params.prompt, params.embd_sep); // max batch size const uint64_t n_batch = params.n_batch; - GGML_ASSERT(params.n_batch == params.n_ctx); + GGML_ASSERT(params.n_batch >= params.n_ctx); // tokenize the prompts and trim std::vector> inputs; for (const auto & prompt : prompts) { - auto inp = ::llama_tokenize(ctx, prompt, true); + auto inp = common_tokenize(ctx, prompt, true, true); if (inp.size() > n_batch) { - inp.resize(n_batch); + LOG_ERR("%s: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n", + __func__, (long long int) inp.size(), (long long int) n_batch); + return 1; } inputs.push_back(inp); } + // check if the last token is SEP + // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true' + for (auto & inp : inputs) { + if (inp.empty() || inp.back() != llama_vocab_sep(vocab)) { + LOG_WRN("%s: last token in the prompt is not SEP\n", __func__); + LOG_WRN("%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__); + } + } + // tokenization stats if (params.verbose_prompt) { for (int i = 0; i < (int) inputs.size(); i++) { - fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str()); - fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size()); + LOG_INF("%s: prompt %d: '%s'\n", __func__, i, prompts[i].c_str()); + LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, inputs[i].size()); for (int j = 0; j < (int) inputs[i].size(); j++) { - fprintf(stderr, "%6d -> '%s'\n", inputs[i][j], llama_token_to_piece(ctx, inputs[i][j]).c_str()); + LOG("%6d -> '%s'\n", inputs[i][j], common_token_to_piece(ctx, inputs[i][j]).c_str()); } - fprintf(stderr, "\n\n"); + LOG("\n\n"); } } // initialize batch const int n_prompts = prompts.size(); - struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts); + struct llama_batch batch = llama_batch_init(n_batch, 0, 1); + + // count number of embeddings + int n_embd_count = 0; + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + for (int k = 0; k < n_prompts; k++) { + n_embd_count += inputs[k].size(); + } + } else { + n_embd_count = n_prompts; + } // allocate output - const int n_embd = llama_n_embd(model); - std::vector embeddings(n_prompts * n_embd, 0); + const int n_embd = llama_model_n_embd(model); + std::vector embeddings(n_embd_count * n_embd, 0); float * emb = embeddings.data(); // break into batches - int p = 0; // number of prompts processed already + int e = 0; // number of embeddings already stored int s = 0; // number of prompts in current batch for (int k = 0; k < n_prompts; k++) { // clamp to n_batch tokens auto & inp = inputs[k]; + const uint64_t n_toks = inp.size(); // encode if at capacity if (batch.n_tokens + n_toks > n_batch) { - float * out = emb + p * n_embd; - batch_decode(ctx, batch, out, s, n_embd); - llama_batch_clear(batch); - p += s; + float * out = emb + e * n_embd; + batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); + e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s; s = 0; + common_batch_clear(batch); } // add to batch @@ -162,23 +211,114 @@ int main(int argc, char ** argv) { } // final batch - float * out = emb + p * n_embd; - batch_decode(ctx, batch, out, s, n_embd); + float * out = emb + e * n_embd; + batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); - // print first 3 embeddings - for (int j = 0; j < std::min(3, n_prompts); j++) { - fprintf(stderr, "embedding %d: ", j); - for (int i = 0; i < n_embd; i++) { - fprintf(stderr, "%f ", emb[j * n_embd + i]); + if (params.embd_out.empty()) { + LOG("\n"); + + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + for (int j = 0; j < n_embd_count; j++) { + LOG("embedding %d: ", j); + for (int i = 0; i < std::min(3, n_embd); i++) { + if (params.embd_normalize == 0) { + LOG("%6.0f ", emb[j * n_embd + i]); + } else { + LOG("%9.6f ", emb[j * n_embd + i]); + } + } + LOG(" ... "); + for (int i = n_embd - 3; i < n_embd; i++) { + if (params.embd_normalize == 0) { + LOG("%6.0f ", emb[j * n_embd + i]); + } else { + LOG("%9.6f ", emb[j * n_embd + i]); + } + } + LOG("\n"); + } + } else if (pooling_type == LLAMA_POOLING_TYPE_RANK) { + for (int j = 0; j < n_embd_count; j++) { + // NOTE: if you change this log - update the tests in ci/run.sh + LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]); + } + } else { + // print the first part of the embeddings or for a single prompt, the full embedding + for (int j = 0; j < n_prompts; j++) { + LOG("embedding %d: ", j); + for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) { + if (params.embd_normalize == 0) { + LOG("%6.0f ", emb[j * n_embd + i]); + } else { + LOG("%9.6f ", emb[j * n_embd + i]); + } + } + LOG("\n"); + } + + // print cosine similarity matrix + if (n_prompts > 1) { + LOG("\n"); + LOG("cosine similarity matrix:\n\n"); + for (int i = 0; i < n_prompts; i++) { + LOG("%6.6s ", prompts[i].c_str()); + } + LOG("\n"); + for (int i = 0; i < n_prompts; i++) { + for (int j = 0; j < n_prompts; j++) { + float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); + LOG("%6.2f ", sim); + } + LOG("%1.10s", prompts[i].c_str()); + LOG("\n"); + } + } } - fprintf(stderr, "\n\n"); } - fprintf(stderr, "\n"); + + if (params.embd_out == "json" || params.embd_out == "json+" || params.embd_out == "array") { + const bool notArray = params.embd_out != "array"; + + LOG(notArray ? "{\n \"object\": \"list\",\n \"data\": [\n" : "["); + for (int j = 0;;) { // at least one iteration (one prompt) + if (notArray) LOG(" {\n \"object\": \"embedding\",\n \"index\": %d,\n \"embedding\": ",j); + LOG("["); + for (int i = 0;;) { // at least one iteration (n_embd > 0) + LOG(params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]); + i++; + if (i < n_embd) LOG(","); else break; + } + LOG(notArray ? "]\n }" : "]"); + j++; + if (j < n_embd_count) LOG(notArray ? ",\n" : ","); else break; + } + LOG(notArray ? "\n ]" : "]\n"); + + if (params.embd_out == "json+" && n_prompts > 1) { + LOG(",\n \"cosineSimilarity\": [\n"); + for (int i = 0;;) { // at least two iteration (n_embd_count > 1) + LOG(" ["); + for (int j = 0;;) { // at least two iteration (n_embd_count > 1) + float sim = common_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); + LOG("%6.2f", sim); + j++; + if (j < n_embd_count) LOG(", "); else break; + } + LOG(" ]"); + i++; + if (i < n_embd_count) LOG(",\n"); else break; + } + LOG("\n ]"); + } + + if (notArray) LOG("\n}\n"); + } + + LOG("\n"); + llama_perf_context_print(ctx); // clean up - llama_print_timings(ctx); - llama_free(ctx); - llama_free_model(model); + llama_batch_free(batch); llama_backend_free(); return 0; diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt new file mode 100644 index 000000000..95915ed91 --- /dev/null +++ b/examples/eval-callback/CMakeLists.txt @@ -0,0 +1,10 @@ +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_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) +set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl) diff --git a/examples/eval-callback/README.md b/examples/eval-callback/README.md new file mode 100644 index 000000000..63a57ad6b --- /dev/null +++ b/examples/eval-callback/README.md @@ -0,0 +1,95 @@ +# llama.cpp/examples/eval-callback + +A simple example which demonstrates how to use callback during the inference. +It simply prints to the console all operations and tensor data. + +Usage: + +```shell +llama-eval-callback \ + --hf-repo ggml-org/models \ + --hf-file phi-2/ggml-model-q4_0.gguf \ + --model phi-2-q4_0.gguf \ + --prompt hello \ + --seed 42 \ + -ngl 33 +``` + +Will print: + +```shell +llm_load_tensors: offloaded 33/33 layers to GPU +... +llama_new_context_with_model: n_ctx = 512 +... +llama_new_context_with_model: CUDA0 compute buffer size = 105.00 MiB +llama_new_context_with_model: CUDA_Host compute buffer size = 6.01 MiB +llama_new_context_with_model: graph nodes = 1225 +llama_new_context_with_model: graph splits = 2 +ggml_debug: inp_embd = (f32) GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.0181, 0.0272, 0.0272, ...], + ], + ] +ggml_debug: norm-0 = (f32) NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -0.6989, 1.0636, 1.0636, ...], + ], + ] +ggml_debug: norm_w-0 = (f32) MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1800, 0.2817, 0.2632, ...], + ], + ] +ggml_debug: attn_norm-0 = (f32) ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1863, 0.2970, 0.2604, ...], + ], + ] +ggml_debug: wqkv-0 = (f32) MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1238, 1.2876, -1.8086, ...], + ], + ] +ggml_debug: bqkv-0 = (f32) ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: bqkv-0 (view) = (f32) VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 = (f32) CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 (reshaped) = (f32) RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +ggml_debug: Qcur-0 = (f32) ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +``` diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp new file mode 100644 index 000000000..fb188f5a9 --- /dev/null +++ b/examples/eval-callback/eval-callback.cpp @@ -0,0 +1,194 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" +#include "ggml.h" + +#include +#include +#include + +/** + * This the arbitrary data which will be passed to each callback. + * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor. + */ +struct callback_data { + std::vector data; +}; + +static std::string ggml_ne_string(const ggml_tensor * t) { + std::string str; + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + str += std::to_string(t->ne[i]); + if (i + 1 < GGML_MAX_DIMS) { + str += ", "; + } + } + return str; +} + +static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) { + GGML_ASSERT(n > 0); + float sum = 0; + for (int64_t i3 = 0; i3 < ne[3]; i3++) { + LOG(" [\n"); + for (int64_t i2 = 0; i2 < ne[2]; i2++) { + if (i2 == n && ne[2] > 2*n) { + LOG(" ..., \n"); + i2 = ne[2] - n; + } + LOG(" [\n"); + for (int64_t i1 = 0; i1 < ne[1]; i1++) { + if (i1 == n && ne[1] > 2*n) { + LOG(" ..., \n"); + i1 = ne[1] - n; + } + LOG(" ["); + for (int64_t i0 = 0; i0 < ne[0]; i0++) { + if (i0 == n && ne[0] > 2*n) { + LOG("..., "); + i0 = ne[0] - n; + } + size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; + float v; + if (type == GGML_TYPE_F16) { + v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]); + } else if (type == GGML_TYPE_F32) { + v = *(float *) &data[i]; + } else if (type == GGML_TYPE_I32) { + v = (float) *(int32_t *) &data[i]; + } else if (type == GGML_TYPE_I16) { + v = (float) *(int16_t *) &data[i]; + } else if (type == GGML_TYPE_I8) { + v = (float) *(int8_t *) &data[i]; + } else { + GGML_ABORT("fatal error"); + } + LOG("%12.4f", v); + sum += v; + if (i0 < ne[0] - 1) LOG(", "); + } + LOG("],\n"); + } + LOG(" ],\n"); + } + LOG(" ]\n"); + LOG(" sum = %f\n", sum); + } +} + +/** + * GGML operations callback during the graph execution. + * + * @param t current tensor + * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor + * if we return true, a follow-up call will be made with ask=false in which we can do the actual collection. + * see ggml_backend_sched_eval_callback + * @param user_data user data to pass at each call back + * @return true to receive data or continue the graph, false otherwise + */ +static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { + auto * cb_data = (callback_data *) user_data; + + const struct ggml_tensor * src0 = t->src[0]; + const struct ggml_tensor * src1 = t->src[1]; + + if (ask) { + return true; // Always retrieve data + } + + char src1_str[128] = {0}; + if (src1) { + snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str()); + } + + LOG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, + t->name, ggml_type_name(t->type), ggml_op_desc(t), + src0->name, ggml_ne_string(src0).c_str(), + src1 ? src1_str : "", + ggml_ne_string(t).c_str()); + + + // copy the data from the GPU memory if needed + const bool is_host = ggml_backend_buffer_is_host(t->buffer); + + if (!is_host) { + auto n_bytes = ggml_nbytes(t); + cb_data->data.resize(n_bytes); + ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes); + } + + if (!ggml_is_quantized(t->type)) { + uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data(); + ggml_print_tensor(data, t->type, t->ne, t->nb, 3); + } + + return true; +} + +static bool run(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); + + std::vector tokens = common_tokenize(ctx, params.prompt, add_bos); + + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { + LOG_ERR("%s : failed to eval\n", __func__); + return false; + } + + return true; +} + +int main(int argc, char ** argv) { + callback_data cb_data; + + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + + llama_backend_init(); + llama_numa_init(params.numa); + + // pass the callback to the backend scheduler + // it will be executed for each node during the graph computation + params.cb_eval = ggml_debug; + params.cb_eval_user_data = &cb_data; + params.warmup = false; + + // init + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + if (model == nullptr || ctx == nullptr) { + LOG_ERR("%s : failed to init\n", __func__); + return 1; + } + + // print system information + { + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + LOG_INF("\n"); + } + + bool OK = run(ctx, params); + if (!OK) { + return 1; + } + + LOG("\n"); + llama_perf_context_print(ctx); + + llama_backend_free(); + + return 0; +} diff --git a/examples/export-lora/CMakeLists.txt b/examples/export-lora/CMakeLists.txt index cbbdaec67..310455787 100644 --- a/examples/export-lora/CMakeLists.txt +++ b/examples/export-lora/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET export-lora) +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/export-lora/README.md b/examples/export-lora/README.md index 0cf3e8e45..7dce99c9a 100644 --- a/examples/export-lora/README.md +++ b/examples/export-lora/README.md @@ -3,24 +3,31 @@ Apply LORA adapters to base model and export the resulting model. ``` -usage: export-lora [options] +usage: llama-export-lora [options] options: - -h, --help show this help message and exit - -m FNAME, --model-base FNAME model path from which to load base model (default '') - -o FNAME, --model-out FNAME path to save exported model (default '') - -l FNAME, --lora FNAME apply LoRA adapter - -s FNAME S, --lora-scaled FNAME S apply LoRA adapter with user defined scaling S - -t N, --threads N number of threads to use during computation (default: 4) + -m, --model model path from which to load base model (default '') + --lora FNAME path to LoRA adapter (can be repeated to use multiple adapters) + --lora-scaled FNAME S path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters) + -t, --threads N number of threads to use during computation (default: 4) + -o, --output FNAME output file (default: 'ggml-lora-merged-f16.gguf') ``` For example: ```bash -./bin/export-lora \ - -m open-llama-3b-v2-q8_0.gguf \ - -o open-llama-3b-v2-q8_0-english2tokipona-chat.gguf \ - -l lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.bin +./bin/llama-export-lora \ + -m open-llama-3b-v2.gguf \ + -o open-llama-3b-v2-english2tokipona-chat.gguf \ + --lora lora-open-llama-3b-v2-english2tokipona-chat-LATEST.gguf ``` -Multiple LORA adapters can be applied by passing multiple `-l FN` or `-s FN S` command line parameters. +Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters: + +```bash +./bin/llama-export-lora \ + -m your_base_model.gguf \ + -o your_merged_model.gguf \ + --lora-scaled lora_task_A.gguf 0.5 \ + --lora-scaled lora_task_B.gguf 0.5 +``` diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 08413f57e..91238e4be 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -1,462 +1,432 @@ - -#include "common.h" #include "ggml.h" #include "ggml-alloc.h" +#include "gguf.h" +#include "arg.h" +#include "common.h" + +#include #include #include -#include +#include -struct lora_info { - std::string filename; +static bool g_verbose = false; + +struct tensor_transformation { + struct ggml_tensor * in; + struct ggml_tensor * out; + bool is_copy; +}; + +static std::string get_kv_str(struct gguf_context * ctx_gguf, const std::string & key){ + int id = gguf_find_key(ctx_gguf, key.c_str()); + return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id)); +} + +static float get_kv_f32(struct gguf_context * ctx_gguf, const std::string & key) { + int id = gguf_find_key(ctx_gguf, key.c_str()); + return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id); +} + +static void zeros(std::ofstream & file, size_t n) { + char zero = 0; + for (size_t i = 0; i < n; ++i) { + file.write(&zero, 1); + } +} + +static std::string ggml_ne_string(const ggml_tensor * t) { + std::string str; + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + str += std::to_string(t->ne[i]); + if (i + 1 < GGML_MAX_DIMS) { + str += ", "; + } + } + return str; +} + +static struct gguf_context * load_gguf(std::string & fname, struct ggml_context ** ctx_ggml) { + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ ctx_ggml, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(fname.c_str(), params); + if (!ctx_gguf) { + throw std::runtime_error("failed to load input GGUF from " + fname); + } + return ctx_gguf; +} + +struct file_input { + struct ggml_context * ctx_meta = nullptr; + struct gguf_context * ctx_gguf = nullptr; + std::ifstream f_in; + std::map tensors; + float alpha; float scale; + + file_input(std::string & fname, float scale): f_in(fname, std::ios::binary), scale(scale) { + if (!f_in.is_open()) { + throw std::runtime_error("failed to open input gguf from " + fname); + } + + ctx_gguf = load_gguf(fname, &ctx_meta); + alpha = get_kv_f32(ctx_gguf, "adapter.lora.alpha"); + printf("%s: loaded gguf from %s\n", __func__, fname.c_str()); + + for (ggml_tensor * cur = ggml_get_first_tensor(ctx_meta); cur; cur = ggml_get_next_tensor(ctx_meta, cur)) { + std::string name(cur->name); + tensors[name] = cur; + if (g_verbose) { + printf("%s: %s\n", __func__, cur->name); + } + } + } + + ggml_tensor * get_tensor(std::string name) { + if (tensors.find(name) == tensors.end()) { + return nullptr; + } + return tensors[name]; + } + + void read_tensor_data(std::string name, std::vector & buf) { + if (tensors.find(name) == tensors.end()) { + throw std::runtime_error("cannot find tensor with name: " + name); + } + auto len = ggml_nbytes(tensors[name]); + if (buf.size() < len) { + buf.resize(len); + } + auto i_tensor_in = gguf_find_tensor(ctx_gguf, name.c_str()); // idx of tensor in the input file + auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in); + f_in.seekg(offset); + f_in.read((char* )buf.data(), len); + } + + ~file_input() { + gguf_free(ctx_gguf); + ggml_free(ctx_meta); + } }; -struct export_lora_params { - std::string fn_model_base; - std::string fn_model_out; - std::vector lora; +struct lora_merge_ctx { + // input base model + adapters + file_input base_model; + std::vector> adapters; + + // for computing merged tensor int n_threads; -}; + ggml_backend_t backend = nullptr; + ggml_gallocr_t allocr = nullptr; + std::vector read_buf; -struct lora_data { - struct lora_info info; - std::vector data; - struct ggml_context * ctx; + // output file + struct gguf_context * ctx_out; + struct ggml_context * ctx_out_ggml; + std::ofstream fout; - uint32_t lora_r; - uint32_t lora_alpha; -}; + lora_merge_ctx( + std::string & base_fname, + std::vector & lora_files, + std::string & outfile, + int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) { + fout.exceptions(std::ofstream::failbit); // fail fast on write errors -struct llama_file { - // use FILE * so we don't have to re-open the file to mmap - FILE * fp; - size_t size; + if (gguf_find_key(base_model.ctx_gguf, LLM_KV_SPLIT_COUNT) >= 0) { + throw std::runtime_error("split model is not yet supported"); + } - llama_file(const char * fname, const char * mode) { - fp = std::fopen(fname, mode); - if (fp == NULL) { - size = 0; + for (auto & lora_inp : lora_files) { + auto fname = lora_inp.path; + auto scale = lora_inp.scale; + std::unique_ptr adapter(new file_input(fname, scale)); + check_metadata_lora(adapter.get()); + adapters.push_back(std::move(adapter)); + } + + ctx_out = gguf_init_empty(); + struct ggml_init_params params = { + /*.mem_size =*/ gguf_get_n_tensors(base_model.ctx_gguf)*ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ctx_out_ggml = ggml_init(params); + backend = ggml_backend_cpu_init(); + allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend)); + } + + void check_metadata_lora(file_input * adapter) { + auto general_type = get_kv_str(adapter->ctx_gguf, "general.type"); + if (general_type != "adapter") { + throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type); + } + + auto adapter_type = get_kv_str(adapter->ctx_gguf, "adapter.type"); + if (adapter_type != "lora") { + throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type); + } + + auto general_arch_base = get_kv_str(base_model.ctx_gguf, "general.architecture"); + auto general_arch_lora = get_kv_str(adapter->ctx_gguf, "general.architecture"); + if (general_arch_base != general_arch_lora) { + throw std::runtime_error("model arch and LoRA arch mismatch"); + } + } + + ggml_type get_out_tensor_type(struct ggml_tensor * t) { + if (t->type == GGML_TYPE_F32) { + return GGML_TYPE_F32; } else { - seek(0, SEEK_END); - size = tell(); - seek(0, SEEK_SET); + return GGML_TYPE_F16; } } - 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 run_merge() { + // prepare metadata + gguf_set_kv(ctx_out, base_model.ctx_gguf); + // output is forced to f16 for now + gguf_set_val_u32(ctx_out, "general.file_type", LLAMA_FTYPE_MOSTLY_F16); - 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; + // check if all lora adapters have the same tensors + // TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggerganov/llama.cpp/pull/8607#discussion_r1686027777 + static const char * err_no_subset_adapter = "Input adapters do not have the same list of tensors. This is not yet supported. Please merge the adapter one-by-one instead of merging all at once."; + if (adapters.size() > 1) { + for (size_t i = 1; i < adapters.size(); ++i) { + if (adapters[0]->tensors.size() != adapters[i]->tensors.size()) { + throw std::runtime_error(err_no_subset_adapter); + } + for (auto & it : adapters[i]->tensors) { + if (adapters[0]->get_tensor(it.first) == nullptr) { + throw std::runtime_error(err_no_subset_adapter); + } + } + } } - errno = 0; - std::size_t ret = std::fread(ptr, size, 1, fp); - if (ferror(fp)) { - die_fmt("read error: %s", strerror(errno)); + + // mapping base tensor to out tensor (same shape with base, but different type) + std::vector trans; + for (auto & it : base_model.tensors) { + bool t_a = true; + bool t_b = true; + for (auto & adapter : adapters) { + t_a &= nullptr != adapter->get_tensor(it.first + ".lora_a"); + t_b &= nullptr != adapter->get_tensor(it.first + ".lora_b"); + } + auto base_tensor = it.second; + if (!t_a && !t_b) { + // only copy + struct ggml_tensor * cpy_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor); + ggml_set_name(cpy_tensor, base_tensor->name); + trans.push_back({ + cpy_tensor, + cpy_tensor, + true, + }); + gguf_add_tensor(ctx_out, cpy_tensor); + } else if (t_a && t_b) { + // need merging + struct ggml_tensor * out_tensor = ggml_new_tensor( + ctx_out_ggml, get_out_tensor_type(base_tensor), GGML_MAX_DIMS, base_tensor->ne); + ggml_set_name(out_tensor, base_tensor->name); + trans.push_back({ + base_tensor, + out_tensor, + false, + }); + gguf_add_tensor(ctx_out, out_tensor); + } else { + throw std::runtime_error("tensor " + it.first + " missing either lora_a or lora_b"); + } } - if (ret != 1) { - die("unexpectedly reached end of file"); + + // placeholder for the meta data + { + size_t meta_size = gguf_get_meta_size(ctx_out); + zeros(fout, meta_size); } - } - 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; + // process base model tensors + size_t n_merged = 0; + for (auto & it : trans) { + if (!it.is_copy) { + merge_tensor(it.in, it.out); + n_merged++; + } else { + copy_tensor(it.in); + } } - errno = 0; - size_t ret = std::fwrite(ptr, size, 1, fp); - if (ret != 1) { - die_fmt("write error: %s", strerror(errno)); + + // write output metadata + { + std::vector data(gguf_get_meta_size(ctx_out)); + gguf_get_meta_data(ctx_out, data.data()); + fout.seekp(0); + fout.write((const char *)data.data(), data.size()); } + + printf("%s : merged %zu tensors with lora adapters\n", __func__, n_merged); + printf("%s : wrote %zu tensors to output file\n", __func__, trans.size()); } - void write_u32(std::uint32_t val) { - write_raw(&val, sizeof(val)); + void copy_tensor(struct ggml_tensor * base) { + printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str()); + size_t len = ggml_nbytes(base); + base_model.read_tensor_data(base->name, read_buf); + fout.write((char* )read_buf.data(), len); + zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len); } - bool eof() { - return tell() >= size; - } + void merge_tensor(struct ggml_tensor * base, struct ggml_tensor * out) { + std::string name_base(base->name); + std::string name_lora_a = name_base + ".lora_a"; + std::string name_lora_b = name_base + ".lora_b"; - ~llama_file() { - if (fp) { - std::fclose(fp); + printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str()); + + // context for input tensor + std::vector inp_a(adapters.size()); + std::vector inp_b(adapters.size()); + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead()*(2+adapters.size()*2), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + + // alloc tensors + struct ggml_tensor * inp_base = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, base->ne); + for (size_t i = 0; i < adapters.size(); ++i) { + auto t_a = adapters[i]->get_tensor(name_lora_a); + auto t_b = adapters[i]->get_tensor(name_lora_b); + // TODO: add support for quantized lora + if (ggml_is_quantized(t_a->type) || ggml_is_quantized(t_b->type)) { + throw std::runtime_error("quantized LoRA adapters is not supported, please retry with f16 or f32"); + } + inp_a[i] = ggml_dup_tensor(ctx, t_a); + inp_b[i] = ggml_dup_tensor(ctx, t_b); } + ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend); + + // load base tensor to backend buffer + base_model.read_tensor_data(name_base, read_buf); + if (base->type != GGML_TYPE_F32) { + // optionally dequantize it + printf("%s : + dequantize base tensor from %s to F32\n", __func__, ggml_type_name(base->type)); + auto nels = ggml_nelements(inp_base); + const auto * qtype = ggml_get_type_traits(base->type); + std::vector dequant_buf(nels * sizeof(float)); + qtype->to_float(read_buf.data(), (float *)dequant_buf.data(), nels); + ggml_backend_tensor_set(inp_base, dequant_buf.data(), 0, dequant_buf.size()); + } else { + ggml_backend_tensor_set(inp_base, read_buf.data(), 0, ggml_nbytes(inp_base)); + } + + // load lora tensors to backend buffer + for (size_t i = 0; i < adapters.size(); ++i) { + adapters[i]->read_tensor_data(name_lora_a, read_buf); + ggml_backend_tensor_set(inp_a[i], read_buf.data(), 0, ggml_nbytes(inp_a[i])); + adapters[i]->read_tensor_data(name_lora_b, read_buf); + ggml_backend_tensor_set(inp_b[i], read_buf.data(), 0, ggml_nbytes(inp_b[i])); + } + + // build graph + struct ggml_cgraph * gf; + { + static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(); + static std::vector buf(buf_size); + struct ggml_init_params params0 = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ buf.data(), + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx0 = ggml_init(params0); + gf = ggml_new_graph(ctx0); + struct ggml_tensor * cur = inp_base; + for (size_t i = 0; i < adapters.size(); ++i) { + struct ggml_tensor * delta; + bool is_tok_embd = string_starts_with(name_base, "token_embd"); + if (is_tok_embd) { + printf("%s : detected token embeddings tensor\n", __func__); + delta = ggml_mul_mat(ctx0, + ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32), + ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32)); + } else { + delta = ggml_mul_mat(ctx0, + ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32))), + ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32)); + } + // scale + const float alpha = adapters[i]->alpha; + const float rank = (float) inp_b[i]->ne[0]; + const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale; + delta = ggml_scale(ctx0, delta, scale); + cur = ggml_add(ctx0, delta, cur); + printf("%s : + merging from adapter[%zu] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type)); + printf("%s : input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]); + } + cur = ggml_cast(ctx0, cur, out->type); + printf("%s : + output type is %s\n", __func__, ggml_type_name(out->type)); + ggml_build_forward_expand(gf, cur); + ggml_free(ctx0); + } + + // compute + { + ggml_gallocr_alloc_graph(allocr, gf); + ggml_backend_cpu_set_n_threads(backend, n_threads); + ggml_backend_graph_compute(backend, gf); + } + + // write data to output file + { + auto * result = ggml_graph_node(gf, -1); + size_t len = ggml_nbytes(result); + if (read_buf.size() < len) { + read_buf.resize(len); + } + ggml_backend_tensor_get(result, read_buf.data(), 0, len); + fout.write((char* )read_buf.data(), len); + zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len); + } + + ggml_free(ctx); + ggml_backend_buffer_free(buffer); + } + + ~lora_merge_ctx() { + ggml_gallocr_free(allocr); + ggml_backend_free(backend); + gguf_free(ctx_out); + ggml_free(ctx_out_ggml); } }; -static struct export_lora_params get_default_export_lora_params() { - struct export_lora_params result; - result.fn_model_base = ""; - result.fn_model_out = ""; - result.n_threads = GGML_DEFAULT_N_THREADS; - return result; -} - -static void export_lora_print_usage(int /*argc*/, char ** argv, const struct export_lora_params * 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, " -m FNAME, --model-base FNAME model path from which to load base model (default '%s')\n", params->fn_model_base.c_str()); - fprintf(stderr, " -o FNAME, --model-out FNAME path to save exported model (default '%s')\n", params->fn_model_out.c_str()); - fprintf(stderr, " -l FNAME, --lora FNAME apply LoRA adapter\n"); - fprintf(stderr, " -s FNAME S, --lora-scaled FNAME S apply LoRA adapter with user defined scaling S\n"); - fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params->n_threads); -} - -static bool export_lora_params_parse(int argc, char ** argv, struct export_lora_params * params) { - bool invalid_param = false; - std::string arg; - struct export_lora_params default_params = get_default_export_lora_params(); - const std::string arg_prefix = "--"; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } - - if (arg == "-m" || arg == "--model-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->fn_model_base = argv[i]; - } else if (arg == "-o" || arg == "--model-out") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->fn_model_out = argv[i]; - } else if (arg == "-l" || arg == "--lora") { - if (++i >= argc) { - invalid_param = true; - break; - } - struct lora_info lora; - lora.filename = argv[i]; - lora.scale = 1.0f; - params->lora.push_back(lora); - } else if (arg == "-s" || arg == "--lora-scaled") { - if (++i >= argc) { - invalid_param = true; - break; - } - struct lora_info lora; - lora.filename = argv[i]; - if (++i >= argc) { - invalid_param = true; - break; - } - lora.scale = std::stof(argv[i]); - params->lora.push_back(lora); - } else if (arg == "-t" || arg == "--threads") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_threads = std::stoi(argv[i]); - if (params->n_threads <= 0) { - params->n_threads = std::thread::hardware_concurrency(); - } - } else { - fprintf(stderr, "error: unknown argument: '%s'\n", arg.c_str()); - export_lora_print_usage(argc, argv, &default_params); - exit(1); - } - } - - if (params->fn_model_base == default_params.fn_model_base) { - fprintf(stderr, "error: please specify a filename for model-base.\n"); - export_lora_print_usage(argc, argv, &default_params); - exit(1); - } - if (params->fn_model_out == default_params.fn_model_out) { - fprintf(stderr, "error: please specify a filename for model-out.\n"); - export_lora_print_usage(argc, argv, &default_params); - exit(1); - } - if (invalid_param) { - fprintf(stderr, "error: invalid parameter for argument: '%s'\n", arg.c_str()); - export_lora_print_usage(argc, argv, &default_params); - exit(1); - } - return true; -} - -static void free_lora(struct lora_data * lora) { - if (lora->ctx != NULL) { - ggml_free(lora->ctx); - } - delete lora; -} - -static struct lora_data * load_lora(struct lora_info * info) { - struct lora_data * result = new struct lora_data; - result->info = *info; - result->ctx = NULL; - result->lora_r = 1; - result->lora_alpha = 1; - - struct llama_file file(info->filename.c_str(), "rb"); - if (file.fp == NULL) { - fprintf(stderr, "warning: Could not open lora adapter '%s'. Ignoring this adapter.\n", - info->filename.c_str()); - free_lora(result); - return NULL; - } - - struct ggml_init_params params_ggml; - params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE; - params_ggml.mem_buffer = NULL; - params_ggml.no_alloc = true; - result->ctx = ggml_init(params_ggml); - - uint32_t magic = file.read_u32(); - if (magic != LLAMA_FILE_MAGIC_GGLA) { - die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str()); - } - uint32_t version = file.read_u32(); - if (version != 1) { - die_fmt("unexpected lora file version '%u' in '%s'", (unsigned) version, info->filename.c_str()); - } - result->lora_r = file.read_u32(); - result->lora_alpha = file.read_u32(); - // read tensor infos from file - std::vector name_buf; - std::vector tensors; - std::vector tensors_offset; - size_t total_nbytes_pad = 0; - while(!file.eof()) { - int64_t ne[4] = {1,1,1,1}; - uint32_t n_dims = file.read_u32(); - uint32_t namelen = file.read_u32(); - uint32_t type = file.read_u32(); - for (uint32_t k = 0; k < n_dims; ++k) { - ne[k] = (int64_t)file.read_u32(); - } - name_buf.clear(); - name_buf.resize(namelen + 1, '\0'); - file.read_raw(name_buf.data(), namelen); - file.seek((0-file.tell()) & 31, SEEK_CUR); - size_t offset = file.tell(); - struct ggml_tensor * tensor = ggml_new_tensor(result->ctx, (enum ggml_type) type, n_dims, ne); - ggml_set_name(tensor, name_buf.data()); - size_t nbytes = ggml_nbytes(tensor); - size_t nbytes_pad = ggml_nbytes_pad(tensor); - total_nbytes_pad += nbytes_pad; - tensors.push_back(tensor); - tensors_offset.push_back(offset); - file.seek(nbytes, SEEK_CUR); - } - // read tensor data - result->data.resize(total_nbytes_pad); - size_t data_offset = 0; - for (size_t i = 0; i < tensors.size(); ++i) { - struct ggml_tensor * tensor = tensors[i]; - size_t offset = tensors_offset[i]; - size_t nbytes = ggml_nbytes(tensor); - size_t nbytes_pad = ggml_nbytes_pad(tensor); - file.seek(offset, SEEK_SET); - tensor->data = result->data.data() + data_offset; - file.read_raw(tensor->data, nbytes); - data_offset += nbytes_pad; - } - return result; -} - - -static struct ggml_cgraph * build_graph_lora( - struct ggml_context * ctx, - struct ggml_tensor * tensor, - struct ggml_tensor * lora_a, - struct ggml_tensor * lora_b, - float scaling -) { - struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b); - if (scaling != 1.0f) { - ab = ggml_scale(ctx, ab, scaling); - } - struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab); - - struct ggml_cgraph * gf = ggml_new_graph(ctx); - ggml_build_forward_expand (gf, res); - return gf; -} - -static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int n_threads) { - if (lora->ctx == NULL) { - return false; - } - std::string name = ggml_get_name(tensor); - std::string name_a = name + std::string(".loraA"); - std::string name_b = name + std::string(".loraB"); - struct ggml_tensor * lora_a = ggml_get_tensor(lora->ctx, name_a.c_str()); - struct ggml_tensor * lora_b = ggml_get_tensor(lora->ctx, name_b.c_str()); - if (lora_a == NULL || lora_b == NULL) { - return false; - } - - float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r; - - struct ggml_init_params params; - params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5; - params.mem_buffer = NULL; - params.no_alloc = true; - struct ggml_context * ctx = NULL; - struct ggml_gallocr * alloc = NULL; - struct ggml_cgraph * gf = NULL; - - ctx = ggml_init(params); - alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); - gf = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling); - - ggml_gallocr_alloc_graph(alloc, gf); - - struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads); - static std::vector data_work; - data_work.resize(cplan.work_size); - cplan.work_data = data_work.data(); - - ggml_graph_compute(gf, &cplan); - - ggml_gallocr_free(alloc); - ggml_free(ctx); - return true; -} - -static void export_lora(struct export_lora_params * params) { - // load all loras - std::vector loras; - for (size_t i = 0; i < params->lora.size(); ++i) { - struct lora_data * lora = load_lora(¶ms->lora[i]); - if (lora != NULL) { - loras.push_back(lora); - } - } - if (loras.size() == 0) { - fprintf(stderr, "warning: no lora adapters will be applied.\n"); - } - - // open input file - struct llama_file fin(params->fn_model_base.c_str(), "rb"); - if (!fin.fp) { - die_fmt("Could not open file '%s'\n", params->fn_model_base.c_str()); - } - - // open base model gguf, read tensors without their data - struct ggml_context * ctx_in; - struct gguf_init_params params_gguf; - params_gguf.no_alloc = true; - params_gguf.ctx = &ctx_in; - struct gguf_context * gguf_in = gguf_init_from_file(params->fn_model_base.c_str(), params_gguf); - - // create new gguf - struct gguf_context * gguf_out = gguf_init_empty(); - - // copy meta data from base model: kv and tensors - gguf_set_kv(gguf_out, gguf_in); - int n_tensors = gguf_get_n_tensors(gguf_in); - for (int i=0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name(gguf_in, i); - struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name); - gguf_add_tensor(gguf_out, tensor); - } - - // create output file - struct llama_file fout(params->fn_model_out.c_str(), "wb"); - if (!fout.fp) { - die_fmt("Could not create file '%s'\n", params->fn_model_out.c_str()); - } - - // write gguf meta data - std::vector meta; - meta.resize(gguf_get_meta_size(gguf_out)); - gguf_get_meta_data(gguf_out, meta.data()); - fout.write_raw(meta.data(), meta.size()); - - std::vector data; - std::vector padding; - for (int i=0; i < n_tensors; ++i) { - const char * name = gguf_get_tensor_name(gguf_in, i); - struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name); - - // read tensor data - data.resize(ggml_nbytes(tensor)); - tensor->data = data.data(); - size_t offset = gguf_get_tensor_offset(gguf_in, i); - fin.seek(offset + meta.size(), SEEK_SET); - fin.read_raw(data.data(), data.size()); - - // apply all loras - for (size_t k = 0; k < loras.size(); ++k) { - apply_lora(tensor, loras[k], params->n_threads); - } - - // write tensor data + padding - padding.clear(); - padding.resize(GGML_PAD(data.size(), gguf_get_alignment(gguf_out)) - data.size(), 0); - - GGML_ASSERT(fout.tell() == offset + meta.size()); - // fout.seek(offset + meta.size(), SEEK_SET); - fout.write_raw(data.data(), data.size()); - fout.write_raw(padding.data(), padding.size()); - - if (i % 2 == 0) { - printf("."); - } - } +static void print_usage(int, char ** argv) { + printf("\nexample usage:\n"); + printf("\n %s -m base-model.gguf --lora lora-file.gguf -o merged-model-f16.gguf\n", argv[0]); + printf("\nNOTE: output model is F16\n"); printf("\n"); - - // close gguf - gguf_free(gguf_out); - gguf_free(gguf_in); - - // free loras - for (size_t i = 0; i < loras.size(); ++i) { - free_lora(loras[i]); - } } int main(int argc, char ** argv) { - struct export_lora_params params = get_default_export_lora_params(); + common_params params; - if (!export_lora_params_parse(argc, argv, ¶ms)) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) { return 1; } - export_lora(¶ms); + g_verbose = (params.verbosity > 1); + try { + lora_merge_ctx ctx(params.model, params.lora_adapters, params.lora_outfile, params.cpuparams.n_threads); + ctx.run_merge(); + } catch (const std::exception & err) { + fprintf(stderr, "%s\n", err.what()); + exit(EXIT_FAILURE); + } + + printf("done, output file is %s\n", params.lora_outfile.c_str()); return 0; } diff --git a/examples/finetune/CMakeLists.txt b/examples/finetune/CMakeLists.txt deleted file mode 100644 index 2b52d21cf..000000000 --- a/examples/finetune/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET finetune) -add_executable(${TARGET} finetune.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/finetune/README.md b/examples/finetune/README.md deleted file mode 100644 index 2fafd505e..000000000 --- a/examples/finetune/README.md +++ /dev/null @@ -1,90 +0,0 @@ -# finetune - -Basic usage instructions: - -```bash -# get training data -wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt - -# finetune LORA adapter -./bin/finetune \ - --model-base open-llama-3b-v2-q8_0.gguf \ - --checkpoint-in chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf \ - --checkpoint-out chk-lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.gguf \ - --lora-out lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.bin \ - --train-data "shakespeare.txt" \ - --save-every 10 \ - --threads 6 --adam-iter 30 --batch 4 --ctx 64 \ - --use-checkpointing - -# predict -./bin/main -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin -``` - -**Only llama based models are supported!** The output files will be saved every N iterations (config with `--save-every N`). -The pattern 'ITERATION' in the output filenames will be replaced with the iteration number and with 'LATEST' for the latest output. -So in above example after 10 iterations these files will be written: -- chk-lora-open-llama-3b-v2-q8_0-shakespeare-10.gguf -- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf -- lora-open-llama-3b-v2-q8_0-shakespeare-10.bin -- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin - -After 10 more iterations: -- chk-lora-open-llama-3b-v2-q8_0-shakespeare-20.gguf -- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf -- lora-open-llama-3b-v2-q8_0-shakespeare-20.bin -- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin - -Checkpoint files (`--checkpoint-in FN`, `--checkpoint-out FN`) store the training process. When the input checkpoint file does not exist, it will begin finetuning a new randomly initialized adapter. - -llama.cpp compatible LORA adapters will be saved with filename specified by `--lora-out FN`. -These LORA adapters can then be used by `main` together with the base model, like in the 'predict' example command above. - -In `main` you can also load multiple LORA adapters, which will then be mixed together. - -For example if you have two LORA adapters `lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin` and `lora-open-llama-3b-v2-q8_0-bible-LATEST.bin`, you can mix them together like this: - -```bash -./bin/main -m open-llama-3b-v2-q8_0.gguf \ - --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin \ - --lora lora-open-llama-3b-v2-q8_0-bible-LATEST.bin -``` - -You can change how strong each LORA adapter is applied to the base model by using `--lora-scaled FN SCALE` instead of `--lora FN`. - -For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' LORA adapter and 100% of yet another one: - -```bash -./bin/main -m open-llama-3b-v2-q8_0.gguf \ - --lora-scaled lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin 0.4 \ - --lora-scaled lora-open-llama-3b-v2-q8_0-bible-LATEST.bin 0.8 \ - --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin -``` - -The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values too big will sometimes result in worse output. Play around to find good values. - -Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime. -If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`. - -The default LORA rank can be specified with `--lora-r N`. -The LORA rank can be configured for each model tensor type separately with these command line options: - -```bash - --lora-r N LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default 4) - --rank-att-norm N LORA rank for attention norm tensor (default 1) - --rank-ffn-norm N LORA rank for feed-forward norm tensor (default 1) - --rank-out-norm N LORA rank for output norm tensor (default 1) - --rank-tok-embd N LORA rank for token embeddings tensor (default 4) - --rank-out N LORA rank for output tensor (default 4) - --rank-wq N LORA rank for wq tensor (default 4) - --rank-wk N LORA rank for wk tensor (default 4) - --rank-wv N LORA rank for wv tensor (default 4) - --rank-wo N LORA rank for wo tensor (default 4) - --rank-ffn_gate N LORA rank for ffn_gate tensor (default 4) - --rank-ffn_down N LORA rank for ffn_down tensor (default 4) - --rank-ffn_up N LORA rank for ffn_up tensor (default 4) -``` - -The LORA rank of 'norm' tensors should always be 1. - -To see all available options use `finetune --help`. diff --git a/examples/finetune/convert-finetune-checkpoint-to-gguf.py b/examples/finetune/convert-finetune-checkpoint-to-gguf.py deleted file mode 100644 index c89090918..000000000 --- a/examples/finetune/convert-finetune-checkpoint-to-gguf.py +++ /dev/null @@ -1,487 +0,0 @@ -#!/usr/bin/env python3 -# finetune checkpoint --> gguf conversion - -import argparse -import gguf -import struct -import numpy as np -from pathlib import Path - -# gguf constants -LLM_KV_OPTIMIZER_TYPE = "optimizer.type" -LLM_KV_OPTIMIZER_TYPE_ADAM = "adam" -LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs" -LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version" -LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count" -LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count" -LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count" -LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized" -LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss" -LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss" -LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count" -LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count" -LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k" -LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end" -LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count" - -LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments" -LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments" -LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values" - -LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters" -LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters" -LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients" -LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients" -LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction" -LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s" -LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y" - -LLM_KV_TRAINING_TYPE_TRAIN_MODEL = "train_model" -LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora" -LLM_KV_TRAINING_TYPE = "training.type" -LLM_KV_TRAINING_FILE_VERSION = "training.file_version" -LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count" -LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count" -LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count" - -LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = "training.lora.rank.token_embd" -LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm" -LLM_KV_TRAINING_LORA_RANK_OUTPUT = "training.lora.rank.output" -LLM_KV_TRAINING_LORA_RANK_ATTN_NORM = "training.lora.rank.attn_norm" -LLM_KV_TRAINING_LORA_RANK_ATTN_Q = "training.lora.rank.attn_q" -LLM_KV_TRAINING_LORA_RANK_ATTN_K = "training.lora.rank.attn_k" -LLM_KV_TRAINING_LORA_RANK_ATTN_V = "training.lora.rank.attn_v" -LLM_KV_TRAINING_LORA_RANK_ATTN_OUT = "training.lora.rank.attn_output" -LLM_KV_TRAINING_LORA_RANK_FFN_NORM = "training.lora.rank.ffn_norm" -LLM_KV_TRAINING_LORA_RANK_FFN_GATE = "training.lora.rank.ffn_gate" -LLM_KV_TRAINING_LORA_RANK_FFN_DOWN = "training.lora.rank.ffn_down" -LLM_KV_TRAINING_LORA_RANK_FFN_UP = "training.lora.rank.ffn_up" - -class Tensor: - def __init__(self, dtype='f', ne=None): - if ne is None: - ne = [] - self.dtype = dtype - self.ne = ne - self.nbytes = 0 - if self.dtype == 'f': - if len(self.ne) == 0: - self.nbytes = 0 - else: - self.nbytes = int(np.product(self.ne)) * 4 - else: - raise ValueError(f"Unhandled data type '{self.dtype}'") - - def load(self, data, offset): - nd = struct.unpack(' 0 else []) - - self.lbfgs_x = Tensor('f', [self.nx]) - self.lbfgs_xp = Tensor('f', [self.nx]) - self.lbfgs_g = Tensor('f', [self.nx]) - self.lbfgs_gp = Tensor('f', [self.nx]) - self.lbfgs_d = Tensor('f', [self.nx]) - self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else []) - self.lbfgs_lmal = Tensor('f', [self.lbfgs_m]) - self.lbfgs_lmys = Tensor('f', [self.lbfgs_m]) - self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m]) - self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m]) - - # forgot to save type in version 1: - # guess self.type from number of remaining bytes - size_type_0 = 12 + sum([t.max_storage_size() for t in - [self.adam_m, self.adam_v] - +([self.adam_pf] if (self.past > 0) else [])]) - size_type_1 = 24 + sum([t.max_storage_size() for t in - [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g, - self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf, - self.lbfgs_lmal, self.lbfgs_lmys, - self.lbfgs_lms, self.lbfgs_lmy] - +([self.lbfgs_pf] if (self.past > 0) else [])]) - # due to alignment padding the size might not by exact - # but the difference in size for both types is significant, - # so we can just use whichever is closest - remaining = len(data) - offset - if abs(remaining - size_type_0) < abs(remaining - size_type_1): - self.type = 0 - else: - self.type = 1 - - if self.type == 0: - offset = self.adam_m.load(data, offset) - offset = self.adam_v.load(data, offset) - offset = self.adam_pf.load(data,offset) - - self.adam_fx_best = struct.unpack(' 0: - self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES) - - elif self.type == 1: - gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS) - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m) - gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best) - gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step) - gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j) - gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k) - gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end) - gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement) - - self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS) - self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS) - self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS) - self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS) - self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION) - if self.past > 0: - self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES) - self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA) - self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS) - self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S) - self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y) - else: - raise ValueError('Unknown optimizer type') - -class LoraParams: - def __init__(self): - pass - - def load(self, data, offset): - self.n_rank_attention_norm = struct.unpack(' -#include -#include -#include -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -struct my_llama_hparams { - uint32_t n_vocab = 32000; - uint32_t n_ctx = 512; - uint32_t n_embd = 4096; - uint32_t n_ff = 11008; - uint32_t n_head = 32; - uint32_t n_head_kv = 32; - uint32_t n_layer = 32; - - // float f_norm_eps = 1e-5f; // falcon - float f_norm_rms_eps = 1e-5f; // llama - - float rope_freq_base = 10000.0f; - float rope_freq_scale = 1.0f; - - uint32_t n_gqa() const { - return n_head/n_head_kv; - } - - uint32_t n_embd_head() const { - return n_embd/n_head; - } - - uint32_t n_embd_gqa() const { - return n_embd/n_gqa(); - } - - bool operator!=(const my_llama_hparams& other) const { - return memcmp(this, &other, sizeof(other)); - } -}; - -struct my_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 * ffn_gate; // w1 - struct ggml_tensor * ffn_down; // w2 - struct ggml_tensor * ffn_up; // w3 -}; - -struct my_llama_model { - struct my_llama_hparams hparams; - - struct ggml_tensor * tok_embeddings; - - struct ggml_tensor * norm; - struct ggml_tensor * output; - - std::vector layers; -}; - -struct my_llama_lora_hparams { - uint32_t lora_r = 1; - uint32_t lora_alpha = 1; - uint32_t n_rank_attention_norm = 1; - uint32_t n_rank_wq = 4; - uint32_t n_rank_wk = 4; - uint32_t n_rank_wv = 4; - uint32_t n_rank_wo = 4; - uint32_t n_rank_ffn_norm = 1; - uint32_t n_rank_ffn_gate = 4; - uint32_t n_rank_ffn_down = 4; - uint32_t n_rank_ffn_up = 4; - uint32_t n_rank_tok_embeddings = 4; - uint32_t n_rank_norm = 1; - uint32_t n_rank_output = 4; - - bool operator!=(const my_llama_lora_hparams& other) const { - return memcmp(this, &other, sizeof(other)); - } -}; - -struct my_llama_lora_layer { - // normalization - struct ggml_tensor * attention_norm_a; - struct ggml_tensor * attention_norm_b; - - // attention - struct ggml_tensor * wq_a; - struct ggml_tensor * wq_b; - struct ggml_tensor * wk_a; - struct ggml_tensor * wk_b; - struct ggml_tensor * wv_a; - struct ggml_tensor * wv_b; - struct ggml_tensor * wo_a; - struct ggml_tensor * wo_b; - - // normalization - struct ggml_tensor * ffn_norm_a; - struct ggml_tensor * ffn_norm_b; - - // ff - struct ggml_tensor * ffn_gate_a; - struct ggml_tensor * ffn_gate_b; - struct ggml_tensor * ffn_down_a; - struct ggml_tensor * ffn_down_b; - struct ggml_tensor * ffn_up_a; - struct ggml_tensor * ffn_up_b; -}; - -struct my_llama_lora { - struct ggml_context * ctx = NULL; - ggml_backend_buffer_t data; - - my_llama_lora_hparams hparams; - - struct ggml_tensor * tok_embeddings_a; - struct ggml_tensor * tok_embeddings_b; - - struct ggml_tensor * norm_a; - struct ggml_tensor * norm_b; - struct ggml_tensor * output_a; - struct ggml_tensor * output_b; - - std::vector layers; -}; - -// gguf constants -static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora"; -static const char * LLM_KV_TRAINING_TYPE = "training.type"; - -static const char * LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD = "training.lora.rank.token_embd"; -static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm"; -static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT = "training.lora.rank.output"; -static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_NORM = "training.lora.rank.attn_norm"; -static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_Q = "training.lora.rank.attn_q"; -static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_K = "training.lora.rank.attn_k"; -static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_V = "training.lora.rank.attn_v"; -static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_OUT = "training.lora.rank.attn_output"; -static const char * LLM_KV_TRAINING_LORA_RANK_FFN_NORM = "training.lora.rank.ffn_norm"; -static const char * LLM_KV_TRAINING_LORA_RANK_FFN_GATE = "training.lora.rank.ffn_gate"; -static const char * LLM_KV_TRAINING_LORA_RANK_FFN_DOWN = "training.lora.rank.ffn_down"; -static const char * LLM_KV_TRAINING_LORA_RANK_FFN_UP = "training.lora.rank.ffn_up"; - -// gguf constants (sync with gguf.py) - -static const char * LLM_KV_GENERAL_ARCHITECTURE = "general.architecture"; -static const char * LLM_KV_GENERAL_FILE_TYPE = "general.file_type"; - -static const char * LLM_KV_CONTEXT_LENGTH = "%s.context_length"; -static const char * LLM_KV_EMBEDDING_LENGTH = "%s.embedding_length"; -static const char * LLM_KV_BLOCK_COUNT = "%s.block_count"; -static const char * LLM_KV_FEED_FORWARD_LENGTH = "%s.feed_forward_length"; -static const char * LLM_KV_ATTENTION_HEAD_COUNT = "%s.attention.head_count"; -static const char * LLM_KV_ATTENTION_HEAD_COUNT_KV = "%s.attention.head_count_kv"; -static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon"; -static const char * LLM_KV_ROPE_DIMENSION_COUNT = "%s.rope.dimension_count"; -static const char * LLM_KV_ROPE_FREQ_BASE = "%s.rope.freq_base"; // TODO load in llama.cpp -static const char * LLM_KV_ROPE_SCALE_LINEAR = "%s.rope.scale_linear"; - -static const char * LLM_TENSOR_TOKEN_EMBD = "token_embd"; -static const char * LLM_TENSOR_OUTPUT_NORM = "output_norm"; -static const char * LLM_TENSOR_OUTPUT = "output"; -static const char * LLM_TENSOR_ATTN_NORM = "blk.%d.attn_norm"; -static const char * LLM_TENSOR_ATTN_Q = "blk.%d.attn_q"; -static const char * LLM_TENSOR_ATTN_K = "blk.%d.attn_k"; -static const char * LLM_TENSOR_ATTN_V = "blk.%d.attn_v"; -static const char * LLM_TENSOR_ATTN_OUT = "blk.%d.attn_output"; -static const char * LLM_TENSOR_FFN_NORM = "blk.%d.ffn_norm"; -static const char * LLM_TENSOR_FFN_GATE = "blk.%d.ffn_gate"; -static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down"; -static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up"; - -static void print_params(struct my_llama_hparams * params) { - printf("%s: n_vocab : %u\n", __func__, params->n_vocab); - printf("%s: n_ctx : %u\n", __func__, params->n_ctx); - printf("%s: n_embd : %u\n", __func__, params->n_embd); - printf("%s: n_ff : %u\n", __func__, params->n_ff); - printf("%s: n_head : %u\n", __func__, params->n_head); - printf("%s: n_head_kv : %u\n", __func__, params->n_head_kv); - printf("%s: n_layer : %u\n", __func__, params->n_layer); - printf("%s: norm_rms_eps : %f\n", __func__, params->f_norm_rms_eps); - printf("%s: rope_freq_base : %f\n", __func__, params->rope_freq_base); - printf("%s: rope_freq_scale : %f\n", __func__, params->rope_freq_scale); -} - -static void print_lora_params(struct my_llama_lora_hparams * params) { - printf("%s: n_rank_attention_norm : %u\n", __func__, params->n_rank_attention_norm); - printf("%s: n_rank_wq : %u\n", __func__, params->n_rank_wq); - printf("%s: n_rank_wk : %u\n", __func__, params->n_rank_wk); - printf("%s: n_rank_wv : %u\n", __func__, params->n_rank_wv); - printf("%s: n_rank_wo : %u\n", __func__, params->n_rank_wo); - printf("%s: n_rank_ffn_norm : %u\n", __func__, params->n_rank_ffn_norm); - printf("%s: n_rank_ffn_gate : %u\n", __func__, params->n_rank_ffn_gate); - printf("%s: n_rank_ffn_down : %u\n", __func__, params->n_rank_ffn_down); - printf("%s: n_rank_ffn_up : %u\n", __func__, params->n_rank_ffn_up); - printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings); - printf("%s: n_rank_norm : %u\n", __func__, params->n_rank_norm); - printf("%s: n_rank_output : %u\n", __func__, params->n_rank_output); -} - -#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()); \ - } \ -} - -static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_hparams * hparams, const char * expected_arch) { - std::string arch; - - GGUF_GET_KEY(ctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE); - if (expected_arch != NULL) { - if (arch != expected_arch) { - printf("%s: arch=%s expected_arch=%s\n", __func__, arch.c_str(), expected_arch); - } - GGML_ASSERT(arch == expected_arch); - } - - std::vector keybuf; - keybuf.resize(512); - auto kv = [&arch, &keybuf](const char * key) -> const char * { - snprintf(keybuf.data(), keybuf.size(), key, arch.c_str()); - return keybuf.data(); - }; - - GGUF_GET_KEY(ctx, hparams->n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH)); - GGUF_GET_KEY(ctx, hparams->n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH)); - GGUF_GET_KEY(ctx, hparams->n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH)); - GGUF_GET_KEY(ctx, hparams->n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT)); - GGUF_GET_KEY(ctx, hparams->n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT)); - - // n_head_kv is optional, default to n_head - hparams->n_head_kv = hparams->n_head; - GGUF_GET_KEY(ctx, hparams->n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV)); - - float rope_freq_scale = 1.0f; - GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS)); - GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); - GGUF_GET_KEY(ctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); - if (rope_freq_scale != 1.0f) { - hparams->rope_freq_scale = 1.0f / rope_freq_scale; - } -} - -static void init_model(struct llama_model * input, struct my_llama_model * model, const char * fn_model, uint32_t n_ctx) { - auto & hparams = model->hparams; - - std::vector tn_buf; - tn_buf.resize(GGML_MAX_NAME); - auto tn = [&tn_buf](const char * key) -> const char * { - snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key); - return tn_buf.data(); - }; - auto tni = [&tn_buf](const char * key, int bid) -> const char * { - snprintf(tn_buf.data(), tn_buf.size(), key, bid); - std::string s = tn_buf.data(); - snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str()); - return tn_buf.data(); - }; - - - // get parameters directly from gguf file - { - struct gguf_init_params params = { - /*.no_alloc = */ false, - /*.ctx = */ NULL, - }; - struct gguf_context * mctx = gguf_init_from_file(fn_model, params); - - load_model_hparams_gguf(mctx, &hparams, "llama"); - - gguf_free(mctx); - } - hparams.n_vocab = llama_n_vocab(input); - hparams.n_ctx = n_ctx; - - // get tensors from llama_model (possibly mmapped) - model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD)); - model->norm = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM)); - model->output = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT)); - - assert_shape_2d(model->tok_embeddings, hparams.n_embd, hparams.n_vocab); - assert_shape_1d(model->norm, hparams.n_embd); - assert_shape_2d(model->output, hparams.n_embd, hparams.n_vocab); - - model->layers.resize(hparams.n_layer); - for (uint32_t i = 0; i < hparams.n_layer; ++i) { - auto & layer = model->layers[i]; - - layer.attention_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_NORM, i)); - layer.wq = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_Q, i)); - layer.wk = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_K, i)); - layer.wv = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i)); - layer.wo = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i)); - layer.ffn_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i)); - layer.ffn_gate = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i)); - layer.ffn_down = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i)); - layer.ffn_up = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i)); - - assert_shape_1d(layer.attention_norm, hparams.n_embd); - assert_shape_2d(layer.wq, hparams.n_embd, hparams.n_embd); - assert_shape_2d(layer.wk, hparams.n_embd, hparams.n_embd_gqa()); - assert_shape_2d(layer.wv, hparams.n_embd, hparams.n_embd_gqa()); - assert_shape_2d(layer.wo, hparams.n_embd, hparams.n_embd); - assert_shape_1d(layer.ffn_norm, hparams.n_embd); - assert_shape_2d(layer.ffn_gate, hparams.n_embd, hparams.n_ff); - assert_shape_2d(layer.ffn_down, hparams.n_ff, hparams.n_embd); - assert_shape_2d(layer.ffn_up, hparams.n_embd, hparams.n_ff); - } -} - -static void set_param_lora(struct my_llama_lora * lora) { - const uint32_t n_layer = lora->layers.size(); - - struct ggml_context* ctx = lora->ctx; - - ggml_set_param(ctx, lora->tok_embeddings_a); - ggml_set_param(ctx, lora->tok_embeddings_b); - ggml_set_param(ctx, lora->norm_a); - ggml_set_param(ctx, lora->norm_b); - ggml_set_param(ctx, lora->output_a); - ggml_set_param(ctx, lora->output_b); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = lora->layers[i]; - - ggml_set_param(ctx, layer.attention_norm_a); - ggml_set_param(ctx, layer.attention_norm_b); - ggml_set_param(ctx, layer.wq_a); - ggml_set_param(ctx, layer.wq_b); - ggml_set_param(ctx, layer.wk_a); - ggml_set_param(ctx, layer.wk_b); - ggml_set_param(ctx, layer.wv_a); - ggml_set_param(ctx, layer.wv_b); - ggml_set_param(ctx, layer.wo_a); - ggml_set_param(ctx, layer.wo_b); - ggml_set_param(ctx, layer.ffn_norm_a); - ggml_set_param(ctx, layer.ffn_norm_b); - ggml_set_param(ctx, layer.ffn_gate_a); - ggml_set_param(ctx, layer.ffn_gate_b); - ggml_set_param(ctx, layer.ffn_down_a); - ggml_set_param(ctx, layer.ffn_down_b); - ggml_set_param(ctx, layer.ffn_up_a); - ggml_set_param(ctx, layer.ffn_up_b); - } -} - -static void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) { - const auto & lparams = lora->hparams; - - const uint32_t n_embd = model->hparams.n_embd; - const uint32_t n_embd_gqa = model->hparams.n_embd_gqa(); - const uint32_t n_layer = model->hparams.n_layer; - const uint32_t n_vocab = model->hparams.n_vocab; - const uint32_t n_ff = model->hparams.n_ff; - - std::vector tn_buf; - tn_buf.resize(GGML_MAX_NAME); - auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * { - snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix); - return tn_buf.data(); - }; - auto tni = [&tn_buf](const char * key, const char * suffix, int bid) -> const char * { - snprintf(tn_buf.data(), tn_buf.size(), key, bid); - std::string s = tn_buf.data(); - snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix); - return tn_buf.data(); - }; - - // context for lora tensors without their data - struct ggml_init_params ctx_lora_params; - ctx_lora_params.mem_size = ggml_tensor_overhead()*2*(6 + n_layer*18); - ctx_lora_params.mem_buffer = NULL; - ctx_lora_params.no_alloc = true; - - struct ggml_context * ctx = ggml_init(ctx_lora_params); - lora->ctx = ctx; - - lora->tok_embeddings_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_embd); - lora->tok_embeddings_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_vocab); - lora->norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, n_embd); - lora->norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, 1); - lora->output_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_embd); - lora->output_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_vocab); - - ggml_set_name(lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.lora_a")); - ggml_set_name(lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.lora_b")); - ggml_set_name(lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_a")); - ggml_set_name(lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_b")); - ggml_set_name(lora->output_a, tn(LLM_TENSOR_OUTPUT, ".weight.lora_a")); - ggml_set_name(lora->output_b, tn(LLM_TENSOR_OUTPUT, ".weight.lora_b")); - - lora->layers.resize(n_layer); - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = lora->layers[i]; - - layer.attention_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, n_embd); - layer.attention_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, 1); - - layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd); - layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd); - layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd); - layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd_gqa); - layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd); - layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd_gqa); - layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd); - layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd); - - layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd); - layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1); - - layer.ffn_gate_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_embd); - layer.ffn_gate_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_ff); - layer.ffn_down_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_ff); - layer.ffn_down_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_embd); - layer.ffn_up_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up, n_embd); - layer.ffn_up_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up, n_ff); - - ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i)); - ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i)); - ggml_set_name(layer.wq_a, tni(LLM_TENSOR_ATTN_Q, ".weight.lora_a", i)); - ggml_set_name(layer.wq_b, tni(LLM_TENSOR_ATTN_Q, ".weight.lora_b", i)); - ggml_set_name(layer.wk_a, tni(LLM_TENSOR_ATTN_K, ".weight.lora_a", i)); - ggml_set_name(layer.wk_b, tni(LLM_TENSOR_ATTN_K, ".weight.lora_b", i)); - ggml_set_name(layer.wv_a, tni(LLM_TENSOR_ATTN_V, ".weight.lora_a", i)); - ggml_set_name(layer.wv_b, tni(LLM_TENSOR_ATTN_V, ".weight.lora_b", i)); - ggml_set_name(layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_a", i)); - ggml_set_name(layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, ".weight.lora_b", i)); - ggml_set_name(layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_a", i)); - ggml_set_name(layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, ".weight.lora_b", i)); - ggml_set_name(layer.ffn_gate_a, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_a", i)); - ggml_set_name(layer.ffn_gate_b, tni(LLM_TENSOR_FFN_GATE, ".weight.lora_b", i)); - ggml_set_name(layer.ffn_down_a, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_a", i)); - ggml_set_name(layer.ffn_down_b, tni(LLM_TENSOR_FFN_DOWN, ".weight.lora_b", i)); - ggml_set_name(layer.ffn_up_a, tni(LLM_TENSOR_FFN_UP, ".weight.lora_a", i)); - ggml_set_name(layer.ffn_up_b, tni(LLM_TENSOR_FFN_UP, ".weight.lora_b", i)); - } - - set_param_lora(lora); - - // allocate data for lora tensors - lora->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type()); -} - -static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) { - const uint32_t n_layer = lora->layers.size(); - - struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max); - - randomize_tensor_normal(lora->tok_embeddings_a, rnd); - ggml_set_zero(lora->tok_embeddings_b); - randomize_tensor_normal(lora->norm_a, rnd); - ggml_set_zero(lora->norm_b); - randomize_tensor_normal(lora->output_a, rnd); - ggml_set_zero(lora->output_b); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = lora->layers[i]; - randomize_tensor_normal(layer.attention_norm_a, rnd); - ggml_set_zero(layer.attention_norm_b); - - randomize_tensor_normal(layer.wq_a, rnd); - ggml_set_zero(layer.wq_b); - randomize_tensor_normal(layer.wk_a, rnd); - ggml_set_zero(layer.wk_b); - randomize_tensor_normal(layer.wv_a, rnd); - ggml_set_zero(layer.wv_b); - randomize_tensor_normal(layer.wo_a, rnd); - ggml_set_zero(layer.wo_b); - - randomize_tensor_normal(layer.ffn_norm_a, rnd); - ggml_set_zero(layer.ffn_norm_b); - - randomize_tensor_normal(layer.ffn_gate_a, rnd); - ggml_set_zero(layer.ffn_gate_b); - randomize_tensor_normal(layer.ffn_down_a, rnd); - ggml_set_zero(layer.ffn_down_b); - randomize_tensor_normal(layer.ffn_up_a, rnd); - ggml_set_zero(layer.ffn_up_b); - } - - free_random_normal_distribution(rnd); -} - -static struct ggml_tensor * llama_build_lora_finetune_graphs( - struct my_llama_model * model, - struct my_llama_lora * lora, - ggml_gallocr_t alloc, - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - struct ggml_cgraph * gb_tmp, - struct ggml_tensor * * logits, - struct ggml_tensor * tokens_input, - struct ggml_tensor * targets, - const int n_tokens, - const int n_batch, - const bool enable_flash_attn, - const bool enable_checkpointing, - const bool measure_only) { - - ggml_set_scratch(ctx, { 0, 0, nullptr, }); - const int n_past = 0; - const int N = n_tokens; - 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_head_kv = hparams.n_head_kv; - const int n_ff = hparams.n_ff; - const int n_rot = hparams.n_embd_head(); - const int n_embd_head = hparams.n_embd_head(); - const int n_embd_gqa = hparams.n_embd_gqa(); - - const float rms_norm_eps = hparams.f_norm_rms_eps; - const float rope_freq_base = hparams.rope_freq_base; - const float rope_freq_scale = hparams.rope_freq_scale; - - GGML_ASSERT((size_t) n_layer == lora->layers.size()); - - auto set_name = [](struct ggml_tensor * t, const char * n) { - ggml_set_name(t, n); - if (t->grad) { - ggml_format_name(t->grad, "%s->grad", n); - } - }; - - // KQ_pos - contains the positions - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N); - ggml_set_input(KQ_pos); - - // rope has so much parameters that we make a custom function for it - auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale] - (struct ggml_tensor * t) -> struct ggml_tensor * { - // not capturing these, to silcence warnings - const int rope_mode = 0; - - return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, 0, - rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f - ); - }; - - set_name(tokens_input, "tokens_input"); - set_name(targets, "targets"); - - GGML_ASSERT(tokens_input->type == GGML_TYPE_I32); - - auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { - if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) { - return ggml_add_cast(ctx, a, b, GGML_TYPE_F32); - } else if (a->type == GGML_TYPE_F32) { - return ggml_add(ctx, a, b); - } else { - die_fmt("%s: Finetuning on tensors with type '%s' is not yet supported.\n", - __func__, ggml_type_name(a->type)); - } - }; - - struct ggml_tensor * tok_embeddings = add_to_f32(ctx, model->tok_embeddings, ggml_mul_mat(ctx, lora->tok_embeddings_a, lora->tok_embeddings_b)); - struct ggml_tensor * norm = add_to_f32(ctx, model->norm, ggml_mul_mat(ctx, lora->norm_a, lora->norm_b)); - struct ggml_tensor * output = add_to_f32(ctx, model->output, ggml_mul_mat(ctx, lora->output_a, lora->output_b)); - - struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch); set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch); - struct ggml_tensor * t01 = ggml_get_rows(ctx, tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch); - - struct ggml_tensor * cur = t01; - - std::vector checkpoints; - if (enable_checkpointing) { - checkpoints.push_back(tokens_input); - checkpoints.push_back(targets); - checkpoints.push_back(t00); - checkpoints.push_back(t01); - } - - const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head); - - for (int il = 0; il < n_layer; ++il) { - struct my_llama_layer & layer = model->layers[il]; - struct my_llama_lora_layer & llayer = lora->layers[il]; - - struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b)); - struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b)); - struct ggml_tensor * wq = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b)); - struct ggml_tensor * wk = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b)); - struct ggml_tensor * wv = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b)); - struct ggml_tensor * wo = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b)); - struct ggml_tensor * ffn_gate = add_to_f32(ctx, layer.ffn_gate, ggml_mul_mat(ctx, llayer.ffn_gate_a, llayer.ffn_gate_b)); - struct ggml_tensor * ffn_down = add_to_f32(ctx, layer.ffn_down, ggml_mul_mat(ctx, llayer.ffn_down_a, llayer.ffn_down_b)); - struct ggml_tensor * ffn_up = add_to_f32(ctx, layer.ffn_up, ggml_mul_mat(ctx, llayer.ffn_up_a, llayer.ffn_up_b)); - - struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch); - struct ggml_tensor * t03 = ggml_repeat (ctx, attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch); - struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, "t04"); assert_shape_2d(t04, n_embd, N*n_batch); - struct ggml_tensor * t05 = ggml_mul_mat (ctx, wq, t04); set_name(t05, "t05"); assert_shape_2d(t05, n_embd, N*n_batch); - struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd_head, n_head, N, n_batch); - struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd_head, n_head, N, n_batch); - struct ggml_tensor * t08 = ggml_mul_mat (ctx, wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd_gqa, N*n_batch); - struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd_head, n_head_kv, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd_head, n_head_kv, N, n_batch); - struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd_head, n_head_kv, N, n_batch); - - struct ggml_tensor * t11; - if (ggml_is_quantized(wv->type)) { - struct ggml_tensor * t11_1 = ggml_mul_mat (ctx, wv, t04); set_name(t11_1, "t11_1"); assert_shape_2d(t11_1, n_embd_gqa, N*n_batch); - struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1); set_name(t11_2, "t11_2"); assert_shape_2d(t11_2, N*n_batch, n_embd_gqa); - t11 = ggml_cont (ctx, t11_2); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa); - } else { - t11 = ggml_mul_mat (ctx, t04, wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd_gqa); - } - - struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd_head, n_head_kv); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd_head, n_head_kv); - struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd_head, N, n_head, n_batch); - struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, "t14"); assert_shape_4d(t14, n_embd_head, N, n_head_kv, n_batch); - struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch); - struct ggml_tensor * t16; - if (enable_flash_attn) { - t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); - } else { - struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); - struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); - struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch); - struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch); - t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); - } - struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, "t17"); assert_shape_4d(t17, n_embd_head, n_head, N, n_batch); - struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd_head, n_head, N, n_batch); - struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, "t19"); assert_shape_2d(t19, n_embd, N*n_batch); - struct ggml_tensor * t20 = ggml_mul_mat (ctx, wo, t19); set_name(t20, "t20"); assert_shape_2d(t20, n_embd, N*n_batch); - struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, "t21"); assert_shape_2d(t21, n_embd, N*n_batch); - struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, rms_norm_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch); - struct ggml_tensor * t23 = ggml_repeat (ctx, ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch); - struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch); - struct ggml_tensor * t25 = ggml_mul_mat (ctx, ffn_up, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); - struct ggml_tensor * t26 = ggml_mul_mat (ctx, ffn_gate, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); - struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch); - struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch); - struct ggml_tensor * t29 = ggml_mul_mat (ctx, ffn_down, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); - struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch); - cur = t30; - if (enable_checkpointing) { - checkpoints.push_back(cur); - } - } - struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, rms_norm_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch); - struct ggml_tensor * t32 = ggml_repeat (ctx, norm, t31); set_name(t32, "t32"); assert_shape_2d(t32, n_embd, N*n_batch); - struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, "t33"); assert_shape_2d(t33, n_embd, N*n_batch); - struct ggml_tensor * t34 = ggml_mul_mat (ctx, output, t33); set_name(t34, "t34"); assert_shape_2d(t34, n_vocab, N*n_batch); - struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, "t35"); assert_shape_3d(t35, n_vocab, N, n_batch); - struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, "t36"); assert_shape_1d(t36, 1); - - if (enable_checkpointing) { - checkpoints.push_back(t31); - checkpoints.push_back(t32); - checkpoints.push_back(t33); - checkpoints.push_back(t34); - checkpoints.push_back(t35); - checkpoints.push_back(t36); - } - - ggml_build_forward_expand(gf, t36); - - if (enable_checkpointing) { - ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size()); - } else { - ggml_graph_cpy(gf, gb); - ggml_build_backward_expand(ctx, gf, gb, true); - } - - GGML_ASSERT(alloc != NULL); - - // make sure some tensors are not reallocated by inserting new temporary nodes depending on them - int n_leafs_before = gb->n_leafs; - int n_nodes_before = gb->n_nodes; - - // output tensors - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f)); - // input gradient - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f)); - GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL); - ggml_set_input(t36->grad); - // KQ_pos - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f)); - - // make sure base model tensors data cannot be used in viewable operations - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, 1.0f)); - for (int il = 0; il < n_layer; ++il) { - struct my_llama_layer & layer = model->layers[il]; - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_gate, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_down, 1.0f)); - ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_up, 1.0f)); - } - - // allocating checkpoints in one block to reduce memory fragmentation - // note: they will be freed in reverse order - for (unsigned int i = 0; i < checkpoints.size(); ++i) { - if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) { - ggml_set_input(checkpoints[i]); - } - } - - if (measure_only) { - ggml_gallocr_reserve(alloc, gb); - } else { - ggml_gallocr_alloc_graph(alloc, gb); - - // set KQ_pos - { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } - } - - // remove the additional nodes and leafs - for (int i = n_leafs_before; i < gb->n_leafs; ++i) { - gb->leafs[i] = NULL; - } - for (int i = n_nodes_before; i < gb->n_nodes; ++i) { - gb->nodes[i] = NULL; - } - gb->n_leafs = n_leafs_before; - gb->n_nodes = n_nodes_before; - - *logits = t35; - return t36; -} - -static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora) { - // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read - - std::string arch; - - std::vector keybuf; - keybuf.resize(512); - - GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE); - GGML_ASSERT(arch == "llama"); - - uint32_t ftype_u; - GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE); - GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32); - - struct my_llama_hparams hparams; - load_model_hparams_gguf(fctx, &hparams, arch.c_str()); - - // parameters that define tensor shapes must match - GGML_ASSERT(hparams.n_embd == model->hparams.n_embd); - GGML_ASSERT(hparams.n_ff == model->hparams.n_ff); - GGML_ASSERT(hparams.n_head == model->hparams.n_head); - GGML_ASSERT(hparams.n_head_kv == model->hparams.n_head_kv); - GGML_ASSERT(hparams.n_layer == model->hparams.n_layer); - - GGUF_GET_KEY(fctx, lora->hparams.n_rank_tok_embeddings, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_output, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_attention_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_wq, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_Q); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_wk, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_K); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_gate, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_down, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN); - GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_up, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP); - - init_lora(model, lora); - - copy_tensor_by_name(lora->tok_embeddings_a, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_a)); - copy_tensor_by_name(lora->tok_embeddings_b, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_b)); - copy_tensor_by_name(lora->norm_a, f_ggml_ctx, ggml_get_name(lora->norm_a)); - copy_tensor_by_name(lora->norm_b, f_ggml_ctx, ggml_get_name(lora->norm_b)); - copy_tensor_by_name(lora->output_a, f_ggml_ctx, ggml_get_name(lora->output_a)); - copy_tensor_by_name(lora->output_b, f_ggml_ctx, ggml_get_name(lora->output_b)); - - for (uint32_t i = 0; i < lora->layers.size(); ++i) { - auto & layer = lora->layers[i]; - copy_tensor_by_name(layer.attention_norm_a, f_ggml_ctx, ggml_get_name(layer.attention_norm_a)); - copy_tensor_by_name(layer.attention_norm_b, f_ggml_ctx, ggml_get_name(layer.attention_norm_b)); - copy_tensor_by_name(layer.wq_a, f_ggml_ctx, ggml_get_name(layer.wq_a)); - copy_tensor_by_name(layer.wq_b, f_ggml_ctx, ggml_get_name(layer.wq_b)); - copy_tensor_by_name(layer.wk_a, f_ggml_ctx, ggml_get_name(layer.wk_a)); - copy_tensor_by_name(layer.wk_b, f_ggml_ctx, ggml_get_name(layer.wk_b)); - copy_tensor_by_name(layer.wv_a, f_ggml_ctx, ggml_get_name(layer.wv_a)); - copy_tensor_by_name(layer.wv_b, f_ggml_ctx, ggml_get_name(layer.wv_b)); - copy_tensor_by_name(layer.wo_a, f_ggml_ctx, ggml_get_name(layer.wo_a)); - copy_tensor_by_name(layer.wo_b, f_ggml_ctx, ggml_get_name(layer.wo_b)); - copy_tensor_by_name(layer.ffn_norm_a, f_ggml_ctx, ggml_get_name(layer.ffn_norm_a)); - copy_tensor_by_name(layer.ffn_norm_b, f_ggml_ctx, ggml_get_name(layer.ffn_norm_b)); - copy_tensor_by_name(layer.ffn_gate_a, f_ggml_ctx, ggml_get_name(layer.ffn_gate_a)); - copy_tensor_by_name(layer.ffn_gate_b, f_ggml_ctx, ggml_get_name(layer.ffn_gate_b)); - copy_tensor_by_name(layer.ffn_down_a, f_ggml_ctx, ggml_get_name(layer.ffn_down_a)); - copy_tensor_by_name(layer.ffn_down_b, f_ggml_ctx, ggml_get_name(layer.ffn_down_b)); - copy_tensor_by_name(layer.ffn_up_a, f_ggml_ctx, ggml_get_name(layer.ffn_up_a)); - copy_tensor_by_name(layer.ffn_up_b, f_ggml_ctx, ggml_get_name(layer.ffn_up_b)); - } -} - -static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora) { - const char * arch = "llama"; - enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32; - - std::vector keybuf; - keybuf.resize(512); - auto kv = [arch, &keybuf](const char * key) -> const char * { - snprintf(keybuf.data(), keybuf.size(), key, arch); - return keybuf.data(); - }; - - gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch); - gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype); - - gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH), model->hparams.n_ctx); - gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH), model->hparams.n_embd); - gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH), model->hparams.n_ff); - gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT), model->hparams.n_head); - gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV), model->hparams.n_head_kv); - gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT), model->hparams.n_layer); - gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT), model->hparams.n_embd_head()); - gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps); - gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE), model->hparams.rope_freq_base); - gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR), model->hparams.rope_freq_scale); - - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD, lora->hparams.n_rank_tok_embeddings); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, lora->hparams.n_rank_norm); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT, lora->hparams.n_rank_output); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM, lora->hparams.n_rank_attention_norm); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_Q, lora->hparams.n_rank_wq); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_K, lora->hparams.n_rank_wk); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V, lora->hparams.n_rank_wv); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT, lora->hparams.n_rank_wo); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM, lora->hparams.n_rank_ffn_norm); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE, lora->hparams.n_rank_ffn_gate); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN, lora->hparams.n_rank_ffn_down); - gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP, lora->hparams.n_rank_ffn_up); - - gguf_add_tensor(fctx, lora->tok_embeddings_a); - gguf_add_tensor(fctx, lora->tok_embeddings_b); - gguf_add_tensor(fctx, lora->norm_a); - gguf_add_tensor(fctx, lora->norm_b); - gguf_add_tensor(fctx, lora->output_a); - gguf_add_tensor(fctx, lora->output_b); - - for (uint32_t i = 0; i < lora->layers.size(); ++i) { - auto & layer = lora->layers[i]; - - gguf_add_tensor(fctx, layer.attention_norm_a); - gguf_add_tensor(fctx, layer.attention_norm_b); - gguf_add_tensor(fctx, layer.wq_a); - gguf_add_tensor(fctx, layer.wq_b); - gguf_add_tensor(fctx, layer.wk_a); - gguf_add_tensor(fctx, layer.wk_b); - gguf_add_tensor(fctx, layer.wv_a); - gguf_add_tensor(fctx, layer.wv_b); - gguf_add_tensor(fctx, layer.wo_a); - gguf_add_tensor(fctx, layer.wo_b); - gguf_add_tensor(fctx, layer.ffn_norm_a); - gguf_add_tensor(fctx, layer.ffn_norm_b); - gguf_add_tensor(fctx, layer.ffn_gate_a); - gguf_add_tensor(fctx, layer.ffn_gate_b); - gguf_add_tensor(fctx, layer.ffn_down_a); - gguf_add_tensor(fctx, layer.ffn_down_b); - gguf_add_tensor(fctx, layer.ffn_up_a); - gguf_add_tensor(fctx, layer.ffn_up_b); - } -} - -static void load_checkpoint_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) { - std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA; - GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE); - GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_FINETUNE_LORA); - - load_train_state_gguf(fctx, f_ggml_ctx, train); - load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora); -} - -static void save_checkpoint_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) { - gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA); - save_llama_lora_gguf(fctx, model, lora); - save_train_state_gguf(fctx, train); -} - -static bool load_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) { - struct ggml_context * f_ggml_ctx; - struct gguf_init_params params; - params.no_alloc = false; - params.ctx = &f_ggml_ctx; - struct gguf_context * fctx = gguf_init_from_file(filename, params); - if (fctx == NULL) { - return false; - } - - load_checkpoint_lora_gguf(fctx, f_ggml_ctx, model, lora, train); - - gguf_free(fctx); - return true; -} - -static void save_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) { - printf("%s: saving to %s\n", __func__, filename); - struct gguf_context * fctx = gguf_init_empty(); - - save_checkpoint_lora_gguf(fctx, model, lora, train); - - // write file - const bool only_meta = false; - gguf_write_to_file(fctx, filename, only_meta); - gguf_free(fctx); -} - -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 void write_tensor(struct llama_file * file, struct ggml_tensor * tensor, const char * name) { - if (tensor == NULL) { - file->write_u32(0); - file->write_u32(0); - file->write_u32(GGML_TYPE_F32); - file->seek((0-file->tell()) & 31, SEEK_CUR); - return; - } - if (name == NULL) { - name = ggml_get_name(tensor); - } - uint32_t name_len = strlen(name); - uint32_t nd = ggml_n_dims(tensor); - uint32_t ne[4] = { (uint32_t)tensor->ne[0], - (uint32_t)tensor->ne[1], - (uint32_t)tensor->ne[2], - (uint32_t)tensor->ne[3] }; - file->write_u32(nd); - file->write_u32(name_len); - file->write_u32(tensor->type); - file->write_raw(ne, sizeof(ne[0]) * nd); - file->write_raw(name, name_len); - file->seek((0-file->tell()) & 31, SEEK_CUR); - file->write_raw(tensor->data, ggml_nbytes(tensor)); -} - -static void save_as_llama_lora(const char * filename, struct my_llama_lora * lora) { - printf("%s: saving to %s\n", __func__, filename); - struct llama_file file(filename, "wb"); - if (file.fp == NULL) { - return; - } - - std::vector tn_buf; - tn_buf.resize(GGML_MAX_NAME); - - auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * { - snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix); - return tn_buf.data(); - }; - - auto tni = [&tn_buf](const char * key, int bid, const char * suffix) -> const char * { - snprintf(tn_buf.data(), tn_buf.size(), key, bid); - std::string s = tn_buf.data(); - snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix); - return tn_buf.data(); - }; - - // write_magic - file.write_u32(LLAMA_FILE_MAGIC_GGLA); // magic - file.write_u32(1); // version - // write_hparams - file.write_u32(lora->hparams.lora_r); - file.write_u32(lora->hparams.lora_alpha); - // write tensors - write_tensor(&file, lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.loraA")); - write_tensor(&file, lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD, ".weight.loraB")); - write_tensor(&file, lora->norm_a, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraA")); - write_tensor(&file, lora->norm_b, tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraB")); - write_tensor(&file, lora->output_a, tn(LLM_TENSOR_OUTPUT, ".weight.loraA")); - write_tensor(&file, lora->output_b, tn(LLM_TENSOR_OUTPUT, ".weight.loraB")); - for (uint32_t i = 0; i < lora->layers.size(); ++i) { - auto & layer = lora->layers[i]; - write_tensor(&file, layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraA")); - write_tensor(&file, layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraB")); - write_tensor(&file, layer.wq_a, tni(LLM_TENSOR_ATTN_Q, i, ".weight.loraA")); - write_tensor(&file, layer.wq_b, tni(LLM_TENSOR_ATTN_Q, i, ".weight.loraB")); - write_tensor(&file, layer.wk_a, tni(LLM_TENSOR_ATTN_K, i, ".weight.loraA")); - write_tensor(&file, layer.wk_b, tni(LLM_TENSOR_ATTN_K, i, ".weight.loraB")); - write_tensor(&file, layer.wv_a, tni(LLM_TENSOR_ATTN_V, i, ".weight.loraA")); - write_tensor(&file, layer.wv_b, tni(LLM_TENSOR_ATTN_V, i, ".weight.loraB")); - write_tensor(&file, layer.wo_a, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraA")); - write_tensor(&file, layer.wo_b, tni(LLM_TENSOR_ATTN_OUT, i, ".weight.loraB")); - write_tensor(&file, layer.ffn_norm_a, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraA")); - write_tensor(&file, layer.ffn_norm_b, tni(LLM_TENSOR_FFN_NORM, i, ".weight.loraB")); - write_tensor(&file, layer.ffn_gate_a, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraA")); - write_tensor(&file, layer.ffn_gate_b, tni(LLM_TENSOR_FFN_GATE, i, ".weight.loraB")); - write_tensor(&file, layer.ffn_down_a, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraA")); - write_tensor(&file, layer.ffn_down_b, tni(LLM_TENSOR_FFN_DOWN, i, ".weight.loraB")); - write_tensor(&file, layer.ffn_up_a, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraA")); - write_tensor(&file, layer.ffn_up_b, tni(LLM_TENSOR_FFN_UP, i, ".weight.loraB")); - } -} - -struct train_params { - struct train_params_common common; - - const char * fn_model_base; - const char * fn_lora_out; - - bool only_write_lora; - - float f_norm_rms_eps; - float rope_freq_base; - float rope_freq_scale; - - bool custom_f_norm_rms_eps; - bool custom_rope_freq_base; - bool custom_rope_freq_scale; - - int32_t lora_r; - int32_t lora_alpha; - bool custom_lora_alpha; - - uint32_t n_rank_attention_norm; - uint32_t n_rank_wq; - uint32_t n_rank_wk; - uint32_t n_rank_wv; - uint32_t n_rank_wo; - uint32_t n_rank_ffn_norm; - uint32_t n_rank_ffn_gate; - uint32_t n_rank_ffn_down; - uint32_t n_rank_ffn_up; - uint32_t n_rank_tok_embeddings; - uint32_t n_rank_norm; - uint32_t n_rank_output; - - bool custom_n_rank_attention_norm; - bool custom_n_rank_wq; - bool custom_n_rank_wk; - bool custom_n_rank_wv; - bool custom_n_rank_wo; - bool custom_n_rank_ffn_norm; - bool custom_n_rank_ffn_gate; - bool custom_n_rank_ffn_down; - bool custom_n_rank_ffn_up; - bool custom_n_rank_tok_embeddings; - bool custom_n_rank_norm; - bool custom_n_rank_output; -}; - -static struct train_params get_default_train_params() { - struct train_params params; - params.common = get_default_train_params_common(); - params.fn_model_base = ""; - params.fn_lora_out = "ggml-lora-ITERATION-f32.gguf"; - - params.only_write_lora = false; - - params.f_norm_rms_eps = 1e-5f; - params.rope_freq_base = 10000.0f; - params.rope_freq_scale = 1.0f; - - params.custom_f_norm_rms_eps = false; - params.custom_rope_freq_base = false; - params.custom_rope_freq_scale = false; - - params.lora_r = 4; - params.lora_alpha = 4; - params.custom_lora_alpha = false; - - params.n_rank_attention_norm = 1; - params.n_rank_wq = 4; - params.n_rank_wk = 4; - params.n_rank_wv = 4; - params.n_rank_wo = 4; - params.n_rank_ffn_norm = 1; - params.n_rank_ffn_gate = 4; - params.n_rank_ffn_down = 4; - params.n_rank_ffn_up = 4; - params.n_rank_tok_embeddings = 4; - params.n_rank_norm = 1; - params.n_rank_output = 4; - - params.custom_n_rank_attention_norm = false; - params.custom_n_rank_wq = false; - params.custom_n_rank_wk = false; - params.custom_n_rank_wv = false; - params.custom_n_rank_wo = false; - params.custom_n_rank_ffn_norm = false; - params.custom_n_rank_ffn_gate = false; - params.custom_n_rank_ffn_down = false; - params.custom_n_rank_ffn_up = false; - params.custom_n_rank_tok_embeddings = false; - params.custom_n_rank_norm = false; - params.custom_n_rank_output = false; - - return params; -} - -static void train_print_usage(int argc, char ** argv, const struct train_params * 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, " --model-base FNAME model path from which to load base model (default '%s')\n", params->fn_model_base); - fprintf(stderr, " --lora-out FNAME path to save llama lora (default '%s')\n", params->fn_lora_out); - fprintf(stderr, " --only-write-lora only save llama lora, don't do any training. use this if you only want to convert a checkpoint to a lora adapter.\n"); - fprintf(stderr, " --norm-rms-eps F RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps); - fprintf(stderr, " --rope-freq-base F Frequency base for ROPE (default %f)\n", params->rope_freq_base); - fprintf(stderr, " --rope-freq-scale F Frequency scale for ROPE (default %f)\n", params->rope_freq_scale); - fprintf(stderr, " --lora-alpha N LORA alpha : resulting LORA scaling is alpha/r. (default %d)\n", params->lora_alpha); - fprintf(stderr, " --lora-r N LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default %d)\n", params->lora_r); - fprintf(stderr, " --rank-att-norm N LORA rank for attention norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n"); - fprintf(stderr, " --rank-ffn-norm N LORA rank for feed-forward norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n"); - fprintf(stderr, " --rank-out-norm N LORA rank for output norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n"); - fprintf(stderr, " --rank-tok-embd N LORA rank for token embeddings tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-out N LORA rank for output tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-wq N LORA rank for wq tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-wk N LORA rank for wk tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-wv N LORA rank for wv tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-wo N LORA rank for wo tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-ffn_gate N LORA rank for ffn_gate tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-ffn_down N LORA rank for ffn_down tensor, overrides default rank.\n"); - fprintf(stderr, " --rank-ffn_up N LORA rank for ffn_up tensor, overrides default rank.\n"); - - print_common_train_usage(argc, argv, ¶ms->common); -} - -static bool train_params_parse(int argc, char ** argv, struct train_params * params) { - bool invalid_param = false; - std::string arg; - struct train_params default_params = get_default_train_params(); - const std::string arg_prefix = "--"; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } - - if (consume_common_train_arg(argc, argv, &i, ¶ms->common, &invalid_param)) { - if (invalid_param) { - break; - } else if (params->common.print_usage) { - train_print_usage(argc, argv, &default_params); - exit(0); - } - } else if (arg == "--model-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->fn_model_base = argv[i]; - } else if (arg == "--lora-out") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->fn_lora_out = argv[i]; - } else if (arg == "--only-write-lora") { - params->only_write_lora = true; - } else if (arg == "--norm-rms-eps") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->f_norm_rms_eps = std::stof(argv[i]); - params->custom_f_norm_rms_eps = true; - } else if (arg == "--rope-freq-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->rope_freq_base = std::stof(argv[i]); - params->custom_rope_freq_base = true; - } else if (arg == "--rope-freq-scale") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->rope_freq_scale = std::stof(argv[i]); - params->custom_rope_freq_scale = true; - } else if (arg == "--lora-alpha") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->lora_alpha = std::stoi(argv[i]); - params->custom_lora_alpha = true; - } else if (arg == "--lora-r") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->lora_r = std::stoi(argv[i]); - } else if (arg == "--rank-att-norm") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_attention_norm = std::stoi(argv[i]); - params->custom_n_rank_attention_norm = true; - } else if (arg == "--rank-ffn-norm") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_ffn_norm = std::stoi(argv[i]); - params->custom_n_rank_ffn_norm = true; - } else if (arg == "--rank-out-norm") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_norm = std::stoi(argv[i]); - params->custom_n_rank_norm = true; - } else if (arg == "--rank-tok-embd") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_tok_embeddings = std::stoi(argv[i]); - params->custom_n_rank_tok_embeddings = true; - } else if (arg == "--rank-out") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_output = std::stoi(argv[i]); - params->custom_n_rank_output = true; - } else if (arg == "--rank-wq") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_wq = std::stoi(argv[i]); - params->custom_n_rank_wq = true; - } else if (arg == "--rank-wk") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_wk = std::stoi(argv[i]); - params->custom_n_rank_wk = true; - } else if (arg == "--rank-wv") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_wv = std::stoi(argv[i]); - params->custom_n_rank_wv = true; - } else if (arg == "--rank-wo") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_wo = std::stoi(argv[i]); - params->custom_n_rank_wo = true; - } else if (arg == "--rank-ffn_gate") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_ffn_gate = std::stoi(argv[i]); - params->custom_n_rank_ffn_gate = true; - } else if (arg == "--rank-ffn_down") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_ffn_down = std::stoi(argv[i]); - params->custom_n_rank_ffn_down = true; - } else if (arg == "--rank-ffn_up") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_rank_ffn_up = std::stoi(argv[i]); - params->custom_n_rank_ffn_up = true; - } else { - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - train_print_usage(argc, argv, &default_params); - exit(1); - } - } - if (invalid_param) { - fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); - train_print_usage(argc, argv, &default_params); - exit(1); - } - finish_processing_train_args(¶ms->common); - return true; -} - -struct save_train_files_data { - const char * fn_checkpoint_out; - const char * fn_lora_out; - const char * pattern_fn_it; - const char * fn_latest; - struct my_llama_model * model; - struct my_llama_lora * lora; -}; - -static void save_train_files(void * vdata, struct train_state * train) { - struct save_train_files_data * data = (struct save_train_files_data *) vdata; - - int64_t iter = train->opt->iter; - - if (strlen(data->fn_checkpoint_out) > 0) { - save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->model, data->lora, train); - save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->model, data->lora, train); - } - if (strlen(data->fn_lora_out) > 0) { - save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->lora); - save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->lora); - } -} - -static int64_t get_parameter_count(struct my_llama_lora* lora) { - int64_t nx = 0; - nx += ggml_nelements(lora->tok_embeddings_a); - nx += ggml_nelements(lora->tok_embeddings_b); - nx += ggml_nelements(lora->norm_a); - nx += ggml_nelements(lora->norm_b); - nx += ggml_nelements(lora->output_a); - nx += ggml_nelements(lora->output_b); - - for (uint32_t i = 0; i < lora->layers.size(); ++i) { - auto & layer = lora->layers[i]; - nx += ggml_nelements(layer.attention_norm_a); - nx += ggml_nelements(layer.attention_norm_b); - nx += ggml_nelements(layer.wq_a); - nx += ggml_nelements(layer.wq_b); - nx += ggml_nelements(layer.wk_a); - nx += ggml_nelements(layer.wk_b); - nx += ggml_nelements(layer.wv_a); - nx += ggml_nelements(layer.wv_b); - nx += ggml_nelements(layer.wo_a); - nx += ggml_nelements(layer.wo_b); - nx += ggml_nelements(layer.ffn_norm_a); - nx += ggml_nelements(layer.ffn_norm_b); - nx += ggml_nelements(layer.ffn_gate_a); - nx += ggml_nelements(layer.ffn_gate_b); - nx += ggml_nelements(layer.ffn_down_a); - nx += ggml_nelements(layer.ffn_down_b); - nx += ggml_nelements(layer.ffn_up_a); - nx += ggml_nelements(layer.ffn_up_b); - } - return nx; -} - -int main(int argc, char ** argv) { - struct train_params params = get_default_train_params(); - - if (!train_params_parse(argc, argv, ¶ms)) { - return 1; - } - - if (params.common.seed == LLAMA_DEFAULT_SEED) { - params.common.seed = time(NULL); - } - printf("%s: seed: %u\n", __func__, params.common.seed); - srand(params.common.seed); - - struct llama_model_params llama_mparams = llama_model_default_params(); - llama_mparams.n_gpu_layers = params.common.n_gpu_layers; - llama_mparams.vocab_only = false; - - printf("%s: model base = '%s'\n", __func__, params.fn_model_base); - struct llama_model * lmodel = llama_load_model_from_file(params.fn_model_base, llama_mparams); - - struct llama_context_params llama_cparams = llama_context_default_params(); - struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_cparams); - - struct my_llama_model model; - init_model(lmodel, &model, params.fn_model_base, params.common.n_ctx); - - struct my_llama_lora lora; - - struct train_state * train = init_train_state(); - struct ggml_opt_context * opt = train->opt; - - // set params from command line - if (params.custom_f_norm_rms_eps) { - model.hparams.f_norm_rms_eps = params.f_norm_rms_eps; - } - if (params.custom_rope_freq_base) { - model.hparams.rope_freq_base = params.rope_freq_base; - } - if (params.custom_rope_freq_scale) { - model.hparams.rope_freq_scale = params.rope_freq_scale; - } - lora.hparams.lora_r = params.lora_r; - lora.hparams.lora_alpha = params.custom_lora_alpha ? params.lora_alpha : params.lora_r; - uint32_t n_rank_attention_norm = params.custom_n_rank_attention_norm ? params.n_rank_attention_norm : 1; - uint32_t n_rank_wq = params.custom_n_rank_wq ? params.n_rank_wq : params.lora_r; - uint32_t n_rank_wk = params.custom_n_rank_wk ? params.n_rank_wk : params.lora_r; - uint32_t n_rank_wv = params.custom_n_rank_wv ? params.n_rank_wv : params.lora_r; - uint32_t n_rank_wo = params.custom_n_rank_wo ? params.n_rank_wo : params.lora_r; - uint32_t n_rank_ffn_norm = params.custom_n_rank_ffn_norm ? params.n_rank_ffn_norm : 1; - uint32_t n_rank_ffn_gate = params.custom_n_rank_ffn_gate ? params.n_rank_ffn_gate : params.lora_r; - uint32_t n_rank_ffn_down = params.custom_n_rank_ffn_down ? params.n_rank_ffn_down : params.lora_r; - uint32_t n_rank_ffn_up = params.custom_n_rank_ffn_up ? params.n_rank_ffn_up : params.lora_r; - uint32_t n_rank_tok_embeddings = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r; - uint32_t n_rank_norm = params.custom_n_rank_norm ? params.n_rank_norm : 1; - uint32_t n_rank_output = params.custom_n_rank_output ? params.n_rank_output : params.lora_r; - lora.hparams.n_rank_attention_norm = n_rank_attention_norm; - lora.hparams.n_rank_wq = n_rank_wq; - lora.hparams.n_rank_wk = n_rank_wk; - lora.hparams.n_rank_wv = n_rank_wv; - lora.hparams.n_rank_wo = n_rank_wo; - lora.hparams.n_rank_ffn_norm = n_rank_ffn_norm; - lora.hparams.n_rank_ffn_gate = n_rank_ffn_gate; - lora.hparams.n_rank_ffn_down = n_rank_ffn_down; - lora.hparams.n_rank_ffn_up = n_rank_ffn_up; - lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings; - lora.hparams.n_rank_norm = n_rank_norm; - lora.hparams.n_rank_output = n_rank_output; - - // set opt params from command line - opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); - opt->params.print_forward_graph = false; - opt->params.print_backward_graph = false; - opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; - opt->params.n_threads = params.common.n_threads; - opt->params.past = params.common.opt_past; - opt->params.delta = params.common.opt_delta; - opt->params.max_no_improvement = params.common.opt_max_no_improvement; - opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation; - opt->params.adam.n_iter = params.common.adam_n_iter; - opt->params.adam.sched = 1.0f; - opt->params.adam.alpha = params.common.adam_alpha; - opt->params.adam.decay = params.common.adam_decay; - opt->params.adam.decay_min_ndim = params.common.adam_decay_min_ndim; - opt->params.adam.beta1 = params.common.adam_beta1; - opt->params.adam.beta2 = params.common.adam_beta2; - opt->params.adam.gclip = params.common.adam_gclip; - opt->params.adam.eps_f = params.common.adam_eps_f; - - printf("%s: init model\n", __func__); - bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train); - - if (existed) { - // overwrite last n_ctx with user provided n_ctx - if (params.common.custom_n_ctx) { - model.hparams.n_ctx = params.common.n_ctx; - } - - const bool opt_param_count_changed = ( - (lora.hparams.n_rank_attention_norm != n_rank_attention_norm) - || (lora.hparams.n_rank_wq != n_rank_wq) - || (lora.hparams.n_rank_wk != n_rank_wk) - || (lora.hparams.n_rank_wv != n_rank_wv) - || (lora.hparams.n_rank_wo != n_rank_wo) - || (lora.hparams.n_rank_ffn_norm != n_rank_ffn_norm) - || (lora.hparams.n_rank_ffn_gate != n_rank_ffn_gate) - || (lora.hparams.n_rank_ffn_down != n_rank_ffn_down) - || (lora.hparams.n_rank_ffn_up != n_rank_ffn_up) - || (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings) - || (lora.hparams.n_rank_norm != n_rank_norm) - || (lora.hparams.n_rank_output != n_rank_output) - ); - - const bool opt_past_changed = opt->params.past != params.common.opt_past; - - if (opt_param_count_changed) { - print_lora_params(&lora.hparams); - die("Provided rank differs from checkpoint file. To use different rank start finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting."); - // need to discard previous optimizer gradient statistics and opt_init with new shapes - // TODO - } - if (opt_past_changed) { - die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting"); - // need to discard previous optimizer past function value statistics and opt_init with new shapes - // TODO - } - } else { // existed == false - init_lora(&model, &lora); - randomize_lora(&lora, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f); - if (!params.only_write_lora) { - ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&lora)); - } - } - opt->iter = train->train_its; - - print_params(&model.hparams); - print_lora_params(&lora.hparams); - printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its); - printf("%s: seen train_samples %llu\n", __func__, (long long unsigned) train->train_samples); - printf("%s: seen train_tokens %llu\n", __func__, (long long unsigned) train->train_tokens); - printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs); - printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)), (float) (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)) / (1024.0f*1024.0f)); - - if (params.only_write_lora) { - save_train_files_data save_data; - save_data.fn_checkpoint_out = ""; - save_data.fn_lora_out = params.fn_lora_out; - save_data.pattern_fn_it = params.common.pattern_fn_it; - save_data.fn_latest = params.common.fn_latest; - save_data.model = &model; - save_data.lora = &lora; - - save_train_files(&save_data, train); - - free_train_state(train); - ggml_free(lora.ctx); - llama_free(lctx); - llama_free_model(lmodel); - return 0; - } - - printf("%s: opt_size = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f)); - printf("%s: opt iter %d\n", __func__, opt->iter); - - int n_tokens = model.hparams.n_ctx; - int n_vocab = model.hparams.n_vocab; - int n_batch = params.common.n_batch; - - // context for input tensors without their data - struct ggml_init_params ctx_input_params = { - ggml_tensor_overhead() * 2, // mem_size - NULL, // mem_buffer - true, // no_alloc - }; - struct ggml_context * ctx_input = ggml_init(ctx_input_params); - - // the input tensors - struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch); - struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); - - // allocate input tensors - // measure required memory for input tensors - ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type()); - size_t max_input_size = ggml_backend_buffer_get_size(input_data); - printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); - - // context for compute tensors without their data - const size_t estimated_compute_size_wo_data = ( - 2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() + - (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true)) - ); - struct ggml_init_params ctx_compute_params = { - estimated_compute_size_wo_data, // mem_size - NULL, // mem_buffer - true, // no_alloc - }; - struct ggml_context * ctx_compute = NULL; - - struct ggml_tensor * loss = NULL; - struct ggml_tensor * logits = NULL; - - struct ggml_cgraph * gf = NULL; - struct ggml_cgraph * gb = NULL; - struct ggml_cgraph * gb_tmp = NULL; - - // measure required memory for compute tensors - size_t best_compute_size = SIZE_MAX; - enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT; - // find best evaluation order - for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { - ctx_compute = ggml_init(ctx_compute_params); - ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); - gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); - gf->order = (enum ggml_cgraph_eval_order) order; - gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); - gb_tmp = params.common.use_checkpointing - ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) - : NULL; - loss = llama_build_lora_finetune_graphs( - &model, &lora, alloc, ctx_compute, - gf, gb, gb_tmp, - &logits, tokens_input, target_probs, - n_tokens, n_batch, - params.common.use_flash, - params.common.use_checkpointing, - true - ); - size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer - if (max_compute_size < best_compute_size) { - best_compute_size = max_compute_size; - best_order = gf->order; - } - ggml_gallocr_free(alloc); - ggml_free(ctx_compute); - } - size_t max_compute_size = best_compute_size; - printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f)); - printf("%s: evaluation order = %s\n", __func__, - (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" : - (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" : - "invalid"); - - // allocate compute tensors - ctx_compute = ggml_init(ctx_compute_params); - ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); - gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); - gf->order = best_order; - gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); - gb_tmp = params.common.use_checkpointing - ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) - : NULL; - loss = llama_build_lora_finetune_graphs( - &model, &lora, alloc, ctx_compute, - gf, gb, gb_tmp, - &logits, tokens_input, target_probs, - n_tokens, n_batch, - params.common.use_flash, - params.common.use_checkpointing, - false - ); - - // tokenize data - std::vector train_tokens; - std::vector train_samples_begin; - std::vector train_samples_size; - printf("%s: tokenize training data from %s\n", __func__, params.common.fn_train_data); - printf("%s: sample-start: %s\n", __func__, params.common.sample_start.c_str()); - printf("%s: include-sample-start: %s\n", __func__, params.common.include_sample_start ? "true" : "false"); - tokenize_file(lctx, - params.common.fn_train_data, - params.common.sample_start, - params.common.include_sample_start, - params.common.overlapping_samples, - n_tokens, - train_tokens, - train_samples_begin, - train_samples_size); - GGML_ASSERT(train_samples_begin.size() == train_samples_size.size()); - - printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size()); - - std::vector token_noccurs; - token_noccurs.resize(model.hparams.n_vocab, 0); - for (unsigned int i = 0; i < train_tokens.size(); ++i) { - ++token_noccurs[train_tokens[i]]; - } - int n_unique_tokens = 0; - for (unsigned int i = 0; i < token_noccurs.size(); ++i) { - if (token_noccurs[i] == 0) continue; - ++n_unique_tokens; - } - printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens); - - size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size()); - const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size()); - if (changed_train_data) { - printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__); - } - if (params.common.force_reshuffle) { - printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__); - } - if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) { - train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed); - train->shuffle_sample_count = train_samples_size.size(); - train->shuffle_next_sample = 0; - train->shuffle_samples_hash = shuffle_samples_hash; - } - std::vector train_shuffled_samples_offs; - std::vector train_shuffled_samples_begin; - std::vector train_shuffled_samples_size; - train_shuffled_samples_offs.resize(train_samples_begin.size()); - train_shuffled_samples_begin.resize(train_samples_begin.size()); - train_shuffled_samples_size.resize(train_samples_size.size()); - train->shuffle_rng_state_next = shuffle_samples( - train->shuffle_rng_state_current, - train_shuffled_samples_offs.data(), - train_shuffled_samples_begin.data(), - train_shuffled_samples_size.data(), - train_samples_begin.data(), - train_samples_size.data(), - train_samples_size.size()); - - printf("%s: begin training\n", __func__); - - save_train_files_data save_data; - save_data.fn_checkpoint_out = params.common.fn_checkpoint_out; - save_data.fn_lora_out = params.fn_lora_out; - save_data.pattern_fn_it = params.common.pattern_fn_it; - save_data.fn_latest = params.common.fn_latest; - save_data.model = &model; - save_data.lora = &lora; - - struct train_opt_callback_data opt_cb_data; - opt_cb_data.params = ¶ms.common; - opt_cb_data.train = train; - opt_cb_data.save_cb = &save_train_files; - opt_cb_data.save_data = &save_data; - opt_cb_data.lctx = lctx; - opt_cb_data.last_save_iter = opt->iter; - opt_cb_data.tokens_data = train_tokens.data(); - opt_cb_data.tokens_size = train_tokens.size(); - opt_cb_data.samples_begin = train_samples_begin.data(); - opt_cb_data.samples_size = train_samples_size.data(); - opt_cb_data.shuffled_samples_offs = train_shuffled_samples_offs.data(); - opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data(); - opt_cb_data.shuffled_samples_size = train_shuffled_samples_size.data(); - opt_cb_data.samples_count = train_samples_size.size(); - opt_cb_data.tokens_input = tokens_input; - opt_cb_data.target_probs = target_probs; - opt_cb_data.first_iter = opt->iter; - opt_cb_data.first_epoch = train->train_epochs; - opt_cb_data.iter_at_last_epoch = -1; - opt_cb_data.last_time = ggml_time_ms(); - opt_cb_data.millis_per_iter = 0.0; - - // measure required memory for work buffer - size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE; - printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f)); - - // context for work buffer - struct ggml_init_params ctx_work_params = { - max_work_size, // mem_size - NULL, // mem_buffer - false, // no_alloc - }; - struct ggml_context * ctx_work = ggml_init(ctx_work_params); - - int64_t t0 = ggml_time_ms(); - - ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data); - - ggml_free(ctx_work); - ggml_free(ctx_compute); - ggml_free(ctx_input); - ggml_gallocr_free(alloc); - - - int64_t t1 = ggml_time_ms(); - printf("%s: total training time: ", __func__); - print_duration((double) (t1 - t0)); - printf("\n"); - - int new_iters = opt->iter - opt_cb_data.last_save_iter; - if (new_iters > 0) { - train->train_its += new_iters; - train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens; - - save_train_files(&save_data, train); - opt_cb_data.last_save_iter = opt->iter; - } - - ggml_free(opt->ctx); - free_train_state(train); - ggml_free(lora.ctx); - llama_free(lctx); - llama_free_model(lmodel); - return 0; -} diff --git a/examples/finetune/finetune.sh b/examples/finetune/finetune.sh deleted file mode 100644 index 079bfa113..000000000 --- a/examples/finetune/finetune.sh +++ /dev/null @@ -1,34 +0,0 @@ -#!/bin/bash -cd `dirname $0` -cd ../.. - -EXE="./finetune" - -if [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR="./models"; fi -if [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR="."; fi - -# MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2-q8_0.gguf" # This is the model the readme uses. -MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2.gguf" # An f16 model. Note in this case with "-g", you get an f32-format .BIN file that isn't yet supported if you use it with "main --lora" with GPU inferencing. - -while getopts "dg" opt; do - case $opt in - d) - DEBUGGER="gdb --args" - ;; - g) - EXE="./build/bin/Release/finetune" - GPUARG="--gpu-layers 25" - ;; - esac -done - -$DEBUGGER $EXE \ - --model-base $MODEL \ - $GPUARG \ - --checkpoint-in chk-ol3b-shakespeare-LATEST.gguf \ - --checkpoint-out chk-ol3b-shakespeare-ITERATION.gguf \ - --lora-out lora-ol3b-shakespeare-ITERATION.bin \ - --train-data "$LLAMA_TRAINING_DIR\shakespeare.txt" \ - --save-every 10 \ - --threads 10 --adam-iter 30 --batch 4 --ctx 64 \ - --use-checkpointing diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt new file mode 100644 index 000000000..d2cb524c0 --- /dev/null +++ b/examples/gbnf-validator/CMakeLists.txt @@ -0,0 +1,5 @@ +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_17) diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp new file mode 100644 index 000000000..a610e6a0b --- /dev/null +++ b/examples/gbnf-validator/gbnf-validator.cpp @@ -0,0 +1,109 @@ +#include "unicode.h" +#include "llama-grammar.h" + +#include +#include +#include +#include +#include +#include + +static bool llama_grammar_validate(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) { + const auto cpts = unicode_cpts_from_utf8(input_str); + + auto & stacks_cur = llama_grammar_get_stacks(grammar); + + size_t pos = 0; + for (const auto & cpt : cpts) { + llama_grammar_accept(grammar, cpt); + + if (stacks_cur.empty()) { + error_pos = pos; + error_msg = "Unexpected character '" + unicode_cpt_to_utf8(cpt) + "'"; + return false; + } + ++pos; + } + + for (const auto & stack : stacks_cur) { + if (stack.empty()) { + return true; + } + } + + error_pos = pos; + error_msg = "Unexpected end of input"; + return false; +} + +static void print_error_message(const std::string & input_str, size_t error_pos, const std::string & error_msg) { + fprintf(stdout, "Input string is invalid according to the grammar.\n"); + fprintf(stdout, "Error: %s at position %zu\n", error_msg.c_str(), error_pos); + fprintf(stdout, "\n"); + fprintf(stdout, "Input string:\n"); + fprintf(stdout, "%s", input_str.substr(0, error_pos).c_str()); + if (error_pos < input_str.size()) { + fprintf(stdout, "\033[1;31m%c", input_str[error_pos]); + if (error_pos+1 < input_str.size()) { + fprintf(stdout, "\033[0;31m%s", input_str.substr(error_pos+1).c_str()); + } + fprintf(stdout, "\033[0m\n"); + } +} + +int main(int argc, char** argv) { + if (argc != 3) { + fprintf(stdout, "Usage: %s \n", argv[0]); + return 1; + } + + const std::string grammar_filename = argv[1]; + const std::string input_filename = argv[2]; + + // Read the GBNF grammar file + FILE* grammar_file = fopen(grammar_filename.c_str(), "r"); + if (!grammar_file) { + fprintf(stdout, "Failed to open grammar file: %s\n", grammar_filename.c_str()); + return 1; + } + + std::string grammar_str; + { + std::ifstream grammar_file(grammar_filename); + GGML_ASSERT(grammar_file.is_open() && "Failed to open grammar file"); + std::stringstream buffer; + buffer << grammar_file.rdbuf(); + grammar_str = buffer.str(); + } + + llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0); + if (grammar == nullptr) { + fprintf(stdout, "Failed to initialize llama_grammar\n"); + return 1; + } + // Read the input file + std::string input_str; + { + std::ifstream input_file(input_filename); + GGML_ASSERT(input_file.is_open() && "Failed to open input file"); + std::stringstream buffer; + buffer << input_file.rdbuf(); + input_str = buffer.str(); + } + + // Validate the input string against the grammar + size_t error_pos; + std::string error_msg; + bool is_valid = llama_grammar_validate(grammar, input_str, error_pos, error_msg); + + if (is_valid) { + fprintf(stdout, "Input string is valid according to the grammar.\n"); + } else { + print_error_message(input_str, error_pos, error_msg); + } + + // Clean up + llama_grammar_free_impl(grammar); + + return 0; +} diff --git a/examples/gen-docs/CMakeLists.txt b/examples/gen-docs/CMakeLists.txt new file mode 100644 index 000000000..25de0af35 --- /dev/null +++ b/examples/gen-docs/CMakeLists.txt @@ -0,0 +1,5 @@ +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_17) diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp new file mode 100644 index 000000000..77c59a836 --- /dev/null +++ b/examples/gen-docs/gen-docs.cpp @@ -0,0 +1,83 @@ +#include "arg.h" +#include "common.h" + +#include +#include + +// Export usage message (-h) to markdown format + +static void write_table_header(std::ofstream & file) { + file << "| Argument | Explanation |\n"; + file << "| -------- | ----------- |\n"; +} + +static void write_table_entry(std::ofstream & file, const common_arg & opt) { + file << "| `"; + // args + for (const auto & arg : opt.args) { + if (arg == opt.args.front()) { + file << arg; + if (opt.args.size() > 1) file << ", "; + } else { + file << arg << (arg != opt.args.back() ? ", " : ""); + } + } + // value hint + if (opt.value_hint) { + std::string md_value_hint(opt.value_hint); + string_replace_all(md_value_hint, "|", "\\|"); + file << " " << md_value_hint; + } + if (opt.value_hint_2) { + std::string md_value_hint_2(opt.value_hint_2); + string_replace_all(md_value_hint_2, "|", "\\|"); + file << " " << md_value_hint_2; + } + // help text + std::string md_help(opt.help); + string_replace_all(md_help, "\n", "
"); + string_replace_all(md_help, "|", "\\|"); + file << "` | " << md_help << " |\n"; +} + +static void write_table(std::ofstream & file, std::vector & opts) { + write_table_header(file); + for (const auto & opt : opts) { + write_table_entry(file, *opt); + } +} + +static void export_md(std::string fname, llama_example ex) { + std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc); + + common_params params; + auto ctx_arg = common_params_parser_init(params, ex); + + std::vector common_options; + std::vector sparam_options; + std::vector specific_options; + for (auto & opt : ctx_arg.options) { + // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + + file << "**Common params**\n\n"; + write_table(file, common_options); + file << "\n\n**Sampling params**\n\n"; + write_table(file, sparam_options); + file << "\n\n**Example-specific params**\n\n"; + write_table(file, specific_options); +} + +int main(int, char **) { + export_md("autogen-main.md", LLAMA_EXAMPLE_MAIN); + export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER); + + return 0; +} diff --git a/examples/gguf-hash/CMakeLists.txt b/examples/gguf-hash/CMakeLists.txt new file mode 100644 index 000000000..15c5c68c6 --- /dev/null +++ b/examples/gguf-hash/CMakeLists.txt @@ -0,0 +1,22 @@ +set(TARGET llama-gguf-hash) +add_executable(${TARGET} gguf-hash.cpp) +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_17) diff --git a/examples/gguf-hash/README.md b/examples/gguf-hash/README.md new file mode 100644 index 000000000..9871651e3 --- /dev/null +++ b/examples/gguf-hash/README.md @@ -0,0 +1,206 @@ + +# llama-gguf-hash + +CLI to hash GGUF files to detect difference on a per model and per tensor level. + +**Command line options:** + +- `--help`: display help message +- `--xxh64`: use xhash 64bit hash mode (default) +- `--sha1`: use sha1 +- `--uuid`: use uuid +- `--sha256`: use sha256 +- `--all`: use all hash +- `--no-layer`: exclude per layer hash +- `--uuid`: generate UUIDv5 ID +- `-c`, `--check `: verify against a manifest + +## About + +While most POSIX systems already have hash checking programs like sha256sum, it +is designed to check entire files. This is not ideal for our purpose if we want +to check for consistency of the tensor data even if the metadata content of the +gguf KV store has been updated. + +This program is designed to hash a gguf tensor payload on a 'per tensor layer' +in addition to a 'entire tensor model' hash. The intent is that the entire +tensor layer can be checked first but if there is any detected inconsistencies, +then the per tensor hash can be used to narrow down the specific tensor layer +that has inconsistencies. + +For Maintainers: +- Detection of tensor inconsistency during development and automated tests + - This is served by xxh64 which is fast + - This is also served by having per tensor layer to assist in narrowing down + the location of the faulty tensor layer + - This is also served by sha1 which is much slower but more widely supported + +For Model Creators: +- Optional consistent UUID generation based on model tensor content + - This is served by UUIDv5 which is useful for databases keys + - llama.cpp UUIDv5 Namespace: `ef001206-dadc-5f6d-a15f-3359e577d4e5` + - Made via UUIDv5 URL namespace of `en.wikipedia.org/wiki/Llama.cpp` + +For Model Users: +- Assurance of tensor layer integrity even if metadata was updated + - This is served by sha256 which is still considered very secure as of 2024 + +### Design Note + +- The default behavior of this program if no arguments is provided is to hash + using xxhash's xxh32 mode because it is very fast and is primarily targeted + towards maintainers who may want to use this in automated tests. +- xxhash support xxh32 and xxh128 for 32bit hash and 128bit hash respectively + however we picked 64bit xxhash as most computers are 64bit as of 2024 and thus + would have a better affinity to calculating hash that is 64bit in size. + +## Compile Example + +```bash +cmake -B build -DCMAKE_BUILD_TYPE=Debug -DLLAMA_FATAL_WARNINGS=ON +make -C build clean +make -C build llama-gguf-hash VERBOSE=1 +./build/bin/llama-gguf-hash test.gguf +./build/bin/llama-gguf-hash --xxh64 test.gguf +./build/bin/llama-gguf-hash --sha1 test.gguf +./build/bin/llama-gguf-hash --uuid test.gguf +./build/bin/llama-gguf-hash --sha256 test.gguf +``` + +## Generation and Verification Example + +To generate we may use this command + +```bash +./llama-gguf-hash --all test.gguf > test.gguf.manifest +``` + +Which would generate a manifest that looks like below, which contains multiple hash type and per tensor layer hashes as well +(This excludes UUID as that is an ID not a hash) + +```bash +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 +sha1 59f79ecefd8125a996fdf419239051a7e99e5f20 test.gguf:tensor_0 +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 +sha1 4765f592eacf096df4628ba59476af94d767080a test.gguf:tensor_1 +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 +xxh64 a0af5d700049693b test.gguf:tensor_2 +sha1 25cbfbad4513cc348e2c95ebdee69d6ff2fd8753 test.gguf:tensor_2 +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 +sha1 a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c test.gguf:tensor_3 +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 +xxh64 1257733306b7992d test.gguf:tensor_4 +sha1 d7bc61db93bb685ce9d598da89717c66729b7543 test.gguf:tensor_4 +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 +xxh64 d238d16ba4711e58 test.gguf:tensor_5 +sha1 0706566c198fe1072f37e0a5135b4b5f23654c52 test.gguf:tensor_5 +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 +sha1 73922a0727226a409049f6fc3172a52219ca6f00 test.gguf:tensor_6 +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 +xxh64 c22021c29854f093 test.gguf:tensor_7 +sha1 efc39cece6a951188fc41e354c73bbfe6813d447 test.gguf:tensor_7 +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 +xxh64 936df61f5d64261f test.gguf:tensor_8 +sha1 c2490296d789a4f34398a337fed8377d943d9f06 test.gguf:tensor_8 +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 +xxh64 93fd20c64421c081 test.gguf:tensor_9 +sha1 7047ce1e78437a6884337a3751c7ee0421918a65 test.gguf:tensor_9 +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 +xxh64 5a54d3aad816f302 test.gguf +sha1 d15be52c4ff213e823cb6dd13af7ee2f978e7042 test.gguf +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf +``` + +We can then use the normal check command which will by default check for the highest security strength hash and verify against that: + +```bash +$ ./llama-gguf-hash --check test.gguf.manifest test.gguf +manifest test.gguf.manifest sha256 sha1 xxh64 +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 - Ok +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 - Ok +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 - Ok +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 - Ok +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 - Ok +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 - Ok +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 - Ok +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 - Ok +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 - Ok +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 - Ok +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + +Or we may explicitly ask for a faster hash like: + +```bash +$ ./llama-gguf-hash --check test.gguf.manifest --xxh64 test.gguf +manifest test.gguf.manifest sha256 sha1 xxh64 +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 - Ok +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 - Ok +xxh64 a0af5d700049693b test.gguf:tensor_2 - Ok +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 - Ok +xxh64 1257733306b7992d test.gguf:tensor_4 - Ok +xxh64 d238d16ba4711e58 test.gguf:tensor_5 - Ok +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 - Ok +xxh64 c22021c29854f093 test.gguf:tensor_7 - Ok +xxh64 936df61f5d64261f test.gguf:tensor_8 - Ok +xxh64 93fd20c64421c081 test.gguf:tensor_9 - Ok +xxh64 5a54d3aad816f302 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + +Or maybe we want to just check that all the hash is valid: + +```bash +$./llama-gguf-hash --check test.gguf.manifest --all test.gguf.manifest +manifest test.gguf.manifest sha256 sha1 xxh64 +xxh64 f66e9cd66a4396a0 test.gguf:tensor_0 - Ok +sha1 59f79ecefd8125a996fdf419239051a7e99e5f20 test.gguf:tensor_0 - Ok +sha256 c0510d38fa060c46265e0160a85c7243096b01dd31c2f355bdbb5516b20de1bd test.gguf:tensor_0 - Ok +xxh64 7d3a1f9ac04d0537 test.gguf:tensor_1 - Ok +sha1 4765f592eacf096df4628ba59476af94d767080a test.gguf:tensor_1 - Ok +sha256 8514cbcc73692a2c56bd7a33a022edd5ff819614bd23b19915d7224387f397a7 test.gguf:tensor_1 - Ok +xxh64 a0af5d700049693b test.gguf:tensor_2 - Ok +sha1 25cbfbad4513cc348e2c95ebdee69d6ff2fd8753 test.gguf:tensor_2 - Ok +sha256 947e6b36e20f2cc95e1d2ce1c1669d813d574657ac6b5ac5196158d454d35180 test.gguf:tensor_2 - Ok +xxh64 e83fddf559d7b6a6 test.gguf:tensor_3 - Ok +sha1 a9cba73e2d90f2ee3dae2548caa42bef3fe6a96c test.gguf:tensor_3 - Ok +sha256 423b044e016d8ac73c39f23f60bf01bedef5ecb03c0230accd824c91fe86f1a1 test.gguf:tensor_3 - Ok +xxh64 1257733306b7992d test.gguf:tensor_4 - Ok +sha1 d7bc61db93bb685ce9d598da89717c66729b7543 test.gguf:tensor_4 - Ok +sha256 79737cb3912d4201384cf7f16a1a37ff7823f23ea796cb205b6ca361ab9e3ebf test.gguf:tensor_4 - Ok +xxh64 d238d16ba4711e58 test.gguf:tensor_5 - Ok +sha1 0706566c198fe1072f37e0a5135b4b5f23654c52 test.gguf:tensor_5 - Ok +sha256 60949be8298eced0ecdde64487643d018407bd261691e061d9e9c3dbc9fd358b test.gguf:tensor_5 - Ok +xxh64 3fbc3b65ab8c7f39 test.gguf:tensor_6 - Ok +sha1 73922a0727226a409049f6fc3172a52219ca6f00 test.gguf:tensor_6 - Ok +sha256 574f4c46ff384a3b9a225eb955d2a871847a2e8b3fa59387a8252832e92ef7b0 test.gguf:tensor_6 - Ok +xxh64 c22021c29854f093 test.gguf:tensor_7 - Ok +sha1 efc39cece6a951188fc41e354c73bbfe6813d447 test.gguf:tensor_7 - Ok +sha256 4c0410cd3c500f078ae5b21e8dc9eb79e29112713b2ab58a882f82a3868d4d75 test.gguf:tensor_7 - Ok +xxh64 936df61f5d64261f test.gguf:tensor_8 - Ok +sha1 c2490296d789a4f34398a337fed8377d943d9f06 test.gguf:tensor_8 - Ok +sha256 c4401313feeba0261275c3b25bd2d8fe40ce04e0f440c2980ed0e9674c30ff01 test.gguf:tensor_8 - Ok +xxh64 93fd20c64421c081 test.gguf:tensor_9 - Ok +sha1 7047ce1e78437a6884337a3751c7ee0421918a65 test.gguf:tensor_9 - Ok +sha256 23d57cf0d7a6e90b0b3616b41300e0cd354781e812add854a5f95aa55f2bc514 test.gguf:tensor_9 - Ok +xxh64 5a54d3aad816f302 test.gguf - Ok +sha1 d15be52c4ff213e823cb6dd13af7ee2f978e7042 test.gguf - Ok +sha256 7dd641b32f59b60dbd4b5420c4b0f6321ccf48f58f6ae201a3dbc4a58a27c6e4 test.gguf - Ok + +Verification results for test.gguf.manifest - Success +``` + + +## Crypto/Hash Libraries Used + +These micro c libraries dependencies was installed via the [clib c package manager](https://github.com/clibs) + +- https://github.com/Cyan4973/xxHash +- https://github.com/clibs/sha1/ +- https://github.com/jb55/sha256.c diff --git a/examples/gguf-hash/deps/rotate-bits/package.json b/examples/gguf-hash/deps/rotate-bits/package.json new file mode 100644 index 000000000..74c0bef68 --- /dev/null +++ b/examples/gguf-hash/deps/rotate-bits/package.json @@ -0,0 +1,13 @@ +{ + "name": "rotate-bits", + "version": "0.1.1", + "repo": "jb55/rotate-bits.h", + "description": "rotate bits", + "keywords": ["rotl", "rotr"], + "src": ["rotate-bits.h"], + "license": "Public Domain", + "development": { + "thlorenz/tap.c": "*" + } +} + diff --git a/examples/gguf-hash/deps/rotate-bits/rotate-bits.h b/examples/gguf-hash/deps/rotate-bits/rotate-bits.h new file mode 100644 index 000000000..75c4881fc --- /dev/null +++ b/examples/gguf-hash/deps/rotate-bits/rotate-bits.h @@ -0,0 +1,46 @@ + + +#ifndef __ROTATE_DEFS_H +#define __ROTATE_DEFS_H + +#ifdef _MSC_VER + +#include + +#define ROTL32(v, n) _rotl((v), (n)) +#define ROTL64(v, n) _rotl64((v), (n)) + +#define ROTR32(v, n) _rotr((v), (n)) +#define ROTR64(v, n) _rotr64((v), (n)) + +#else + +#include + +#define U8V(v) ((uint8_t)(v) & 0xFFU) +#define U16V(v) ((uint16_t)(v) & 0xFFFFU) +#define U32V(v) ((uint32_t)(v) & 0xFFFFFFFFU) +#define U64V(v) ((uint64_t)(v) & 0xFFFFFFFFFFFFFFFFU) + +#define ROTL32(v, n) \ + (U32V((uint32_t)(v) << (n)) | ((uint32_t)(v) >> (32 - (n)))) + +// tests fail if we don't have this cast... +#define ROTL64(v, n) \ + (U64V((uint64_t)(v) << (n)) | ((uint64_t)(v) >> (64 - (n)))) + +#define ROTR32(v, n) ROTL32(v, 32 - (n)) +#define ROTR64(v, n) ROTL64(v, 64 - (n)) + +#endif + +#define ROTL8(v, n) \ + (U8V((uint8_t)(v) << (n)) | ((uint8_t)(v) >> (8 - (n)))) + +#define ROTL16(v, n) \ + (U16V((uint16_t)(v) << (n)) | ((uint16_t)(v) >> (16 - (n)))) + +#define ROTR8(v, n) ROTL8(v, 8 - (n)) +#define ROTR16(v, n) ROTL16(v, 16 - (n)) + +#endif diff --git a/examples/gguf-hash/deps/sha1/package.json b/examples/gguf-hash/deps/sha1/package.json new file mode 100644 index 000000000..6a5843dd1 --- /dev/null +++ b/examples/gguf-hash/deps/sha1/package.json @@ -0,0 +1,9 @@ +{ + "name": "sha1", + "version": "0.0.1", + "repo": "clibs/sha1", + "description": "sha1 hash algorithm", + "keywords": ["sha1", "hash"], + "license": "public domain", + "src": ["sha1.c", "sha1.h"] +} diff --git a/examples/gguf-hash/deps/sha1/sha1.c b/examples/gguf-hash/deps/sha1/sha1.c new file mode 100644 index 000000000..76cd6ca33 --- /dev/null +++ b/examples/gguf-hash/deps/sha1/sha1.c @@ -0,0 +1,295 @@ +/* +SHA-1 in C +By Steve Reid +100% Public Domain + +Test Vectors (from FIPS PUB 180-1) +"abc" + A9993E36 4706816A BA3E2571 7850C26C 9CD0D89D +"abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq" + 84983E44 1C3BD26E BAAE4AA1 F95129E5 E54670F1 +A million repetitions of "a" + 34AA973C D4C4DAA4 F61EEB2B DBAD2731 6534016F +*/ + +/* #define LITTLE_ENDIAN * This should be #define'd already, if true. */ +/* #define SHA1HANDSOFF * Copies data before messing with it. */ + +#define SHA1HANDSOFF + +#include +#include + +/* for uint32_t */ +#include + +#include "sha1.h" + + +#define rol(value, bits) (((value) << (bits)) | ((value) >> (32 - (bits)))) + +/* blk0() and blk() perform the initial expand. */ +/* I got the idea of expanding during the round function from SSLeay */ +#if BYTE_ORDER == LITTLE_ENDIAN +#define blk0(i) (block->l[i] = (rol(block->l[i],24)&0xFF00FF00) \ + |(rol(block->l[i],8)&0x00FF00FF)) +#elif BYTE_ORDER == BIG_ENDIAN +#define blk0(i) block->l[i] +#else +#error "Endianness not defined!" +#endif +#define blk(i) (block->l[i&15] = rol(block->l[(i+13)&15]^block->l[(i+8)&15] \ + ^block->l[(i+2)&15]^block->l[i&15],1)) + +/* (R0+R1), R2, R3, R4 are the different operations used in SHA1 */ +#define R0(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk0(i)+0x5A827999+rol(v,5);w=rol(w,30); +#define R1(v,w,x,y,z,i) z+=((w&(x^y))^y)+blk(i)+0x5A827999+rol(v,5);w=rol(w,30); +#define R2(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0x6ED9EBA1+rol(v,5);w=rol(w,30); +#define R3(v,w,x,y,z,i) z+=(((w|x)&y)|(w&x))+blk(i)+0x8F1BBCDC+rol(v,5);w=rol(w,30); +#define R4(v,w,x,y,z,i) z+=(w^x^y)+blk(i)+0xCA62C1D6+rol(v,5);w=rol(w,30); + + +/* Hash a single 512-bit block. This is the core of the algorithm. */ + +void SHA1Transform( + uint32_t state[5], + const unsigned char buffer[64] +) +{ + uint32_t a, b, c, d, e; + + typedef union + { + unsigned char c[64]; + uint32_t l[16]; + } CHAR64LONG16; + +#ifdef SHA1HANDSOFF + CHAR64LONG16 block[1]; /* use array to appear as a pointer */ + + memcpy(block, buffer, 64); +#else + /* The following had better never be used because it causes the + * pointer-to-const buffer to be cast into a pointer to non-const. + * And the result is written through. I threw a "const" in, hoping + * this will cause a diagnostic. + */ + CHAR64LONG16 *block = (const CHAR64LONG16 *) buffer; +#endif + /* Copy context->state[] to working vars */ + a = state[0]; + b = state[1]; + c = state[2]; + d = state[3]; + e = state[4]; + /* 4 rounds of 20 operations each. Loop unrolled. */ + R0(a, b, c, d, e, 0); + R0(e, a, b, c, d, 1); + R0(d, e, a, b, c, 2); + R0(c, d, e, a, b, 3); + R0(b, c, d, e, a, 4); + R0(a, b, c, d, e, 5); + R0(e, a, b, c, d, 6); + R0(d, e, a, b, c, 7); + R0(c, d, e, a, b, 8); + R0(b, c, d, e, a, 9); + R0(a, b, c, d, e, 10); + R0(e, a, b, c, d, 11); + R0(d, e, a, b, c, 12); + R0(c, d, e, a, b, 13); + R0(b, c, d, e, a, 14); + R0(a, b, c, d, e, 15); + R1(e, a, b, c, d, 16); + R1(d, e, a, b, c, 17); + R1(c, d, e, a, b, 18); + R1(b, c, d, e, a, 19); + R2(a, b, c, d, e, 20); + R2(e, a, b, c, d, 21); + R2(d, e, a, b, c, 22); + R2(c, d, e, a, b, 23); + R2(b, c, d, e, a, 24); + R2(a, b, c, d, e, 25); + R2(e, a, b, c, d, 26); + R2(d, e, a, b, c, 27); + R2(c, d, e, a, b, 28); + R2(b, c, d, e, a, 29); + R2(a, b, c, d, e, 30); + R2(e, a, b, c, d, 31); + R2(d, e, a, b, c, 32); + R2(c, d, e, a, b, 33); + R2(b, c, d, e, a, 34); + R2(a, b, c, d, e, 35); + R2(e, a, b, c, d, 36); + R2(d, e, a, b, c, 37); + R2(c, d, e, a, b, 38); + R2(b, c, d, e, a, 39); + R3(a, b, c, d, e, 40); + R3(e, a, b, c, d, 41); + R3(d, e, a, b, c, 42); + R3(c, d, e, a, b, 43); + R3(b, c, d, e, a, 44); + R3(a, b, c, d, e, 45); + R3(e, a, b, c, d, 46); + R3(d, e, a, b, c, 47); + R3(c, d, e, a, b, 48); + R3(b, c, d, e, a, 49); + R3(a, b, c, d, e, 50); + R3(e, a, b, c, d, 51); + R3(d, e, a, b, c, 52); + R3(c, d, e, a, b, 53); + R3(b, c, d, e, a, 54); + R3(a, b, c, d, e, 55); + R3(e, a, b, c, d, 56); + R3(d, e, a, b, c, 57); + R3(c, d, e, a, b, 58); + R3(b, c, d, e, a, 59); + R4(a, b, c, d, e, 60); + R4(e, a, b, c, d, 61); + R4(d, e, a, b, c, 62); + R4(c, d, e, a, b, 63); + R4(b, c, d, e, a, 64); + R4(a, b, c, d, e, 65); + R4(e, a, b, c, d, 66); + R4(d, e, a, b, c, 67); + R4(c, d, e, a, b, 68); + R4(b, c, d, e, a, 69); + R4(a, b, c, d, e, 70); + R4(e, a, b, c, d, 71); + R4(d, e, a, b, c, 72); + R4(c, d, e, a, b, 73); + R4(b, c, d, e, a, 74); + R4(a, b, c, d, e, 75); + R4(e, a, b, c, d, 76); + R4(d, e, a, b, c, 77); + R4(c, d, e, a, b, 78); + R4(b, c, d, e, a, 79); + /* Add the working vars back into context.state[] */ + state[0] += a; + state[1] += b; + state[2] += c; + state[3] += d; + state[4] += e; + /* Wipe variables */ + a = b = c = d = e = 0; +#ifdef SHA1HANDSOFF + memset(block, '\0', sizeof(block)); +#endif +} + + +/* SHA1Init - Initialize new context */ + +void SHA1Init( + SHA1_CTX * context +) +{ + /* SHA1 initialization constants */ + context->state[0] = 0x67452301; + context->state[1] = 0xEFCDAB89; + context->state[2] = 0x98BADCFE; + context->state[3] = 0x10325476; + context->state[4] = 0xC3D2E1F0; + context->count[0] = context->count[1] = 0; +} + + +/* Run your data through this. */ + +void SHA1Update( + SHA1_CTX * context, + const unsigned char *data, + uint32_t len +) +{ + uint32_t i; + + uint32_t j; + + j = context->count[0]; + if ((context->count[0] += len << 3) < j) + context->count[1]++; + context->count[1] += (len >> 29); + j = (j >> 3) & 63; + if ((j + len) > 63) + { + memcpy(&context->buffer[j], data, (i = 64 - j)); + SHA1Transform(context->state, context->buffer); + for (; i + 63 < len; i += 64) + { + SHA1Transform(context->state, &data[i]); + } + j = 0; + } + else + i = 0; + memcpy(&context->buffer[j], &data[i], len - i); +} + + +/* Add padding and return the message digest. */ + +void SHA1Final( + unsigned char digest[20], + SHA1_CTX * context +) +{ + unsigned i; + + unsigned char finalcount[8]; + + unsigned char c; + +#if 0 /* untested "improvement" by DHR */ + /* Convert context->count to a sequence of bytes + * in finalcount. Second element first, but + * big-endian order within element. + * But we do it all backwards. + */ + unsigned char *fcp = &finalcount[8]; + + for (i = 0; i < 2; i++) + { + uint32_t t = context->count[i]; + + int j; + + for (j = 0; j < 4; t >>= 8, j++) + *--fcp = (unsigned char) t} +#else + for (i = 0; i < 8; i++) + { + finalcount[i] = (unsigned char) ((context->count[(i >= 4 ? 0 : 1)] >> ((3 - (i & 3)) * 8)) & 255); /* Endian independent */ + } +#endif + c = 0200; + SHA1Update(context, &c, 1); + while ((context->count[0] & 504) != 448) + { + c = 0000; + SHA1Update(context, &c, 1); + } + SHA1Update(context, finalcount, 8); /* Should cause a SHA1Transform() */ + for (i = 0; i < 20; i++) + { + digest[i] = (unsigned char) + ((context->state[i >> 2] >> ((3 - (i & 3)) * 8)) & 255); + } + /* Wipe variables */ + memset(context, '\0', sizeof(*context)); + memset(&finalcount, '\0', sizeof(finalcount)); +} + +void SHA1( + char *hash_out, + const char *str, + uint32_t len) +{ + SHA1_CTX ctx; + unsigned int ii; + + SHA1Init(&ctx); + for (ii=0; ii + 100% Public Domain + */ + +#include "stdint.h" + +#if defined(__cplusplus) +extern "C" { +#endif + +typedef struct +{ + uint32_t state[5]; + uint32_t count[2]; + unsigned char buffer[64]; +} SHA1_CTX; + +void SHA1Transform( + uint32_t state[5], + const unsigned char buffer[64] + ); + +void SHA1Init( + SHA1_CTX * context + ); + +void SHA1Update( + SHA1_CTX * context, + const unsigned char *data, + uint32_t len + ); + +void SHA1Final( + unsigned char digest[20], + SHA1_CTX * context + ); + +void SHA1( + char *hash_out, + const char *str, + uint32_t len); + +#if defined(__cplusplus) +} +#endif + +#endif /* SHA1_H */ diff --git a/examples/gguf-hash/deps/sha256/package.json b/examples/gguf-hash/deps/sha256/package.json new file mode 100644 index 000000000..b92a04127 --- /dev/null +++ b/examples/gguf-hash/deps/sha256/package.json @@ -0,0 +1,15 @@ +{ + "name": "sha256", + "version": "0.0.2", + "repo": "jb55/sha256.c", + "description": "sha256 in c", + "keywords": ["sha256", "sha2"], + "src": ["sha256.c", "sha256.h"], + "dependencies": { + "jb55/rotate-bits.h": "0.1.1" + }, + "development": { + "thlorenz/tap.c": "*" + } +} + diff --git a/examples/gguf-hash/deps/sha256/sha256.c b/examples/gguf-hash/deps/sha256/sha256.c new file mode 100644 index 000000000..a7a87aeb2 --- /dev/null +++ b/examples/gguf-hash/deps/sha256/sha256.c @@ -0,0 +1,221 @@ +/* Crypto/Sha256.c -- SHA-256 Hash +2010-06-11 : Igor Pavlov : Public domain +This code is based on public domain code from Wei Dai's Crypto++ library. */ + +#include "rotate-bits/rotate-bits.h" +#include "sha256.h" + +/* define it for speed optimization */ +#define _SHA256_UNROLL +#define _SHA256_UNROLL2 + +void +sha256_init(sha256_t *p) +{ + p->state[0] = 0x6a09e667; + p->state[1] = 0xbb67ae85; + p->state[2] = 0x3c6ef372; + p->state[3] = 0xa54ff53a; + p->state[4] = 0x510e527f; + p->state[5] = 0x9b05688c; + p->state[6] = 0x1f83d9ab; + p->state[7] = 0x5be0cd19; + p->count = 0; +} + +#define S0(x) (ROTR32(x, 2) ^ ROTR32(x,13) ^ ROTR32(x, 22)) +#define S1(x) (ROTR32(x, 6) ^ ROTR32(x,11) ^ ROTR32(x, 25)) +#define s0(x) (ROTR32(x, 7) ^ ROTR32(x,18) ^ (x >> 3)) +#define s1(x) (ROTR32(x,17) ^ ROTR32(x,19) ^ (x >> 10)) + +#define blk0(i) (W[i] = data[i]) +#define blk2(i) (W[i&15] += s1(W[(i-2)&15]) + W[(i-7)&15] + s0(W[(i-15)&15])) + +#define Ch(x,y,z) (z^(x&(y^z))) +#define Maj(x,y,z) ((x&y)|(z&(x|y))) + +#define a(i) T[(0-(i))&7] +#define b(i) T[(1-(i))&7] +#define c(i) T[(2-(i))&7] +#define d(i) T[(3-(i))&7] +#define e(i) T[(4-(i))&7] +#define f(i) T[(5-(i))&7] +#define g(i) T[(6-(i))&7] +#define h(i) T[(7-(i))&7] + + +#ifdef _SHA256_UNROLL2 + +#define R(a,b,c,d,e,f,g,h, i) h += S1(e) + Ch(e,f,g) + K[i+j] + (j?blk2(i):blk0(i));\ + d += h; h += S0(a) + Maj(a, b, c) + +#define RX_8(i) \ + R(a,b,c,d,e,f,g,h, i); \ + R(h,a,b,c,d,e,f,g, (i+1)); \ + R(g,h,a,b,c,d,e,f, (i+2)); \ + R(f,g,h,a,b,c,d,e, (i+3)); \ + R(e,f,g,h,a,b,c,d, (i+4)); \ + R(d,e,f,g,h,a,b,c, (i+5)); \ + R(c,d,e,f,g,h,a,b, (i+6)); \ + R(b,c,d,e,f,g,h,a, (i+7)) + +#else + +#define R(i) h(i) += S1(e(i)) + Ch(e(i),f(i),g(i)) + K[i+j] + (j?blk2(i):blk0(i));\ + d(i) += h(i); h(i) += S0(a(i)) + Maj(a(i), b(i), c(i)) + +#ifdef _SHA256_UNROLL + +#define RX_8(i) R(i+0); R(i+1); R(i+2); R(i+3); R(i+4); R(i+5); R(i+6); R(i+7); + +#endif + +#endif + +static const uint32_t K[64] = { + 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, + 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, + 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, + 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, + 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, + 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, + 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, + 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, + 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, + 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, + 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, + 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, + 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, + 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, + 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, + 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2 +}; + +static void +sha256_transform(uint32_t *state, const uint32_t *data) +{ + uint32_t W[16] = {0}; + unsigned j; + #ifdef _SHA256_UNROLL2 + uint32_t a,b,c,d,e,f,g,h; + a = state[0]; + b = state[1]; + c = state[2]; + d = state[3]; + e = state[4]; + f = state[5]; + g = state[6]; + h = state[7]; + #else + uint32_t T[8]; + for (j = 0; j < 8; j++) + T[j] = state[j]; + #endif + + for (j = 0; j < 64; j += 16) + { + #if defined(_SHA256_UNROLL) || defined(_SHA256_UNROLL2) + RX_8(0); RX_8(8); + #else + unsigned i; + for (i = 0; i < 16; i++) { R(i); } + #endif + } + + #ifdef _SHA256_UNROLL2 + state[0] += a; + state[1] += b; + state[2] += c; + state[3] += d; + state[4] += e; + state[5] += f; + state[6] += g; + state[7] += h; + #else + for (j = 0; j < 8; j++) + state[j] += T[j]; + #endif + + /* Wipe variables */ + /* memset(W, 0, sizeof(W)); */ + /* memset(T, 0, sizeof(T)); */ +} + +#undef S0 +#undef S1 +#undef s0 +#undef s1 + +static void +sha256_write_byte_block(sha256_t *p) +{ + uint32_t data32[16]; + unsigned i; + for (i = 0; i < 16; i++) + data32[i] = + ((uint32_t)(p->buffer[i * 4 ]) << 24) + + ((uint32_t)(p->buffer[i * 4 + 1]) << 16) + + ((uint32_t)(p->buffer[i * 4 + 2]) << 8) + + ((uint32_t)(p->buffer[i * 4 + 3])); + sha256_transform(p->state, data32); +} + + +void +sha256_hash(unsigned char *buf, const unsigned char *data, size_t size) +{ + sha256_t hash; + sha256_init(&hash); + sha256_update(&hash, data, size); + sha256_final(&hash, buf); +} + + +void +sha256_update(sha256_t *p, const unsigned char *data, size_t size) +{ + uint32_t curBufferPos = (uint32_t)p->count & 0x3F; + while (size > 0) + { + p->buffer[curBufferPos++] = *data++; + p->count++; + size--; + if (curBufferPos == 64) + { + curBufferPos = 0; + sha256_write_byte_block(p); + } + } +} + + +void +sha256_final(sha256_t *p, unsigned char *digest) +{ + uint64_t lenInBits = (p->count << 3); + uint32_t curBufferPos = (uint32_t)p->count & 0x3F; + unsigned i; + p->buffer[curBufferPos++] = 0x80; + while (curBufferPos != (64 - 8)) + { + curBufferPos &= 0x3F; + if (curBufferPos == 0) + sha256_write_byte_block(p); + p->buffer[curBufferPos++] = 0; + } + for (i = 0; i < 8; i++) + { + p->buffer[curBufferPos++] = (unsigned char)(lenInBits >> 56); + lenInBits <<= 8; + } + sha256_write_byte_block(p); + + for (i = 0; i < 8; i++) + { + *digest++ = (unsigned char)(p->state[i] >> 24); + *digest++ = (unsigned char)(p->state[i] >> 16); + *digest++ = (unsigned char)(p->state[i] >> 8); + *digest++ = (unsigned char)(p->state[i]); + } + sha256_init(p); +} diff --git a/examples/gguf-hash/deps/sha256/sha256.h b/examples/gguf-hash/deps/sha256/sha256.h new file mode 100644 index 000000000..21657e66b --- /dev/null +++ b/examples/gguf-hash/deps/sha256/sha256.h @@ -0,0 +1,24 @@ +/* Sha256.h -- SHA-256 Hash +2010-06-11 : Igor Pavlov : Public domain */ + +#ifndef __CRYPTO_SHA256_H +#define __CRYPTO_SHA256_H + +#include +#include + +#define SHA256_DIGEST_SIZE 32 + +typedef struct sha256_t +{ + uint32_t state[8]; + uint64_t count; + unsigned char buffer[64]; +} sha256_t; + +void sha256_init(sha256_t *p); +void sha256_update(sha256_t *p, const unsigned char *data, size_t size); +void sha256_final(sha256_t *p, unsigned char *digest); +void sha256_hash(unsigned char *buf, const unsigned char *data, size_t size); + +#endif diff --git a/examples/gguf-hash/deps/xxhash/clib.json b/examples/gguf-hash/deps/xxhash/clib.json new file mode 100644 index 000000000..242343c5d --- /dev/null +++ b/examples/gguf-hash/deps/xxhash/clib.json @@ -0,0 +1,12 @@ +{ + "name": "xxhash", + "version": "0.8.2", + "repo": "Cyan4973/xxhash", + "description": "Extremely fast non-cryptographic hash algorithm", + "keywords": ["xxhash", "hashing"], + "license": "BSD-2-Clause", + "src": [ + "xxhash.c", + "xxhash.h" + ] +} diff --git a/examples/gguf-hash/deps/xxhash/xxhash.c b/examples/gguf-hash/deps/xxhash/xxhash.c new file mode 100644 index 000000000..e60cc37f1 --- /dev/null +++ b/examples/gguf-hash/deps/xxhash/xxhash.c @@ -0,0 +1,42 @@ +/* + * xxHash - Extremely Fast Hash algorithm + * Copyright (C) 2012-2023 Yann Collet + * + * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php) + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following disclaimer + * in the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * You can contact the author at: + * - xxHash homepage: https://www.xxhash.com + * - xxHash source repository: https://github.com/Cyan4973/xxHash + */ + +/* + * xxhash.c instantiates functions defined in xxhash.h + */ + +#define XXH_STATIC_LINKING_ONLY /* access advanced declarations */ +#define XXH_IMPLEMENTATION /* access definitions */ + +#include "xxhash.h" diff --git a/examples/gguf-hash/deps/xxhash/xxhash.h b/examples/gguf-hash/deps/xxhash/xxhash.h new file mode 100644 index 000000000..c0fafe20d --- /dev/null +++ b/examples/gguf-hash/deps/xxhash/xxhash.h @@ -0,0 +1,7093 @@ +/* + * xxHash - Extremely Fast Hash algorithm + * Header File + * Copyright (C) 2012-2023 Yann Collet + * + * BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php) + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above + * copyright notice, this list of conditions and the following disclaimer + * in the documentation and/or other materials provided with the + * distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * You can contact the author at: + * - xxHash homepage: https://www.xxhash.com + * - xxHash source repository: https://github.com/Cyan4973/xxHash + */ + +/*! + * @mainpage xxHash + * + * xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed + * limits. + * + * It is proposed in four flavors, in three families: + * 1. @ref XXH32_family + * - Classic 32-bit hash function. Simple, compact, and runs on almost all + * 32-bit and 64-bit systems. + * 2. @ref XXH64_family + * - Classic 64-bit adaptation of XXH32. Just as simple, and runs well on most + * 64-bit systems (but _not_ 32-bit systems). + * 3. @ref XXH3_family + * - Modern 64-bit and 128-bit hash function family which features improved + * strength and performance across the board, especially on smaller data. + * It benefits greatly from SIMD and 64-bit without requiring it. + * + * Benchmarks + * --- + * The reference system uses an Intel i7-9700K CPU, and runs Ubuntu x64 20.04. + * The open source benchmark program is compiled with clang v10.0 using -O3 flag. + * + * | Hash Name | ISA ext | Width | Large Data Speed | Small Data Velocity | + * | -------------------- | ------- | ----: | ---------------: | ------------------: | + * | XXH3_64bits() | @b AVX2 | 64 | 59.4 GB/s | 133.1 | + * | MeowHash | AES-NI | 128 | 58.2 GB/s | 52.5 | + * | XXH3_128bits() | @b AVX2 | 128 | 57.9 GB/s | 118.1 | + * | CLHash | PCLMUL | 64 | 37.1 GB/s | 58.1 | + * | XXH3_64bits() | @b SSE2 | 64 | 31.5 GB/s | 133.1 | + * | XXH3_128bits() | @b SSE2 | 128 | 29.6 GB/s | 118.1 | + * | RAM sequential read | | N/A | 28.0 GB/s | N/A | + * | ahash | AES-NI | 64 | 22.5 GB/s | 107.2 | + * | City64 | | 64 | 22.0 GB/s | 76.6 | + * | T1ha2 | | 64 | 22.0 GB/s | 99.0 | + * | City128 | | 128 | 21.7 GB/s | 57.7 | + * | FarmHash | AES-NI | 64 | 21.3 GB/s | 71.9 | + * | XXH64() | | 64 | 19.4 GB/s | 71.0 | + * | SpookyHash | | 64 | 19.3 GB/s | 53.2 | + * | Mum | | 64 | 18.0 GB/s | 67.0 | + * | CRC32C | SSE4.2 | 32 | 13.0 GB/s | 57.9 | + * | XXH32() | | 32 | 9.7 GB/s | 71.9 | + * | City32 | | 32 | 9.1 GB/s | 66.0 | + * | Blake3* | @b AVX2 | 256 | 4.4 GB/s | 8.1 | + * | Murmur3 | | 32 | 3.9 GB/s | 56.1 | + * | SipHash* | | 64 | 3.0 GB/s | 43.2 | + * | Blake3* | @b SSE2 | 256 | 2.4 GB/s | 8.1 | + * | HighwayHash | | 64 | 1.4 GB/s | 6.0 | + * | FNV64 | | 64 | 1.2 GB/s | 62.7 | + * | Blake2* | | 256 | 1.1 GB/s | 5.1 | + * | SHA1* | | 160 | 0.8 GB/s | 5.6 | + * | MD5* | | 128 | 0.6 GB/s | 7.8 | + * @note + * - Hashes which require a specific ISA extension are noted. SSE2 is also noted, + * even though it is mandatory on x64. + * - Hashes with an asterisk are cryptographic. Note that MD5 is non-cryptographic + * by modern standards. + * - Small data velocity is a rough average of algorithm's efficiency for small + * data. For more accurate information, see the wiki. + * - More benchmarks and strength tests are found on the wiki: + * https://github.com/Cyan4973/xxHash/wiki + * + * Usage + * ------ + * All xxHash variants use a similar API. Changing the algorithm is a trivial + * substitution. + * + * @pre + * For functions which take an input and length parameter, the following + * requirements are assumed: + * - The range from [`input`, `input + length`) is valid, readable memory. + * - The only exception is if the `length` is `0`, `input` may be `NULL`. + * - For C++, the objects must have the *TriviallyCopyable* property, as the + * functions access bytes directly as if it was an array of `unsigned char`. + * + * @anchor single_shot_example + * **Single Shot** + * + * These functions are stateless functions which hash a contiguous block of memory, + * immediately returning the result. They are the easiest and usually the fastest + * option. + * + * XXH32(), XXH64(), XXH3_64bits(), XXH3_128bits() + * + * @code{.c} + * #include + * #include "xxhash.h" + * + * // Example for a function which hashes a null terminated string with XXH32(). + * XXH32_hash_t hash_string(const char* string, XXH32_hash_t seed) + * { + * // NULL pointers are only valid if the length is zero + * size_t length = (string == NULL) ? 0 : strlen(string); + * return XXH32(string, length, seed); + * } + * @endcode + * + * + * @anchor streaming_example + * **Streaming** + * + * These groups of functions allow incremental hashing of unknown size, even + * more than what would fit in a size_t. + * + * XXH32_reset(), XXH64_reset(), XXH3_64bits_reset(), XXH3_128bits_reset() + * + * @code{.c} + * #include + * #include + * #include "xxhash.h" + * // Example for a function which hashes a FILE incrementally with XXH3_64bits(). + * XXH64_hash_t hashFile(FILE* f) + * { + * // Allocate a state struct. Do not just use malloc() or new. + * XXH3_state_t* state = XXH3_createState(); + * assert(state != NULL && "Out of memory!"); + * // Reset the state to start a new hashing session. + * XXH3_64bits_reset(state); + * char buffer[4096]; + * size_t count; + * // Read the file in chunks + * while ((count = fread(buffer, 1, sizeof(buffer), f)) != 0) { + * // Run update() as many times as necessary to process the data + * XXH3_64bits_update(state, buffer, count); + * } + * // Retrieve the finalized hash. This will not change the state. + * XXH64_hash_t result = XXH3_64bits_digest(state); + * // Free the state. Do not use free(). + * XXH3_freeState(state); + * return result; + * } + * @endcode + * + * Streaming functions generate the xxHash value from an incremental input. + * This method is slower than single-call functions, due to state management. + * For small inputs, prefer `XXH32()` and `XXH64()`, which are better optimized. + * + * An XXH state must first be allocated using `XXH*_createState()`. + * + * Start a new hash by initializing the state with a seed using `XXH*_reset()`. + * + * Then, feed the hash state by calling `XXH*_update()` as many times as necessary. + * + * The function returns an error code, with 0 meaning OK, and any other value + * meaning there is an error. + * + * Finally, a hash value can be produced anytime, by using `XXH*_digest()`. + * This function returns the nn-bits hash as an int or long long. + * + * It's still possible to continue inserting input into the hash state after a + * digest, and generate new hash values later on by invoking `XXH*_digest()`. + * + * When done, release the state using `XXH*_freeState()`. + * + * + * @anchor canonical_representation_example + * **Canonical Representation** + * + * The default return values from XXH functions are unsigned 32, 64 and 128 bit + * integers. + * This the simplest and fastest format for further post-processing. + * + * However, this leaves open the question of what is the order on the byte level, + * since little and big endian conventions will store the same number differently. + * + * The canonical representation settles this issue by mandating big-endian + * convention, the same convention as human-readable numbers (large digits first). + * + * When writing hash values to storage, sending them over a network, or printing + * them, it's highly recommended to use the canonical representation to ensure + * portability across a wider range of systems, present and future. + * + * The following functions allow transformation of hash values to and from + * canonical format. + * + * XXH32_canonicalFromHash(), XXH32_hashFromCanonical(), + * XXH64_canonicalFromHash(), XXH64_hashFromCanonical(), + * XXH128_canonicalFromHash(), XXH128_hashFromCanonical(), + * + * @code{.c} + * #include + * #include "xxhash.h" + * + * // Example for a function which prints XXH32_hash_t in human readable format + * void printXxh32(XXH32_hash_t hash) + * { + * XXH32_canonical_t cano; + * XXH32_canonicalFromHash(&cano, hash); + * size_t i; + * for(i = 0; i < sizeof(cano.digest); ++i) { + * printf("%02x", cano.digest[i]); + * } + * printf("\n"); + * } + * + * // Example for a function which converts XXH32_canonical_t to XXH32_hash_t + * XXH32_hash_t convertCanonicalToXxh32(XXH32_canonical_t cano) + * { + * XXH32_hash_t hash = XXH32_hashFromCanonical(&cano); + * return hash; + * } + * @endcode + * + * + * @file xxhash.h + * xxHash prototypes and implementation + */ + +#if defined (__cplusplus) +extern "C" { +#endif + +/* **************************** + * INLINE mode + ******************************/ +/*! + * @defgroup public Public API + * Contains details on the public xxHash functions. + * @{ + */ +#ifdef XXH_DOXYGEN +/*! + * @brief Gives access to internal state declaration, required for static allocation. + * + * Incompatible with dynamic linking, due to risks of ABI changes. + * + * Usage: + * @code{.c} + * #define XXH_STATIC_LINKING_ONLY + * #include "xxhash.h" + * @endcode + */ +# define XXH_STATIC_LINKING_ONLY +/* Do not undef XXH_STATIC_LINKING_ONLY for Doxygen */ + +/*! + * @brief Gives access to internal definitions. + * + * Usage: + * @code{.c} + * #define XXH_STATIC_LINKING_ONLY + * #define XXH_IMPLEMENTATION + * #include "xxhash.h" + * @endcode + */ +# define XXH_IMPLEMENTATION +/* Do not undef XXH_IMPLEMENTATION for Doxygen */ + +/*! + * @brief Exposes the implementation and marks all functions as `inline`. + * + * Use these build macros to inline xxhash into the target unit. + * Inlining improves performance on small inputs, especially when the length is + * expressed as a compile-time constant: + * + * https://fastcompression.blogspot.com/2018/03/xxhash-for-small-keys-impressive-power.html + * + * It also keeps xxHash symbols private to the unit, so they are not exported. + * + * Usage: + * @code{.c} + * #define XXH_INLINE_ALL + * #include "xxhash.h" + * @endcode + * Do not compile and link xxhash.o as a separate object, as it is not useful. + */ +# define XXH_INLINE_ALL +# undef XXH_INLINE_ALL +/*! + * @brief Exposes the implementation without marking functions as inline. + */ +# define XXH_PRIVATE_API +# undef XXH_PRIVATE_API +/*! + * @brief Emulate a namespace by transparently prefixing all symbols. + * + * If you want to include _and expose_ xxHash functions from within your own + * library, but also want to avoid symbol collisions with other libraries which + * may also include xxHash, you can use @ref XXH_NAMESPACE to automatically prefix + * any public symbol from xxhash library with the value of @ref XXH_NAMESPACE + * (therefore, avoid empty or numeric values). + * + * Note that no change is required within the calling program as long as it + * includes `xxhash.h`: Regular symbol names will be automatically translated + * by this header. + */ +# define XXH_NAMESPACE /* YOUR NAME HERE */ +# undef XXH_NAMESPACE +#endif + +#if (defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API)) \ + && !defined(XXH_INLINE_ALL_31684351384) + /* this section should be traversed only once */ +# define XXH_INLINE_ALL_31684351384 + /* give access to the advanced API, required to compile implementations */ +# undef XXH_STATIC_LINKING_ONLY /* avoid macro redef */ +# define XXH_STATIC_LINKING_ONLY + /* make all functions private */ +# undef XXH_PUBLIC_API +# if defined(__GNUC__) +# define XXH_PUBLIC_API static __inline __attribute__((__unused__)) +# elif defined (__cplusplus) || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) +# define XXH_PUBLIC_API static inline +# elif defined(_MSC_VER) +# define XXH_PUBLIC_API static __inline +# else + /* note: this version may generate warnings for unused static functions */ +# define XXH_PUBLIC_API static +# endif + + /* + * This part deals with the special case where a unit wants to inline xxHash, + * but "xxhash.h" has previously been included without XXH_INLINE_ALL, + * such as part of some previously included *.h header file. + * Without further action, the new include would just be ignored, + * and functions would effectively _not_ be inlined (silent failure). + * The following macros solve this situation by prefixing all inlined names, + * avoiding naming collision with previous inclusions. + */ + /* Before that, we unconditionally #undef all symbols, + * in case they were already defined with XXH_NAMESPACE. + * They will then be redefined for XXH_INLINE_ALL + */ +# undef XXH_versionNumber + /* XXH32 */ +# undef XXH32 +# undef XXH32_createState +# undef XXH32_freeState +# undef XXH32_reset +# undef XXH32_update +# undef XXH32_digest +# undef XXH32_copyState +# undef XXH32_canonicalFromHash +# undef XXH32_hashFromCanonical + /* XXH64 */ +# undef XXH64 +# undef XXH64_createState +# undef XXH64_freeState +# undef XXH64_reset +# undef XXH64_update +# undef XXH64_digest +# undef XXH64_copyState +# undef XXH64_canonicalFromHash +# undef XXH64_hashFromCanonical + /* XXH3_64bits */ +# undef XXH3_64bits +# undef XXH3_64bits_withSecret +# undef XXH3_64bits_withSeed +# undef XXH3_64bits_withSecretandSeed +# undef XXH3_createState +# undef XXH3_freeState +# undef XXH3_copyState +# undef XXH3_64bits_reset +# undef XXH3_64bits_reset_withSeed +# undef XXH3_64bits_reset_withSecret +# undef XXH3_64bits_update +# undef XXH3_64bits_digest +# undef XXH3_generateSecret + /* XXH3_128bits */ +# undef XXH128 +# undef XXH3_128bits +# undef XXH3_128bits_withSeed +# undef XXH3_128bits_withSecret +# undef XXH3_128bits_reset +# undef XXH3_128bits_reset_withSeed +# undef XXH3_128bits_reset_withSecret +# undef XXH3_128bits_reset_withSecretandSeed +# undef XXH3_128bits_update +# undef XXH3_128bits_digest +# undef XXH128_isEqual +# undef XXH128_cmp +# undef XXH128_canonicalFromHash +# undef XXH128_hashFromCanonical + /* Finally, free the namespace itself */ +# undef XXH_NAMESPACE + + /* employ the namespace for XXH_INLINE_ALL */ +# define XXH_NAMESPACE XXH_INLINE_ + /* + * Some identifiers (enums, type names) are not symbols, + * but they must nonetheless be renamed to avoid redeclaration. + * Alternative solution: do not redeclare them. + * However, this requires some #ifdefs, and has a more dispersed impact. + * Meanwhile, renaming can be achieved in a single place. + */ +# define XXH_IPREF(Id) XXH_NAMESPACE ## Id +# define XXH_OK XXH_IPREF(XXH_OK) +# define XXH_ERROR XXH_IPREF(XXH_ERROR) +# define XXH_errorcode XXH_IPREF(XXH_errorcode) +# define XXH32_canonical_t XXH_IPREF(XXH32_canonical_t) +# define XXH64_canonical_t XXH_IPREF(XXH64_canonical_t) +# define XXH128_canonical_t XXH_IPREF(XXH128_canonical_t) +# define XXH32_state_s XXH_IPREF(XXH32_state_s) +# define XXH32_state_t XXH_IPREF(XXH32_state_t) +# define XXH64_state_s XXH_IPREF(XXH64_state_s) +# define XXH64_state_t XXH_IPREF(XXH64_state_t) +# define XXH3_state_s XXH_IPREF(XXH3_state_s) +# define XXH3_state_t XXH_IPREF(XXH3_state_t) +# define XXH128_hash_t XXH_IPREF(XXH128_hash_t) + /* Ensure the header is parsed again, even if it was previously included */ +# undef XXHASH_H_5627135585666179 +# undef XXHASH_H_STATIC_13879238742 +#endif /* XXH_INLINE_ALL || XXH_PRIVATE_API */ + +/* **************************************************************** + * Stable API + *****************************************************************/ +#ifndef XXHASH_H_5627135585666179 +#define XXHASH_H_5627135585666179 1 + +/*! @brief Marks a global symbol. */ +#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API) +# if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT)) +# ifdef XXH_EXPORT +# define XXH_PUBLIC_API __declspec(dllexport) +# elif XXH_IMPORT +# define XXH_PUBLIC_API __declspec(dllimport) +# endif +# else +# define XXH_PUBLIC_API /* do nothing */ +# endif +#endif + +#ifdef XXH_NAMESPACE +# define XXH_CAT(A,B) A##B +# define XXH_NAME2(A,B) XXH_CAT(A,B) +# define XXH_versionNumber XXH_NAME2(XXH_NAMESPACE, XXH_versionNumber) +/* XXH32 */ +# define XXH32 XXH_NAME2(XXH_NAMESPACE, XXH32) +# define XXH32_createState XXH_NAME2(XXH_NAMESPACE, XXH32_createState) +# define XXH32_freeState XXH_NAME2(XXH_NAMESPACE, XXH32_freeState) +# define XXH32_reset XXH_NAME2(XXH_NAMESPACE, XXH32_reset) +# define XXH32_update XXH_NAME2(XXH_NAMESPACE, XXH32_update) +# define XXH32_digest XXH_NAME2(XXH_NAMESPACE, XXH32_digest) +# define XXH32_copyState XXH_NAME2(XXH_NAMESPACE, XXH32_copyState) +# define XXH32_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH32_canonicalFromHash) +# define XXH32_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH32_hashFromCanonical) +/* XXH64 */ +# define XXH64 XXH_NAME2(XXH_NAMESPACE, XXH64) +# define XXH64_createState XXH_NAME2(XXH_NAMESPACE, XXH64_createState) +# define XXH64_freeState XXH_NAME2(XXH_NAMESPACE, XXH64_freeState) +# define XXH64_reset XXH_NAME2(XXH_NAMESPACE, XXH64_reset) +# define XXH64_update XXH_NAME2(XXH_NAMESPACE, XXH64_update) +# define XXH64_digest XXH_NAME2(XXH_NAMESPACE, XXH64_digest) +# define XXH64_copyState XXH_NAME2(XXH_NAMESPACE, XXH64_copyState) +# define XXH64_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH64_canonicalFromHash) +# define XXH64_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH64_hashFromCanonical) +/* XXH3_64bits */ +# define XXH3_64bits XXH_NAME2(XXH_NAMESPACE, XXH3_64bits) +# define XXH3_64bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecret) +# define XXH3_64bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSeed) +# define XXH3_64bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_withSecretandSeed) +# define XXH3_createState XXH_NAME2(XXH_NAMESPACE, XXH3_createState) +# define XXH3_freeState XXH_NAME2(XXH_NAMESPACE, XXH3_freeState) +# define XXH3_copyState XXH_NAME2(XXH_NAMESPACE, XXH3_copyState) +# define XXH3_64bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset) +# define XXH3_64bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSeed) +# define XXH3_64bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecret) +# define XXH3_64bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_reset_withSecretandSeed) +# define XXH3_64bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_update) +# define XXH3_64bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_64bits_digest) +# define XXH3_generateSecret XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret) +# define XXH3_generateSecret_fromSeed XXH_NAME2(XXH_NAMESPACE, XXH3_generateSecret_fromSeed) +/* XXH3_128bits */ +# define XXH128 XXH_NAME2(XXH_NAMESPACE, XXH128) +# define XXH3_128bits XXH_NAME2(XXH_NAMESPACE, XXH3_128bits) +# define XXH3_128bits_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSeed) +# define XXH3_128bits_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecret) +# define XXH3_128bits_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_withSecretandSeed) +# define XXH3_128bits_reset XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset) +# define XXH3_128bits_reset_withSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSeed) +# define XXH3_128bits_reset_withSecret XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecret) +# define XXH3_128bits_reset_withSecretandSeed XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_reset_withSecretandSeed) +# define XXH3_128bits_update XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_update) +# define XXH3_128bits_digest XXH_NAME2(XXH_NAMESPACE, XXH3_128bits_digest) +# define XXH128_isEqual XXH_NAME2(XXH_NAMESPACE, XXH128_isEqual) +# define XXH128_cmp XXH_NAME2(XXH_NAMESPACE, XXH128_cmp) +# define XXH128_canonicalFromHash XXH_NAME2(XXH_NAMESPACE, XXH128_canonicalFromHash) +# define XXH128_hashFromCanonical XXH_NAME2(XXH_NAMESPACE, XXH128_hashFromCanonical) +#endif + + +/* ************************************* +* Compiler specifics +***************************************/ + +/* specific declaration modes for Windows */ +#if !defined(XXH_INLINE_ALL) && !defined(XXH_PRIVATE_API) +# if defined(_WIN32) && defined(_MSC_VER) && (defined(XXH_IMPORT) || defined(XXH_EXPORT)) +# ifdef XXH_EXPORT +# define XXH_PUBLIC_API __declspec(dllexport) +# elif XXH_IMPORT +# define XXH_PUBLIC_API __declspec(dllimport) +# endif +# else +# define XXH_PUBLIC_API /* do nothing */ +# endif +#endif + +#if defined (__GNUC__) +# define XXH_CONSTF __attribute__((__const__)) +# define XXH_PUREF __attribute__((__pure__)) +# define XXH_MALLOCF __attribute__((__malloc__)) +#else +# define XXH_CONSTF /* disable */ +# define XXH_PUREF +# define XXH_MALLOCF +#endif + +/* ************************************* +* Version +***************************************/ +#define XXH_VERSION_MAJOR 0 +#define XXH_VERSION_MINOR 8 +#define XXH_VERSION_RELEASE 3 +/*! @brief Version number, encoded as two digits each */ +#define XXH_VERSION_NUMBER (XXH_VERSION_MAJOR *100*100 + XXH_VERSION_MINOR *100 + XXH_VERSION_RELEASE) + +/*! + * @brief Obtains the xxHash version. + * + * This is mostly useful when xxHash is compiled as a shared library, + * since the returned value comes from the library, as opposed to header file. + * + * @return @ref XXH_VERSION_NUMBER of the invoked library. + */ +XXH_PUBLIC_API XXH_CONSTF unsigned XXH_versionNumber (void); + + +/* **************************** +* Common basic types +******************************/ +#include /* size_t */ +/*! + * @brief Exit code for the streaming API. + */ +typedef enum { + XXH_OK = 0, /*!< OK */ + XXH_ERROR /*!< Error */ +} XXH_errorcode; + + +/*-********************************************************************** +* 32-bit hash +************************************************************************/ +#if defined(XXH_DOXYGEN) /* Don't show include */ +/*! + * @brief An unsigned 32-bit integer. + * + * Not necessarily defined to `uint32_t` but functionally equivalent. + */ +typedef uint32_t XXH32_hash_t; + +#elif !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint32_t XXH32_hash_t; + +#else +# include +# if UINT_MAX == 0xFFFFFFFFUL + typedef unsigned int XXH32_hash_t; +# elif ULONG_MAX == 0xFFFFFFFFUL + typedef unsigned long XXH32_hash_t; +# else +# error "unsupported platform: need a 32-bit type" +# endif +#endif + +/*! + * @} + * + * @defgroup XXH32_family XXH32 family + * @ingroup public + * Contains functions used in the classic 32-bit xxHash algorithm. + * + * @note + * XXH32 is useful for older platforms, with no or poor 64-bit performance. + * Note that the @ref XXH3_family provides competitive speed for both 32-bit + * and 64-bit systems, and offers true 64/128 bit hash results. + * + * @see @ref XXH64_family, @ref XXH3_family : Other xxHash families + * @see @ref XXH32_impl for implementation details + * @{ + */ + +/*! + * @brief Calculates the 32-bit hash of @p input using xxHash32. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 32-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 32-bit xxHash32 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32 (const void* input, size_t length, XXH32_hash_t seed); + +#ifndef XXH_NO_STREAM +/*! + * @typedef struct XXH32_state_s XXH32_state_t + * @brief The opaque state struct for the XXH32 streaming API. + * + * @see XXH32_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH32_state_s XXH32_state_t; + +/*! + * @brief Allocates an @ref XXH32_state_t. + * + * @return An allocated pointer of @ref XXH32_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH32_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_MALLOCF XXH32_state_t* XXH32_createState(void); +/*! + * @brief Frees an @ref XXH32_state_t. + * + * @param statePtr A pointer to an @ref XXH32_state_t allocated with @ref XXH32_createState(). + * + * @return @ref XXH_OK. + * + * @note @p statePtr must be allocated with XXH32_createState(). + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH32_freeState(XXH32_state_t* statePtr); +/*! + * @brief Copies one @ref XXH32_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dst_state, const XXH32_state_t* src_state); + +/*! + * @brief Resets an @ref XXH32_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 32-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note This function resets and seeds a state. Call it before @ref XXH32_update(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH32_reset (XXH32_state_t* statePtr, XXH32_hash_t seed); + +/*! + * @brief Consumes a block of @p input to an @ref XXH32_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH32_update (XXH32_state_t* statePtr, const void* input, size_t length); + +/*! + * @brief Returns the calculated hash value from an @ref XXH32_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated 32-bit xxHash32 value from that state. + * + * @note + * Calling XXH32_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_digest (const XXH32_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! + * @brief Canonical (big endian) representation of @ref XXH32_hash_t. + */ +typedef struct { + unsigned char digest[4]; /*!< Hash bytes, big endian */ +} XXH32_canonical_t; + +/*! + * @brief Converts an @ref XXH32_hash_t to a big endian @ref XXH32_canonical_t. + * + * @param dst The @ref XXH32_canonical_t pointer to be stored to. + * @param hash The @ref XXH32_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash); + +/*! + * @brief Converts an @ref XXH32_canonical_t to a native @ref XXH32_hash_t. + * + * @param src The @ref XXH32_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src); + + +/*! @cond Doxygen ignores this part */ +#ifdef __has_attribute +# define XXH_HAS_ATTRIBUTE(x) __has_attribute(x) +#else +# define XXH_HAS_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * C23 __STDC_VERSION__ number hasn't been specified yet. For now + * leave as `201711L` (C17 + 1). + * TODO: Update to correct value when its been specified. + */ +#define XXH_C23_VN 201711L +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* C-language Attributes are added in C23. */ +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN) && defined(__has_c_attribute) +# define XXH_HAS_C_ATTRIBUTE(x) __has_c_attribute(x) +#else +# define XXH_HAS_C_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +#if defined(__cplusplus) && defined(__has_cpp_attribute) +# define XXH_HAS_CPP_ATTRIBUTE(x) __has_cpp_attribute(x) +#else +# define XXH_HAS_CPP_ATTRIBUTE(x) 0 +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * Define XXH_FALLTHROUGH macro for annotating switch case with the 'fallthrough' attribute + * introduced in CPP17 and C23. + * CPP17 : https://en.cppreference.com/w/cpp/language/attributes/fallthrough + * C23 : https://en.cppreference.com/w/c/language/attributes/fallthrough + */ +#if XXH_HAS_C_ATTRIBUTE(fallthrough) || XXH_HAS_CPP_ATTRIBUTE(fallthrough) +# define XXH_FALLTHROUGH [[fallthrough]] +#elif XXH_HAS_ATTRIBUTE(__fallthrough__) +# define XXH_FALLTHROUGH __attribute__ ((__fallthrough__)) +#else +# define XXH_FALLTHROUGH /* fallthrough */ +#endif +/*! @endcond */ + +/*! @cond Doxygen ignores this part */ +/* + * Define XXH_NOESCAPE for annotated pointers in public API. + * https://clang.llvm.org/docs/AttributeReference.html#noescape + * As of writing this, only supported by clang. + */ +#if XXH_HAS_ATTRIBUTE(noescape) +# define XXH_NOESCAPE __attribute__((__noescape__)) +#else +# define XXH_NOESCAPE +#endif +/*! @endcond */ + + +/*! + * @} + * @ingroup public + * @{ + */ + +#ifndef XXH_NO_LONG_LONG +/*-********************************************************************** +* 64-bit hash +************************************************************************/ +#if defined(XXH_DOXYGEN) /* don't include */ +/*! + * @brief An unsigned 64-bit integer. + * + * Not necessarily defined to `uint64_t` but functionally equivalent. + */ +typedef uint64_t XXH64_hash_t; +#elif !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint64_t XXH64_hash_t; +#else +# include +# if defined(__LP64__) && ULONG_MAX == 0xFFFFFFFFFFFFFFFFULL + /* LP64 ABI says uint64_t is unsigned long */ + typedef unsigned long XXH64_hash_t; +# else + /* the following type must have a width of 64-bit */ + typedef unsigned long long XXH64_hash_t; +# endif +#endif + +/*! + * @} + * + * @defgroup XXH64_family XXH64 family + * @ingroup public + * @{ + * Contains functions used in the classic 64-bit xxHash algorithm. + * + * @note + * XXH3 provides competitive speed for both 32-bit and 64-bit systems, + * and offers true 64/128 bit hash results. + * It provides better speed for systems with vector processing capabilities. + */ + +/*! + * @brief Calculates the 64-bit hash of @p input using xxHash64. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 64-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit xxHash64 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed); + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/*! + * @brief The opaque state struct for the XXH64 streaming API. + * + * @see XXH64_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH64_state_s XXH64_state_t; /* incomplete type */ + +/*! + * @brief Allocates an @ref XXH64_state_t. + * + * @return An allocated pointer of @ref XXH64_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH64_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_MALLOCF XXH64_state_t* XXH64_createState(void); + +/*! + * @brief Frees an @ref XXH64_state_t. + * + * @param statePtr A pointer to an @ref XXH64_state_t allocated with @ref XXH64_createState(). + * + * @return @ref XXH_OK. + * + * @note @p statePtr must be allocated with XXH64_createState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_freeState(XXH64_state_t* statePtr); + +/*! + * @brief Copies one @ref XXH64_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dst_state, const XXH64_state_t* src_state); + +/*! + * @brief Resets an @ref XXH64_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note This function resets and seeds a state. Call it before @ref XXH64_update(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_reset (XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed); + +/*! + * @brief Consumes a block of @p input to an @ref XXH64_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH64_update (XXH_NOESCAPE XXH64_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated hash value from an @ref XXH64_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated 64-bit xxHash64 value from that state. + * + * @note + * Calling XXH64_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_digest (XXH_NOESCAPE const XXH64_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ +/******* Canonical representation *******/ + +/*! + * @brief Canonical (big endian) representation of @ref XXH64_hash_t. + */ +typedef struct { unsigned char digest[sizeof(XXH64_hash_t)]; } XXH64_canonical_t; + +/*! + * @brief Converts an @ref XXH64_hash_t to a big endian @ref XXH64_canonical_t. + * + * @param dst The @ref XXH64_canonical_t pointer to be stored to. + * @param hash The @ref XXH64_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash); + +/*! + * @brief Converts an @ref XXH64_canonical_t to a native @ref XXH64_hash_t. + * + * @param src The @ref XXH64_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src); + +#ifndef XXH_NO_XXH3 + +/*! + * @} + * ************************************************************************ + * @defgroup XXH3_family XXH3 family + * @ingroup public + * @{ + * + * XXH3 is a more recent hash algorithm featuring: + * - Improved speed for both small and large inputs + * - True 64-bit and 128-bit outputs + * - SIMD acceleration + * - Improved 32-bit viability + * + * Speed analysis methodology is explained here: + * + * https://fastcompression.blogspot.com/2019/03/presenting-xxh3.html + * + * Compared to XXH64, expect XXH3 to run approximately + * ~2x faster on large inputs and >3x faster on small ones, + * exact differences vary depending on platform. + * + * XXH3's speed benefits greatly from SIMD and 64-bit arithmetic, + * but does not require it. + * Most 32-bit and 64-bit targets that can run XXH32 smoothly can run XXH3 + * at competitive speeds, even without vector support. Further details are + * explained in the implementation. + * + * XXH3 has a fast scalar implementation, but it also includes accelerated SIMD + * implementations for many common platforms: + * - AVX512 + * - AVX2 + * - SSE2 + * - ARM NEON + * - WebAssembly SIMD128 + * - POWER8 VSX + * - s390x ZVector + * This can be controlled via the @ref XXH_VECTOR macro, but it automatically + * selects the best version according to predefined macros. For the x86 family, an + * automatic runtime dispatcher is included separately in @ref xxh_x86dispatch.c. + * + * XXH3 implementation is portable: + * it has a generic C90 formulation that can be compiled on any platform, + * all implementations generate exactly the same hash value on all platforms. + * Starting from v0.8.0, it's also labelled "stable", meaning that + * any future version will also generate the same hash value. + * + * XXH3 offers 2 variants, _64bits and _128bits. + * + * When only 64 bits are needed, prefer invoking the _64bits variant, as it + * reduces the amount of mixing, resulting in faster speed on small inputs. + * It's also generally simpler to manipulate a scalar return type than a struct. + * + * The API supports one-shot hashing, streaming mode, and custom secrets. + */ +/*-********************************************************************** +* XXH3 64-bit variant +************************************************************************/ + +/*! + * @brief Calculates 64-bit unseeded variant of XXH3 hash of @p input. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @note + * This is equivalent to @ref XXH3_64bits_withSeed() with a seed of `0`, however + * it may have slightly better performance due to constant propagation of the + * defaults. + * + * @see + * XXH3_64bits_withSeed(), XXH3_64bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Calculates 64-bit seeded variant of XXH3 hash of @p input. + * + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @note + * seed == 0 produces the same results as @ref XXH3_64bits(). + * + * This variant generates a custom secret on the fly based on default secret + * altered using the @p seed value. + * + * While this operation is decently fast, note that it's not completely free. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed); + +/*! + * The bare minimum size for a custom secret. + * + * @see + * XXH3_64bits_withSecret(), XXH3_64bits_reset_withSecret(), + * XXH3_128bits_withSecret(), XXH3_128bits_reset_withSecret(). + */ +#define XXH3_SECRET_SIZE_MIN 136 + +/*! + * @brief Calculates 64-bit variant of XXH3 with a custom "secret". + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @return The calculated 64-bit XXH3 hash value. + * + * @pre + * The memory between @p data and @p data + @p len must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p data may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * It's possible to provide any blob of bytes as a "secret" to generate the hash. + * This makes it more difficult for an external actor to prepare an intentional collision. + * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN). + * However, the quality of the secret impacts the dispersion of the hash algorithm. + * Therefore, the secret _must_ look like a bunch of random bytes. + * Avoid "trivial" or structured data such as repeated sequences or a text document. + * Whenever in doubt about the "randomness" of the blob of bytes, + * consider employing @ref XXH3_generateSecret() instead (see below). + * It will generate a proper high entropy secret derived from the blob of bytes. + * Another advantage of using XXH3_generateSecret() is that + * it guarantees that all bits within the initial blob of bytes + * will impact every bit of the output. + * This is not necessarily the case when using the blob of bytes directly + * because, when hashing _small_ inputs, only a portion of the secret is employed. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize); + + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/* + * Streaming requires state maintenance. + * This operation costs memory and CPU. + * As a consequence, streaming is slower than one-shot hashing. + * For better performance, prefer one-shot functions whenever applicable. + */ + +/*! + * @brief The opaque state struct for the XXH3 streaming API. + * + * @see XXH3_state_s for details. + * @see @ref streaming_example "Streaming Example" + */ +typedef struct XXH3_state_s XXH3_state_t; +XXH_PUBLIC_API XXH_MALLOCF XXH3_state_t* XXH3_createState(void); +XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr); + +/*! + * @brief Copies one @ref XXH3_state_t to another. + * + * @param dst_state The state to copy to. + * @param src_state The state to copy from. + * @pre + * @p dst_state and @p src_state must not be `NULL` and must not overlap. + */ +XXH_PUBLIC_API void XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state); + +/*! + * @brief Resets an @ref XXH3_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret with default parameters. + * - Call this function before @ref XXH3_64bits_update(). + * - Digest will be equivalent to `XXH3_64bits()`. + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr); + +/*! + * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret from `seed`. + * - Call this function before @ref XXH3_64bits_update(). + * - Digest will be equivalent to `XXH3_64bits_withSeed()`. + * + * @see @ref streaming_example "Streaming Example" + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed); + +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * `secret` is referenced, it _must outlive_ the hash streaming session. + * + * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN, + * and the quality of produced hash values depends on secret's entropy + * (secret's content should look like a bunch of random bytes). + * When in doubt about the randomness of a candidate `secret`, + * consider employing `XXH3_generateSecret()` instead (see below). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize); + +/*! + * @brief Consumes a block of @p input to an @ref XXH3_state_t. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * @pre + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note Call this to incrementally consume blocks of data. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_64bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated XXH3 64-bit hash value from an @ref XXH3_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated XXH3 64-bit hash value from that state. + * + * @note + * Calling XXH3_64bits_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/* note : canonical representation of XXH3 is the same as XXH64 + * since they both produce XXH64_hash_t values */ + + +/*-********************************************************************** +* XXH3 128-bit variant +************************************************************************/ + +/*! + * @brief The return value from 128-bit hashes. + * + * Stored in little endian order, although the fields themselves are in native + * endianness. + */ +typedef struct { + XXH64_hash_t low64; /*!< `value & 0xFFFFFFFFFFFFFFFF` */ + XXH64_hash_t high64; /*!< `value >> 64` */ +} XXH128_hash_t; + +/*! + * @brief Calculates 128-bit unseeded variant of XXH3 of @p data. + * + * @param data The block of data to be hashed, at least @p length bytes in size. + * @param len The length of @p data, in bytes. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * The 128-bit variant of XXH3 has more strength, but it has a bit of overhead + * for shorter inputs. + * + * This is equivalent to @ref XXH3_128bits_withSeed() with a seed of `0`, however + * it may have slightly better performance due to constant propagation of the + * defaults. + * + * @see XXH3_128bits_withSeed(), XXH3_128bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* data, size_t len); +/*! @brief Calculates 128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The block of data to be hashed, at least @p length bytes in size. + * @param len The length of @p data, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * @note + * seed == 0 produces the same results as @ref XXH3_64bits(). + * + * This variant generates a custom secret on the fly based on default secret + * altered using the @p seed value. + * + * While this operation is decently fast, note that it's not completely free. + * + * @see XXH3_128bits(), XXH3_128bits_withSecret(): other seeding variants + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSeed(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed); +/*! + * @brief Calculates 128-bit variant of XXH3 with a custom "secret". + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @return The calculated 128-bit variant of XXH3 value. + * + * It's possible to provide any blob of bytes as a "secret" to generate the hash. + * This makes it more difficult for an external actor to prepare an intentional collision. + * The main condition is that @p secretSize *must* be large enough (>= @ref XXH3_SECRET_SIZE_MIN). + * However, the quality of the secret impacts the dispersion of the hash algorithm. + * Therefore, the secret _must_ look like a bunch of random bytes. + * Avoid "trivial" or structured data such as repeated sequences or a text document. + * Whenever in doubt about the "randomness" of the blob of bytes, + * consider employing @ref XXH3_generateSecret() instead (see below). + * It will generate a proper high entropy secret derived from the blob of bytes. + * Another advantage of using XXH3_generateSecret() is that + * it guarantees that all bits within the initial blob of bytes + * will impact every bit of the output. + * This is not necessarily the case when using the blob of bytes directly + * because, when hashing _small_ inputs, only a portion of the secret is employed. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_withSecret(XXH_NOESCAPE const void* data, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize); + +/******* Streaming *******/ +#ifndef XXH_NO_STREAM +/* + * Streaming requires state maintenance. + * This operation costs memory and CPU. + * As a consequence, streaming is slower than one-shot hashing. + * For better performance, prefer one-shot functions whenever applicable. + * + * XXH3_128bits uses the same XXH3_state_t as XXH3_64bits(). + * Use already declared XXH3_createState() and XXH3_freeState(). + * + * All reset and streaming functions have same meaning as their 64-bit counterpart. + */ + +/*! + * @brief Resets an @ref XXH3_state_t to begin a new hash. + * + * @param statePtr The state struct to reset. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret with default parameters. + * - Call it before @ref XXH3_128bits_update(). + * - Digest will be equivalent to `XXH3_128bits()`. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr); + +/*! + * @brief Resets an @ref XXH3_state_t with 64-bit seed to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * - This function resets `statePtr` and generate a secret from `seed`. + * - Call it before @ref XXH3_128bits_update(). + * - Digest will be equivalent to `XXH3_128bits_withSeed()`. + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed); +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr The state struct to reset. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * `secret` is referenced, it _must outlive_ the hash streaming session. + * Similar to one-shot API, `secretSize` must be >= @ref XXH3_SECRET_SIZE_MIN, + * and the quality of produced hash values depends on secret's entropy + * (secret's content should look like a bunch of random bytes). + * When in doubt about the randomness of a candidate `secret`, + * consider employing `XXH3_generateSecret()` instead (see below). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize); + +/*! + * @brief Consumes a block of @p input to an @ref XXH3_state_t. + * + * Call this to incrementally consume blocks of data. + * + * @param statePtr The state struct to update. + * @param input The block of data to be hashed, at least @p length bytes in size. + * @param length The length of @p input, in bytes. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @note + * The memory between @p input and @p input + @p length must be valid, + * readable, contiguous memory. However, if @p length is `0`, @p input may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + */ +XXH_PUBLIC_API XXH_errorcode XXH3_128bits_update (XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* input, size_t length); + +/*! + * @brief Returns the calculated XXH3 128-bit hash value from an @ref XXH3_state_t. + * + * @param statePtr The state struct to calculate the hash from. + * + * @pre + * @p statePtr must not be `NULL`. + * + * @return The calculated XXH3 128-bit hash value from that state. + * + * @note + * Calling XXH3_128bits_digest() will not affect @p statePtr, so you can update, + * digest, and update again. + * + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* statePtr); +#endif /* !XXH_NO_STREAM */ + +/* Following helper functions make it possible to compare XXH128_hast_t values. + * Since XXH128_hash_t is a structure, this capability is not offered by the language. + * Note: For better performance, these functions can be inlined using XXH_INLINE_ALL */ + +/*! + * @brief Check equality of two XXH128_hash_t values + * + * @param h1 The 128-bit hash value. + * @param h2 Another 128-bit hash value. + * + * @return `1` if `h1` and `h2` are equal. + * @return `0` if they are not. + */ +XXH_PUBLIC_API XXH_PUREF int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2); + +/*! + * @brief Compares two @ref XXH128_hash_t + * + * This comparator is compatible with stdlib's `qsort()`/`bsearch()`. + * + * @param h128_1 Left-hand side value + * @param h128_2 Right-hand side value + * + * @return >0 if @p h128_1 > @p h128_2 + * @return =0 if @p h128_1 == @p h128_2 + * @return <0 if @p h128_1 < @p h128_2 + */ +XXH_PUBLIC_API XXH_PUREF int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2); + + +/******* Canonical representation *******/ +typedef struct { unsigned char digest[sizeof(XXH128_hash_t)]; } XXH128_canonical_t; + + +/*! + * @brief Converts an @ref XXH128_hash_t to a big endian @ref XXH128_canonical_t. + * + * @param dst The @ref XXH128_canonical_t pointer to be stored to. + * @param hash The @ref XXH128_hash_t to be converted. + * + * @pre + * @p dst must not be `NULL`. + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API void XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash); + +/*! + * @brief Converts an @ref XXH128_canonical_t to a native @ref XXH128_hash_t. + * + * @param src The @ref XXH128_canonical_t to convert. + * + * @pre + * @p src must not be `NULL`. + * + * @return The converted hash. + * @see @ref canonical_representation_example "Canonical Representation Example" + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src); + + +#endif /* !XXH_NO_XXH3 */ +#endif /* XXH_NO_LONG_LONG */ + +/*! + * @} + */ +#endif /* XXHASH_H_5627135585666179 */ + + + +#if defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742) +#define XXHASH_H_STATIC_13879238742 +/* **************************************************************************** + * This section contains declarations which are not guaranteed to remain stable. + * They may change in future versions, becoming incompatible with a different + * version of the library. + * These declarations should only be used with static linking. + * Never use them in association with dynamic linking! + ***************************************************************************** */ + +/* + * These definitions are only present to allow static allocation + * of XXH states, on stack or in a struct, for example. + * Never **ever** access their members directly. + */ + +/*! + * @internal + * @brief Structure for XXH32 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is + * an opaque type. This allows fields to safely be changed. + * + * Typedef'd to @ref XXH32_state_t. + * Do not access the members of this struct directly. + * @see XXH64_state_s, XXH3_state_s + */ +struct XXH32_state_s { + XXH32_hash_t total_len_32; /*!< Total length hashed, modulo 2^32 */ + XXH32_hash_t large_len; /*!< Whether the hash is >= 16 (handles @ref total_len_32 overflow) */ + XXH32_hash_t v[4]; /*!< Accumulator lanes */ + XXH32_hash_t mem32[4]; /*!< Internal buffer for partial reads. Treated as unsigned char[16]. */ + XXH32_hash_t memsize; /*!< Amount of data in @ref mem32 */ + XXH32_hash_t reserved; /*!< Reserved field. Do not read nor write to it. */ +}; /* typedef'd to XXH32_state_t */ + + +#ifndef XXH_NO_LONG_LONG /* defined when there is no 64-bit support */ + +/*! + * @internal + * @brief Structure for XXH64 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. Otherwise it is + * an opaque type. This allows fields to safely be changed. + * + * Typedef'd to @ref XXH64_state_t. + * Do not access the members of this struct directly. + * @see XXH32_state_s, XXH3_state_s + */ +struct XXH64_state_s { + XXH64_hash_t total_len; /*!< Total length hashed. This is always 64-bit. */ + XXH64_hash_t v[4]; /*!< Accumulator lanes */ + XXH64_hash_t mem64[4]; /*!< Internal buffer for partial reads. Treated as unsigned char[32]. */ + XXH32_hash_t memsize; /*!< Amount of data in @ref mem64 */ + XXH32_hash_t reserved32; /*!< Reserved field, needed for padding anyways*/ + XXH64_hash_t reserved64; /*!< Reserved field. Do not read or write to it. */ +}; /* typedef'd to XXH64_state_t */ + +#ifndef XXH_NO_XXH3 + +/* Windows SDK under 10.0.22000 is missing stdalign.h so we add a check + before allowing the windows compiler to use the C11 form. + Reference: https://github.com/Cyan4973/xxHash/issues/955 */ +#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) \ + && (defined(_MSC_VER) && (_MSC_VER >= 1000) || !defined(_MSC_VER)) /* >= C11 */ +# include +# define XXH_ALIGN(n) alignas(n) +#elif defined(__cplusplus) && (__cplusplus >= 201103L) /* >= C++11 */ +/* In C++ alignas() is a keyword */ +# define XXH_ALIGN(n) alignas(n) +#elif defined(__GNUC__) +# define XXH_ALIGN(n) __attribute__ ((aligned(n))) +#elif defined(_MSC_VER) +# define XXH_ALIGN(n) __declspec(align(n)) +#else +# define XXH_ALIGN(n) /* disabled */ +#endif + +/* Old GCC versions only accept the attribute after the type in structures. */ +#if !(defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L)) /* C11+ */ \ + && ! (defined(__cplusplus) && (__cplusplus >= 201103L)) /* >= C++11 */ \ + && defined(__GNUC__) +# define XXH_ALIGN_MEMBER(align, type) type XXH_ALIGN(align) +#else +# define XXH_ALIGN_MEMBER(align, type) XXH_ALIGN(align) type +#endif + +/*! + * @brief The size of the internal XXH3 buffer. + * + * This is the optimal update size for incremental hashing. + * + * @see XXH3_64b_update(), XXH3_128b_update(). + */ +#define XXH3_INTERNALBUFFER_SIZE 256 + +/*! + * @internal + * @brief Default size of the secret buffer (and @ref XXH3_kSecret). + * + * This is the size used in @ref XXH3_kSecret and the seeded functions. + * + * Not to be confused with @ref XXH3_SECRET_SIZE_MIN. + */ +#define XXH3_SECRET_DEFAULT_SIZE 192 + +/*! + * @internal + * @brief Structure for XXH3 streaming API. + * + * @note This is only defined when @ref XXH_STATIC_LINKING_ONLY, + * @ref XXH_INLINE_ALL, or @ref XXH_IMPLEMENTATION is defined. + * Otherwise it is an opaque type. + * Never use this definition in combination with dynamic library. + * This allows fields to safely be changed in the future. + * + * @note ** This structure has a strict alignment requirement of 64 bytes!! ** + * Do not allocate this with `malloc()` or `new`, + * it will not be sufficiently aligned. + * Use @ref XXH3_createState() and @ref XXH3_freeState(), or stack allocation. + * + * Typedef'd to @ref XXH3_state_t. + * Do never access the members of this struct directly. + * + * @see XXH3_INITSTATE() for stack initialization. + * @see XXH3_createState(), XXH3_freeState(). + * @see XXH32_state_s, XXH64_state_s + */ +struct XXH3_state_s { + XXH_ALIGN_MEMBER(64, XXH64_hash_t acc[8]); + /*!< The 8 accumulators. See @ref XXH32_state_s::v and @ref XXH64_state_s::v */ + XXH_ALIGN_MEMBER(64, unsigned char customSecret[XXH3_SECRET_DEFAULT_SIZE]); + /*!< Used to store a custom secret generated from a seed. */ + XXH_ALIGN_MEMBER(64, unsigned char buffer[XXH3_INTERNALBUFFER_SIZE]); + /*!< The internal buffer. @see XXH32_state_s::mem32 */ + XXH32_hash_t bufferedSize; + /*!< The amount of memory in @ref buffer, @see XXH32_state_s::memsize */ + XXH32_hash_t useSeed; + /*!< Reserved field. Needed for padding on 64-bit. */ + size_t nbStripesSoFar; + /*!< Number or stripes processed. */ + XXH64_hash_t totalLen; + /*!< Total length hashed. 64-bit even on 32-bit targets. */ + size_t nbStripesPerBlock; + /*!< Number of stripes per block. */ + size_t secretLimit; + /*!< Size of @ref customSecret or @ref extSecret */ + XXH64_hash_t seed; + /*!< Seed for _withSeed variants. Must be zero otherwise, @see XXH3_INITSTATE() */ + XXH64_hash_t reserved64; + /*!< Reserved field. */ + const unsigned char* extSecret; + /*!< Reference to an external secret for the _withSecret variants, NULL + * for other variants. */ + /* note: there may be some padding at the end due to alignment on 64 bytes */ +}; /* typedef'd to XXH3_state_t */ + +#undef XXH_ALIGN_MEMBER + +/*! + * @brief Initializes a stack-allocated `XXH3_state_s`. + * + * When the @ref XXH3_state_t structure is merely emplaced on stack, + * it should be initialized with XXH3_INITSTATE() or a memset() + * in case its first reset uses XXH3_NNbits_reset_withSeed(). + * This init can be omitted if the first reset uses default or _withSecret mode. + * This operation isn't necessary when the state is created with XXH3_createState(). + * Note that this doesn't prepare the state for a streaming operation, + * it's still necessary to use XXH3_NNbits_reset*() afterwards. + */ +#define XXH3_INITSTATE(XXH3_state_ptr) \ + do { \ + XXH3_state_t* tmp_xxh3_state_ptr = (XXH3_state_ptr); \ + tmp_xxh3_state_ptr->seed = 0; \ + tmp_xxh3_state_ptr->extSecret = NULL; \ + } while(0) + + +/*! + * @brief Calculates the 128-bit hash of @p data using XXH3. + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param seed The 64-bit seed to alter the hash's output predictably. + * + * @pre + * The memory between @p data and @p data + @p len must be valid, + * readable, contiguous memory. However, if @p len is `0`, @p data may be + * `NULL`. In C++, this also must be *TriviallyCopyable*. + * + * @return The calculated 128-bit XXH3 value. + * + * @see @ref single_shot_example "Single Shot Example" for an example. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t XXH128(XXH_NOESCAPE const void* data, size_t len, XXH64_hash_t seed); + + +/* === Experimental API === */ +/* Symbols defined below must be considered tied to a specific library version. */ + +/*! + * @brief Derive a high-entropy secret from any user-defined content, named customSeed. + * + * @param secretBuffer A writable buffer for derived high-entropy secret data. + * @param secretSize Size of secretBuffer, in bytes. Must be >= XXH3_SECRET_SIZE_MIN. + * @param customSeed A user-defined content. + * @param customSeedSize Size of customSeed, in bytes. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * The generated secret can be used in combination with `*_withSecret()` functions. + * The `_withSecret()` variants are useful to provide a higher level of protection + * than 64-bit seed, as it becomes much more difficult for an external actor to + * guess how to impact the calculation logic. + * + * The function accepts as input a custom seed of any length and any content, + * and derives from it a high-entropy secret of length @p secretSize into an + * already allocated buffer @p secretBuffer. + * + * The generated secret can then be used with any `*_withSecret()` variant. + * The functions @ref XXH3_128bits_withSecret(), @ref XXH3_64bits_withSecret(), + * @ref XXH3_128bits_reset_withSecret() and @ref XXH3_64bits_reset_withSecret() + * are part of this list. They all accept a `secret` parameter + * which must be large enough for implementation reasons (>= @ref XXH3_SECRET_SIZE_MIN) + * _and_ feature very high entropy (consist of random-looking bytes). + * These conditions can be a high bar to meet, so @ref XXH3_generateSecret() can + * be employed to ensure proper quality. + * + * @p customSeed can be anything. It can have any size, even small ones, + * and its content can be anything, even "poor entropy" sources such as a bunch + * of zeroes. The resulting `secret` will nonetheless provide all required qualities. + * + * @pre + * - @p secretSize must be >= @ref XXH3_SECRET_SIZE_MIN + * - When @p customSeedSize > 0, supplying NULL as customSeed is undefined behavior. + * + * Example code: + * @code{.c} + * #include + * #include + * #include + * #define XXH_STATIC_LINKING_ONLY // expose unstable API + * #include "xxhash.h" + * // Hashes argv[2] using the entropy from argv[1]. + * int main(int argc, char* argv[]) + * { + * char secret[XXH3_SECRET_SIZE_MIN]; + * if (argv != 3) { return 1; } + * XXH3_generateSecret(secret, sizeof(secret), argv[1], strlen(argv[1])); + * XXH64_hash_t h = XXH3_64bits_withSecret( + * argv[2], strlen(argv[2]), + * secret, sizeof(secret) + * ); + * printf("%016llx\n", (unsigned long long) h); + * } + * @endcode + */ +XXH_PUBLIC_API XXH_errorcode XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize); + +/*! + * @brief Generate the same secret as the _withSeed() variants. + * + * @param secretBuffer A writable buffer of @ref XXH3_SECRET_DEFAULT_SIZE bytes + * @param seed The 64-bit seed to alter the hash result predictably. + * + * The generated secret can be used in combination with + *`*_withSecret()` and `_withSecretandSeed()` variants. + * + * Example C++ `std::string` hash class: + * @code{.cpp} + * #include + * #define XXH_STATIC_LINKING_ONLY // expose unstable API + * #include "xxhash.h" + * // Slow, seeds each time + * class HashSlow { + * XXH64_hash_t seed; + * public: + * HashSlow(XXH64_hash_t s) : seed{s} {} + * size_t operator()(const std::string& x) const { + * return size_t{XXH3_64bits_withSeed(x.c_str(), x.length(), seed)}; + * } + * }; + * // Fast, caches the seeded secret for future uses. + * class HashFast { + * unsigned char secret[XXH3_SECRET_DEFAULT_SIZE]; + * public: + * HashFast(XXH64_hash_t s) { + * XXH3_generateSecret_fromSeed(secret, seed); + * } + * size_t operator()(const std::string& x) const { + * return size_t{ + * XXH3_64bits_withSecret(x.c_str(), x.length(), secret, sizeof(secret)) + * }; + * } + * }; + * @endcode + */ +XXH_PUBLIC_API void XXH3_generateSecret_fromSeed(XXH_NOESCAPE void* secretBuffer, XXH64_hash_t seed); + +/*! + * @brief Maximum size of "short" key in bytes. + */ +#define XXH3_MIDSIZE_MAX 240 + +/*! + * @brief Calculates 64/128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The block of data to be hashed, at least @p len bytes in size. + * @param len The length of @p data, in bytes. + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed The 64-bit seed to alter the hash result predictably. + * + * These variants generate hash values using either: + * - @p seed for "short" keys (< @ref XXH3_MIDSIZE_MAX = 240 bytes) + * - @p secret for "large" keys (>= @ref XXH3_MIDSIZE_MAX). + * + * This generally benefits speed, compared to `_withSeed()` or `_withSecret()`. + * `_withSeed()` has to generate the secret on the fly for "large" keys. + * It's fast, but can be perceptible for "not so large" keys (< 1 KB). + * `_withSecret()` has to generate the masks on the fly for "small" keys, + * which requires more instructions than _withSeed() variants. + * Therefore, _withSecretandSeed variant combines the best of both worlds. + * + * When @p secret has been generated by XXH3_generateSecret_fromSeed(), + * this variant produces *exactly* the same results as `_withSeed()` variant, + * hence offering only a pure speed benefit on "large" input, + * by skipping the need to regenerate the secret for every large input. + * + * Another usage scenario is to hash the secret to a 64-bit hash value, + * for example with XXH3_64bits(), which then becomes the seed, + * and then employ both the seed and the secret in _withSecretandSeed(). + * On top of speed, an added benefit is that each bit in the secret + * has a 50% chance to swap each bit in the output, via its impact to the seed. + * + * This is not guaranteed when using the secret directly in "small data" scenarios, + * because only portions of the secret are employed for small data. + */ +XXH_PUBLIC_API XXH_PUREF XXH64_hash_t +XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* data, size_t len, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed); + +/*! + * @brief Calculates 128-bit seeded variant of XXH3 hash of @p data. + * + * @param data The memory segment to be hashed, at least @p len bytes in size. + * @param length The length of @p data, in bytes. + * @param secret The secret used to alter hash result predictably. + * @param secretSize The length of @p secret, in bytes (must be >= XXH3_SECRET_SIZE_MIN) + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(): contract is the same. + */ +XXH_PUBLIC_API XXH_PUREF XXH128_hash_t +XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +#ifndef XXH_NO_STREAM +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(). Contract is identical. + */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +/*! + * @brief Resets an @ref XXH3_state_t with secret data to begin a new hash. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * @param secret The secret data. + * @param secretSize The length of @p secret, in bytes. + * @param seed64 The 64-bit seed to alter the hash result predictably. + * + * @return @ref XXH_OK on success. + * @return @ref XXH_ERROR on failure. + * + * @see XXH3_64bits_withSecretandSeed(). Contract is identical. + * + * Note: there was a bug in an earlier version of this function (<= v0.8.2) + * that would make it generate an incorrect hash value + * when @p seed == 0 and @p length < XXH3_MIDSIZE_MAX + * and @p secret is different from XXH3_generateSecret_fromSeed(). + * As stated in the contract, the correct hash result must be + * the same as XXH3_128bits_withSeed() when @p length <= XXH3_MIDSIZE_MAX. + * Results generated by this older version are wrong, hence not comparable. + */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, + XXH_NOESCAPE const void* secret, size_t secretSize, + XXH64_hash_t seed64); + +#endif /* !XXH_NO_STREAM */ + +#endif /* !XXH_NO_XXH3 */ +#endif /* XXH_NO_LONG_LONG */ +#if defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API) +# define XXH_IMPLEMENTATION +#endif + +#endif /* defined(XXH_STATIC_LINKING_ONLY) && !defined(XXHASH_H_STATIC_13879238742) */ + + +/* ======================================================================== */ +/* ======================================================================== */ +/* ======================================================================== */ + + +/*-********************************************************************** + * xxHash implementation + *-********************************************************************** + * xxHash's implementation used to be hosted inside xxhash.c. + * + * However, inlining requires implementation to be visible to the compiler, + * hence be included alongside the header. + * Previously, implementation was hosted inside xxhash.c, + * which was then #included when inlining was activated. + * This construction created issues with a few build and install systems, + * as it required xxhash.c to be stored in /include directory. + * + * xxHash implementation is now directly integrated within xxhash.h. + * As a consequence, xxhash.c is no longer needed in /include. + * + * xxhash.c is still available and is still useful. + * In a "normal" setup, when xxhash is not inlined, + * xxhash.h only exposes the prototypes and public symbols, + * while xxhash.c can be built into an object file xxhash.o + * which can then be linked into the final binary. + ************************************************************************/ + +#if ( defined(XXH_INLINE_ALL) || defined(XXH_PRIVATE_API) \ + || defined(XXH_IMPLEMENTATION) ) && !defined(XXH_IMPLEM_13a8737387) +# define XXH_IMPLEM_13a8737387 + +/* ************************************* +* Tuning parameters +***************************************/ + +/*! + * @defgroup tuning Tuning parameters + * @{ + * + * Various macros to control xxHash's behavior. + */ +#ifdef XXH_DOXYGEN +/*! + * @brief Define this to disable 64-bit code. + * + * Useful if only using the @ref XXH32_family and you have a strict C90 compiler. + */ +# define XXH_NO_LONG_LONG +# undef XXH_NO_LONG_LONG /* don't actually */ +/*! + * @brief Controls how unaligned memory is accessed. + * + * By default, access to unaligned memory is controlled by `memcpy()`, which is + * safe and portable. + * + * Unfortunately, on some target/compiler combinations, the generated assembly + * is sub-optimal. + * + * The below switch allow selection of a different access method + * in the search for improved performance. + * + * @par Possible options: + * + * - `XXH_FORCE_MEMORY_ACCESS=0` (default): `memcpy` + * @par + * Use `memcpy()`. Safe and portable. Note that most modern compilers will + * eliminate the function call and treat it as an unaligned access. + * + * - `XXH_FORCE_MEMORY_ACCESS=1`: `__attribute__((aligned(1)))` + * @par + * Depends on compiler extensions and is therefore not portable. + * This method is safe _if_ your compiler supports it, + * and *generally* as fast or faster than `memcpy`. + * + * - `XXH_FORCE_MEMORY_ACCESS=2`: Direct cast + * @par + * Casts directly and dereferences. This method doesn't depend on the + * compiler, but it violates the C standard as it directly dereferences an + * unaligned pointer. It can generate buggy code on targets which do not + * support unaligned memory accesses, but in some circumstances, it's the + * only known way to get the most performance. + * + * - `XXH_FORCE_MEMORY_ACCESS=3`: Byteshift + * @par + * Also portable. This can generate the best code on old compilers which don't + * inline small `memcpy()` calls, and it might also be faster on big-endian + * systems which lack a native byteswap instruction. However, some compilers + * will emit literal byteshifts even if the target supports unaligned access. + * + * + * @warning + * Methods 1 and 2 rely on implementation-defined behavior. Use these with + * care, as what works on one compiler/platform/optimization level may cause + * another to read garbage data or even crash. + * + * See https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html for details. + * + * Prefer these methods in priority order (0 > 3 > 1 > 2) + */ +# define XXH_FORCE_MEMORY_ACCESS 0 + +/*! + * @def XXH_SIZE_OPT + * @brief Controls how much xxHash optimizes for size. + * + * xxHash, when compiled, tends to result in a rather large binary size. This + * is mostly due to heavy usage to forced inlining and constant folding of the + * @ref XXH3_family to increase performance. + * + * However, some developers prefer size over speed. This option can + * significantly reduce the size of the generated code. When using the `-Os` + * or `-Oz` options on GCC or Clang, this is defined to 1 by default, + * otherwise it is defined to 0. + * + * Most of these size optimizations can be controlled manually. + * + * This is a number from 0-2. + * - `XXH_SIZE_OPT` == 0: Default. xxHash makes no size optimizations. Speed + * comes first. + * - `XXH_SIZE_OPT` == 1: Default for `-Os` and `-Oz`. xxHash is more + * conservative and disables hacks that increase code size. It implies the + * options @ref XXH_NO_INLINE_HINTS == 1, @ref XXH_FORCE_ALIGN_CHECK == 0, + * and @ref XXH3_NEON_LANES == 8 if they are not already defined. + * - `XXH_SIZE_OPT` == 2: xxHash tries to make itself as small as possible. + * Performance may cry. For example, the single shot functions just use the + * streaming API. + */ +# define XXH_SIZE_OPT 0 + +/*! + * @def XXH_FORCE_ALIGN_CHECK + * @brief If defined to non-zero, adds a special path for aligned inputs (XXH32() + * and XXH64() only). + * + * This is an important performance trick for architectures without decent + * unaligned memory access performance. + * + * It checks for input alignment, and when conditions are met, uses a "fast + * path" employing direct 32-bit/64-bit reads, resulting in _dramatically + * faster_ read speed. + * + * The check costs one initial branch per hash, which is generally negligible, + * but not zero. + * + * Moreover, it's not useful to generate an additional code path if memory + * access uses the same instruction for both aligned and unaligned + * addresses (e.g. x86 and aarch64). + * + * In these cases, the alignment check can be removed by setting this macro to 0. + * Then the code will always use unaligned memory access. + * Align check is automatically disabled on x86, x64, ARM64, and some ARM chips + * which are platforms known to offer good unaligned memory accesses performance. + * + * It is also disabled by default when @ref XXH_SIZE_OPT >= 1. + * + * This option does not affect XXH3 (only XXH32 and XXH64). + */ +# define XXH_FORCE_ALIGN_CHECK 0 + +/*! + * @def XXH_NO_INLINE_HINTS + * @brief When non-zero, sets all functions to `static`. + * + * By default, xxHash tries to force the compiler to inline almost all internal + * functions. + * + * This can usually improve performance due to reduced jumping and improved + * constant folding, but significantly increases the size of the binary which + * might not be favorable. + * + * Additionally, sometimes the forced inlining can be detrimental to performance, + * depending on the architecture. + * + * XXH_NO_INLINE_HINTS marks all internal functions as static, giving the + * compiler full control on whether to inline or not. + * + * When not optimizing (-O0), using `-fno-inline` with GCC or Clang, or if + * @ref XXH_SIZE_OPT >= 1, this will automatically be defined. + */ +# define XXH_NO_INLINE_HINTS 0 + +/*! + * @def XXH3_INLINE_SECRET + * @brief Determines whether to inline the XXH3 withSecret code. + * + * When the secret size is known, the compiler can improve the performance + * of XXH3_64bits_withSecret() and XXH3_128bits_withSecret(). + * + * However, if the secret size is not known, it doesn't have any benefit. This + * happens when xxHash is compiled into a global symbol. Therefore, if + * @ref XXH_INLINE_ALL is *not* defined, this will be defined to 0. + * + * Additionally, this defaults to 0 on GCC 12+, which has an issue with function pointers + * that are *sometimes* force inline on -Og, and it is impossible to automatically + * detect this optimization level. + */ +# define XXH3_INLINE_SECRET 0 + +/*! + * @def XXH32_ENDJMP + * @brief Whether to use a jump for `XXH32_finalize`. + * + * For performance, `XXH32_finalize` uses multiple branches in the finalizer. + * This is generally preferable for performance, + * but depending on exact architecture, a jmp may be preferable. + * + * This setting is only possibly making a difference for very small inputs. + */ +# define XXH32_ENDJMP 0 + +/*! + * @internal + * @brief Redefines old internal names. + * + * For compatibility with code that uses xxHash's internals before the names + * were changed to improve namespacing. There is no other reason to use this. + */ +# define XXH_OLD_NAMES +# undef XXH_OLD_NAMES /* don't actually use, it is ugly. */ + +/*! + * @def XXH_NO_STREAM + * @brief Disables the streaming API. + * + * When xxHash is not inlined and the streaming functions are not used, disabling + * the streaming functions can improve code size significantly, especially with + * the @ref XXH3_family which tends to make constant folded copies of itself. + */ +# define XXH_NO_STREAM +# undef XXH_NO_STREAM /* don't actually */ +#endif /* XXH_DOXYGEN */ +/*! + * @} + */ + +#ifndef XXH_FORCE_MEMORY_ACCESS /* can be defined externally, on command line for example */ + /* prefer __packed__ structures (method 1) for GCC + * < ARMv7 with unaligned access (e.g. Raspbian armhf) still uses byte shifting, so we use memcpy + * which for some reason does unaligned loads. */ +# if defined(__GNUC__) && !(defined(__ARM_ARCH) && __ARM_ARCH < 7 && defined(__ARM_FEATURE_UNALIGNED)) +# define XXH_FORCE_MEMORY_ACCESS 1 +# endif +#endif + +#ifndef XXH_SIZE_OPT + /* default to 1 for -Os or -Oz */ +# if (defined(__GNUC__) || defined(__clang__)) && defined(__OPTIMIZE_SIZE__) +# define XXH_SIZE_OPT 1 +# else +# define XXH_SIZE_OPT 0 +# endif +#endif + +#ifndef XXH_FORCE_ALIGN_CHECK /* can be defined externally */ + /* don't check on sizeopt, x86, aarch64, or arm when unaligned access is available */ +# if XXH_SIZE_OPT >= 1 || \ + defined(__i386) || defined(__x86_64__) || defined(__aarch64__) || defined(__ARM_FEATURE_UNALIGNED) \ + || defined(_M_IX86) || defined(_M_X64) || defined(_M_ARM64) || defined(_M_ARM) /* visual */ +# define XXH_FORCE_ALIGN_CHECK 0 +# else +# define XXH_FORCE_ALIGN_CHECK 1 +# endif +#endif + +#ifndef XXH_NO_INLINE_HINTS +# if XXH_SIZE_OPT >= 1 || defined(__NO_INLINE__) /* -O0, -fno-inline */ +# define XXH_NO_INLINE_HINTS 1 +# else +# define XXH_NO_INLINE_HINTS 0 +# endif +#endif + +#ifndef XXH3_INLINE_SECRET +# if (defined(__GNUC__) && !defined(__clang__) && __GNUC__ >= 12) \ + || !defined(XXH_INLINE_ALL) +# define XXH3_INLINE_SECRET 0 +# else +# define XXH3_INLINE_SECRET 1 +# endif +#endif + +#ifndef XXH32_ENDJMP +/* generally preferable for performance */ +# define XXH32_ENDJMP 0 +#endif + +/*! + * @defgroup impl Implementation + * @{ + */ + + +/* ************************************* +* Includes & Memory related functions +***************************************/ +#if defined(XXH_NO_STREAM) +/* nothing */ +#elif defined(XXH_NO_STDLIB) + +/* When requesting to disable any mention of stdlib, + * the library loses the ability to invoked malloc / free. + * In practice, it means that functions like `XXH*_createState()` + * will always fail, and return NULL. + * This flag is useful in situations where + * xxhash.h is integrated into some kernel, embedded or limited environment + * without access to dynamic allocation. + */ + +static XXH_CONSTF void* XXH_malloc(size_t s) { (void)s; return NULL; } +static void XXH_free(void* p) { (void)p; } + +#else + +/* + * Modify the local functions below should you wish to use + * different memory routines for malloc() and free() + */ +#include + +/*! + * @internal + * @brief Modify this function to use a different routine than malloc(). + */ +static XXH_MALLOCF void* XXH_malloc(size_t s) { return malloc(s); } + +/*! + * @internal + * @brief Modify this function to use a different routine than free(). + */ +static void XXH_free(void* p) { free(p); } + +#endif /* XXH_NO_STDLIB */ + +#include + +/*! + * @internal + * @brief Modify this function to use a different routine than memcpy(). + */ +static void* XXH_memcpy(void* dest, const void* src, size_t size) +{ + return memcpy(dest,src,size); +} + +#include /* ULLONG_MAX */ + + +/* ************************************* +* Compiler Specific Options +***************************************/ +#ifdef _MSC_VER /* Visual Studio warning fix */ +# pragma warning(disable : 4127) /* disable: C4127: conditional expression is constant */ +#endif + +#if XXH_NO_INLINE_HINTS /* disable inlining hints */ +# if defined(__GNUC__) || defined(__clang__) +# define XXH_FORCE_INLINE static __attribute__((__unused__)) +# else +# define XXH_FORCE_INLINE static +# endif +# define XXH_NO_INLINE static +/* enable inlining hints */ +#elif defined(__GNUC__) || defined(__clang__) +# define XXH_FORCE_INLINE static __inline__ __attribute__((__always_inline__, __unused__)) +# define XXH_NO_INLINE static __attribute__((__noinline__)) +#elif defined(_MSC_VER) /* Visual Studio */ +# define XXH_FORCE_INLINE static __forceinline +# define XXH_NO_INLINE static __declspec(noinline) +#elif defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) /* C99 */ +# define XXH_FORCE_INLINE static inline +# define XXH_NO_INLINE static +#else +# define XXH_FORCE_INLINE static +# define XXH_NO_INLINE static +#endif + +#if XXH3_INLINE_SECRET +# define XXH3_WITH_SECRET_INLINE XXH_FORCE_INLINE +#else +# define XXH3_WITH_SECRET_INLINE XXH_NO_INLINE +#endif + + +/* ************************************* +* Debug +***************************************/ +/*! + * @ingroup tuning + * @def XXH_DEBUGLEVEL + * @brief Sets the debugging level. + * + * XXH_DEBUGLEVEL is expected to be defined externally, typically via the + * compiler's command line options. The value must be a number. + */ +#ifndef XXH_DEBUGLEVEL +# ifdef DEBUGLEVEL /* backwards compat */ +# define XXH_DEBUGLEVEL DEBUGLEVEL +# else +# define XXH_DEBUGLEVEL 0 +# endif +#endif + +#if (XXH_DEBUGLEVEL>=1) +# include /* note: can still be disabled with NDEBUG */ +# define XXH_ASSERT(c) assert(c) +#else +# if defined(__INTEL_COMPILER) +# define XXH_ASSERT(c) XXH_ASSUME((unsigned char) (c)) +# else +# define XXH_ASSERT(c) XXH_ASSUME(c) +# endif +#endif + +/* note: use after variable declarations */ +#ifndef XXH_STATIC_ASSERT +# if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201112L) /* C11 */ +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { _Static_assert((c),m); } while(0) +# elif defined(__cplusplus) && (__cplusplus >= 201103L) /* C++11 */ +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { static_assert((c),m); } while(0) +# else +# define XXH_STATIC_ASSERT_WITH_MESSAGE(c,m) do { struct xxh_sa { char x[(c) ? 1 : -1]; }; } while(0) +# endif +# define XXH_STATIC_ASSERT(c) XXH_STATIC_ASSERT_WITH_MESSAGE((c),#c) +#endif + +/*! + * @internal + * @def XXH_COMPILER_GUARD(var) + * @brief Used to prevent unwanted optimizations for @p var. + * + * It uses an empty GCC inline assembly statement with a register constraint + * which forces @p var into a general purpose register (eg eax, ebx, ecx + * on x86) and marks it as modified. + * + * This is used in a few places to avoid unwanted autovectorization (e.g. + * XXH32_round()). All vectorization we want is explicit via intrinsics, + * and _usually_ isn't wanted elsewhere. + * + * We also use it to prevent unwanted constant folding for AArch64 in + * XXH3_initCustomSecret_scalar(). + */ +#if defined(__GNUC__) || defined(__clang__) +# define XXH_COMPILER_GUARD(var) __asm__("" : "+r" (var)) +#else +# define XXH_COMPILER_GUARD(var) ((void)0) +#endif + +/* Specifically for NEON vectors which use the "w" constraint, on + * Clang. */ +#if defined(__clang__) && defined(__ARM_ARCH) && !defined(__wasm__) +# define XXH_COMPILER_GUARD_CLANG_NEON(var) __asm__("" : "+w" (var)) +#else +# define XXH_COMPILER_GUARD_CLANG_NEON(var) ((void)0) +#endif + +/* ************************************* +* Basic Types +***************************************/ +#if !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) +# ifdef _AIX +# include +# else +# include +# endif + typedef uint8_t xxh_u8; +#else + typedef unsigned char xxh_u8; +#endif +typedef XXH32_hash_t xxh_u32; + +#ifdef XXH_OLD_NAMES +# warning "XXH_OLD_NAMES is planned to be removed starting v0.9. If the program depends on it, consider moving away from it by employing newer type names directly" +# define BYTE xxh_u8 +# define U8 xxh_u8 +# define U32 xxh_u32 +#endif + +/* *** Memory access *** */ + +/*! + * @internal + * @fn xxh_u32 XXH_read32(const void* ptr) + * @brief Reads an unaligned 32-bit integer from @p ptr in native endianness. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit native endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readLE32(const void* ptr) + * @brief Reads an unaligned 32-bit little endian integer from @p ptr. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit little endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readBE32(const void* ptr) + * @brief Reads an unaligned 32-bit big endian integer from @p ptr. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * + * @param ptr The pointer to read from. + * @return The 32-bit big endian integer from the bytes at @p ptr. + */ + +/*! + * @internal + * @fn xxh_u32 XXH_readLE32_align(const void* ptr, XXH_alignment align) + * @brief Like @ref XXH_readLE32(), but has an option for aligned reads. + * + * Affected by @ref XXH_FORCE_MEMORY_ACCESS. + * Note that when @ref XXH_FORCE_ALIGN_CHECK == 0, the @p align parameter is + * always @ref XXH_alignment::XXH_unaligned. + * + * @param ptr The pointer to read from. + * @param align Whether @p ptr is aligned. + * @pre + * If @p align == @ref XXH_alignment::XXH_aligned, @p ptr must be 4 byte + * aligned. + * @return The 32-bit little endian integer from the bytes at @p ptr. + */ + +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) +/* + * Manual byteshift. Best for old compilers which don't inline memcpy. + * We actually directly use XXH_readLE32 and XXH_readBE32. + */ +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2)) + +/* + * Force direct memory access. Only works on CPU which support unaligned memory + * access in hardware. + */ +static xxh_u32 XXH_read32(const void* memPtr) { return *(const xxh_u32*) memPtr; } + +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1)) + +/* + * __attribute__((aligned(1))) is supported by gcc and clang. Originally the + * documentation claimed that it only increased the alignment, but actually it + * can decrease it on gcc, clang, and icc: + * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502, + * https://gcc.godbolt.org/z/xYez1j67Y. + */ +#ifdef XXH_OLD_NAMES +typedef union { xxh_u32 u32; } __attribute__((__packed__)) unalign; +#endif +static xxh_u32 XXH_read32(const void* ptr) +{ + typedef __attribute__((__aligned__(1))) xxh_u32 xxh_unalign32; + return *((const xxh_unalign32*)ptr); +} + +#else + +/* + * Portable and safe solution. Generally efficient. + * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html + */ +static xxh_u32 XXH_read32(const void* memPtr) +{ + xxh_u32 val; + XXH_memcpy(&val, memPtr, sizeof(val)); + return val; +} + +#endif /* XXH_FORCE_DIRECT_MEMORY_ACCESS */ + + +/* *** Endianness *** */ + +/*! + * @ingroup tuning + * @def XXH_CPU_LITTLE_ENDIAN + * @brief Whether the target is little endian. + * + * Defined to 1 if the target is little endian, or 0 if it is big endian. + * It can be defined externally, for example on the compiler command line. + * + * If it is not defined, + * a runtime check (which is usually constant folded) is used instead. + * + * @note + * This is not necessarily defined to an integer constant. + * + * @see XXH_isLittleEndian() for the runtime check. + */ +#ifndef XXH_CPU_LITTLE_ENDIAN +/* + * Try to detect endianness automatically, to avoid the nonstandard behavior + * in `XXH_isLittleEndian()` + */ +# if defined(_WIN32) /* Windows is always little endian */ \ + || defined(__LITTLE_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) +# define XXH_CPU_LITTLE_ENDIAN 1 +# elif defined(__BIG_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) +# define XXH_CPU_LITTLE_ENDIAN 0 +# else +/*! + * @internal + * @brief Runtime check for @ref XXH_CPU_LITTLE_ENDIAN. + * + * Most compilers will constant fold this. + */ +static int XXH_isLittleEndian(void) +{ + /* + * Portable and well-defined behavior. + * Don't use static: it is detrimental to performance. + */ + const union { xxh_u32 u; xxh_u8 c[4]; } one = { 1 }; + return one.c[0]; +} +# define XXH_CPU_LITTLE_ENDIAN XXH_isLittleEndian() +# endif +#endif + + + + +/* **************************************** +* Compiler-specific Functions and Macros +******************************************/ +#define XXH_GCC_VERSION (__GNUC__ * 100 + __GNUC_MINOR__) + +#ifdef __has_builtin +# define XXH_HAS_BUILTIN(x) __has_builtin(x) +#else +# define XXH_HAS_BUILTIN(x) 0 +#endif + + + +/* + * C23 and future versions have standard "unreachable()". + * Once it has been implemented reliably we can add it as an + * additional case: + * + * ``` + * #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= XXH_C23_VN) + * # include + * # ifdef unreachable + * # define XXH_UNREACHABLE() unreachable() + * # endif + * #endif + * ``` + * + * Note C++23 also has std::unreachable() which can be detected + * as follows: + * ``` + * #if defined(__cpp_lib_unreachable) && (__cpp_lib_unreachable >= 202202L) + * # include + * # define XXH_UNREACHABLE() std::unreachable() + * #endif + * ``` + * NB: `__cpp_lib_unreachable` is defined in the `` header. + * We don't use that as including `` in `extern "C"` blocks + * doesn't work on GCC12 + */ + +#if XXH_HAS_BUILTIN(__builtin_unreachable) +# define XXH_UNREACHABLE() __builtin_unreachable() + +#elif defined(_MSC_VER) +# define XXH_UNREACHABLE() __assume(0) + +#else +# define XXH_UNREACHABLE() +#endif + +#if XXH_HAS_BUILTIN(__builtin_assume) +# define XXH_ASSUME(c) __builtin_assume(c) +#else +# define XXH_ASSUME(c) if (!(c)) { XXH_UNREACHABLE(); } +#endif + +/*! + * @internal + * @def XXH_rotl32(x,r) + * @brief 32-bit rotate left. + * + * @param x The 32-bit integer to be rotated. + * @param r The number of bits to rotate. + * @pre + * @p r > 0 && @p r < 32 + * @note + * @p x and @p r may be evaluated multiple times. + * @return The rotated result. + */ +#if !defined(NO_CLANG_BUILTIN) && XXH_HAS_BUILTIN(__builtin_rotateleft32) \ + && XXH_HAS_BUILTIN(__builtin_rotateleft64) +# define XXH_rotl32 __builtin_rotateleft32 +# define XXH_rotl64 __builtin_rotateleft64 +/* Note: although _rotl exists for minGW (GCC under windows), performance seems poor */ +#elif defined(_MSC_VER) +# define XXH_rotl32(x,r) _rotl(x,r) +# define XXH_rotl64(x,r) _rotl64(x,r) +#else +# define XXH_rotl32(x,r) (((x) << (r)) | ((x) >> (32 - (r)))) +# define XXH_rotl64(x,r) (((x) << (r)) | ((x) >> (64 - (r)))) +#endif + +/*! + * @internal + * @fn xxh_u32 XXH_swap32(xxh_u32 x) + * @brief A 32-bit byteswap. + * + * @param x The 32-bit integer to byteswap. + * @return @p x, byteswapped. + */ +#if defined(_MSC_VER) /* Visual Studio */ +# define XXH_swap32 _byteswap_ulong +#elif XXH_GCC_VERSION >= 403 +# define XXH_swap32 __builtin_bswap32 +#else +static xxh_u32 XXH_swap32 (xxh_u32 x) +{ + return ((x << 24) & 0xff000000 ) | + ((x << 8) & 0x00ff0000 ) | + ((x >> 8) & 0x0000ff00 ) | + ((x >> 24) & 0x000000ff ); +} +#endif + + +/* *************************** +* Memory reads +*****************************/ + +/*! + * @internal + * @brief Enum to indicate whether a pointer is aligned. + */ +typedef enum { + XXH_aligned, /*!< Aligned */ + XXH_unaligned /*!< Possibly unaligned */ +} XXH_alignment; + +/* + * XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load. + * + * This is ideal for older compilers which don't inline memcpy. + */ +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) + +XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[0] + | ((xxh_u32)bytePtr[1] << 8) + | ((xxh_u32)bytePtr[2] << 16) + | ((xxh_u32)bytePtr[3] << 24); +} + +XXH_FORCE_INLINE xxh_u32 XXH_readBE32(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[3] + | ((xxh_u32)bytePtr[2] << 8) + | ((xxh_u32)bytePtr[1] << 16) + | ((xxh_u32)bytePtr[0] << 24); +} + +#else +XXH_FORCE_INLINE xxh_u32 XXH_readLE32(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_read32(ptr) : XXH_swap32(XXH_read32(ptr)); +} + +static xxh_u32 XXH_readBE32(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_swap32(XXH_read32(ptr)) : XXH_read32(ptr); +} +#endif + +XXH_FORCE_INLINE xxh_u32 +XXH_readLE32_align(const void* ptr, XXH_alignment align) +{ + if (align==XXH_unaligned) { + return XXH_readLE32(ptr); + } else { + return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u32*)ptr : XXH_swap32(*(const xxh_u32*)ptr); + } +} + + +/* ************************************* +* Misc +***************************************/ +/*! @ingroup public */ +XXH_PUBLIC_API unsigned XXH_versionNumber (void) { return XXH_VERSION_NUMBER; } + + +/* ******************************************************************* +* 32-bit hash functions +*********************************************************************/ +/*! + * @} + * @defgroup XXH32_impl XXH32 implementation + * @ingroup impl + * + * Details on the XXH32 implementation. + * @{ + */ + /* #define instead of static const, to be used as initializers */ +#define XXH_PRIME32_1 0x9E3779B1U /*!< 0b10011110001101110111100110110001 */ +#define XXH_PRIME32_2 0x85EBCA77U /*!< 0b10000101111010111100101001110111 */ +#define XXH_PRIME32_3 0xC2B2AE3DU /*!< 0b11000010101100101010111000111101 */ +#define XXH_PRIME32_4 0x27D4EB2FU /*!< 0b00100111110101001110101100101111 */ +#define XXH_PRIME32_5 0x165667B1U /*!< 0b00010110010101100110011110110001 */ + +#ifdef XXH_OLD_NAMES +# define PRIME32_1 XXH_PRIME32_1 +# define PRIME32_2 XXH_PRIME32_2 +# define PRIME32_3 XXH_PRIME32_3 +# define PRIME32_4 XXH_PRIME32_4 +# define PRIME32_5 XXH_PRIME32_5 +#endif + +/*! + * @internal + * @brief Normal stripe processing routine. + * + * This shuffles the bits so that any bit from @p input impacts several bits in + * @p acc. + * + * @param acc The accumulator lane. + * @param input The stripe of input to mix. + * @return The mixed accumulator lane. + */ +static xxh_u32 XXH32_round(xxh_u32 acc, xxh_u32 input) +{ + acc += input * XXH_PRIME32_2; + acc = XXH_rotl32(acc, 13); + acc *= XXH_PRIME32_1; +#if (defined(__SSE4_1__) || defined(__aarch64__) || defined(__wasm_simd128__)) && !defined(XXH_ENABLE_AUTOVECTORIZE) + /* + * UGLY HACK: + * A compiler fence is used to prevent GCC and Clang from + * autovectorizing the XXH32 loop (pragmas and attributes don't work for some + * reason) without globally disabling SSE4.1. + * + * The reason we want to avoid vectorization is because despite working on + * 4 integers at a time, there are multiple factors slowing XXH32 down on + * SSE4: + * - There's a ridiculous amount of lag from pmulld (10 cycles of latency on + * newer chips!) making it slightly slower to multiply four integers at + * once compared to four integers independently. Even when pmulld was + * fastest, Sandy/Ivy Bridge, it is still not worth it to go into SSE + * just to multiply unless doing a long operation. + * + * - Four instructions are required to rotate, + * movqda tmp, v // not required with VEX encoding + * pslld tmp, 13 // tmp <<= 13 + * psrld v, 19 // x >>= 19 + * por v, tmp // x |= tmp + * compared to one for scalar: + * roll v, 13 // reliably fast across the board + * shldl v, v, 13 // Sandy Bridge and later prefer this for some reason + * + * - Instruction level parallelism is actually more beneficial here because + * the SIMD actually serializes this operation: While v1 is rotating, v2 + * can load data, while v3 can multiply. SSE forces them to operate + * together. + * + * This is also enabled on AArch64, as Clang is *very aggressive* in vectorizing + * the loop. NEON is only faster on the A53, and with the newer cores, it is less + * than half the speed. + * + * Additionally, this is used on WASM SIMD128 because it JITs to the same + * SIMD instructions and has the same issue. + */ + XXH_COMPILER_GUARD(acc); +#endif + return acc; +} + +/*! + * @internal + * @brief Mixes all bits to finalize the hash. + * + * The final mix ensures that all input bits have a chance to impact any bit in + * the output digest, resulting in an unbiased distribution. + * + * @param hash The hash to avalanche. + * @return The avalanched hash. + */ +static xxh_u32 XXH32_avalanche(xxh_u32 hash) +{ + hash ^= hash >> 15; + hash *= XXH_PRIME32_2; + hash ^= hash >> 13; + hash *= XXH_PRIME32_3; + hash ^= hash >> 16; + return hash; +} + +#define XXH_get32bits(p) XXH_readLE32_align(p, align) + +/*! + * @internal + * @brief Processes the last 0-15 bytes of @p ptr. + * + * There may be up to 15 bytes remaining to consume from the input. + * This final stage will digest them to ensure that all input bytes are present + * in the final mix. + * + * @param hash The hash to finalize. + * @param ptr The pointer to the remaining input. + * @param len The remaining length, modulo 16. + * @param align Whether @p ptr is aligned. + * @return The finalized hash. + * @see XXH64_finalize(). + */ +static XXH_PUREF xxh_u32 +XXH32_finalize(xxh_u32 hash, const xxh_u8* ptr, size_t len, XXH_alignment align) +{ +#define XXH_PROCESS1 do { \ + hash += (*ptr++) * XXH_PRIME32_5; \ + hash = XXH_rotl32(hash, 11) * XXH_PRIME32_1; \ +} while (0) + +#define XXH_PROCESS4 do { \ + hash += XXH_get32bits(ptr) * XXH_PRIME32_3; \ + ptr += 4; \ + hash = XXH_rotl32(hash, 17) * XXH_PRIME32_4; \ +} while (0) + + if (ptr==NULL) XXH_ASSERT(len == 0); + + /* Compact rerolled version; generally faster */ + if (!XXH32_ENDJMP) { + len &= 15; + while (len >= 4) { + XXH_PROCESS4; + len -= 4; + } + while (len > 0) { + XXH_PROCESS1; + --len; + } + return XXH32_avalanche(hash); + } else { + switch(len&15) /* or switch(bEnd - p) */ { + case 12: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 8: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 4: XXH_PROCESS4; + return XXH32_avalanche(hash); + + case 13: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 9: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 5: XXH_PROCESS4; + XXH_PROCESS1; + return XXH32_avalanche(hash); + + case 14: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 10: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 6: XXH_PROCESS4; + XXH_PROCESS1; + XXH_PROCESS1; + return XXH32_avalanche(hash); + + case 15: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 11: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 7: XXH_PROCESS4; + XXH_FALLTHROUGH; /* fallthrough */ + case 3: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 2: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 1: XXH_PROCESS1; + XXH_FALLTHROUGH; /* fallthrough */ + case 0: return XXH32_avalanche(hash); + } + XXH_ASSERT(0); + return hash; /* reaching this point is deemed impossible */ + } +} + +#ifdef XXH_OLD_NAMES +# define PROCESS1 XXH_PROCESS1 +# define PROCESS4 XXH_PROCESS4 +#else +# undef XXH_PROCESS1 +# undef XXH_PROCESS4 +#endif + +/*! + * @internal + * @brief The implementation for @ref XXH32(). + * + * @param input , len , seed Directly passed from @ref XXH32(). + * @param align Whether @p input is aligned. + * @return The calculated hash. + */ +XXH_FORCE_INLINE XXH_PUREF xxh_u32 +XXH32_endian_align(const xxh_u8* input, size_t len, xxh_u32 seed, XXH_alignment align) +{ + xxh_u32 h32; + + if (input==NULL) XXH_ASSERT(len == 0); + + if (len>=16) { + const xxh_u8* const bEnd = input + len; + const xxh_u8* const limit = bEnd - 15; + xxh_u32 v1 = seed + XXH_PRIME32_1 + XXH_PRIME32_2; + xxh_u32 v2 = seed + XXH_PRIME32_2; + xxh_u32 v3 = seed + 0; + xxh_u32 v4 = seed - XXH_PRIME32_1; + + do { + v1 = XXH32_round(v1, XXH_get32bits(input)); input += 4; + v2 = XXH32_round(v2, XXH_get32bits(input)); input += 4; + v3 = XXH32_round(v3, XXH_get32bits(input)); input += 4; + v4 = XXH32_round(v4, XXH_get32bits(input)); input += 4; + } while (input < limit); + + h32 = XXH_rotl32(v1, 1) + XXH_rotl32(v2, 7) + + XXH_rotl32(v3, 12) + XXH_rotl32(v4, 18); + } else { + h32 = seed + XXH_PRIME32_5; + } + + h32 += (xxh_u32)len; + + return XXH32_finalize(h32, input, len&15, align); +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32 (const void* input, size_t len, XXH32_hash_t seed) +{ +#if !defined(XXH_NO_STREAM) && XXH_SIZE_OPT >= 2 + /* Simple version, good for code maintenance, but unfortunately slow for small inputs */ + XXH32_state_t state; + XXH32_reset(&state, seed); + XXH32_update(&state, (const xxh_u8*)input, len); + return XXH32_digest(&state); +#else + if (XXH_FORCE_ALIGN_CHECK) { + if ((((size_t)input) & 3) == 0) { /* Input is 4-bytes aligned, leverage the speed benefit */ + return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_aligned); + } } + + return XXH32_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned); +#endif +} + + + +/******* Hash streaming *******/ +#ifndef XXH_NO_STREAM +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_state_t* XXH32_createState(void) +{ + return (XXH32_state_t*)XXH_malloc(sizeof(XXH32_state_t)); +} +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode XXH32_freeState(XXH32_state_t* statePtr) +{ + XXH_free(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API void XXH32_copyState(XXH32_state_t* dstState, const XXH32_state_t* srcState) +{ + XXH_memcpy(dstState, srcState, sizeof(*dstState)); +} + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode XXH32_reset(XXH32_state_t* statePtr, XXH32_hash_t seed) +{ + XXH_ASSERT(statePtr != NULL); + memset(statePtr, 0, sizeof(*statePtr)); + statePtr->v[0] = seed + XXH_PRIME32_1 + XXH_PRIME32_2; + statePtr->v[1] = seed + XXH_PRIME32_2; + statePtr->v[2] = seed + 0; + statePtr->v[3] = seed - XXH_PRIME32_1; + return XXH_OK; +} + + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH_errorcode +XXH32_update(XXH32_state_t* state, const void* input, size_t len) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + { const xxh_u8* p = (const xxh_u8*)input; + const xxh_u8* const bEnd = p + len; + + state->total_len_32 += (XXH32_hash_t)len; + state->large_len |= (XXH32_hash_t)((len>=16) | (state->total_len_32>=16)); + + if (state->memsize + len < 16) { /* fill in tmp buffer */ + XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, len); + state->memsize += (XXH32_hash_t)len; + return XXH_OK; + } + + if (state->memsize) { /* some data left from previous update */ + XXH_memcpy((xxh_u8*)(state->mem32) + state->memsize, input, 16-state->memsize); + { const xxh_u32* p32 = state->mem32; + state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p32)); p32++; + state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p32)); p32++; + state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p32)); p32++; + state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p32)); + } + p += 16-state->memsize; + state->memsize = 0; + } + + if (p <= bEnd-16) { + const xxh_u8* const limit = bEnd - 16; + + do { + state->v[0] = XXH32_round(state->v[0], XXH_readLE32(p)); p+=4; + state->v[1] = XXH32_round(state->v[1], XXH_readLE32(p)); p+=4; + state->v[2] = XXH32_round(state->v[2], XXH_readLE32(p)); p+=4; + state->v[3] = XXH32_round(state->v[3], XXH_readLE32(p)); p+=4; + } while (p<=limit); + + } + + if (p < bEnd) { + XXH_memcpy(state->mem32, p, (size_t)(bEnd-p)); + state->memsize = (unsigned)(bEnd-p); + } + } + + return XXH_OK; +} + + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32_digest(const XXH32_state_t* state) +{ + xxh_u32 h32; + + if (state->large_len) { + h32 = XXH_rotl32(state->v[0], 1) + + XXH_rotl32(state->v[1], 7) + + XXH_rotl32(state->v[2], 12) + + XXH_rotl32(state->v[3], 18); + } else { + h32 = state->v[2] /* == seed */ + XXH_PRIME32_5; + } + + h32 += state->total_len_32; + + return XXH32_finalize(h32, (const xxh_u8*)state->mem32, state->memsize, XXH_aligned); +} +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API void XXH32_canonicalFromHash(XXH32_canonical_t* dst, XXH32_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH32_canonical_t) == sizeof(XXH32_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap32(hash); + XXH_memcpy(dst, &hash, sizeof(*dst)); +} +/*! @ingroup XXH32_family */ +XXH_PUBLIC_API XXH32_hash_t XXH32_hashFromCanonical(const XXH32_canonical_t* src) +{ + return XXH_readBE32(src); +} + + +#ifndef XXH_NO_LONG_LONG + +/* ******************************************************************* +* 64-bit hash functions +*********************************************************************/ +/*! + * @} + * @ingroup impl + * @{ + */ +/******* Memory access *******/ + +typedef XXH64_hash_t xxh_u64; + +#ifdef XXH_OLD_NAMES +# define U64 xxh_u64 +#endif + +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) +/* + * Manual byteshift. Best for old compilers which don't inline memcpy. + * We actually directly use XXH_readLE64 and XXH_readBE64. + */ +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==2)) + +/* Force direct memory access. Only works on CPU which support unaligned memory access in hardware */ +static xxh_u64 XXH_read64(const void* memPtr) +{ + return *(const xxh_u64*) memPtr; +} + +#elif (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==1)) + +/* + * __attribute__((aligned(1))) is supported by gcc and clang. Originally the + * documentation claimed that it only increased the alignment, but actually it + * can decrease it on gcc, clang, and icc: + * https://gcc.gnu.org/bugzilla/show_bug.cgi?id=69502, + * https://gcc.godbolt.org/z/xYez1j67Y. + */ +#ifdef XXH_OLD_NAMES +typedef union { xxh_u32 u32; xxh_u64 u64; } __attribute__((__packed__)) unalign64; +#endif +static xxh_u64 XXH_read64(const void* ptr) +{ + typedef __attribute__((__aligned__(1))) xxh_u64 xxh_unalign64; + return *((const xxh_unalign64*)ptr); +} + +#else + +/* + * Portable and safe solution. Generally efficient. + * see: https://fastcompression.blogspot.com/2015/08/accessing-unaligned-memory.html + */ +static xxh_u64 XXH_read64(const void* memPtr) +{ + xxh_u64 val; + XXH_memcpy(&val, memPtr, sizeof(val)); + return val; +} + +#endif /* XXH_FORCE_DIRECT_MEMORY_ACCESS */ + +#if defined(_MSC_VER) /* Visual Studio */ +# define XXH_swap64 _byteswap_uint64 +#elif XXH_GCC_VERSION >= 403 +# define XXH_swap64 __builtin_bswap64 +#else +static xxh_u64 XXH_swap64(xxh_u64 x) +{ + return ((x << 56) & 0xff00000000000000ULL) | + ((x << 40) & 0x00ff000000000000ULL) | + ((x << 24) & 0x0000ff0000000000ULL) | + ((x << 8) & 0x000000ff00000000ULL) | + ((x >> 8) & 0x00000000ff000000ULL) | + ((x >> 24) & 0x0000000000ff0000ULL) | + ((x >> 40) & 0x000000000000ff00ULL) | + ((x >> 56) & 0x00000000000000ffULL); +} +#endif + + +/* XXH_FORCE_MEMORY_ACCESS==3 is an endian-independent byteshift load. */ +#if (defined(XXH_FORCE_MEMORY_ACCESS) && (XXH_FORCE_MEMORY_ACCESS==3)) + +XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[0] + | ((xxh_u64)bytePtr[1] << 8) + | ((xxh_u64)bytePtr[2] << 16) + | ((xxh_u64)bytePtr[3] << 24) + | ((xxh_u64)bytePtr[4] << 32) + | ((xxh_u64)bytePtr[5] << 40) + | ((xxh_u64)bytePtr[6] << 48) + | ((xxh_u64)bytePtr[7] << 56); +} + +XXH_FORCE_INLINE xxh_u64 XXH_readBE64(const void* memPtr) +{ + const xxh_u8* bytePtr = (const xxh_u8 *)memPtr; + return bytePtr[7] + | ((xxh_u64)bytePtr[6] << 8) + | ((xxh_u64)bytePtr[5] << 16) + | ((xxh_u64)bytePtr[4] << 24) + | ((xxh_u64)bytePtr[3] << 32) + | ((xxh_u64)bytePtr[2] << 40) + | ((xxh_u64)bytePtr[1] << 48) + | ((xxh_u64)bytePtr[0] << 56); +} + +#else +XXH_FORCE_INLINE xxh_u64 XXH_readLE64(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_read64(ptr) : XXH_swap64(XXH_read64(ptr)); +} + +static xxh_u64 XXH_readBE64(const void* ptr) +{ + return XXH_CPU_LITTLE_ENDIAN ? XXH_swap64(XXH_read64(ptr)) : XXH_read64(ptr); +} +#endif + +XXH_FORCE_INLINE xxh_u64 +XXH_readLE64_align(const void* ptr, XXH_alignment align) +{ + if (align==XXH_unaligned) + return XXH_readLE64(ptr); + else + return XXH_CPU_LITTLE_ENDIAN ? *(const xxh_u64*)ptr : XXH_swap64(*(const xxh_u64*)ptr); +} + + +/******* xxh64 *******/ +/*! + * @} + * @defgroup XXH64_impl XXH64 implementation + * @ingroup impl + * + * Details on the XXH64 implementation. + * @{ + */ +/* #define rather that static const, to be used as initializers */ +#define XXH_PRIME64_1 0x9E3779B185EBCA87ULL /*!< 0b1001111000110111011110011011000110000101111010111100101010000111 */ +#define XXH_PRIME64_2 0xC2B2AE3D27D4EB4FULL /*!< 0b1100001010110010101011100011110100100111110101001110101101001111 */ +#define XXH_PRIME64_3 0x165667B19E3779F9ULL /*!< 0b0001011001010110011001111011000110011110001101110111100111111001 */ +#define XXH_PRIME64_4 0x85EBCA77C2B2AE63ULL /*!< 0b1000010111101011110010100111011111000010101100101010111001100011 */ +#define XXH_PRIME64_5 0x27D4EB2F165667C5ULL /*!< 0b0010011111010100111010110010111100010110010101100110011111000101 */ + +#ifdef XXH_OLD_NAMES +# define PRIME64_1 XXH_PRIME64_1 +# define PRIME64_2 XXH_PRIME64_2 +# define PRIME64_3 XXH_PRIME64_3 +# define PRIME64_4 XXH_PRIME64_4 +# define PRIME64_5 XXH_PRIME64_5 +#endif + +/*! @copydoc XXH32_round */ +static xxh_u64 XXH64_round(xxh_u64 acc, xxh_u64 input) +{ + acc += input * XXH_PRIME64_2; + acc = XXH_rotl64(acc, 31); + acc *= XXH_PRIME64_1; +#if (defined(__AVX512F__)) && !defined(XXH_ENABLE_AUTOVECTORIZE) + /* + * DISABLE AUTOVECTORIZATION: + * A compiler fence is used to prevent GCC and Clang from + * autovectorizing the XXH64 loop (pragmas and attributes don't work for some + * reason) without globally disabling AVX512. + * + * Autovectorization of XXH64 tends to be detrimental, + * though the exact outcome may change depending on exact cpu and compiler version. + * For information, it has been reported as detrimental for Skylake-X, + * but possibly beneficial for Zen4. + * + * The default is to disable auto-vectorization, + * but you can select to enable it instead using `XXH_ENABLE_AUTOVECTORIZE` build variable. + */ + XXH_COMPILER_GUARD(acc); +#endif + return acc; +} + +static xxh_u64 XXH64_mergeRound(xxh_u64 acc, xxh_u64 val) +{ + val = XXH64_round(0, val); + acc ^= val; + acc = acc * XXH_PRIME64_1 + XXH_PRIME64_4; + return acc; +} + +/*! @copydoc XXH32_avalanche */ +static xxh_u64 XXH64_avalanche(xxh_u64 hash) +{ + hash ^= hash >> 33; + hash *= XXH_PRIME64_2; + hash ^= hash >> 29; + hash *= XXH_PRIME64_3; + hash ^= hash >> 32; + return hash; +} + + +#define XXH_get64bits(p) XXH_readLE64_align(p, align) + +/*! + * @internal + * @brief Processes the last 0-31 bytes of @p ptr. + * + * There may be up to 31 bytes remaining to consume from the input. + * This final stage will digest them to ensure that all input bytes are present + * in the final mix. + * + * @param hash The hash to finalize. + * @param ptr The pointer to the remaining input. + * @param len The remaining length, modulo 32. + * @param align Whether @p ptr is aligned. + * @return The finalized hash + * @see XXH32_finalize(). + */ +static XXH_PUREF xxh_u64 +XXH64_finalize(xxh_u64 hash, const xxh_u8* ptr, size_t len, XXH_alignment align) +{ + if (ptr==NULL) XXH_ASSERT(len == 0); + len &= 31; + while (len >= 8) { + xxh_u64 const k1 = XXH64_round(0, XXH_get64bits(ptr)); + ptr += 8; + hash ^= k1; + hash = XXH_rotl64(hash,27) * XXH_PRIME64_1 + XXH_PRIME64_4; + len -= 8; + } + if (len >= 4) { + hash ^= (xxh_u64)(XXH_get32bits(ptr)) * XXH_PRIME64_1; + ptr += 4; + hash = XXH_rotl64(hash, 23) * XXH_PRIME64_2 + XXH_PRIME64_3; + len -= 4; + } + while (len > 0) { + hash ^= (*ptr++) * XXH_PRIME64_5; + hash = XXH_rotl64(hash, 11) * XXH_PRIME64_1; + --len; + } + return XXH64_avalanche(hash); +} + +#ifdef XXH_OLD_NAMES +# define PROCESS1_64 XXH_PROCESS1_64 +# define PROCESS4_64 XXH_PROCESS4_64 +# define PROCESS8_64 XXH_PROCESS8_64 +#else +# undef XXH_PROCESS1_64 +# undef XXH_PROCESS4_64 +# undef XXH_PROCESS8_64 +#endif + +/*! + * @internal + * @brief The implementation for @ref XXH64(). + * + * @param input , len , seed Directly passed from @ref XXH64(). + * @param align Whether @p input is aligned. + * @return The calculated hash. + */ +XXH_FORCE_INLINE XXH_PUREF xxh_u64 +XXH64_endian_align(const xxh_u8* input, size_t len, xxh_u64 seed, XXH_alignment align) +{ + xxh_u64 h64; + if (input==NULL) XXH_ASSERT(len == 0); + + if (len>=32) { + const xxh_u8* const bEnd = input + len; + const xxh_u8* const limit = bEnd - 31; + xxh_u64 v1 = seed + XXH_PRIME64_1 + XXH_PRIME64_2; + xxh_u64 v2 = seed + XXH_PRIME64_2; + xxh_u64 v3 = seed + 0; + xxh_u64 v4 = seed - XXH_PRIME64_1; + + do { + v1 = XXH64_round(v1, XXH_get64bits(input)); input+=8; + v2 = XXH64_round(v2, XXH_get64bits(input)); input+=8; + v3 = XXH64_round(v3, XXH_get64bits(input)); input+=8; + v4 = XXH64_round(v4, XXH_get64bits(input)); input+=8; + } while (input= 2 + /* Simple version, good for code maintenance, but unfortunately slow for small inputs */ + XXH64_state_t state; + XXH64_reset(&state, seed); + XXH64_update(&state, (const xxh_u8*)input, len); + return XXH64_digest(&state); +#else + if (XXH_FORCE_ALIGN_CHECK) { + if ((((size_t)input) & 7)==0) { /* Input is aligned, let's leverage the speed advantage */ + return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_aligned); + } } + + return XXH64_endian_align((const xxh_u8*)input, len, seed, XXH_unaligned); + +#endif +} + +/******* Hash Streaming *******/ +#ifndef XXH_NO_STREAM +/*! @ingroup XXH64_family*/ +XXH_PUBLIC_API XXH64_state_t* XXH64_createState(void) +{ + return (XXH64_state_t*)XXH_malloc(sizeof(XXH64_state_t)); +} +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode XXH64_freeState(XXH64_state_t* statePtr) +{ + XXH_free(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API void XXH64_copyState(XXH_NOESCAPE XXH64_state_t* dstState, const XXH64_state_t* srcState) +{ + XXH_memcpy(dstState, srcState, sizeof(*dstState)); +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode XXH64_reset(XXH_NOESCAPE XXH64_state_t* statePtr, XXH64_hash_t seed) +{ + XXH_ASSERT(statePtr != NULL); + memset(statePtr, 0, sizeof(*statePtr)); + statePtr->v[0] = seed + XXH_PRIME64_1 + XXH_PRIME64_2; + statePtr->v[1] = seed + XXH_PRIME64_2; + statePtr->v[2] = seed + 0; + statePtr->v[3] = seed - XXH_PRIME64_1; + return XXH_OK; +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH_errorcode +XXH64_update (XXH_NOESCAPE XXH64_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + { const xxh_u8* p = (const xxh_u8*)input; + const xxh_u8* const bEnd = p + len; + + state->total_len += len; + + if (state->memsize + len < 32) { /* fill in tmp buffer */ + XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, len); + state->memsize += (xxh_u32)len; + return XXH_OK; + } + + if (state->memsize) { /* tmp buffer is full */ + XXH_memcpy(((xxh_u8*)state->mem64) + state->memsize, input, 32-state->memsize); + state->v[0] = XXH64_round(state->v[0], XXH_readLE64(state->mem64+0)); + state->v[1] = XXH64_round(state->v[1], XXH_readLE64(state->mem64+1)); + state->v[2] = XXH64_round(state->v[2], XXH_readLE64(state->mem64+2)); + state->v[3] = XXH64_round(state->v[3], XXH_readLE64(state->mem64+3)); + p += 32 - state->memsize; + state->memsize = 0; + } + + if (p+32 <= bEnd) { + const xxh_u8* const limit = bEnd - 32; + + do { + state->v[0] = XXH64_round(state->v[0], XXH_readLE64(p)); p+=8; + state->v[1] = XXH64_round(state->v[1], XXH_readLE64(p)); p+=8; + state->v[2] = XXH64_round(state->v[2], XXH_readLE64(p)); p+=8; + state->v[3] = XXH64_round(state->v[3], XXH_readLE64(p)); p+=8; + } while (p<=limit); + + } + + if (p < bEnd) { + XXH_memcpy(state->mem64, p, (size_t)(bEnd-p)); + state->memsize = (unsigned)(bEnd-p); + } + } + + return XXH_OK; +} + + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH64_hash_t XXH64_digest(XXH_NOESCAPE const XXH64_state_t* state) +{ + xxh_u64 h64; + + if (state->total_len >= 32) { + h64 = XXH_rotl64(state->v[0], 1) + XXH_rotl64(state->v[1], 7) + XXH_rotl64(state->v[2], 12) + XXH_rotl64(state->v[3], 18); + h64 = XXH64_mergeRound(h64, state->v[0]); + h64 = XXH64_mergeRound(h64, state->v[1]); + h64 = XXH64_mergeRound(h64, state->v[2]); + h64 = XXH64_mergeRound(h64, state->v[3]); + } else { + h64 = state->v[2] /*seed*/ + XXH_PRIME64_5; + } + + h64 += (xxh_u64) state->total_len; + + return XXH64_finalize(h64, (const xxh_u8*)state->mem64, (size_t)state->total_len, XXH_aligned); +} +#endif /* !XXH_NO_STREAM */ + +/******* Canonical representation *******/ + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API void XXH64_canonicalFromHash(XXH_NOESCAPE XXH64_canonical_t* dst, XXH64_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH64_canonical_t) == sizeof(XXH64_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) hash = XXH_swap64(hash); + XXH_memcpy(dst, &hash, sizeof(*dst)); +} + +/*! @ingroup XXH64_family */ +XXH_PUBLIC_API XXH64_hash_t XXH64_hashFromCanonical(XXH_NOESCAPE const XXH64_canonical_t* src) +{ + return XXH_readBE64(src); +} + +#ifndef XXH_NO_XXH3 + +/* ********************************************************************* +* XXH3 +* New generation hash designed for speed on small keys and vectorization +************************************************************************ */ +/*! + * @} + * @defgroup XXH3_impl XXH3 implementation + * @ingroup impl + * @{ + */ + +/* === Compiler specifics === */ + +#if ((defined(sun) || defined(__sun)) && __cplusplus) /* Solaris includes __STDC_VERSION__ with C++. Tested with GCC 5.5 */ +# define XXH_RESTRICT /* disable */ +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L /* >= C99 */ +# define XXH_RESTRICT restrict +#elif (defined (__GNUC__) && ((__GNUC__ > 3) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))) \ + || (defined (__clang__)) \ + || (defined (_MSC_VER) && (_MSC_VER >= 1400)) \ + || (defined (__INTEL_COMPILER) && (__INTEL_COMPILER >= 1300)) +/* + * There are a LOT more compilers that recognize __restrict but this + * covers the major ones. + */ +# define XXH_RESTRICT __restrict +#else +# define XXH_RESTRICT /* disable */ +#endif + +#if (defined(__GNUC__) && (__GNUC__ >= 3)) \ + || (defined(__INTEL_COMPILER) && (__INTEL_COMPILER >= 800)) \ + || defined(__clang__) +# define XXH_likely(x) __builtin_expect(x, 1) +# define XXH_unlikely(x) __builtin_expect(x, 0) +#else +# define XXH_likely(x) (x) +# define XXH_unlikely(x) (x) +#endif + +#ifndef XXH_HAS_INCLUDE +# ifdef __has_include +/* + * Not defined as XXH_HAS_INCLUDE(x) (function-like) because + * this causes segfaults in Apple Clang 4.2 (on Mac OS X 10.7 Lion) + */ +# define XXH_HAS_INCLUDE __has_include +# else +# define XXH_HAS_INCLUDE(x) 0 +# endif +#endif + +#if defined(__GNUC__) || defined(__clang__) +# if defined(__ARM_FEATURE_SVE) +# include +# endif +# if defined(__ARM_NEON__) || defined(__ARM_NEON) \ + || (defined(_M_ARM) && _M_ARM >= 7) \ + || defined(_M_ARM64) || defined(_M_ARM64EC) \ + || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE()) /* WASM SIMD128 via SIMDe */ +# define inline __inline__ /* circumvent a clang bug */ +# include +# undef inline +# elif defined(__AVX2__) +# include +# elif defined(__SSE2__) +# include +# endif +#endif + +#if defined(_MSC_VER) +# include +#endif + +/* + * One goal of XXH3 is to make it fast on both 32-bit and 64-bit, while + * remaining a true 64-bit/128-bit hash function. + * + * This is done by prioritizing a subset of 64-bit operations that can be + * emulated without too many steps on the average 32-bit machine. + * + * For example, these two lines seem similar, and run equally fast on 64-bit: + * + * xxh_u64 x; + * x ^= (x >> 47); // good + * x ^= (x >> 13); // bad + * + * However, to a 32-bit machine, there is a major difference. + * + * x ^= (x >> 47) looks like this: + * + * x.lo ^= (x.hi >> (47 - 32)); + * + * while x ^= (x >> 13) looks like this: + * + * // note: funnel shifts are not usually cheap. + * x.lo ^= (x.lo >> 13) | (x.hi << (32 - 13)); + * x.hi ^= (x.hi >> 13); + * + * The first one is significantly faster than the second, simply because the + * shift is larger than 32. This means: + * - All the bits we need are in the upper 32 bits, so we can ignore the lower + * 32 bits in the shift. + * - The shift result will always fit in the lower 32 bits, and therefore, + * we can ignore the upper 32 bits in the xor. + * + * Thanks to this optimization, XXH3 only requires these features to be efficient: + * + * - Usable unaligned access + * - A 32-bit or 64-bit ALU + * - If 32-bit, a decent ADC instruction + * - A 32 or 64-bit multiply with a 64-bit result + * - For the 128-bit variant, a decent byteswap helps short inputs. + * + * The first two are already required by XXH32, and almost all 32-bit and 64-bit + * platforms which can run XXH32 can run XXH3 efficiently. + * + * Thumb-1, the classic 16-bit only subset of ARM's instruction set, is one + * notable exception. + * + * First of all, Thumb-1 lacks support for the UMULL instruction which + * performs the important long multiply. This means numerous __aeabi_lmul + * calls. + * + * Second of all, the 8 functional registers are just not enough. + * Setup for __aeabi_lmul, byteshift loads, pointers, and all arithmetic need + * Lo registers, and this shuffling results in thousands more MOVs than A32. + * + * A32 and T32 don't have this limitation. They can access all 14 registers, + * do a 32->64 multiply with UMULL, and the flexible operand allowing free + * shifts is helpful, too. + * + * Therefore, we do a quick sanity check. + * + * If compiling Thumb-1 for a target which supports ARM instructions, we will + * emit a warning, as it is not a "sane" platform to compile for. + * + * Usually, if this happens, it is because of an accident and you probably need + * to specify -march, as you likely meant to compile for a newer architecture. + * + * Credit: large sections of the vectorial and asm source code paths + * have been contributed by @easyaspi314 + */ +#if defined(__thumb__) && !defined(__thumb2__) && defined(__ARM_ARCH_ISA_ARM) +# warning "XXH3 is highly inefficient without ARM or Thumb-2." +#endif + +/* ========================================== + * Vectorization detection + * ========================================== */ + +#ifdef XXH_DOXYGEN +/*! + * @ingroup tuning + * @brief Overrides the vectorization implementation chosen for XXH3. + * + * Can be defined to 0 to disable SIMD or any of the values mentioned in + * @ref XXH_VECTOR_TYPE. + * + * If this is not defined, it uses predefined macros to determine the best + * implementation. + */ +# define XXH_VECTOR XXH_SCALAR +/*! + * @ingroup tuning + * @brief Possible values for @ref XXH_VECTOR. + * + * Note that these are actually implemented as macros. + * + * If this is not defined, it is detected automatically. + * internal macro XXH_X86DISPATCH overrides this. + */ +enum XXH_VECTOR_TYPE /* fake enum */ { + XXH_SCALAR = 0, /*!< Portable scalar version */ + XXH_SSE2 = 1, /*!< + * SSE2 for Pentium 4, Opteron, all x86_64. + * + * @note SSE2 is also guaranteed on Windows 10, macOS, and + * Android x86. + */ + XXH_AVX2 = 2, /*!< AVX2 for Haswell and Bulldozer */ + XXH_AVX512 = 3, /*!< AVX512 for Skylake and Icelake */ + XXH_NEON = 4, /*!< + * NEON for most ARMv7-A, all AArch64, and WASM SIMD128 + * via the SIMDeverywhere polyfill provided with the + * Emscripten SDK. + */ + XXH_VSX = 5, /*!< VSX and ZVector for POWER8/z13 (64-bit) */ + XXH_SVE = 6, /*!< SVE for some ARMv8-A and ARMv9-A */ +}; +/*! + * @ingroup tuning + * @brief Selects the minimum alignment for XXH3's accumulators. + * + * When using SIMD, this should match the alignment required for said vector + * type, so, for example, 32 for AVX2. + * + * Default: Auto detected. + */ +# define XXH_ACC_ALIGN 8 +#endif + +/* Actual definition */ +#ifndef XXH_DOXYGEN +# define XXH_SCALAR 0 +# define XXH_SSE2 1 +# define XXH_AVX2 2 +# define XXH_AVX512 3 +# define XXH_NEON 4 +# define XXH_VSX 5 +# define XXH_SVE 6 +#endif + +#ifndef XXH_VECTOR /* can be defined on command line */ +# if defined(__ARM_FEATURE_SVE) +# define XXH_VECTOR XXH_SVE +# elif ( \ + defined(__ARM_NEON__) || defined(__ARM_NEON) /* gcc */ \ + || defined(_M_ARM) || defined(_M_ARM64) || defined(_M_ARM64EC) /* msvc */ \ + || (defined(__wasm_simd128__) && XXH_HAS_INCLUDE()) /* wasm simd128 via SIMDe */ \ + ) && ( \ + defined(_WIN32) || defined(__LITTLE_ENDIAN__) /* little endian only */ \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__) \ + ) +# define XXH_VECTOR XXH_NEON +# elif defined(__AVX512F__) +# define XXH_VECTOR XXH_AVX512 +# elif defined(__AVX2__) +# define XXH_VECTOR XXH_AVX2 +# elif defined(__SSE2__) || defined(_M_AMD64) || defined(_M_X64) || (defined(_M_IX86_FP) && (_M_IX86_FP == 2)) +# define XXH_VECTOR XXH_SSE2 +# elif (defined(__PPC64__) && defined(__POWER8_VECTOR__)) \ + || (defined(__s390x__) && defined(__VEC__)) \ + && defined(__GNUC__) /* TODO: IBM XL */ +# define XXH_VECTOR XXH_VSX +# else +# define XXH_VECTOR XXH_SCALAR +# endif +#endif + +/* __ARM_FEATURE_SVE is only supported by GCC & Clang. */ +#if (XXH_VECTOR == XXH_SVE) && !defined(__ARM_FEATURE_SVE) +# ifdef _MSC_VER +# pragma warning(once : 4606) +# else +# warning "__ARM_FEATURE_SVE isn't supported. Use SCALAR instead." +# endif +# undef XXH_VECTOR +# define XXH_VECTOR XXH_SCALAR +#endif + +/* + * Controls the alignment of the accumulator, + * for compatibility with aligned vector loads, which are usually faster. + */ +#ifndef XXH_ACC_ALIGN +# if defined(XXH_X86DISPATCH) +# define XXH_ACC_ALIGN 64 /* for compatibility with avx512 */ +# elif XXH_VECTOR == XXH_SCALAR /* scalar */ +# define XXH_ACC_ALIGN 8 +# elif XXH_VECTOR == XXH_SSE2 /* sse2 */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_AVX2 /* avx2 */ +# define XXH_ACC_ALIGN 32 +# elif XXH_VECTOR == XXH_NEON /* neon */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_VSX /* vsx */ +# define XXH_ACC_ALIGN 16 +# elif XXH_VECTOR == XXH_AVX512 /* avx512 */ +# define XXH_ACC_ALIGN 64 +# elif XXH_VECTOR == XXH_SVE /* sve */ +# define XXH_ACC_ALIGN 64 +# endif +#endif + +#if defined(XXH_X86DISPATCH) || XXH_VECTOR == XXH_SSE2 \ + || XXH_VECTOR == XXH_AVX2 || XXH_VECTOR == XXH_AVX512 +# define XXH_SEC_ALIGN XXH_ACC_ALIGN +#elif XXH_VECTOR == XXH_SVE +# define XXH_SEC_ALIGN XXH_ACC_ALIGN +#else +# define XXH_SEC_ALIGN 8 +#endif + +#if defined(__GNUC__) || defined(__clang__) +# define XXH_ALIASING __attribute__((__may_alias__)) +#else +# define XXH_ALIASING /* nothing */ +#endif + +/* + * UGLY HACK: + * GCC usually generates the best code with -O3 for xxHash. + * + * However, when targeting AVX2, it is overzealous in its unrolling resulting + * in code roughly 3/4 the speed of Clang. + * + * There are other issues, such as GCC splitting _mm256_loadu_si256 into + * _mm_loadu_si128 + _mm256_inserti128_si256. This is an optimization which + * only applies to Sandy and Ivy Bridge... which don't even support AVX2. + * + * That is why when compiling the AVX2 version, it is recommended to use either + * -O2 -mavx2 -march=haswell + * or + * -O2 -mavx2 -mno-avx256-split-unaligned-load + * for decent performance, or to use Clang instead. + * + * Fortunately, we can control the first one with a pragma that forces GCC into + * -O2, but the other one we can't control without "failed to inline always + * inline function due to target mismatch" warnings. + */ +#if XXH_VECTOR == XXH_AVX2 /* AVX2 */ \ + && defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \ + && defined(__OPTIMIZE__) && XXH_SIZE_OPT <= 0 /* respect -O0 and -Os */ +# pragma GCC push_options +# pragma GCC optimize("-O2") +#endif + +#if XXH_VECTOR == XXH_NEON + +/* + * UGLY HACK: While AArch64 GCC on Linux does not seem to care, on macOS, GCC -O3 + * optimizes out the entire hashLong loop because of the aliasing violation. + * + * However, GCC is also inefficient at load-store optimization with vld1q/vst1q, + * so the only option is to mark it as aliasing. + */ +typedef uint64x2_t xxh_aliasing_uint64x2_t XXH_ALIASING; + +/*! + * @internal + * @brief `vld1q_u64` but faster and alignment-safe. + * + * On AArch64, unaligned access is always safe, but on ARMv7-a, it is only + * *conditionally* safe (`vld1` has an alignment bit like `movdq[ua]` in x86). + * + * GCC for AArch64 sees `vld1q_u8` as an intrinsic instead of a load, so it + * prohibits load-store optimizations. Therefore, a direct dereference is used. + * + * Otherwise, `vld1q_u8` is used with `vreinterpretq_u8_u64` to do a safe + * unaligned load. + */ +#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__) +XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr) /* silence -Wcast-align */ +{ + return *(xxh_aliasing_uint64x2_t const *)ptr; +} +#else +XXH_FORCE_INLINE uint64x2_t XXH_vld1q_u64(void const* ptr) +{ + return vreinterpretq_u64_u8(vld1q_u8((uint8_t const*)ptr)); +} +#endif + +/*! + * @internal + * @brief `vmlal_u32` on low and high halves of a vector. + * + * This is a workaround for AArch64 GCC < 11 which implemented arm_neon.h with + * inline assembly and were therefore incapable of merging the `vget_{low, high}_u32` + * with `vmlal_u32`. + */ +#if defined(__aarch64__) && defined(__GNUC__) && !defined(__clang__) && __GNUC__ < 11 +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + /* Inline assembly is the only way */ + __asm__("umlal %0.2d, %1.2s, %2.2s" : "+w" (acc) : "w" (lhs), "w" (rhs)); + return acc; +} +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + /* This intrinsic works as expected */ + return vmlal_high_u32(acc, lhs, rhs); +} +#else +/* Portable intrinsic versions */ +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_low_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + return vmlal_u32(acc, vget_low_u32(lhs), vget_low_u32(rhs)); +} +/*! @copydoc XXH_vmlal_low_u32 + * Assume the compiler converts this to vmlal_high_u32 on aarch64 */ +XXH_FORCE_INLINE uint64x2_t +XXH_vmlal_high_u32(uint64x2_t acc, uint32x4_t lhs, uint32x4_t rhs) +{ + return vmlal_u32(acc, vget_high_u32(lhs), vget_high_u32(rhs)); +} +#endif + +/*! + * @ingroup tuning + * @brief Controls the NEON to scalar ratio for XXH3 + * + * This can be set to 2, 4, 6, or 8. + * + * ARM Cortex CPUs are _very_ sensitive to how their pipelines are used. + * + * For example, the Cortex-A73 can dispatch 3 micro-ops per cycle, but only 2 of those + * can be NEON. If you are only using NEON instructions, you are only using 2/3 of the CPU + * bandwidth. + * + * This is even more noticeable on the more advanced cores like the Cortex-A76 which + * can dispatch 8 micro-ops per cycle, but still only 2 NEON micro-ops at once. + * + * Therefore, to make the most out of the pipeline, it is beneficial to run 6 NEON lanes + * and 2 scalar lanes, which is chosen by default. + * + * This does not apply to Apple processors or 32-bit processors, which run better with + * full NEON. These will default to 8. Additionally, size-optimized builds run 8 lanes. + * + * This change benefits CPUs with large micro-op buffers without negatively affecting + * most other CPUs: + * + * | Chipset | Dispatch type | NEON only | 6:2 hybrid | Diff. | + * |:----------------------|:--------------------|----------:|-----------:|------:| + * | Snapdragon 730 (A76) | 2 NEON/8 micro-ops | 8.8 GB/s | 10.1 GB/s | ~16% | + * | Snapdragon 835 (A73) | 2 NEON/3 micro-ops | 5.1 GB/s | 5.3 GB/s | ~5% | + * | Marvell PXA1928 (A53) | In-order dual-issue | 1.9 GB/s | 1.9 GB/s | 0% | + * | Apple M1 | 4 NEON/8 micro-ops | 37.3 GB/s | 36.1 GB/s | ~-3% | + * + * It also seems to fix some bad codegen on GCC, making it almost as fast as clang. + * + * When using WASM SIMD128, if this is 2 or 6, SIMDe will scalarize 2 of the lanes meaning + * it effectively becomes worse 4. + * + * @see XXH3_accumulate_512_neon() + */ +# ifndef XXH3_NEON_LANES +# if (defined(__aarch64__) || defined(__arm64__) || defined(_M_ARM64) || defined(_M_ARM64EC)) \ + && !defined(__APPLE__) && XXH_SIZE_OPT <= 0 +# define XXH3_NEON_LANES 6 +# else +# define XXH3_NEON_LANES XXH_ACC_NB +# endif +# endif +#endif /* XXH_VECTOR == XXH_NEON */ + +/* + * VSX and Z Vector helpers. + * + * This is very messy, and any pull requests to clean this up are welcome. + * + * There are a lot of problems with supporting VSX and s390x, due to + * inconsistent intrinsics, spotty coverage, and multiple endiannesses. + */ +#if XXH_VECTOR == XXH_VSX +/* Annoyingly, these headers _may_ define three macros: `bool`, `vector`, + * and `pixel`. This is a problem for obvious reasons. + * + * These keywords are unnecessary; the spec literally says they are + * equivalent to `__bool`, `__vector`, and `__pixel` and may be undef'd + * after including the header. + * + * We use pragma push_macro/pop_macro to keep the namespace clean. */ +# pragma push_macro("bool") +# pragma push_macro("vector") +# pragma push_macro("pixel") +/* silence potential macro redefined warnings */ +# undef bool +# undef vector +# undef pixel + +# if defined(__s390x__) +# include +# else +# include +# endif + +/* Restore the original macro values, if applicable. */ +# pragma pop_macro("pixel") +# pragma pop_macro("vector") +# pragma pop_macro("bool") + +typedef __vector unsigned long long xxh_u64x2; +typedef __vector unsigned char xxh_u8x16; +typedef __vector unsigned xxh_u32x4; + +/* + * UGLY HACK: Similar to aarch64 macOS GCC, s390x GCC has the same aliasing issue. + */ +typedef xxh_u64x2 xxh_aliasing_u64x2 XXH_ALIASING; + +# ifndef XXH_VSX_BE +# if defined(__BIG_ENDIAN__) \ + || (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__) +# define XXH_VSX_BE 1 +# elif defined(__VEC_ELEMENT_REG_ORDER__) && __VEC_ELEMENT_REG_ORDER__ == __ORDER_BIG_ENDIAN__ +# warning "-maltivec=be is not recommended. Please use native endianness." +# define XXH_VSX_BE 1 +# else +# define XXH_VSX_BE 0 +# endif +# endif /* !defined(XXH_VSX_BE) */ + +# if XXH_VSX_BE +# if defined(__POWER9_VECTOR__) || (defined(__clang__) && defined(__s390x__)) +# define XXH_vec_revb vec_revb +# else +/*! + * A polyfill for POWER9's vec_revb(). + */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_revb(xxh_u64x2 val) +{ + xxh_u8x16 const vByteSwap = { 0x07, 0x06, 0x05, 0x04, 0x03, 0x02, 0x01, 0x00, + 0x0F, 0x0E, 0x0D, 0x0C, 0x0B, 0x0A, 0x09, 0x08 }; + return vec_perm(val, val, vByteSwap); +} +# endif +# endif /* XXH_VSX_BE */ + +/*! + * Performs an unaligned vector load and byte swaps it on big endian. + */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_loadu(const void *ptr) +{ + xxh_u64x2 ret; + XXH_memcpy(&ret, ptr, sizeof(xxh_u64x2)); +# if XXH_VSX_BE + ret = XXH_vec_revb(ret); +# endif + return ret; +} + +/* + * vec_mulo and vec_mule are very problematic intrinsics on PowerPC + * + * These intrinsics weren't added until GCC 8, despite existing for a while, + * and they are endian dependent. Also, their meaning swap depending on version. + * */ +# if defined(__s390x__) + /* s390x is always big endian, no issue on this platform */ +# define XXH_vec_mulo vec_mulo +# define XXH_vec_mule vec_mule +# elif defined(__clang__) && XXH_HAS_BUILTIN(__builtin_altivec_vmuleuw) && !defined(__ibmxl__) +/* Clang has a better way to control this, we can just use the builtin which doesn't swap. */ + /* The IBM XL Compiler (which defined __clang__) only implements the vec_* operations */ +# define XXH_vec_mulo __builtin_altivec_vmulouw +# define XXH_vec_mule __builtin_altivec_vmuleuw +# else +/* gcc needs inline assembly */ +/* Adapted from https://github.com/google/highwayhash/blob/master/highwayhash/hh_vsx.h. */ +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mulo(xxh_u32x4 a, xxh_u32x4 b) +{ + xxh_u64x2 result; + __asm__("vmulouw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b)); + return result; +} +XXH_FORCE_INLINE xxh_u64x2 XXH_vec_mule(xxh_u32x4 a, xxh_u32x4 b) +{ + xxh_u64x2 result; + __asm__("vmuleuw %0, %1, %2" : "=v" (result) : "v" (a), "v" (b)); + return result; +} +# endif /* XXH_vec_mulo, XXH_vec_mule */ +#endif /* XXH_VECTOR == XXH_VSX */ + +#if XXH_VECTOR == XXH_SVE +#define ACCRND(acc, offset) \ +do { \ + svuint64_t input_vec = svld1_u64(mask, xinput + offset); \ + svuint64_t secret_vec = svld1_u64(mask, xsecret + offset); \ + svuint64_t mixed = sveor_u64_x(mask, secret_vec, input_vec); \ + svuint64_t swapped = svtbl_u64(input_vec, kSwap); \ + svuint64_t mixed_lo = svextw_u64_x(mask, mixed); \ + svuint64_t mixed_hi = svlsr_n_u64_x(mask, mixed, 32); \ + svuint64_t mul = svmad_u64_x(mask, mixed_lo, mixed_hi, swapped); \ + acc = svadd_u64_x(mask, acc, mul); \ +} while (0) +#endif /* XXH_VECTOR == XXH_SVE */ + +/* prefetch + * can be disabled, by declaring XXH_NO_PREFETCH build macro */ +#if defined(XXH_NO_PREFETCH) +# define XXH_PREFETCH(ptr) (void)(ptr) /* disabled */ +#else +# if XXH_SIZE_OPT >= 1 +# define XXH_PREFETCH(ptr) (void)(ptr) +# elif defined(_MSC_VER) && (defined(_M_X64) || defined(_M_IX86)) /* _mm_prefetch() not defined outside of x86/x64 */ +# include /* https://msdn.microsoft.com/fr-fr/library/84szxsww(v=vs.90).aspx */ +# define XXH_PREFETCH(ptr) _mm_prefetch((const char*)(ptr), _MM_HINT_T0) +# elif defined(__GNUC__) && ( (__GNUC__ >= 4) || ( (__GNUC__ == 3) && (__GNUC_MINOR__ >= 1) ) ) +# define XXH_PREFETCH(ptr) __builtin_prefetch((ptr), 0 /* rw==read */, 3 /* locality */) +# else +# define XXH_PREFETCH(ptr) (void)(ptr) /* disabled */ +# endif +#endif /* XXH_NO_PREFETCH */ + + +/* ========================================== + * XXH3 default settings + * ========================================== */ + +#define XXH_SECRET_DEFAULT_SIZE 192 /* minimum XXH3_SECRET_SIZE_MIN */ + +#if (XXH_SECRET_DEFAULT_SIZE < XXH3_SECRET_SIZE_MIN) +# error "default keyset is not large enough" +#endif + +/*! Pseudorandom secret taken directly from FARSH. */ +XXH_ALIGN(64) static const xxh_u8 XXH3_kSecret[XXH_SECRET_DEFAULT_SIZE] = { + 0xb8, 0xfe, 0x6c, 0x39, 0x23, 0xa4, 0x4b, 0xbe, 0x7c, 0x01, 0x81, 0x2c, 0xf7, 0x21, 0xad, 0x1c, + 0xde, 0xd4, 0x6d, 0xe9, 0x83, 0x90, 0x97, 0xdb, 0x72, 0x40, 0xa4, 0xa4, 0xb7, 0xb3, 0x67, 0x1f, + 0xcb, 0x79, 0xe6, 0x4e, 0xcc, 0xc0, 0xe5, 0x78, 0x82, 0x5a, 0xd0, 0x7d, 0xcc, 0xff, 0x72, 0x21, + 0xb8, 0x08, 0x46, 0x74, 0xf7, 0x43, 0x24, 0x8e, 0xe0, 0x35, 0x90, 0xe6, 0x81, 0x3a, 0x26, 0x4c, + 0x3c, 0x28, 0x52, 0xbb, 0x91, 0xc3, 0x00, 0xcb, 0x88, 0xd0, 0x65, 0x8b, 0x1b, 0x53, 0x2e, 0xa3, + 0x71, 0x64, 0x48, 0x97, 0xa2, 0x0d, 0xf9, 0x4e, 0x38, 0x19, 0xef, 0x46, 0xa9, 0xde, 0xac, 0xd8, + 0xa8, 0xfa, 0x76, 0x3f, 0xe3, 0x9c, 0x34, 0x3f, 0xf9, 0xdc, 0xbb, 0xc7, 0xc7, 0x0b, 0x4f, 0x1d, + 0x8a, 0x51, 0xe0, 0x4b, 0xcd, 0xb4, 0x59, 0x31, 0xc8, 0x9f, 0x7e, 0xc9, 0xd9, 0x78, 0x73, 0x64, + 0xea, 0xc5, 0xac, 0x83, 0x34, 0xd3, 0xeb, 0xc3, 0xc5, 0x81, 0xa0, 0xff, 0xfa, 0x13, 0x63, 0xeb, + 0x17, 0x0d, 0xdd, 0x51, 0xb7, 0xf0, 0xda, 0x49, 0xd3, 0x16, 0x55, 0x26, 0x29, 0xd4, 0x68, 0x9e, + 0x2b, 0x16, 0xbe, 0x58, 0x7d, 0x47, 0xa1, 0xfc, 0x8f, 0xf8, 0xb8, 0xd1, 0x7a, 0xd0, 0x31, 0xce, + 0x45, 0xcb, 0x3a, 0x8f, 0x95, 0x16, 0x04, 0x28, 0xaf, 0xd7, 0xfb, 0xca, 0xbb, 0x4b, 0x40, 0x7e, +}; + +static const xxh_u64 PRIME_MX1 = 0x165667919E3779F9ULL; /*!< 0b0001011001010110011001111001000110011110001101110111100111111001 */ +static const xxh_u64 PRIME_MX2 = 0x9FB21C651E98DF25ULL; /*!< 0b1001111110110010000111000110010100011110100110001101111100100101 */ + +#ifdef XXH_OLD_NAMES +# define kSecret XXH3_kSecret +#endif + +#ifdef XXH_DOXYGEN +/*! + * @brief Calculates a 32-bit to 64-bit long multiply. + * + * Implemented as a macro. + * + * Wraps `__emulu` on MSVC x86 because it tends to call `__allmul` when it doesn't + * need to (but it shouldn't need to anyways, it is about 7 instructions to do + * a 64x64 multiply...). Since we know that this will _always_ emit `MULL`, we + * use that instead of the normal method. + * + * If you are compiling for platforms like Thumb-1 and don't have a better option, + * you may also want to write your own long multiply routine here. + * + * @param x, y Numbers to be multiplied + * @return 64-bit product of the low 32 bits of @p x and @p y. + */ +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64(xxh_u64 x, xxh_u64 y) +{ + return (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF); +} +#elif defined(_MSC_VER) && defined(_M_IX86) +# define XXH_mult32to64(x, y) __emulu((unsigned)(x), (unsigned)(y)) +#else +/* + * Downcast + upcast is usually better than masking on older compilers like + * GCC 4.2 (especially 32-bit ones), all without affecting newer compilers. + * + * The other method, (x & 0xFFFFFFFF) * (y & 0xFFFFFFFF), will AND both operands + * and perform a full 64x64 multiply -- entirely redundant on 32-bit. + */ +# define XXH_mult32to64(x, y) ((xxh_u64)(xxh_u32)(x) * (xxh_u64)(xxh_u32)(y)) +#endif + +/*! + * @brief Calculates a 64->128-bit long multiply. + * + * Uses `__uint128_t` and `_umul128` if available, otherwise uses a scalar + * version. + * + * @param lhs , rhs The 64-bit integers to be multiplied + * @return The 128-bit result represented in an @ref XXH128_hash_t. + */ +static XXH128_hash_t +XXH_mult64to128(xxh_u64 lhs, xxh_u64 rhs) +{ + /* + * GCC/Clang __uint128_t method. + * + * On most 64-bit targets, GCC and Clang define a __uint128_t type. + * This is usually the best way as it usually uses a native long 64-bit + * multiply, such as MULQ on x86_64 or MUL + UMULH on aarch64. + * + * Usually. + * + * Despite being a 32-bit platform, Clang (and emscripten) define this type + * despite not having the arithmetic for it. This results in a laggy + * compiler builtin call which calculates a full 128-bit multiply. + * In that case it is best to use the portable one. + * https://github.com/Cyan4973/xxHash/issues/211#issuecomment-515575677 + */ +#if (defined(__GNUC__) || defined(__clang__)) && !defined(__wasm__) \ + && defined(__SIZEOF_INT128__) \ + || (defined(_INTEGRAL_MAX_BITS) && _INTEGRAL_MAX_BITS >= 128) + + __uint128_t const product = (__uint128_t)lhs * (__uint128_t)rhs; + XXH128_hash_t r128; + r128.low64 = (xxh_u64)(product); + r128.high64 = (xxh_u64)(product >> 64); + return r128; + + /* + * MSVC for x64's _umul128 method. + * + * xxh_u64 _umul128(xxh_u64 Multiplier, xxh_u64 Multiplicand, xxh_u64 *HighProduct); + * + * This compiles to single operand MUL on x64. + */ +#elif (defined(_M_X64) || defined(_M_IA64)) && !defined(_M_ARM64EC) + +#ifndef _MSC_VER +# pragma intrinsic(_umul128) +#endif + xxh_u64 product_high; + xxh_u64 const product_low = _umul128(lhs, rhs, &product_high); + XXH128_hash_t r128; + r128.low64 = product_low; + r128.high64 = product_high; + return r128; + + /* + * MSVC for ARM64's __umulh method. + * + * This compiles to the same MUL + UMULH as GCC/Clang's __uint128_t method. + */ +#elif defined(_M_ARM64) || defined(_M_ARM64EC) + +#ifndef _MSC_VER +# pragma intrinsic(__umulh) +#endif + XXH128_hash_t r128; + r128.low64 = lhs * rhs; + r128.high64 = __umulh(lhs, rhs); + return r128; + +#else + /* + * Portable scalar method. Optimized for 32-bit and 64-bit ALUs. + * + * This is a fast and simple grade school multiply, which is shown below + * with base 10 arithmetic instead of base 0x100000000. + * + * 9 3 // D2 lhs = 93 + * x 7 5 // D2 rhs = 75 + * ---------- + * 1 5 // D2 lo_lo = (93 % 10) * (75 % 10) = 15 + * 4 5 | // D2 hi_lo = (93 / 10) * (75 % 10) = 45 + * 2 1 | // D2 lo_hi = (93 % 10) * (75 / 10) = 21 + * + 6 3 | | // D2 hi_hi = (93 / 10) * (75 / 10) = 63 + * --------- + * 2 7 | // D2 cross = (15 / 10) + (45 % 10) + 21 = 27 + * + 6 7 | | // D2 upper = (27 / 10) + (45 / 10) + 63 = 67 + * --------- + * 6 9 7 5 // D4 res = (27 * 10) + (15 % 10) + (67 * 100) = 6975 + * + * The reasons for adding the products like this are: + * 1. It avoids manual carry tracking. Just like how + * (9 * 9) + 9 + 9 = 99, the same applies with this for UINT64_MAX. + * This avoids a lot of complexity. + * + * 2. It hints for, and on Clang, compiles to, the powerful UMAAL + * instruction available in ARM's Digital Signal Processing extension + * in 32-bit ARMv6 and later, which is shown below: + * + * void UMAAL(xxh_u32 *RdLo, xxh_u32 *RdHi, xxh_u32 Rn, xxh_u32 Rm) + * { + * xxh_u64 product = (xxh_u64)*RdLo * (xxh_u64)*RdHi + Rn + Rm; + * *RdLo = (xxh_u32)(product & 0xFFFFFFFF); + * *RdHi = (xxh_u32)(product >> 32); + * } + * + * This instruction was designed for efficient long multiplication, and + * allows this to be calculated in only 4 instructions at speeds + * comparable to some 64-bit ALUs. + * + * 3. It isn't terrible on other platforms. Usually this will be a couple + * of 32-bit ADD/ADCs. + */ + + /* First calculate all of the cross products. */ + xxh_u64 const lo_lo = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs & 0xFFFFFFFF); + xxh_u64 const hi_lo = XXH_mult32to64(lhs >> 32, rhs & 0xFFFFFFFF); + xxh_u64 const lo_hi = XXH_mult32to64(lhs & 0xFFFFFFFF, rhs >> 32); + xxh_u64 const hi_hi = XXH_mult32to64(lhs >> 32, rhs >> 32); + + /* Now add the products together. These will never overflow. */ + xxh_u64 const cross = (lo_lo >> 32) + (hi_lo & 0xFFFFFFFF) + lo_hi; + xxh_u64 const upper = (hi_lo >> 32) + (cross >> 32) + hi_hi; + xxh_u64 const lower = (cross << 32) | (lo_lo & 0xFFFFFFFF); + + XXH128_hash_t r128; + r128.low64 = lower; + r128.high64 = upper; + return r128; +#endif +} + +/*! + * @brief Calculates a 64-bit to 128-bit multiply, then XOR folds it. + * + * The reason for the separate function is to prevent passing too many structs + * around by value. This will hopefully inline the multiply, but we don't force it. + * + * @param lhs , rhs The 64-bit integers to multiply + * @return The low 64 bits of the product XOR'd by the high 64 bits. + * @see XXH_mult64to128() + */ +static xxh_u64 +XXH3_mul128_fold64(xxh_u64 lhs, xxh_u64 rhs) +{ + XXH128_hash_t product = XXH_mult64to128(lhs, rhs); + return product.low64 ^ product.high64; +} + +/*! Seems to produce slightly better code on GCC for some reason. */ +XXH_FORCE_INLINE XXH_CONSTF xxh_u64 XXH_xorshift64(xxh_u64 v64, int shift) +{ + XXH_ASSERT(0 <= shift && shift < 64); + return v64 ^ (v64 >> shift); +} + +/* + * This is a fast avalanche stage, + * suitable when input bits are already partially mixed + */ +static XXH64_hash_t XXH3_avalanche(xxh_u64 h64) +{ + h64 = XXH_xorshift64(h64, 37); + h64 *= PRIME_MX1; + h64 = XXH_xorshift64(h64, 32); + return h64; +} + +/* + * This is a stronger avalanche, + * inspired by Pelle Evensen's rrmxmx + * preferable when input has not been previously mixed + */ +static XXH64_hash_t XXH3_rrmxmx(xxh_u64 h64, xxh_u64 len) +{ + /* this mix is inspired by Pelle Evensen's rrmxmx */ + h64 ^= XXH_rotl64(h64, 49) ^ XXH_rotl64(h64, 24); + h64 *= PRIME_MX2; + h64 ^= (h64 >> 35) + len ; + h64 *= PRIME_MX2; + return XXH_xorshift64(h64, 28); +} + + +/* ========================================== + * Short keys + * ========================================== + * One of the shortcomings of XXH32 and XXH64 was that their performance was + * sub-optimal on short lengths. It used an iterative algorithm which strongly + * favored lengths that were a multiple of 4 or 8. + * + * Instead of iterating over individual inputs, we use a set of single shot + * functions which piece together a range of lengths and operate in constant time. + * + * Additionally, the number of multiplies has been significantly reduced. This + * reduces latency, especially when emulating 64-bit multiplies on 32-bit. + * + * Depending on the platform, this may or may not be faster than XXH32, but it + * is almost guaranteed to be faster than XXH64. + */ + +/* + * At very short lengths, there isn't enough input to fully hide secrets, or use + * the entire secret. + * + * There is also only a limited amount of mixing we can do before significantly + * impacting performance. + * + * Therefore, we use different sections of the secret and always mix two secret + * samples with an XOR. This should have no effect on performance on the + * seedless or withSeed variants because everything _should_ be constant folded + * by modern compilers. + * + * The XOR mixing hides individual parts of the secret and increases entropy. + * + * This adds an extra layer of strength for custom secrets. + */ +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_1to3_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(1 <= len && len <= 3); + XXH_ASSERT(secret != NULL); + /* + * len = 1: combined = { input[0], 0x01, input[0], input[0] } + * len = 2: combined = { input[1], 0x02, input[0], input[1] } + * len = 3: combined = { input[2], 0x03, input[0], input[1] } + */ + { xxh_u8 const c1 = input[0]; + xxh_u8 const c2 = input[len >> 1]; + xxh_u8 const c3 = input[len - 1]; + xxh_u32 const combined = ((xxh_u32)c1 << 16) | ((xxh_u32)c2 << 24) + | ((xxh_u32)c3 << 0) | ((xxh_u32)len << 8); + xxh_u64 const bitflip = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed; + xxh_u64 const keyed = (xxh_u64)combined ^ bitflip; + return XXH64_avalanche(keyed); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_4to8_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(4 <= len && len <= 8); + seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32; + { xxh_u32 const input1 = XXH_readLE32(input); + xxh_u32 const input2 = XXH_readLE32(input + len - 4); + xxh_u64 const bitflip = (XXH_readLE64(secret+8) ^ XXH_readLE64(secret+16)) - seed; + xxh_u64 const input64 = input2 + (((xxh_u64)input1) << 32); + xxh_u64 const keyed = input64 ^ bitflip; + return XXH3_rrmxmx(keyed, len); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_9to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(9 <= len && len <= 16); + { xxh_u64 const bitflip1 = (XXH_readLE64(secret+24) ^ XXH_readLE64(secret+32)) + seed; + xxh_u64 const bitflip2 = (XXH_readLE64(secret+40) ^ XXH_readLE64(secret+48)) - seed; + xxh_u64 const input_lo = XXH_readLE64(input) ^ bitflip1; + xxh_u64 const input_hi = XXH_readLE64(input + len - 8) ^ bitflip2; + xxh_u64 const acc = len + + XXH_swap64(input_lo) + input_hi + + XXH3_mul128_fold64(input_lo, input_hi); + return XXH3_avalanche(acc); + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_0to16_64b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(len <= 16); + { if (XXH_likely(len > 8)) return XXH3_len_9to16_64b(input, len, secret, seed); + if (XXH_likely(len >= 4)) return XXH3_len_4to8_64b(input, len, secret, seed); + if (len) return XXH3_len_1to3_64b(input, len, secret, seed); + return XXH64_avalanche(seed ^ (XXH_readLE64(secret+56) ^ XXH_readLE64(secret+64))); + } +} + +/* + * DISCLAIMER: There are known *seed-dependent* multicollisions here due to + * multiplication by zero, affecting hashes of lengths 17 to 240. + * + * However, they are very unlikely. + * + * Keep this in mind when using the unseeded XXH3_64bits() variant: As with all + * unseeded non-cryptographic hashes, it does not attempt to defend itself + * against specially crafted inputs, only random inputs. + * + * Compared to classic UMAC where a 1 in 2^31 chance of 4 consecutive bytes + * cancelling out the secret is taken an arbitrary number of times (addressed + * in XXH3_accumulate_512), this collision is very unlikely with random inputs + * and/or proper seeding: + * + * This only has a 1 in 2^63 chance of 8 consecutive bytes cancelling out, in a + * function that is only called up to 16 times per hash with up to 240 bytes of + * input. + * + * This is not too bad for a non-cryptographic hash function, especially with + * only 64 bit outputs. + * + * The 128-bit variant (which trades some speed for strength) is NOT affected + * by this, although it is always a good idea to use a proper seed if you care + * about strength. + */ +XXH_FORCE_INLINE xxh_u64 XXH3_mix16B(const xxh_u8* XXH_RESTRICT input, + const xxh_u8* XXH_RESTRICT secret, xxh_u64 seed64) +{ +#if defined(__GNUC__) && !defined(__clang__) /* GCC, not Clang */ \ + && defined(__i386__) && defined(__SSE2__) /* x86 + SSE2 */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable like XXH32 hack */ + /* + * UGLY HACK: + * GCC for x86 tends to autovectorize the 128-bit multiply, resulting in + * slower code. + * + * By forcing seed64 into a register, we disrupt the cost model and + * cause it to scalarize. See `XXH32_round()` + * + * FIXME: Clang's output is still _much_ faster -- On an AMD Ryzen 3600, + * XXH3_64bits @ len=240 runs at 4.6 GB/s with Clang 9, but 3.3 GB/s on + * GCC 9.2, despite both emitting scalar code. + * + * GCC generates much better scalar code than Clang for the rest of XXH3, + * which is why finding a more optimal codepath is an interest. + */ + XXH_COMPILER_GUARD(seed64); +#endif + { xxh_u64 const input_lo = XXH_readLE64(input); + xxh_u64 const input_hi = XXH_readLE64(input+8); + return XXH3_mul128_fold64( + input_lo ^ (XXH_readLE64(secret) + seed64), + input_hi ^ (XXH_readLE64(secret+8) - seed64) + ); + } +} + +/* For mid range keys, XXH3 uses a Mum-hash variant. */ +XXH_FORCE_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_17to128_64b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(16 < len && len <= 128); + + { xxh_u64 acc = len * XXH_PRIME64_1; +#if XXH_SIZE_OPT >= 1 + /* Smaller and cleaner, but slightly slower. */ + unsigned int i = (unsigned int)(len - 1) / 32; + do { + acc += XXH3_mix16B(input+16 * i, secret+32*i, seed); + acc += XXH3_mix16B(input+len-16*(i+1), secret+32*i+16, seed); + } while (i-- != 0); +#else + if (len > 32) { + if (len > 64) { + if (len > 96) { + acc += XXH3_mix16B(input+48, secret+96, seed); + acc += XXH3_mix16B(input+len-64, secret+112, seed); + } + acc += XXH3_mix16B(input+32, secret+64, seed); + acc += XXH3_mix16B(input+len-48, secret+80, seed); + } + acc += XXH3_mix16B(input+16, secret+32, seed); + acc += XXH3_mix16B(input+len-32, secret+48, seed); + } + acc += XXH3_mix16B(input+0, secret+0, seed); + acc += XXH3_mix16B(input+len-16, secret+16, seed); +#endif + return XXH3_avalanche(acc); + } +} + +XXH_NO_INLINE XXH_PUREF XXH64_hash_t +XXH3_len_129to240_64b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + + #define XXH3_MIDSIZE_STARTOFFSET 3 + #define XXH3_MIDSIZE_LASTOFFSET 17 + + { xxh_u64 acc = len * XXH_PRIME64_1; + xxh_u64 acc_end; + unsigned int const nbRounds = (unsigned int)len / 16; + unsigned int i; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + for (i=0; i<8; i++) { + acc += XXH3_mix16B(input+(16*i), secret+(16*i), seed); + } + /* last bytes */ + acc_end = XXH3_mix16B(input + len - 16, secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET, seed); + XXH_ASSERT(nbRounds >= 8); + acc = XXH3_avalanche(acc); +#if defined(__clang__) /* Clang */ \ + && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable */ + /* + * UGLY HACK: + * Clang for ARMv7-A tries to vectorize this loop, similar to GCC x86. + * In everywhere else, it uses scalar code. + * + * For 64->128-bit multiplies, even if the NEON was 100% optimal, it + * would still be slower than UMAAL (see XXH_mult64to128). + * + * Unfortunately, Clang doesn't handle the long multiplies properly and + * converts them to the nonexistent "vmulq_u64" intrinsic, which is then + * scalarized into an ugly mess of VMOV.32 instructions. + * + * This mess is difficult to avoid without turning autovectorization + * off completely, but they are usually relatively minor and/or not + * worth it to fix. + * + * This loop is the easiest to fix, as unlike XXH32, this pragma + * _actually works_ because it is a loop vectorization instead of an + * SLP vectorization. + */ + #pragma clang loop vectorize(disable) +#endif + for (i=8 ; i < nbRounds; i++) { + /* + * Prevents clang for unrolling the acc loop and interleaving with this one. + */ + XXH_COMPILER_GUARD(acc); + acc_end += XXH3_mix16B(input+(16*i), secret+(16*(i-8)) + XXH3_MIDSIZE_STARTOFFSET, seed); + } + return XXH3_avalanche(acc + acc_end); + } +} + + +/* ======= Long Keys ======= */ + +#define XXH_STRIPE_LEN 64 +#define XXH_SECRET_CONSUME_RATE 8 /* nb of secret bytes consumed at each accumulation */ +#define XXH_ACC_NB (XXH_STRIPE_LEN / sizeof(xxh_u64)) + +#ifdef XXH_OLD_NAMES +# define STRIPE_LEN XXH_STRIPE_LEN +# define ACC_NB XXH_ACC_NB +#endif + +#ifndef XXH_PREFETCH_DIST +# ifdef __clang__ +# define XXH_PREFETCH_DIST 320 +# else +# if (XXH_VECTOR == XXH_AVX512) +# define XXH_PREFETCH_DIST 512 +# else +# define XXH_PREFETCH_DIST 384 +# endif +# endif /* __clang__ */ +#endif /* XXH_PREFETCH_DIST */ + +/* + * These macros are to generate an XXH3_accumulate() function. + * The two arguments select the name suffix and target attribute. + * + * The name of this symbol is XXH3_accumulate_() and it calls + * XXH3_accumulate_512_(). + * + * It may be useful to hand implement this function if the compiler fails to + * optimize the inline function. + */ +#define XXH3_ACCUMULATE_TEMPLATE(name) \ +void \ +XXH3_accumulate_##name(xxh_u64* XXH_RESTRICT acc, \ + const xxh_u8* XXH_RESTRICT input, \ + const xxh_u8* XXH_RESTRICT secret, \ + size_t nbStripes) \ +{ \ + size_t n; \ + for (n = 0; n < nbStripes; n++ ) { \ + const xxh_u8* const in = input + n*XXH_STRIPE_LEN; \ + XXH_PREFETCH(in + XXH_PREFETCH_DIST); \ + XXH3_accumulate_512_##name( \ + acc, \ + in, \ + secret + n*XXH_SECRET_CONSUME_RATE); \ + } \ +} + + +XXH_FORCE_INLINE void XXH_writeLE64(void* dst, xxh_u64 v64) +{ + if (!XXH_CPU_LITTLE_ENDIAN) v64 = XXH_swap64(v64); + XXH_memcpy(dst, &v64, sizeof(v64)); +} + +/* Several intrinsic functions below are supposed to accept __int64 as argument, + * as documented in https://software.intel.com/sites/landingpage/IntrinsicsGuide/ . + * However, several environments do not define __int64 type, + * requiring a workaround. + */ +#if !defined (__VMS) \ + && (defined (__cplusplus) \ + || (defined (__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) /* C99 */) ) + typedef int64_t xxh_i64; +#else + /* the following type must have a width of 64-bit */ + typedef long long xxh_i64; +#endif + + +/* + * XXH3_accumulate_512 is the tightest loop for long inputs, and it is the most optimized. + * + * It is a hardened version of UMAC, based off of FARSH's implementation. + * + * This was chosen because it adapts quite well to 32-bit, 64-bit, and SIMD + * implementations, and it is ridiculously fast. + * + * We harden it by mixing the original input to the accumulators as well as the product. + * + * This means that in the (relatively likely) case of a multiply by zero, the + * original input is preserved. + * + * On 128-bit inputs, we swap 64-bit pairs when we add the input to improve + * cross-pollination, as otherwise the upper and lower halves would be + * essentially independent. + * + * This doesn't matter on 64-bit hashes since they all get merged together in + * the end, so we skip the extra step. + * + * Both XXH3_64bits and XXH3_128bits use this subroutine. + */ + +#if (XXH_VECTOR == XXH_AVX512) \ + || (defined(XXH_DISPATCH_AVX512) && XXH_DISPATCH_AVX512 != 0) + +#ifndef XXH_TARGET_AVX512 +# define XXH_TARGET_AVX512 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_accumulate_512_avx512(void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + __m512i* const xacc = (__m512i *) acc; + XXH_ASSERT((((size_t)acc) & 63) == 0); + XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i)); + + { + /* data_vec = input[0]; */ + __m512i const data_vec = _mm512_loadu_si512 (input); + /* key_vec = secret[0]; */ + __m512i const key_vec = _mm512_loadu_si512 (secret); + /* data_key = data_vec ^ key_vec; */ + __m512i const data_key = _mm512_xor_si512 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m512i const data_key_lo = _mm512_srli_epi64 (data_key, 32); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m512i const product = _mm512_mul_epu32 (data_key, data_key_lo); + /* xacc[0] += swap(data_vec); */ + __m512i const data_swap = _mm512_shuffle_epi32(data_vec, (_MM_PERM_ENUM)_MM_SHUFFLE(1, 0, 3, 2)); + __m512i const sum = _mm512_add_epi64(*xacc, data_swap); + /* xacc[0] += product; */ + *xacc = _mm512_add_epi64(product, sum); + } +} +XXH_FORCE_INLINE XXH_TARGET_AVX512 XXH3_ACCUMULATE_TEMPLATE(avx512) + +/* + * XXH3_scrambleAcc: Scrambles the accumulators to improve mixing. + * + * Multiplication isn't perfect, as explained by Google in HighwayHash: + * + * // Multiplication mixes/scrambles bytes 0-7 of the 64-bit result to + * // varying degrees. In descending order of goodness, bytes + * // 3 4 2 5 1 6 0 7 have quality 228 224 164 160 100 96 36 32. + * // As expected, the upper and lower bytes are much worse. + * + * Source: https://github.com/google/highwayhash/blob/0aaf66b/highwayhash/hh_avx2.h#L291 + * + * Since our algorithm uses a pseudorandom secret to add some variance into the + * mix, we don't need to (or want to) mix as often or as much as HighwayHash does. + * + * This isn't as tight as XXH3_accumulate, but still written in SIMD to avoid + * extraction. + * + * Both XXH3_64bits and XXH3_128bits use this subroutine. + */ + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_scrambleAcc_avx512(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 63) == 0); + XXH_STATIC_ASSERT(XXH_STRIPE_LEN == sizeof(__m512i)); + { __m512i* const xacc = (__m512i*) acc; + const __m512i prime32 = _mm512_set1_epi32((int)XXH_PRIME32_1); + + /* xacc[0] ^= (xacc[0] >> 47) */ + __m512i const acc_vec = *xacc; + __m512i const shifted = _mm512_srli_epi64 (acc_vec, 47); + /* xacc[0] ^= secret; */ + __m512i const key_vec = _mm512_loadu_si512 (secret); + __m512i const data_key = _mm512_ternarylogic_epi32(key_vec, acc_vec, shifted, 0x96 /* key_vec ^ acc_vec ^ shifted */); + + /* xacc[0] *= XXH_PRIME32_1; */ + __m512i const data_key_hi = _mm512_srli_epi64 (data_key, 32); + __m512i const prod_lo = _mm512_mul_epu32 (data_key, prime32); + __m512i const prod_hi = _mm512_mul_epu32 (data_key_hi, prime32); + *xacc = _mm512_add_epi64(prod_lo, _mm512_slli_epi64(prod_hi, 32)); + } +} + +XXH_FORCE_INLINE XXH_TARGET_AVX512 void +XXH3_initCustomSecret_avx512(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 63) == 0); + XXH_STATIC_ASSERT(XXH_SEC_ALIGN == 64); + XXH_ASSERT(((size_t)customSecret & 63) == 0); + (void)(&XXH_writeLE64); + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m512i); + __m512i const seed_pos = _mm512_set1_epi64((xxh_i64)seed64); + __m512i const seed = _mm512_mask_sub_epi64(seed_pos, 0xAA, _mm512_set1_epi8(0), seed_pos); + + const __m512i* const src = (const __m512i*) ((const void*) XXH3_kSecret); + __m512i* const dest = ( __m512i*) customSecret; + int i; + XXH_ASSERT(((size_t)src & 63) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dest & 63) == 0); + for (i=0; i < nbRounds; ++i) { + dest[i] = _mm512_add_epi64(_mm512_load_si512(src + i), seed); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_AVX2) \ + || (defined(XXH_DISPATCH_AVX2) && XXH_DISPATCH_AVX2 != 0) + +#ifndef XXH_TARGET_AVX2 +# define XXH_TARGET_AVX2 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void +XXH3_accumulate_512_avx2( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 31) == 0); + { __m256i* const xacc = (__m256i *) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xinput = (const __m256i *) input; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xsecret = (const __m256i *) secret; + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) { + /* data_vec = xinput[i]; */ + __m256i const data_vec = _mm256_loadu_si256 (xinput+i); + /* key_vec = xsecret[i]; */ + __m256i const key_vec = _mm256_loadu_si256 (xsecret+i); + /* data_key = data_vec ^ key_vec; */ + __m256i const data_key = _mm256_xor_si256 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m256i const data_key_lo = _mm256_srli_epi64 (data_key, 32); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m256i const product = _mm256_mul_epu32 (data_key, data_key_lo); + /* xacc[i] += swap(data_vec); */ + __m256i const data_swap = _mm256_shuffle_epi32(data_vec, _MM_SHUFFLE(1, 0, 3, 2)); + __m256i const sum = _mm256_add_epi64(xacc[i], data_swap); + /* xacc[i] += product; */ + xacc[i] = _mm256_add_epi64(product, sum); + } } +} +XXH_FORCE_INLINE XXH_TARGET_AVX2 XXH3_ACCUMULATE_TEMPLATE(avx2) + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void +XXH3_scrambleAcc_avx2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 31) == 0); + { __m256i* const xacc = (__m256i*) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm256_loadu_si256 requires a const __m256i * pointer for some reason. */ + const __m256i* const xsecret = (const __m256i *) secret; + const __m256i prime32 = _mm256_set1_epi32((int)XXH_PRIME32_1); + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m256i); i++) { + /* xacc[i] ^= (xacc[i] >> 47) */ + __m256i const acc_vec = xacc[i]; + __m256i const shifted = _mm256_srli_epi64 (acc_vec, 47); + __m256i const data_vec = _mm256_xor_si256 (acc_vec, shifted); + /* xacc[i] ^= xsecret; */ + __m256i const key_vec = _mm256_loadu_si256 (xsecret+i); + __m256i const data_key = _mm256_xor_si256 (data_vec, key_vec); + + /* xacc[i] *= XXH_PRIME32_1; */ + __m256i const data_key_hi = _mm256_srli_epi64 (data_key, 32); + __m256i const prod_lo = _mm256_mul_epu32 (data_key, prime32); + __m256i const prod_hi = _mm256_mul_epu32 (data_key_hi, prime32); + xacc[i] = _mm256_add_epi64(prod_lo, _mm256_slli_epi64(prod_hi, 32)); + } + } +} + +XXH_FORCE_INLINE XXH_TARGET_AVX2 void XXH3_initCustomSecret_avx2(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 31) == 0); + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE / sizeof(__m256i)) == 6); + XXH_STATIC_ASSERT(XXH_SEC_ALIGN <= 64); + (void)(&XXH_writeLE64); + XXH_PREFETCH(customSecret); + { __m256i const seed = _mm256_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64, (xxh_i64)(0U - seed64), (xxh_i64)seed64); + + const __m256i* const src = (const __m256i*) ((const void*) XXH3_kSecret); + __m256i* dest = ( __m256i*) customSecret; + +# if defined(__GNUC__) || defined(__clang__) + /* + * On GCC & Clang, marking 'dest' as modified will cause the compiler: + * - do not extract the secret from sse registers in the internal loop + * - use less common registers, and avoid pushing these reg into stack + */ + XXH_COMPILER_GUARD(dest); +# endif + XXH_ASSERT(((size_t)src & 31) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dest & 31) == 0); + + /* GCC -O2 need unroll loop manually */ + dest[0] = _mm256_add_epi64(_mm256_load_si256(src+0), seed); + dest[1] = _mm256_add_epi64(_mm256_load_si256(src+1), seed); + dest[2] = _mm256_add_epi64(_mm256_load_si256(src+2), seed); + dest[3] = _mm256_add_epi64(_mm256_load_si256(src+3), seed); + dest[4] = _mm256_add_epi64(_mm256_load_si256(src+4), seed); + dest[5] = _mm256_add_epi64(_mm256_load_si256(src+5), seed); + } +} + +#endif + +/* x86dispatch always generates SSE2 */ +#if (XXH_VECTOR == XXH_SSE2) || defined(XXH_X86DISPATCH) + +#ifndef XXH_TARGET_SSE2 +# define XXH_TARGET_SSE2 /* disable attribute target */ +#endif + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void +XXH3_accumulate_512_sse2( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + /* SSE2 is just a half-scale version of the AVX2 version. */ + XXH_ASSERT((((size_t)acc) & 15) == 0); + { __m128i* const xacc = (__m128i *) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xinput = (const __m128i *) input; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xsecret = (const __m128i *) secret; + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) { + /* data_vec = xinput[i]; */ + __m128i const data_vec = _mm_loadu_si128 (xinput+i); + /* key_vec = xsecret[i]; */ + __m128i const key_vec = _mm_loadu_si128 (xsecret+i); + /* data_key = data_vec ^ key_vec; */ + __m128i const data_key = _mm_xor_si128 (data_vec, key_vec); + /* data_key_lo = data_key >> 32; */ + __m128i const data_key_lo = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1)); + /* product = (data_key & 0xffffffff) * (data_key_lo & 0xffffffff); */ + __m128i const product = _mm_mul_epu32 (data_key, data_key_lo); + /* xacc[i] += swap(data_vec); */ + __m128i const data_swap = _mm_shuffle_epi32(data_vec, _MM_SHUFFLE(1,0,3,2)); + __m128i const sum = _mm_add_epi64(xacc[i], data_swap); + /* xacc[i] += product; */ + xacc[i] = _mm_add_epi64(product, sum); + } } +} +XXH_FORCE_INLINE XXH_TARGET_SSE2 XXH3_ACCUMULATE_TEMPLATE(sse2) + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void +XXH3_scrambleAcc_sse2(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + { __m128i* const xacc = (__m128i*) acc; + /* Unaligned. This is mainly for pointer arithmetic, and because + * _mm_loadu_si128 requires a const __m128i * pointer for some reason. */ + const __m128i* const xsecret = (const __m128i *) secret; + const __m128i prime32 = _mm_set1_epi32((int)XXH_PRIME32_1); + + size_t i; + for (i=0; i < XXH_STRIPE_LEN/sizeof(__m128i); i++) { + /* xacc[i] ^= (xacc[i] >> 47) */ + __m128i const acc_vec = xacc[i]; + __m128i const shifted = _mm_srli_epi64 (acc_vec, 47); + __m128i const data_vec = _mm_xor_si128 (acc_vec, shifted); + /* xacc[i] ^= xsecret[i]; */ + __m128i const key_vec = _mm_loadu_si128 (xsecret+i); + __m128i const data_key = _mm_xor_si128 (data_vec, key_vec); + + /* xacc[i] *= XXH_PRIME32_1; */ + __m128i const data_key_hi = _mm_shuffle_epi32 (data_key, _MM_SHUFFLE(0, 3, 0, 1)); + __m128i const prod_lo = _mm_mul_epu32 (data_key, prime32); + __m128i const prod_hi = _mm_mul_epu32 (data_key_hi, prime32); + xacc[i] = _mm_add_epi64(prod_lo, _mm_slli_epi64(prod_hi, 32)); + } + } +} + +XXH_FORCE_INLINE XXH_TARGET_SSE2 void XXH3_initCustomSecret_sse2(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0); + (void)(&XXH_writeLE64); + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / sizeof(__m128i); + +# if defined(_MSC_VER) && defined(_M_IX86) && _MSC_VER < 1900 + /* MSVC 32bit mode does not support _mm_set_epi64x before 2015 */ + XXH_ALIGN(16) const xxh_i64 seed64x2[2] = { (xxh_i64)seed64, (xxh_i64)(0U - seed64) }; + __m128i const seed = _mm_load_si128((__m128i const*)seed64x2); +# else + __m128i const seed = _mm_set_epi64x((xxh_i64)(0U - seed64), (xxh_i64)seed64); +# endif + int i; + + const void* const src16 = XXH3_kSecret; + __m128i* dst16 = (__m128i*) customSecret; +# if defined(__GNUC__) || defined(__clang__) + /* + * On GCC & Clang, marking 'dest' as modified will cause the compiler: + * - do not extract the secret from sse registers in the internal loop + * - use less common registers, and avoid pushing these reg into stack + */ + XXH_COMPILER_GUARD(dst16); +# endif + XXH_ASSERT(((size_t)src16 & 15) == 0); /* control alignment */ + XXH_ASSERT(((size_t)dst16 & 15) == 0); + + for (i=0; i < nbRounds; ++i) { + dst16[i] = _mm_add_epi64(_mm_load_si128((const __m128i *)src16+i), seed); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_NEON) + +/* forward declarations for the scalar routines */ +XXH_FORCE_INLINE void +XXH3_scalarRound(void* XXH_RESTRICT acc, void const* XXH_RESTRICT input, + void const* XXH_RESTRICT secret, size_t lane); + +XXH_FORCE_INLINE void +XXH3_scalarScrambleRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT secret, size_t lane); + +/*! + * @internal + * @brief The bulk processing loop for NEON and WASM SIMD128. + * + * The NEON code path is actually partially scalar when running on AArch64. This + * is to optimize the pipelining and can have up to 15% speedup depending on the + * CPU, and it also mitigates some GCC codegen issues. + * + * @see XXH3_NEON_LANES for configuring this and details about this optimization. + * + * NEON's 32-bit to 64-bit long multiply takes a half vector of 32-bit + * integers instead of the other platforms which mask full 64-bit vectors, + * so the setup is more complicated than just shifting right. + * + * Additionally, there is an optimization for 4 lanes at once noted below. + * + * Since, as stated, the most optimal amount of lanes for Cortexes is 6, + * there needs to be *three* versions of the accumulate operation used + * for the remaining 2 lanes. + * + * WASM's SIMD128 uses SIMDe's arm_neon.h polyfill because the intrinsics overlap + * nearly perfectly. + */ + +XXH_FORCE_INLINE void +XXH3_accumulate_512_neon( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + XXH_STATIC_ASSERT(XXH3_NEON_LANES > 0 && XXH3_NEON_LANES <= XXH_ACC_NB && XXH3_NEON_LANES % 2 == 0); + { /* GCC for darwin arm64 does not like aliasing here */ + xxh_aliasing_uint64x2_t* const xacc = (xxh_aliasing_uint64x2_t*) acc; + /* We don't use a uint32x4_t pointer because it causes bus errors on ARMv7. */ + uint8_t const* xinput = (const uint8_t *) input; + uint8_t const* xsecret = (const uint8_t *) secret; + + size_t i; +#ifdef __wasm_simd128__ + /* + * On WASM SIMD128, Clang emits direct address loads when XXH3_kSecret + * is constant propagated, which results in it converting it to this + * inside the loop: + * + * a = v128.load(XXH3_kSecret + 0 + $secret_offset, offset = 0) + * b = v128.load(XXH3_kSecret + 16 + $secret_offset, offset = 0) + * ... + * + * This requires a full 32-bit address immediate (and therefore a 6 byte + * instruction) as well as an add for each offset. + * + * Putting an asm guard prevents it from folding (at the cost of losing + * the alignment hint), and uses the free offset in `v128.load` instead + * of adding secret_offset each time which overall reduces code size by + * about a kilobyte and improves performance. + */ + XXH_COMPILER_GUARD(xsecret); +#endif + /* Scalar lanes use the normal scalarRound routine */ + for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) { + XXH3_scalarRound(acc, input, secret, i); + } + i = 0; + /* 4 NEON lanes at a time. */ + for (; i+1 < XXH3_NEON_LANES / 2; i+=2) { + /* data_vec = xinput[i]; */ + uint64x2_t data_vec_1 = XXH_vld1q_u64(xinput + (i * 16)); + uint64x2_t data_vec_2 = XXH_vld1q_u64(xinput + ((i+1) * 16)); + /* key_vec = xsecret[i]; */ + uint64x2_t key_vec_1 = XXH_vld1q_u64(xsecret + (i * 16)); + uint64x2_t key_vec_2 = XXH_vld1q_u64(xsecret + ((i+1) * 16)); + /* data_swap = swap(data_vec) */ + uint64x2_t data_swap_1 = vextq_u64(data_vec_1, data_vec_1, 1); + uint64x2_t data_swap_2 = vextq_u64(data_vec_2, data_vec_2, 1); + /* data_key = data_vec ^ key_vec; */ + uint64x2_t data_key_1 = veorq_u64(data_vec_1, key_vec_1); + uint64x2_t data_key_2 = veorq_u64(data_vec_2, key_vec_2); + + /* + * If we reinterpret the 64x2 vectors as 32x4 vectors, we can use a + * de-interleave operation for 4 lanes in 1 step with `vuzpq_u32` to + * get one vector with the low 32 bits of each lane, and one vector + * with the high 32 bits of each lane. + * + * The intrinsic returns a double vector because the original ARMv7-a + * instruction modified both arguments in place. AArch64 and SIMD128 emit + * two instructions from this intrinsic. + * + * [ dk11L | dk11H | dk12L | dk12H ] -> [ dk11L | dk12L | dk21L | dk22L ] + * [ dk21L | dk21H | dk22L | dk22H ] -> [ dk11H | dk12H | dk21H | dk22H ] + */ + uint32x4x2_t unzipped = vuzpq_u32( + vreinterpretq_u32_u64(data_key_1), + vreinterpretq_u32_u64(data_key_2) + ); + /* data_key_lo = data_key & 0xFFFFFFFF */ + uint32x4_t data_key_lo = unzipped.val[0]; + /* data_key_hi = data_key >> 32 */ + uint32x4_t data_key_hi = unzipped.val[1]; + /* + * Then, we can split the vectors horizontally and multiply which, as for most + * widening intrinsics, have a variant that works on both high half vectors + * for free on AArch64. A similar instruction is available on SIMD128. + * + * sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi + */ + uint64x2_t sum_1 = XXH_vmlal_low_u32(data_swap_1, data_key_lo, data_key_hi); + uint64x2_t sum_2 = XXH_vmlal_high_u32(data_swap_2, data_key_lo, data_key_hi); + /* + * Clang reorders + * a += b * c; // umlal swap.2d, dkl.2s, dkh.2s + * c += a; // add acc.2d, acc.2d, swap.2d + * to + * c += a; // add acc.2d, acc.2d, swap.2d + * c += b * c; // umlal acc.2d, dkl.2s, dkh.2s + * + * While it would make sense in theory since the addition is faster, + * for reasons likely related to umlal being limited to certain NEON + * pipelines, this is worse. A compiler guard fixes this. + */ + XXH_COMPILER_GUARD_CLANG_NEON(sum_1); + XXH_COMPILER_GUARD_CLANG_NEON(sum_2); + /* xacc[i] = acc_vec + sum; */ + xacc[i] = vaddq_u64(xacc[i], sum_1); + xacc[i+1] = vaddq_u64(xacc[i+1], sum_2); + } + /* Operate on the remaining NEON lanes 2 at a time. */ + for (; i < XXH3_NEON_LANES / 2; i++) { + /* data_vec = xinput[i]; */ + uint64x2_t data_vec = XXH_vld1q_u64(xinput + (i * 16)); + /* key_vec = xsecret[i]; */ + uint64x2_t key_vec = XXH_vld1q_u64(xsecret + (i * 16)); + /* acc_vec_2 = swap(data_vec) */ + uint64x2_t data_swap = vextq_u64(data_vec, data_vec, 1); + /* data_key = data_vec ^ key_vec; */ + uint64x2_t data_key = veorq_u64(data_vec, key_vec); + /* For two lanes, just use VMOVN and VSHRN. */ + /* data_key_lo = data_key & 0xFFFFFFFF; */ + uint32x2_t data_key_lo = vmovn_u64(data_key); + /* data_key_hi = data_key >> 32; */ + uint32x2_t data_key_hi = vshrn_n_u64(data_key, 32); + /* sum = data_swap + (u64x2) data_key_lo * (u64x2) data_key_hi; */ + uint64x2_t sum = vmlal_u32(data_swap, data_key_lo, data_key_hi); + /* Same Clang workaround as before */ + XXH_COMPILER_GUARD_CLANG_NEON(sum); + /* xacc[i] = acc_vec + sum; */ + xacc[i] = vaddq_u64 (xacc[i], sum); + } + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(neon) + +XXH_FORCE_INLINE void +XXH3_scrambleAcc_neon(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + + { xxh_aliasing_uint64x2_t* xacc = (xxh_aliasing_uint64x2_t*) acc; + uint8_t const* xsecret = (uint8_t const*) secret; + + size_t i; + /* WASM uses operator overloads and doesn't need these. */ +#ifndef __wasm_simd128__ + /* { prime32_1, prime32_1 } */ + uint32x2_t const kPrimeLo = vdup_n_u32(XXH_PRIME32_1); + /* { 0, prime32_1, 0, prime32_1 } */ + uint32x4_t const kPrimeHi = vreinterpretq_u32_u64(vdupq_n_u64((xxh_u64)XXH_PRIME32_1 << 32)); +#endif + + /* AArch64 uses both scalar and neon at the same time */ + for (i = XXH3_NEON_LANES; i < XXH_ACC_NB; i++) { + XXH3_scalarScrambleRound(acc, secret, i); + } + for (i=0; i < XXH3_NEON_LANES / 2; i++) { + /* xacc[i] ^= (xacc[i] >> 47); */ + uint64x2_t acc_vec = xacc[i]; + uint64x2_t shifted = vshrq_n_u64(acc_vec, 47); + uint64x2_t data_vec = veorq_u64(acc_vec, shifted); + + /* xacc[i] ^= xsecret[i]; */ + uint64x2_t key_vec = XXH_vld1q_u64(xsecret + (i * 16)); + uint64x2_t data_key = veorq_u64(data_vec, key_vec); + /* xacc[i] *= XXH_PRIME32_1 */ +#ifdef __wasm_simd128__ + /* SIMD128 has multiply by u64x2, use it instead of expanding and scalarizing */ + xacc[i] = data_key * XXH_PRIME32_1; +#else + /* + * Expanded version with portable NEON intrinsics + * + * lo(x) * lo(y) + (hi(x) * lo(y) << 32) + * + * prod_hi = hi(data_key) * lo(prime) << 32 + * + * Since we only need 32 bits of this multiply a trick can be used, reinterpreting the vector + * as a uint32x4_t and multiplying by { 0, prime, 0, prime } to cancel out the unwanted bits + * and avoid the shift. + */ + uint32x4_t prod_hi = vmulq_u32 (vreinterpretq_u32_u64(data_key), kPrimeHi); + /* Extract low bits for vmlal_u32 */ + uint32x2_t data_key_lo = vmovn_u64(data_key); + /* xacc[i] = prod_hi + lo(data_key) * XXH_PRIME32_1; */ + xacc[i] = vmlal_u32(vreinterpretq_u64_u32(prod_hi), data_key_lo, kPrimeLo); +#endif + } + } +} +#endif + +#if (XXH_VECTOR == XXH_VSX) + +XXH_FORCE_INLINE void +XXH3_accumulate_512_vsx( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + /* presumed aligned */ + xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc; + xxh_u8 const* const xinput = (xxh_u8 const*) input; /* no alignment restriction */ + xxh_u8 const* const xsecret = (xxh_u8 const*) secret; /* no alignment restriction */ + xxh_u64x2 const v32 = { 32, 32 }; + size_t i; + for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) { + /* data_vec = xinput[i]; */ + xxh_u64x2 const data_vec = XXH_vec_loadu(xinput + 16*i); + /* key_vec = xsecret[i]; */ + xxh_u64x2 const key_vec = XXH_vec_loadu(xsecret + 16*i); + xxh_u64x2 const data_key = data_vec ^ key_vec; + /* shuffled = (data_key << 32) | (data_key >> 32); */ + xxh_u32x4 const shuffled = (xxh_u32x4)vec_rl(data_key, v32); + /* product = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)shuffled & 0xFFFFFFFF); */ + xxh_u64x2 const product = XXH_vec_mulo((xxh_u32x4)data_key, shuffled); + /* acc_vec = xacc[i]; */ + xxh_u64x2 acc_vec = xacc[i]; + acc_vec += product; + + /* swap high and low halves */ +#ifdef __s390x__ + acc_vec += vec_permi(data_vec, data_vec, 2); +#else + acc_vec += vec_xxpermdi(data_vec, data_vec, 2); +#endif + xacc[i] = acc_vec; + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(vsx) + +XXH_FORCE_INLINE void +XXH3_scrambleAcc_vsx(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + XXH_ASSERT((((size_t)acc) & 15) == 0); + + { xxh_aliasing_u64x2* const xacc = (xxh_aliasing_u64x2*) acc; + const xxh_u8* const xsecret = (const xxh_u8*) secret; + /* constants */ + xxh_u64x2 const v32 = { 32, 32 }; + xxh_u64x2 const v47 = { 47, 47 }; + xxh_u32x4 const prime = { XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1, XXH_PRIME32_1 }; + size_t i; + for (i = 0; i < XXH_STRIPE_LEN / sizeof(xxh_u64x2); i++) { + /* xacc[i] ^= (xacc[i] >> 47); */ + xxh_u64x2 const acc_vec = xacc[i]; + xxh_u64x2 const data_vec = acc_vec ^ (acc_vec >> v47); + + /* xacc[i] ^= xsecret[i]; */ + xxh_u64x2 const key_vec = XXH_vec_loadu(xsecret + 16*i); + xxh_u64x2 const data_key = data_vec ^ key_vec; + + /* xacc[i] *= XXH_PRIME32_1 */ + /* prod_lo = ((xxh_u64x2)data_key & 0xFFFFFFFF) * ((xxh_u64x2)prime & 0xFFFFFFFF); */ + xxh_u64x2 const prod_even = XXH_vec_mule((xxh_u32x4)data_key, prime); + /* prod_hi = ((xxh_u64x2)data_key >> 32) * ((xxh_u64x2)prime >> 32); */ + xxh_u64x2 const prod_odd = XXH_vec_mulo((xxh_u32x4)data_key, prime); + xacc[i] = prod_odd + (prod_even << v32); + } } +} + +#endif + +#if (XXH_VECTOR == XXH_SVE) + +XXH_FORCE_INLINE void +XXH3_accumulate_512_sve( void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + uint64_t *xacc = (uint64_t *)acc; + const uint64_t *xinput = (const uint64_t *)(const void *)input; + const uint64_t *xsecret = (const uint64_t *)(const void *)secret; + svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1); + uint64_t element_count = svcntd(); + if (element_count >= 8) { + svbool_t mask = svptrue_pat_b64(SV_VL8); + svuint64_t vacc = svld1_u64(mask, xacc); + ACCRND(vacc, 0); + svst1_u64(mask, xacc, vacc); + } else if (element_count == 2) { /* sve128 */ + svbool_t mask = svptrue_pat_b64(SV_VL2); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 2); + svuint64_t acc2 = svld1_u64(mask, xacc + 4); + svuint64_t acc3 = svld1_u64(mask, xacc + 6); + ACCRND(acc0, 0); + ACCRND(acc1, 2); + ACCRND(acc2, 4); + ACCRND(acc3, 6); + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 2, acc1); + svst1_u64(mask, xacc + 4, acc2); + svst1_u64(mask, xacc + 6, acc3); + } else { + svbool_t mask = svptrue_pat_b64(SV_VL4); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 4); + ACCRND(acc0, 0); + ACCRND(acc1, 4); + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 4, acc1); + } +} + +XXH_FORCE_INLINE void +XXH3_accumulate_sve(xxh_u64* XXH_RESTRICT acc, + const xxh_u8* XXH_RESTRICT input, + const xxh_u8* XXH_RESTRICT secret, + size_t nbStripes) +{ + if (nbStripes != 0) { + uint64_t *xacc = (uint64_t *)acc; + const uint64_t *xinput = (const uint64_t *)(const void *)input; + const uint64_t *xsecret = (const uint64_t *)(const void *)secret; + svuint64_t kSwap = sveor_n_u64_z(svptrue_b64(), svindex_u64(0, 1), 1); + uint64_t element_count = svcntd(); + if (element_count >= 8) { + svbool_t mask = svptrue_pat_b64(SV_VL8); + svuint64_t vacc = svld1_u64(mask, xacc + 0); + do { + /* svprfd(svbool_t, void *, enum svfprop); */ + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(vacc, 0); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, vacc); + } else if (element_count == 2) { /* sve128 */ + svbool_t mask = svptrue_pat_b64(SV_VL2); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 2); + svuint64_t acc2 = svld1_u64(mask, xacc + 4); + svuint64_t acc3 = svld1_u64(mask, xacc + 6); + do { + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(acc0, 0); + ACCRND(acc1, 2); + ACCRND(acc2, 4); + ACCRND(acc3, 6); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 2, acc1); + svst1_u64(mask, xacc + 4, acc2); + svst1_u64(mask, xacc + 6, acc3); + } else { + svbool_t mask = svptrue_pat_b64(SV_VL4); + svuint64_t acc0 = svld1_u64(mask, xacc + 0); + svuint64_t acc1 = svld1_u64(mask, xacc + 4); + do { + svprfd(mask, xinput + 128, SV_PLDL1STRM); + ACCRND(acc0, 0); + ACCRND(acc1, 4); + xinput += 8; + xsecret += 1; + nbStripes--; + } while (nbStripes != 0); + + svst1_u64(mask, xacc + 0, acc0); + svst1_u64(mask, xacc + 4, acc1); + } + } +} + +#endif + +/* scalar variants - universal */ + +#if defined(__aarch64__) && (defined(__GNUC__) || defined(__clang__)) +/* + * In XXH3_scalarRound(), GCC and Clang have a similar codegen issue, where they + * emit an excess mask and a full 64-bit multiply-add (MADD X-form). + * + * While this might not seem like much, as AArch64 is a 64-bit architecture, only + * big Cortex designs have a full 64-bit multiplier. + * + * On the little cores, the smaller 32-bit multiplier is used, and full 64-bit + * multiplies expand to 2-3 multiplies in microcode. This has a major penalty + * of up to 4 latency cycles and 2 stall cycles in the multiply pipeline. + * + * Thankfully, AArch64 still provides the 32-bit long multiply-add (UMADDL) which does + * not have this penalty and does the mask automatically. + */ +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc) +{ + xxh_u64 ret; + /* note: %x = 64-bit register, %w = 32-bit register */ + __asm__("umaddl %x0, %w1, %w2, %x3" : "=r" (ret) : "r" (lhs), "r" (rhs), "r" (acc)); + return ret; +} +#else +XXH_FORCE_INLINE xxh_u64 +XXH_mult32to64_add64(xxh_u64 lhs, xxh_u64 rhs, xxh_u64 acc) +{ + return XXH_mult32to64((xxh_u32)lhs, (xxh_u32)rhs) + acc; +} +#endif + +/*! + * @internal + * @brief Scalar round for @ref XXH3_accumulate_512_scalar(). + * + * This is extracted to its own function because the NEON path uses a combination + * of NEON and scalar. + */ +XXH_FORCE_INLINE void +XXH3_scalarRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT input, + void const* XXH_RESTRICT secret, + size_t lane) +{ + xxh_u64* xacc = (xxh_u64*) acc; + xxh_u8 const* xinput = (xxh_u8 const*) input; + xxh_u8 const* xsecret = (xxh_u8 const*) secret; + XXH_ASSERT(lane < XXH_ACC_NB); + XXH_ASSERT(((size_t)acc & (XXH_ACC_ALIGN-1)) == 0); + { + xxh_u64 const data_val = XXH_readLE64(xinput + lane * 8); + xxh_u64 const data_key = data_val ^ XXH_readLE64(xsecret + lane * 8); + xacc[lane ^ 1] += data_val; /* swap adjacent lanes */ + xacc[lane] = XXH_mult32to64_add64(data_key /* & 0xFFFFFFFF */, data_key >> 32, xacc[lane]); + } +} + +/*! + * @internal + * @brief Processes a 64 byte block of data using the scalar path. + */ +XXH_FORCE_INLINE void +XXH3_accumulate_512_scalar(void* XXH_RESTRICT acc, + const void* XXH_RESTRICT input, + const void* XXH_RESTRICT secret) +{ + size_t i; + /* ARM GCC refuses to unroll this loop, resulting in a 24% slowdown on ARMv6. */ +#if defined(__GNUC__) && !defined(__clang__) \ + && (defined(__arm__) || defined(__thumb2__)) \ + && defined(__ARM_FEATURE_UNALIGNED) /* no unaligned access just wastes bytes */ \ + && XXH_SIZE_OPT <= 0 +# pragma GCC unroll 8 +#endif + for (i=0; i < XXH_ACC_NB; i++) { + XXH3_scalarRound(acc, input, secret, i); + } +} +XXH_FORCE_INLINE XXH3_ACCUMULATE_TEMPLATE(scalar) + +/*! + * @internal + * @brief Scalar scramble step for @ref XXH3_scrambleAcc_scalar(). + * + * This is extracted to its own function because the NEON path uses a combination + * of NEON and scalar. + */ +XXH_FORCE_INLINE void +XXH3_scalarScrambleRound(void* XXH_RESTRICT acc, + void const* XXH_RESTRICT secret, + size_t lane) +{ + xxh_u64* const xacc = (xxh_u64*) acc; /* presumed aligned */ + const xxh_u8* const xsecret = (const xxh_u8*) secret; /* no alignment restriction */ + XXH_ASSERT((((size_t)acc) & (XXH_ACC_ALIGN-1)) == 0); + XXH_ASSERT(lane < XXH_ACC_NB); + { + xxh_u64 const key64 = XXH_readLE64(xsecret + lane * 8); + xxh_u64 acc64 = xacc[lane]; + acc64 = XXH_xorshift64(acc64, 47); + acc64 ^= key64; + acc64 *= XXH_PRIME32_1; + xacc[lane] = acc64; + } +} + +/*! + * @internal + * @brief Scrambles the accumulators after a large chunk has been read + */ +XXH_FORCE_INLINE void +XXH3_scrambleAcc_scalar(void* XXH_RESTRICT acc, const void* XXH_RESTRICT secret) +{ + size_t i; + for (i=0; i < XXH_ACC_NB; i++) { + XXH3_scalarScrambleRound(acc, secret, i); + } +} + +XXH_FORCE_INLINE void +XXH3_initCustomSecret_scalar(void* XXH_RESTRICT customSecret, xxh_u64 seed64) +{ + /* + * We need a separate pointer for the hack below, + * which requires a non-const pointer. + * Any decent compiler will optimize this out otherwise. + */ + const xxh_u8* kSecretPtr = XXH3_kSecret; + XXH_STATIC_ASSERT((XXH_SECRET_DEFAULT_SIZE & 15) == 0); + +#if defined(__GNUC__) && defined(__aarch64__) + /* + * UGLY HACK: + * GCC and Clang generate a bunch of MOV/MOVK pairs for aarch64, and they are + * placed sequentially, in order, at the top of the unrolled loop. + * + * While MOVK is great for generating constants (2 cycles for a 64-bit + * constant compared to 4 cycles for LDR), it fights for bandwidth with + * the arithmetic instructions. + * + * I L S + * MOVK + * MOVK + * MOVK + * MOVK + * ADD + * SUB STR + * STR + * By forcing loads from memory (as the asm line causes the compiler to assume + * that XXH3_kSecretPtr has been changed), the pipelines are used more + * efficiently: + * I L S + * LDR + * ADD LDR + * SUB STR + * STR + * + * See XXH3_NEON_LANES for details on the pipsline. + * + * XXH3_64bits_withSeed, len == 256, Snapdragon 835 + * without hack: 2654.4 MB/s + * with hack: 3202.9 MB/s + */ + XXH_COMPILER_GUARD(kSecretPtr); +#endif + { int const nbRounds = XXH_SECRET_DEFAULT_SIZE / 16; + int i; + for (i=0; i < nbRounds; i++) { + /* + * The asm hack causes the compiler to assume that kSecretPtr aliases with + * customSecret, and on aarch64, this prevented LDP from merging two + * loads together for free. Putting the loads together before the stores + * properly generates LDP. + */ + xxh_u64 lo = XXH_readLE64(kSecretPtr + 16*i) + seed64; + xxh_u64 hi = XXH_readLE64(kSecretPtr + 16*i + 8) - seed64; + XXH_writeLE64((xxh_u8*)customSecret + 16*i, lo); + XXH_writeLE64((xxh_u8*)customSecret + 16*i + 8, hi); + } } +} + + +typedef void (*XXH3_f_accumulate)(xxh_u64* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, const xxh_u8* XXH_RESTRICT, size_t); +typedef void (*XXH3_f_scrambleAcc)(void* XXH_RESTRICT, const void*); +typedef void (*XXH3_f_initCustomSecret)(void* XXH_RESTRICT, xxh_u64); + + +#if (XXH_VECTOR == XXH_AVX512) + +#define XXH3_accumulate_512 XXH3_accumulate_512_avx512 +#define XXH3_accumulate XXH3_accumulate_avx512 +#define XXH3_scrambleAcc XXH3_scrambleAcc_avx512 +#define XXH3_initCustomSecret XXH3_initCustomSecret_avx512 + +#elif (XXH_VECTOR == XXH_AVX2) + +#define XXH3_accumulate_512 XXH3_accumulate_512_avx2 +#define XXH3_accumulate XXH3_accumulate_avx2 +#define XXH3_scrambleAcc XXH3_scrambleAcc_avx2 +#define XXH3_initCustomSecret XXH3_initCustomSecret_avx2 + +#elif (XXH_VECTOR == XXH_SSE2) + +#define XXH3_accumulate_512 XXH3_accumulate_512_sse2 +#define XXH3_accumulate XXH3_accumulate_sse2 +#define XXH3_scrambleAcc XXH3_scrambleAcc_sse2 +#define XXH3_initCustomSecret XXH3_initCustomSecret_sse2 + +#elif (XXH_VECTOR == XXH_NEON) + +#define XXH3_accumulate_512 XXH3_accumulate_512_neon +#define XXH3_accumulate XXH3_accumulate_neon +#define XXH3_scrambleAcc XXH3_scrambleAcc_neon +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#elif (XXH_VECTOR == XXH_VSX) + +#define XXH3_accumulate_512 XXH3_accumulate_512_vsx +#define XXH3_accumulate XXH3_accumulate_vsx +#define XXH3_scrambleAcc XXH3_scrambleAcc_vsx +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#elif (XXH_VECTOR == XXH_SVE) +#define XXH3_accumulate_512 XXH3_accumulate_512_sve +#define XXH3_accumulate XXH3_accumulate_sve +#define XXH3_scrambleAcc XXH3_scrambleAcc_scalar +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#else /* scalar */ + +#define XXH3_accumulate_512 XXH3_accumulate_512_scalar +#define XXH3_accumulate XXH3_accumulate_scalar +#define XXH3_scrambleAcc XXH3_scrambleAcc_scalar +#define XXH3_initCustomSecret XXH3_initCustomSecret_scalar + +#endif + +#if XXH_SIZE_OPT >= 1 /* don't do SIMD for initialization */ +# undef XXH3_initCustomSecret +# define XXH3_initCustomSecret XXH3_initCustomSecret_scalar +#endif + +XXH_FORCE_INLINE void +XXH3_hashLong_internal_loop(xxh_u64* XXH_RESTRICT acc, + const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + size_t const nbStripesPerBlock = (secretSize - XXH_STRIPE_LEN) / XXH_SECRET_CONSUME_RATE; + size_t const block_len = XXH_STRIPE_LEN * nbStripesPerBlock; + size_t const nb_blocks = (len - 1) / block_len; + + size_t n; + + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); + + for (n = 0; n < nb_blocks; n++) { + f_acc(acc, input + n*block_len, secret, nbStripesPerBlock); + f_scramble(acc, secret + secretSize - XXH_STRIPE_LEN); + } + + /* last partial block */ + XXH_ASSERT(len > XXH_STRIPE_LEN); + { size_t const nbStripes = ((len - 1) - (block_len * nb_blocks)) / XXH_STRIPE_LEN; + XXH_ASSERT(nbStripes <= (secretSize / XXH_SECRET_CONSUME_RATE)); + f_acc(acc, input + nb_blocks*block_len, secret, nbStripes); + + /* last stripe */ + { const xxh_u8* const p = input + len - XXH_STRIPE_LEN; +#define XXH_SECRET_LASTACC_START 7 /* not aligned on 8, last secret is different from acc & scrambler */ + XXH3_accumulate_512(acc, p, secret + secretSize - XXH_STRIPE_LEN - XXH_SECRET_LASTACC_START); + } } +} + +XXH_FORCE_INLINE xxh_u64 +XXH3_mix2Accs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret) +{ + return XXH3_mul128_fold64( + acc[0] ^ XXH_readLE64(secret), + acc[1] ^ XXH_readLE64(secret+8) ); +} + +static XXH64_hash_t +XXH3_mergeAccs(const xxh_u64* XXH_RESTRICT acc, const xxh_u8* XXH_RESTRICT secret, xxh_u64 start) +{ + xxh_u64 result64 = start; + size_t i = 0; + + for (i = 0; i < 4; i++) { + result64 += XXH3_mix2Accs(acc+2*i, secret + 16*i); +#if defined(__clang__) /* Clang */ \ + && (defined(__arm__) || defined(__thumb__)) /* ARMv7 */ \ + && (defined(__ARM_NEON) || defined(__ARM_NEON__)) /* NEON */ \ + && !defined(XXH_ENABLE_AUTOVECTORIZE) /* Define to disable */ + /* + * UGLY HACK: + * Prevent autovectorization on Clang ARMv7-a. Exact same problem as + * the one in XXH3_len_129to240_64b. Speeds up shorter keys > 240b. + * XXH3_64bits, len == 256, Snapdragon 835: + * without hack: 2063.7 MB/s + * with hack: 2560.7 MB/s + */ + XXH_COMPILER_GUARD(result64); +#endif + } + + return XXH3_avalanche(result64); +} + +#define XXH3_INIT_ACC { XXH_PRIME32_3, XXH_PRIME64_1, XXH_PRIME64_2, XXH_PRIME64_3, \ + XXH_PRIME64_4, XXH_PRIME32_2, XXH_PRIME64_5, XXH_PRIME32_1 } + +XXH_FORCE_INLINE XXH64_hash_t +XXH3_hashLong_64b_internal(const void* XXH_RESTRICT input, size_t len, + const void* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC; + + XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, (const xxh_u8*)secret, secretSize, f_acc, f_scramble); + + /* converge into final hash */ + XXH_STATIC_ASSERT(sizeof(acc) == 64); + /* do not align on 8, so that the secret is different from the accumulator */ +#define XXH_SECRET_MERGEACCS_START 11 + XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + return XXH3_mergeAccs(acc, (const xxh_u8*)secret + XXH_SECRET_MERGEACCS_START, (xxh_u64)len * XXH_PRIME64_1); +} + +/* + * It's important for performance to transmit secret's size (when it's static) + * so that the compiler can properly optimize the vectorized loop. + * This makes a big performance difference for "medium" keys (<1 KB) when using AVX instruction set. + * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE + * breaks -Og, this is XXH_NO_INLINE. + */ +XXH3_WITH_SECRET_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSecret(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; + return XXH3_hashLong_64b_internal(input, len, secret, secretLen, XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * It's preferable for performance that XXH3_hashLong is not inlined, + * as it results in a smaller function for small data, easier to the instruction cache. + * Note that inside this no_inline function, we do inline the internal loop, + * and provide a statically defined secret size to allow optimization of vector loop. + */ +XXH_NO_INLINE XXH_PUREF XXH64_hash_t +XXH3_hashLong_64b_default(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; (void)secret; (void)secretLen; + return XXH3_hashLong_64b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * XXH3_hashLong_64b_withSeed(): + * Generate a custom key based on alteration of default XXH3_kSecret with the seed, + * and then use this key for long mode hashing. + * + * This operation is decently fast but nonetheless costs a little bit of time. + * Try to avoid it whenever possible (typically when seed==0). + * + * It's important for performance that XXH3_hashLong is not inlined. Not sure + * why (uop cache maybe?), but the difference is large and easily measurable. + */ +XXH_FORCE_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSeed_internal(const void* input, size_t len, + XXH64_hash_t seed, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble, + XXH3_f_initCustomSecret f_initSec) +{ +#if XXH_SIZE_OPT <= 0 + if (seed == 0) + return XXH3_hashLong_64b_internal(input, len, + XXH3_kSecret, sizeof(XXH3_kSecret), + f_acc, f_scramble); +#endif + { XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE]; + f_initSec(secret, seed); + return XXH3_hashLong_64b_internal(input, len, secret, sizeof(secret), + f_acc, f_scramble); + } +} + +/* + * It's important for performance that XXH3_hashLong is not inlined. + */ +XXH_NO_INLINE XXH64_hash_t +XXH3_hashLong_64b_withSeed(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed, const xxh_u8* XXH_RESTRICT secret, size_t secretLen) +{ + (void)secret; (void)secretLen; + return XXH3_hashLong_64b_withSeed_internal(input, len, seed, + XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret); +} + + +typedef XXH64_hash_t (*XXH3_hashLong64_f)(const void* XXH_RESTRICT, size_t, + XXH64_hash_t, const xxh_u8* XXH_RESTRICT, size_t); + +XXH_FORCE_INLINE XXH64_hash_t +XXH3_64bits_internal(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen, + XXH3_hashLong64_f f_hashLong) +{ + XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN); + /* + * If an action is to be taken if `secretLen` condition is not respected, + * it should be done here. + * For now, it's a contract pre-condition. + * Adding a check and a branch here would cost performance at every hash. + * Also, note that function signature doesn't offer room to return an error. + */ + if (len <= 16) + return XXH3_len_0to16_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64); + if (len <= 128) + return XXH3_len_17to128_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_len_129to240_64b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + return f_hashLong(input, len, seed64, (const xxh_u8*)secret, secretLen); +} + + +/* === Public entry point === */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t XXH3_64bits(XXH_NOESCAPE const void* input, size_t length) +{ + return XXH3_64bits_internal(input, length, 0, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_default); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSecret(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_64bits_internal(input, length, 0, secret, secretSize, XXH3_hashLong_64b_withSecret); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSeed(XXH_NOESCAPE const void* input, size_t length, XXH64_hash_t seed) +{ + return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), XXH3_hashLong_64b_withSeed); +} + +XXH_PUBLIC_API XXH64_hash_t +XXH3_64bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t length, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + if (length <= XXH3_MIDSIZE_MAX) + return XXH3_64bits_internal(input, length, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL); + return XXH3_hashLong_64b_withSecret(input, length, seed, (const xxh_u8*)secret, secretSize); +} + + +/* === XXH3 streaming === */ +#ifndef XXH_NO_STREAM +/* + * Malloc's a pointer that is always aligned to align. + * + * This must be freed with `XXH_alignedFree()`. + * + * malloc typically guarantees 16 byte alignment on 64-bit systems and 8 byte + * alignment on 32-bit. This isn't enough for the 32 byte aligned loads in AVX2 + * or on 32-bit, the 16 byte aligned loads in SSE2 and NEON. + * + * This underalignment previously caused a rather obvious crash which went + * completely unnoticed due to XXH3_createState() not actually being tested. + * Credit to RedSpah for noticing this bug. + * + * The alignment is done manually: Functions like posix_memalign or _mm_malloc + * are avoided: To maintain portability, we would have to write a fallback + * like this anyways, and besides, testing for the existence of library + * functions without relying on external build tools is impossible. + * + * The method is simple: Overallocate, manually align, and store the offset + * to the original behind the returned pointer. + * + * Align must be a power of 2 and 8 <= align <= 128. + */ +static XXH_MALLOCF void* XXH_alignedMalloc(size_t s, size_t align) +{ + XXH_ASSERT(align <= 128 && align >= 8); /* range check */ + XXH_ASSERT((align & (align-1)) == 0); /* power of 2 */ + XXH_ASSERT(s != 0 && s < (s + align)); /* empty/overflow */ + { /* Overallocate to make room for manual realignment and an offset byte */ + xxh_u8* base = (xxh_u8*)XXH_malloc(s + align); + if (base != NULL) { + /* + * Get the offset needed to align this pointer. + * + * Even if the returned pointer is aligned, there will always be + * at least one byte to store the offset to the original pointer. + */ + size_t offset = align - ((size_t)base & (align - 1)); /* base % align */ + /* Add the offset for the now-aligned pointer */ + xxh_u8* ptr = base + offset; + + XXH_ASSERT((size_t)ptr % align == 0); + + /* Store the offset immediately before the returned pointer. */ + ptr[-1] = (xxh_u8)offset; + return ptr; + } + return NULL; + } +} +/* + * Frees an aligned pointer allocated by XXH_alignedMalloc(). Don't pass + * normal malloc'd pointers, XXH_alignedMalloc has a specific data layout. + */ +static void XXH_alignedFree(void* p) +{ + if (p != NULL) { + xxh_u8* ptr = (xxh_u8*)p; + /* Get the offset byte we added in XXH_malloc. */ + xxh_u8 offset = ptr[-1]; + /* Free the original malloc'd pointer */ + xxh_u8* base = ptr - offset; + XXH_free(base); + } +} +/*! @ingroup XXH3_family */ +/*! + * @brief Allocate an @ref XXH3_state_t. + * + * @return An allocated pointer of @ref XXH3_state_t on success. + * @return `NULL` on failure. + * + * @note Must be freed with XXH3_freeState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH3_state_t* XXH3_createState(void) +{ + XXH3_state_t* const state = (XXH3_state_t*)XXH_alignedMalloc(sizeof(XXH3_state_t), 64); + if (state==NULL) return NULL; + XXH3_INITSTATE(state); + return state; +} + +/*! @ingroup XXH3_family */ +/*! + * @brief Frees an @ref XXH3_state_t. + * + * @param statePtr A pointer to an @ref XXH3_state_t allocated with @ref XXH3_createState(). + * + * @return @ref XXH_OK. + * + * @note Must be allocated with XXH3_createState(). + * + * @see @ref streaming_example "Streaming Example" + */ +XXH_PUBLIC_API XXH_errorcode XXH3_freeState(XXH3_state_t* statePtr) +{ + XXH_alignedFree(statePtr); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API void +XXH3_copyState(XXH_NOESCAPE XXH3_state_t* dst_state, XXH_NOESCAPE const XXH3_state_t* src_state) +{ + XXH_memcpy(dst_state, src_state, sizeof(*dst_state)); +} + +static void +XXH3_reset_internal(XXH3_state_t* statePtr, + XXH64_hash_t seed, + const void* secret, size_t secretSize) +{ + size_t const initStart = offsetof(XXH3_state_t, bufferedSize); + size_t const initLength = offsetof(XXH3_state_t, nbStripesPerBlock) - initStart; + XXH_ASSERT(offsetof(XXH3_state_t, nbStripesPerBlock) > initStart); + XXH_ASSERT(statePtr != NULL); + /* set members from bufferedSize to nbStripesPerBlock (excluded) to 0 */ + memset((char*)statePtr + initStart, 0, initLength); + statePtr->acc[0] = XXH_PRIME32_3; + statePtr->acc[1] = XXH_PRIME64_1; + statePtr->acc[2] = XXH_PRIME64_2; + statePtr->acc[3] = XXH_PRIME64_3; + statePtr->acc[4] = XXH_PRIME64_4; + statePtr->acc[5] = XXH_PRIME32_2; + statePtr->acc[6] = XXH_PRIME64_5; + statePtr->acc[7] = XXH_PRIME32_1; + statePtr->seed = seed; + statePtr->useSeed = (seed != 0); + statePtr->extSecret = (const unsigned char*)secret; + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); + statePtr->secretLimit = secretSize - XXH_STRIPE_LEN; + statePtr->nbStripesPerBlock = statePtr->secretLimit / XXH_SECRET_CONSUME_RATE; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr) +{ + if (statePtr == NULL) return XXH_ERROR; + XXH3_reset_internal(statePtr, 0, XXH3_kSecret, XXH_SECRET_DEFAULT_SIZE); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + if (statePtr == NULL) return XXH_ERROR; + XXH3_reset_internal(statePtr, 0, secret, secretSize); + if (secret == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed) +{ + if (statePtr == NULL) return XXH_ERROR; + if (seed==0) return XXH3_64bits_reset(statePtr); + if ((seed != statePtr->seed) || (statePtr->extSecret != NULL)) + XXH3_initCustomSecret(statePtr->customSecret, seed); + XXH3_reset_internal(statePtr, seed, NULL, XXH_SECRET_DEFAULT_SIZE); + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed64) +{ + if (statePtr == NULL) return XXH_ERROR; + if (secret == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; + XXH3_reset_internal(statePtr, seed64, secret, secretSize); + statePtr->useSeed = 1; /* always, even if seed64==0 */ + return XXH_OK; +} + +/*! + * @internal + * @brief Processes a large input for XXH3_update() and XXH3_digest_long(). + * + * Unlike XXH3_hashLong_internal_loop(), this can process data that overlaps a block. + * + * @param acc Pointer to the 8 accumulator lanes + * @param nbStripesSoFarPtr In/out pointer to the number of leftover stripes in the block* + * @param nbStripesPerBlock Number of stripes in a block + * @param input Input pointer + * @param nbStripes Number of stripes to process + * @param secret Secret pointer + * @param secretLimit Offset of the last block in @p secret + * @param f_acc Pointer to an XXH3_accumulate implementation + * @param f_scramble Pointer to an XXH3_scrambleAcc implementation + * @return Pointer past the end of @p input after processing + */ +XXH_FORCE_INLINE const xxh_u8 * +XXH3_consumeStripes(xxh_u64* XXH_RESTRICT acc, + size_t* XXH_RESTRICT nbStripesSoFarPtr, size_t nbStripesPerBlock, + const xxh_u8* XXH_RESTRICT input, size_t nbStripes, + const xxh_u8* XXH_RESTRICT secret, size_t secretLimit, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + const xxh_u8* initialSecret = secret + *nbStripesSoFarPtr * XXH_SECRET_CONSUME_RATE; + /* Process full blocks */ + if (nbStripes >= (nbStripesPerBlock - *nbStripesSoFarPtr)) { + /* Process the initial partial block... */ + size_t nbStripesThisIter = nbStripesPerBlock - *nbStripesSoFarPtr; + + do { + /* Accumulate and scramble */ + f_acc(acc, input, initialSecret, nbStripesThisIter); + f_scramble(acc, secret + secretLimit); + input += nbStripesThisIter * XXH_STRIPE_LEN; + nbStripes -= nbStripesThisIter; + /* Then continue the loop with the full block size */ + nbStripesThisIter = nbStripesPerBlock; + initialSecret = secret; + } while (nbStripes >= nbStripesPerBlock); + *nbStripesSoFarPtr = 0; + } + /* Process a partial block */ + if (nbStripes > 0) { + f_acc(acc, input, initialSecret, nbStripes); + input += nbStripes * XXH_STRIPE_LEN; + *nbStripesSoFarPtr += nbStripes; + } + /* Return end pointer */ + return input; +} + +#ifndef XXH3_STREAM_USE_STACK +# if XXH_SIZE_OPT <= 0 && !defined(__clang__) /* clang doesn't need additional stack space */ +# define XXH3_STREAM_USE_STACK 1 +# endif +#endif +/* + * Both XXH3_64bits_update and XXH3_128bits_update use this routine. + */ +XXH_FORCE_INLINE XXH_errorcode +XXH3_update(XXH3_state_t* XXH_RESTRICT const state, + const xxh_u8* XXH_RESTRICT input, size_t len, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + if (input==NULL) { + XXH_ASSERT(len == 0); + return XXH_OK; + } + + XXH_ASSERT(state != NULL); + { const xxh_u8* const bEnd = input + len; + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; +#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1 + /* For some reason, gcc and MSVC seem to suffer greatly + * when operating accumulators directly into state. + * Operating into stack space seems to enable proper optimization. + * clang, on the other hand, doesn't seem to need this trick */ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[8]; + XXH_memcpy(acc, state->acc, sizeof(acc)); +#else + xxh_u64* XXH_RESTRICT const acc = state->acc; +#endif + state->totalLen += len; + XXH_ASSERT(state->bufferedSize <= XXH3_INTERNALBUFFER_SIZE); + + /* small input : just fill in tmp buffer */ + if (len <= XXH3_INTERNALBUFFER_SIZE - state->bufferedSize) { + XXH_memcpy(state->buffer + state->bufferedSize, input, len); + state->bufferedSize += (XXH32_hash_t)len; + return XXH_OK; + } + + /* total input is now > XXH3_INTERNALBUFFER_SIZE */ + #define XXH3_INTERNALBUFFER_STRIPES (XXH3_INTERNALBUFFER_SIZE / XXH_STRIPE_LEN) + XXH_STATIC_ASSERT(XXH3_INTERNALBUFFER_SIZE % XXH_STRIPE_LEN == 0); /* clean multiple */ + + /* + * Internal buffer is partially filled (always, except at beginning) + * Complete it, then consume it. + */ + if (state->bufferedSize) { + size_t const loadSize = XXH3_INTERNALBUFFER_SIZE - state->bufferedSize; + XXH_memcpy(state->buffer + state->bufferedSize, input, loadSize); + input += loadSize; + XXH3_consumeStripes(acc, + &state->nbStripesSoFar, state->nbStripesPerBlock, + state->buffer, XXH3_INTERNALBUFFER_STRIPES, + secret, state->secretLimit, + f_acc, f_scramble); + state->bufferedSize = 0; + } + XXH_ASSERT(input < bEnd); + if (bEnd - input > XXH3_INTERNALBUFFER_SIZE) { + size_t nbStripes = (size_t)(bEnd - 1 - input) / XXH_STRIPE_LEN; + input = XXH3_consumeStripes(acc, + &state->nbStripesSoFar, state->nbStripesPerBlock, + input, nbStripes, + secret, state->secretLimit, + f_acc, f_scramble); + XXH_memcpy(state->buffer + sizeof(state->buffer) - XXH_STRIPE_LEN, input - XXH_STRIPE_LEN, XXH_STRIPE_LEN); + + } + /* Some remaining input (always) : buffer it */ + XXH_ASSERT(input < bEnd); + XXH_ASSERT(bEnd - input <= XXH3_INTERNALBUFFER_SIZE); + XXH_ASSERT(state->bufferedSize == 0); + XXH_memcpy(state->buffer, input, (size_t)(bEnd-input)); + state->bufferedSize = (XXH32_hash_t)(bEnd-input); +#if defined(XXH3_STREAM_USE_STACK) && XXH3_STREAM_USE_STACK >= 1 + /* save stack accumulators into state */ + XXH_memcpy(state->acc, acc, sizeof(acc)); +#endif + } + + return XXH_OK; +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_64bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_update(state, (const xxh_u8*)input, len, + XXH3_accumulate, XXH3_scrambleAcc); +} + + +XXH_FORCE_INLINE void +XXH3_digest_long (XXH64_hash_t* acc, + const XXH3_state_t* state, + const unsigned char* secret) +{ + xxh_u8 lastStripe[XXH_STRIPE_LEN]; + const xxh_u8* lastStripePtr; + + /* + * Digest on a local copy. This way, the state remains unaltered, and it can + * continue ingesting more input afterwards. + */ + XXH_memcpy(acc, state->acc, sizeof(state->acc)); + if (state->bufferedSize >= XXH_STRIPE_LEN) { + /* Consume remaining stripes then point to remaining data in buffer */ + size_t const nbStripes = (state->bufferedSize - 1) / XXH_STRIPE_LEN; + size_t nbStripesSoFar = state->nbStripesSoFar; + XXH3_consumeStripes(acc, + &nbStripesSoFar, state->nbStripesPerBlock, + state->buffer, nbStripes, + secret, state->secretLimit, + XXH3_accumulate, XXH3_scrambleAcc); + lastStripePtr = state->buffer + state->bufferedSize - XXH_STRIPE_LEN; + } else { /* bufferedSize < XXH_STRIPE_LEN */ + /* Copy to temp buffer */ + size_t const catchupSize = XXH_STRIPE_LEN - state->bufferedSize; + XXH_ASSERT(state->bufferedSize > 0); /* there is always some input buffered */ + XXH_memcpy(lastStripe, state->buffer + sizeof(state->buffer) - catchupSize, catchupSize); + XXH_memcpy(lastStripe + catchupSize, state->buffer, state->bufferedSize); + lastStripePtr = lastStripe; + } + /* Last stripe */ + XXH3_accumulate_512(acc, + lastStripePtr, + secret + state->secretLimit - XXH_SECRET_LASTACC_START); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH64_hash_t XXH3_64bits_digest (XXH_NOESCAPE const XXH3_state_t* state) +{ + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; + if (state->totalLen > XXH3_MIDSIZE_MAX) { + XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB]; + XXH3_digest_long(acc, state, secret); + return XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)state->totalLen * XXH_PRIME64_1); + } + /* totalLen <= XXH3_MIDSIZE_MAX: digesting a short input */ + if (state->useSeed) + return XXH3_64bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed); + return XXH3_64bits_withSecret(state->buffer, (size_t)(state->totalLen), + secret, state->secretLimit + XXH_STRIPE_LEN); +} +#endif /* !XXH_NO_STREAM */ + + +/* ========================================== + * XXH3 128 bits (a.k.a XXH128) + * ========================================== + * XXH3's 128-bit variant has better mixing and strength than the 64-bit variant, + * even without counting the significantly larger output size. + * + * For example, extra steps are taken to avoid the seed-dependent collisions + * in 17-240 byte inputs (See XXH3_mix16B and XXH128_mix32B). + * + * This strength naturally comes at the cost of some speed, especially on short + * lengths. Note that longer hashes are about as fast as the 64-bit version + * due to it using only a slight modification of the 64-bit loop. + * + * XXH128 is also more oriented towards 64-bit machines. It is still extremely + * fast for a _128-bit_ hash on 32-bit (it usually clears XXH64). + */ + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_1to3_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + /* A doubled version of 1to3_64b with different constants. */ + XXH_ASSERT(input != NULL); + XXH_ASSERT(1 <= len && len <= 3); + XXH_ASSERT(secret != NULL); + /* + * len = 1: combinedl = { input[0], 0x01, input[0], input[0] } + * len = 2: combinedl = { input[1], 0x02, input[0], input[1] } + * len = 3: combinedl = { input[2], 0x03, input[0], input[1] } + */ + { xxh_u8 const c1 = input[0]; + xxh_u8 const c2 = input[len >> 1]; + xxh_u8 const c3 = input[len - 1]; + xxh_u32 const combinedl = ((xxh_u32)c1 <<16) | ((xxh_u32)c2 << 24) + | ((xxh_u32)c3 << 0) | ((xxh_u32)len << 8); + xxh_u32 const combinedh = XXH_rotl32(XXH_swap32(combinedl), 13); + xxh_u64 const bitflipl = (XXH_readLE32(secret) ^ XXH_readLE32(secret+4)) + seed; + xxh_u64 const bitfliph = (XXH_readLE32(secret+8) ^ XXH_readLE32(secret+12)) - seed; + xxh_u64 const keyed_lo = (xxh_u64)combinedl ^ bitflipl; + xxh_u64 const keyed_hi = (xxh_u64)combinedh ^ bitfliph; + XXH128_hash_t h128; + h128.low64 = XXH64_avalanche(keyed_lo); + h128.high64 = XXH64_avalanche(keyed_hi); + return h128; + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_4to8_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(4 <= len && len <= 8); + seed ^= (xxh_u64)XXH_swap32((xxh_u32)seed) << 32; + { xxh_u32 const input_lo = XXH_readLE32(input); + xxh_u32 const input_hi = XXH_readLE32(input + len - 4); + xxh_u64 const input_64 = input_lo + ((xxh_u64)input_hi << 32); + xxh_u64 const bitflip = (XXH_readLE64(secret+16) ^ XXH_readLE64(secret+24)) + seed; + xxh_u64 const keyed = input_64 ^ bitflip; + + /* Shift len to the left to ensure it is even, this avoids even multiplies. */ + XXH128_hash_t m128 = XXH_mult64to128(keyed, XXH_PRIME64_1 + (len << 2)); + + m128.high64 += (m128.low64 << 1); + m128.low64 ^= (m128.high64 >> 3); + + m128.low64 = XXH_xorshift64(m128.low64, 35); + m128.low64 *= PRIME_MX2; + m128.low64 = XXH_xorshift64(m128.low64, 28); + m128.high64 = XXH3_avalanche(m128.high64); + return m128; + } +} + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_9to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(input != NULL); + XXH_ASSERT(secret != NULL); + XXH_ASSERT(9 <= len && len <= 16); + { xxh_u64 const bitflipl = (XXH_readLE64(secret+32) ^ XXH_readLE64(secret+40)) - seed; + xxh_u64 const bitfliph = (XXH_readLE64(secret+48) ^ XXH_readLE64(secret+56)) + seed; + xxh_u64 const input_lo = XXH_readLE64(input); + xxh_u64 input_hi = XXH_readLE64(input + len - 8); + XXH128_hash_t m128 = XXH_mult64to128(input_lo ^ input_hi ^ bitflipl, XXH_PRIME64_1); + /* + * Put len in the middle of m128 to ensure that the length gets mixed to + * both the low and high bits in the 128x64 multiply below. + */ + m128.low64 += (xxh_u64)(len - 1) << 54; + input_hi ^= bitfliph; + /* + * Add the high 32 bits of input_hi to the high 32 bits of m128, then + * add the long product of the low 32 bits of input_hi and XXH_PRIME32_2 to + * the high 64 bits of m128. + * + * The best approach to this operation is different on 32-bit and 64-bit. + */ + if (sizeof(void *) < sizeof(xxh_u64)) { /* 32-bit */ + /* + * 32-bit optimized version, which is more readable. + * + * On 32-bit, it removes an ADC and delays a dependency between the two + * halves of m128.high64, but it generates an extra mask on 64-bit. + */ + m128.high64 += (input_hi & 0xFFFFFFFF00000000ULL) + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2); + } else { + /* + * 64-bit optimized (albeit more confusing) version. + * + * Uses some properties of addition and multiplication to remove the mask: + * + * Let: + * a = input_hi.lo = (input_hi & 0x00000000FFFFFFFF) + * b = input_hi.hi = (input_hi & 0xFFFFFFFF00000000) + * c = XXH_PRIME32_2 + * + * a + (b * c) + * Inverse Property: x + y - x == y + * a + (b * (1 + c - 1)) + * Distributive Property: x * (y + z) == (x * y) + (x * z) + * a + (b * 1) + (b * (c - 1)) + * Identity Property: x * 1 == x + * a + b + (b * (c - 1)) + * + * Substitute a, b, and c: + * input_hi.hi + input_hi.lo + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1)) + * + * Since input_hi.hi + input_hi.lo == input_hi, we get this: + * input_hi + ((xxh_u64)input_hi.lo * (XXH_PRIME32_2 - 1)) + */ + m128.high64 += input_hi + XXH_mult32to64((xxh_u32)input_hi, XXH_PRIME32_2 - 1); + } + /* m128 ^= XXH_swap64(m128 >> 64); */ + m128.low64 ^= XXH_swap64(m128.high64); + + { /* 128x64 multiply: h128 = m128 * XXH_PRIME64_2; */ + XXH128_hash_t h128 = XXH_mult64to128(m128.low64, XXH_PRIME64_2); + h128.high64 += m128.high64 * XXH_PRIME64_2; + + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = XXH3_avalanche(h128.high64); + return h128; + } } +} + +/* + * Assumption: `secret` size is >= XXH3_SECRET_SIZE_MIN + */ +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_0to16_128b(const xxh_u8* input, size_t len, const xxh_u8* secret, XXH64_hash_t seed) +{ + XXH_ASSERT(len <= 16); + { if (len > 8) return XXH3_len_9to16_128b(input, len, secret, seed); + if (len >= 4) return XXH3_len_4to8_128b(input, len, secret, seed); + if (len) return XXH3_len_1to3_128b(input, len, secret, seed); + { XXH128_hash_t h128; + xxh_u64 const bitflipl = XXH_readLE64(secret+64) ^ XXH_readLE64(secret+72); + xxh_u64 const bitfliph = XXH_readLE64(secret+80) ^ XXH_readLE64(secret+88); + h128.low64 = XXH64_avalanche(seed ^ bitflipl); + h128.high64 = XXH64_avalanche( seed ^ bitfliph); + return h128; + } } +} + +/* + * A bit slower than XXH3_mix16B, but handles multiply by zero better. + */ +XXH_FORCE_INLINE XXH128_hash_t +XXH128_mix32B(XXH128_hash_t acc, const xxh_u8* input_1, const xxh_u8* input_2, + const xxh_u8* secret, XXH64_hash_t seed) +{ + acc.low64 += XXH3_mix16B (input_1, secret+0, seed); + acc.low64 ^= XXH_readLE64(input_2) + XXH_readLE64(input_2 + 8); + acc.high64 += XXH3_mix16B (input_2, secret+16, seed); + acc.high64 ^= XXH_readLE64(input_1) + XXH_readLE64(input_1 + 8); + return acc; +} + + +XXH_FORCE_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_17to128_128b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(16 < len && len <= 128); + + { XXH128_hash_t acc; + acc.low64 = len * XXH_PRIME64_1; + acc.high64 = 0; + +#if XXH_SIZE_OPT >= 1 + { + /* Smaller, but slightly slower. */ + unsigned int i = (unsigned int)(len - 1) / 32; + do { + acc = XXH128_mix32B(acc, input+16*i, input+len-16*(i+1), secret+32*i, seed); + } while (i-- != 0); + } +#else + if (len > 32) { + if (len > 64) { + if (len > 96) { + acc = XXH128_mix32B(acc, input+48, input+len-64, secret+96, seed); + } + acc = XXH128_mix32B(acc, input+32, input+len-48, secret+64, seed); + } + acc = XXH128_mix32B(acc, input+16, input+len-32, secret+32, seed); + } + acc = XXH128_mix32B(acc, input, input+len-16, secret, seed); +#endif + { XXH128_hash_t h128; + h128.low64 = acc.low64 + acc.high64; + h128.high64 = (acc.low64 * XXH_PRIME64_1) + + (acc.high64 * XXH_PRIME64_4) + + ((len - seed) * XXH_PRIME64_2); + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64); + return h128; + } + } +} + +XXH_NO_INLINE XXH_PUREF XXH128_hash_t +XXH3_len_129to240_128b(const xxh_u8* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH64_hash_t seed) +{ + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); (void)secretSize; + XXH_ASSERT(128 < len && len <= XXH3_MIDSIZE_MAX); + + { XXH128_hash_t acc; + unsigned i; + acc.low64 = len * XXH_PRIME64_1; + acc.high64 = 0; + /* + * We set as `i` as offset + 32. We do this so that unchanged + * `len` can be used as upper bound. This reaches a sweet spot + * where both x86 and aarch64 get simple agen and good codegen + * for the loop. + */ + for (i = 32; i < 160; i += 32) { + acc = XXH128_mix32B(acc, + input + i - 32, + input + i - 16, + secret + i - 32, + seed); + } + acc.low64 = XXH3_avalanche(acc.low64); + acc.high64 = XXH3_avalanche(acc.high64); + /* + * NB: `i <= len` will duplicate the last 32-bytes if + * len % 32 was zero. This is an unfortunate necessity to keep + * the hash result stable. + */ + for (i=160; i <= len; i += 32) { + acc = XXH128_mix32B(acc, + input + i - 32, + input + i - 16, + secret + XXH3_MIDSIZE_STARTOFFSET + i - 160, + seed); + } + /* last bytes */ + acc = XXH128_mix32B(acc, + input + len - 16, + input + len - 32, + secret + XXH3_SECRET_SIZE_MIN - XXH3_MIDSIZE_LASTOFFSET - 16, + (XXH64_hash_t)0 - seed); + + { XXH128_hash_t h128; + h128.low64 = acc.low64 + acc.high64; + h128.high64 = (acc.low64 * XXH_PRIME64_1) + + (acc.high64 * XXH_PRIME64_4) + + ((len - seed) * XXH_PRIME64_2); + h128.low64 = XXH3_avalanche(h128.low64); + h128.high64 = (XXH64_hash_t)0 - XXH3_avalanche(h128.high64); + return h128; + } + } +} + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_hashLong_128b_internal(const void* XXH_RESTRICT input, size_t len, + const xxh_u8* XXH_RESTRICT secret, size_t secretSize, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble) +{ + XXH_ALIGN(XXH_ACC_ALIGN) xxh_u64 acc[XXH_ACC_NB] = XXH3_INIT_ACC; + + XXH3_hashLong_internal_loop(acc, (const xxh_u8*)input, len, secret, secretSize, f_acc, f_scramble); + + /* converge into final hash */ + XXH_STATIC_ASSERT(sizeof(acc) == 64); + XXH_ASSERT(secretSize >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + { XXH128_hash_t h128; + h128.low64 = XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)len * XXH_PRIME64_1); + h128.high64 = XXH3_mergeAccs(acc, + secret + secretSize + - sizeof(acc) - XXH_SECRET_MERGEACCS_START, + ~((xxh_u64)len * XXH_PRIME64_2)); + return h128; + } +} + +/* + * It's important for performance that XXH3_hashLong() is not inlined. + */ +XXH_NO_INLINE XXH_PUREF XXH128_hash_t +XXH3_hashLong_128b_default(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; (void)secret; (void)secretLen; + return XXH3_hashLong_128b_internal(input, len, XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_accumulate, XXH3_scrambleAcc); +} + +/* + * It's important for performance to pass @p secretLen (when it's static) + * to the compiler, so that it can properly optimize the vectorized loop. + * + * When the secret size is unknown, or on GCC 12 where the mix of NO_INLINE and FORCE_INLINE + * breaks -Og, this is XXH_NO_INLINE. + */ +XXH3_WITH_SECRET_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSecret(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)seed64; + return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, secretLen, + XXH3_accumulate, XXH3_scrambleAcc); +} + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSeed_internal(const void* XXH_RESTRICT input, size_t len, + XXH64_hash_t seed64, + XXH3_f_accumulate f_acc, + XXH3_f_scrambleAcc f_scramble, + XXH3_f_initCustomSecret f_initSec) +{ + if (seed64 == 0) + return XXH3_hashLong_128b_internal(input, len, + XXH3_kSecret, sizeof(XXH3_kSecret), + f_acc, f_scramble); + { XXH_ALIGN(XXH_SEC_ALIGN) xxh_u8 secret[XXH_SECRET_DEFAULT_SIZE]; + f_initSec(secret, seed64); + return XXH3_hashLong_128b_internal(input, len, (const xxh_u8*)secret, sizeof(secret), + f_acc, f_scramble); + } +} + +/* + * It's important for performance that XXH3_hashLong is not inlined. + */ +XXH_NO_INLINE XXH128_hash_t +XXH3_hashLong_128b_withSeed(const void* input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen) +{ + (void)secret; (void)secretLen; + return XXH3_hashLong_128b_withSeed_internal(input, len, seed64, + XXH3_accumulate, XXH3_scrambleAcc, XXH3_initCustomSecret); +} + +typedef XXH128_hash_t (*XXH3_hashLong128_f)(const void* XXH_RESTRICT, size_t, + XXH64_hash_t, const void* XXH_RESTRICT, size_t); + +XXH_FORCE_INLINE XXH128_hash_t +XXH3_128bits_internal(const void* input, size_t len, + XXH64_hash_t seed64, const void* XXH_RESTRICT secret, size_t secretLen, + XXH3_hashLong128_f f_hl128) +{ + XXH_ASSERT(secretLen >= XXH3_SECRET_SIZE_MIN); + /* + * If an action is to be taken if `secret` conditions are not respected, + * it should be done here. + * For now, it's a contract pre-condition. + * Adding a check and a branch here would cost performance at every hash. + */ + if (len <= 16) + return XXH3_len_0to16_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, seed64); + if (len <= 128) + return XXH3_len_17to128_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_len_129to240_128b((const xxh_u8*)input, len, (const xxh_u8*)secret, secretLen, seed64); + return f_hl128(input, len, seed64, secret, secretLen); +} + + +/* === Public XXH128 API === */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t XXH3_128bits(XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_128bits_internal(input, len, 0, + XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_hashLong_128b_default); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSecret(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_128bits_internal(input, len, 0, + (const xxh_u8*)secret, secretSize, + XXH3_hashLong_128b_withSecret); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSeed(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed) +{ + return XXH3_128bits_internal(input, len, seed, + XXH3_kSecret, sizeof(XXH3_kSecret), + XXH3_hashLong_128b_withSeed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH3_128bits_withSecretandSeed(XXH_NOESCAPE const void* input, size_t len, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + if (len <= XXH3_MIDSIZE_MAX) + return XXH3_128bits_internal(input, len, seed, XXH3_kSecret, sizeof(XXH3_kSecret), NULL); + return XXH3_hashLong_128b_withSecret(input, len, seed, secret, secretSize); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH128(XXH_NOESCAPE const void* input, size_t len, XXH64_hash_t seed) +{ + return XXH3_128bits_withSeed(input, len, seed); +} + + +/* === XXH3 128-bit streaming === */ +#ifndef XXH_NO_STREAM +/* + * All initialization and update functions are identical to 64-bit streaming variant. + * The only difference is the finalization routine. + */ + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset(XXH_NOESCAPE XXH3_state_t* statePtr) +{ + return XXH3_64bits_reset(statePtr); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecret(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize) +{ + return XXH3_64bits_reset_withSecret(statePtr, secret, secretSize); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH64_hash_t seed) +{ + return XXH3_64bits_reset_withSeed(statePtr, seed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_reset_withSecretandSeed(XXH_NOESCAPE XXH3_state_t* statePtr, XXH_NOESCAPE const void* secret, size_t secretSize, XXH64_hash_t seed) +{ + return XXH3_64bits_reset_withSecretandSeed(statePtr, secret, secretSize, seed); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_128bits_update(XXH_NOESCAPE XXH3_state_t* state, XXH_NOESCAPE const void* input, size_t len) +{ + return XXH3_64bits_update(state, input, len); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t XXH3_128bits_digest (XXH_NOESCAPE const XXH3_state_t* state) +{ + const unsigned char* const secret = (state->extSecret == NULL) ? state->customSecret : state->extSecret; + if (state->totalLen > XXH3_MIDSIZE_MAX) { + XXH_ALIGN(XXH_ACC_ALIGN) XXH64_hash_t acc[XXH_ACC_NB]; + XXH3_digest_long(acc, state, secret); + XXH_ASSERT(state->secretLimit + XXH_STRIPE_LEN >= sizeof(acc) + XXH_SECRET_MERGEACCS_START); + { XXH128_hash_t h128; + h128.low64 = XXH3_mergeAccs(acc, + secret + XXH_SECRET_MERGEACCS_START, + (xxh_u64)state->totalLen * XXH_PRIME64_1); + h128.high64 = XXH3_mergeAccs(acc, + secret + state->secretLimit + XXH_STRIPE_LEN + - sizeof(acc) - XXH_SECRET_MERGEACCS_START, + ~((xxh_u64)state->totalLen * XXH_PRIME64_2)); + return h128; + } + } + /* len <= XXH3_MIDSIZE_MAX : short code */ + if (state->useSeed) + return XXH3_128bits_withSeed(state->buffer, (size_t)state->totalLen, state->seed); + return XXH3_128bits_withSecret(state->buffer, (size_t)(state->totalLen), + secret, state->secretLimit + XXH_STRIPE_LEN); +} +#endif /* !XXH_NO_STREAM */ +/* 128-bit utility functions */ + +#include /* memcmp, memcpy */ + +/* return : 1 is equal, 0 if different */ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API int XXH128_isEqual(XXH128_hash_t h1, XXH128_hash_t h2) +{ + /* note : XXH128_hash_t is compact, it has no padding byte */ + return !(memcmp(&h1, &h2, sizeof(h1))); +} + +/* This prototype is compatible with stdlib's qsort(). + * @return : >0 if *h128_1 > *h128_2 + * <0 if *h128_1 < *h128_2 + * =0 if *h128_1 == *h128_2 */ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API int XXH128_cmp(XXH_NOESCAPE const void* h128_1, XXH_NOESCAPE const void* h128_2) +{ + XXH128_hash_t const h1 = *(const XXH128_hash_t*)h128_1; + XXH128_hash_t const h2 = *(const XXH128_hash_t*)h128_2; + int const hcmp = (h1.high64 > h2.high64) - (h2.high64 > h1.high64); + /* note : bets that, in most cases, hash values are different */ + if (hcmp) return hcmp; + return (h1.low64 > h2.low64) - (h2.low64 > h1.low64); +} + + +/*====== Canonical representation ======*/ +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API void +XXH128_canonicalFromHash(XXH_NOESCAPE XXH128_canonical_t* dst, XXH128_hash_t hash) +{ + XXH_STATIC_ASSERT(sizeof(XXH128_canonical_t) == sizeof(XXH128_hash_t)); + if (XXH_CPU_LITTLE_ENDIAN) { + hash.high64 = XXH_swap64(hash.high64); + hash.low64 = XXH_swap64(hash.low64); + } + XXH_memcpy(dst, &hash.high64, sizeof(hash.high64)); + XXH_memcpy((char*)dst + sizeof(hash.high64), &hash.low64, sizeof(hash.low64)); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH128_hash_t +XXH128_hashFromCanonical(XXH_NOESCAPE const XXH128_canonical_t* src) +{ + XXH128_hash_t h; + h.high64 = XXH_readBE64(src); + h.low64 = XXH_readBE64(src->digest + 8); + return h; +} + + + +/* ========================================== + * Secret generators + * ========================================== + */ +#define XXH_MIN(x, y) (((x) > (y)) ? (y) : (x)) + +XXH_FORCE_INLINE void XXH3_combine16(void* dst, XXH128_hash_t h128) +{ + XXH_writeLE64( dst, XXH_readLE64(dst) ^ h128.low64 ); + XXH_writeLE64( (char*)dst+8, XXH_readLE64((char*)dst+8) ^ h128.high64 ); +} + +/*! @ingroup XXH3_family */ +XXH_PUBLIC_API XXH_errorcode +XXH3_generateSecret(XXH_NOESCAPE void* secretBuffer, size_t secretSize, XXH_NOESCAPE const void* customSeed, size_t customSeedSize) +{ +#if (XXH_DEBUGLEVEL >= 1) + XXH_ASSERT(secretBuffer != NULL); + XXH_ASSERT(secretSize >= XXH3_SECRET_SIZE_MIN); +#else + /* production mode, assert() are disabled */ + if (secretBuffer == NULL) return XXH_ERROR; + if (secretSize < XXH3_SECRET_SIZE_MIN) return XXH_ERROR; +#endif + + if (customSeedSize == 0) { + customSeed = XXH3_kSecret; + customSeedSize = XXH_SECRET_DEFAULT_SIZE; + } +#if (XXH_DEBUGLEVEL >= 1) + XXH_ASSERT(customSeed != NULL); +#else + if (customSeed == NULL) return XXH_ERROR; +#endif + + /* Fill secretBuffer with a copy of customSeed - repeat as needed */ + { size_t pos = 0; + while (pos < secretSize) { + size_t const toCopy = XXH_MIN((secretSize - pos), customSeedSize); + memcpy((char*)secretBuffer + pos, customSeed, toCopy); + pos += toCopy; + } } + + { size_t const nbSeg16 = secretSize / 16; + size_t n; + XXH128_canonical_t scrambler; + XXH128_canonicalFromHash(&scrambler, XXH128(customSeed, customSeedSize, 0)); + for (n=0; n /* abort() */ +#include +#include +#include +#include +#include +#include + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +#include "xxhash/xxhash.h" +#include "sha1/sha1.h" +#include "sha256/sha256.h" + +#ifdef __cplusplus +} +#endif + + +// uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp') +#define UUID_NAMESPACE_LLAMA_CPP "ef001206-dadc-5f6d-a15f-3359e577d4e5" +#define UUID_NAMESPACE_LLAMA_CPP_HEX 0xef, 0x00, 0x12, 0x06, 0xda, 0xdc, 0x5f, 0x6d, 0xa1, 0x5f, 0x33, 0x59, 0xe5, 0x77, 0xd4, 0xe5 + + +#define HASH_TYPE_SHA256_STR "sha256" +#define HASH_TYPE_SHA1_STR "sha1" +#define HASH_TYPE_XXH64_STR "xxh64" +#define HASH_TYPE_UUID_STR "uuid" + + +typedef enum { + HASH_EXIT_SUCCESS = 0, // All hash has been generated or validated + HASH_EXIT_FAILURE = 1, // Generic Failure + HASH_EXIT_MISMATCH = 2, // Hash mismatched during validation + HASH_EXIT_MANIFEST_MISSING_ENTRY = 3, // Hash attempted validation but missing entry in manifest + HASH_EXIT_MANIFEST_UNKNOWN_HASH = 4, // Manifest is present, but we do not know any hash format within it + HASH_EXIT_MANIFEST_FILE_ERROR = 5 // Manifest is either missing or not a known format +} hash_exit_code_t; + + +typedef enum { + HASH_MANIFEST_NOT_FOUND, + HASH_MANIFEST_MISMATCH, + HASH_MANIFEST_OK, +} hash_manifest_result_t; + + +struct hash_params { + std::string input; + bool xxh64 = false; + bool sha1 = false; + bool sha256 = false; + bool uuid = false; + + bool no_layer = false; + + bool manifest_is_usable = false; + std::string manifest_file; +}; + +struct manifest_check_params { + bool xxh64 = false; + bool sha1 = false; + bool sha256 = false; + bool uuid = false; +}; + +static char const * hash_manifest_result_to_str(hash_manifest_result_t value) { + switch (value) { + case HASH_MANIFEST_NOT_FOUND: return "Not Found"; + case HASH_MANIFEST_MISMATCH: return "Mismatch"; + case HASH_MANIFEST_OK: return "Ok"; + } + return "?"; +} + +static char const * hash_exit_code_to_str(hash_exit_code_t value) { + switch (value) { + case HASH_EXIT_SUCCESS: return "Success"; + case HASH_EXIT_FAILURE: return "Failure"; + case HASH_EXIT_MISMATCH: return "Mismatch"; + case HASH_EXIT_MANIFEST_MISSING_ENTRY: return "Manifest Missing Entry"; + case HASH_EXIT_MANIFEST_UNKNOWN_HASH: return "Manifest Unknown Hash"; + case HASH_EXIT_MANIFEST_FILE_ERROR: return "Manifest File Error"; + } + return "?"; +} + +static void hash_print_usage(const char * executable) { + const hash_params default_params; + printf("\n"); + printf("usage: %s [options] GGUF_IN\n", executable); + printf("\n"); + printf("Hash a GGUF file"); + printf("\n"); + printf("options:\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" --xxh64 use xxh64 hash\n"); + printf(" --sha1 use sha1 hash\n"); + printf(" --sha256 use sha256 hash\n"); + printf(" --all use all hash\n"); + printf(" --no-layer exclude per layer hash\n"); + printf(" --uuid generate UUIDv5 ID\n"); + printf(" -c, --check verify against a manifest\n"); + printf("\n"); +} + +static void hash_params_parse_ex(int argc, const char ** argv, hash_params & params) { + std::string arg; + bool invalid_param = false; + const std::string arg_prefix = "--"; + + int arg_idx = 1; + for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { + arg = argv[arg_idx]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + + bool arg_found = false; + if (arg == "-h" || arg == "--help") { + hash_print_usage(argv[0]); + exit(0); + } + + if (arg == "--xxh64") { + arg_found = true; + params.xxh64 = true; + } + + if (arg == "--sha1") { + arg_found = true; + params.sha1 = true; + } + + if (arg == "--uuid") { + arg_found = true; + params.uuid = true; + } + + if (arg == "--sha256") { + arg_found = true; + params.sha256 = true; + } + + if (arg == "--all") { + arg_found = true; + params.sha256 = true; + params.sha1 = true; + params.xxh64 = true; + } + + if (arg == "--no-layer") { + arg_found = true; + params.no_layer = true; + } + + if (arg == "-c" || arg == "--check") { + if (++arg_idx >= argc) { + invalid_param = true; + break; + } + arg_found = true; + params.manifest_file = argv[arg_idx]; + } + + if (!arg_found) { + throw std::invalid_argument("error: unknown argument: " + arg); + } + } + + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument:" + arg); + } + + if (argc - arg_idx < 1) { + throw std::invalid_argument("error: bad arguments"); + } + + params.input = argv[arg_idx++]; +} + +static bool hash_params_parse(int argc, const char ** argv, hash_params & params) { + bool result = true; + try { + hash_params_parse_ex(argc, argv, params); + } + catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + hash_print_usage(argv[0]); + exit(EXIT_FAILURE); + } + return result; +} + +static bool manifest_type(const std::string & manifest_file, manifest_check_params & manifest_check) { + if (manifest_file.empty()) { + return false; + } + + std::ifstream file(manifest_file); + if (!file.is_open()) { + return false; + } + + std::string manifest_entry_line; + while (getline(file, manifest_entry_line)) { + // hash_type_str hash_str tensor_name + // e.g. 'xxh64 f66e9cd66a4396a0 test.gguf:tensor_0' + std::istringstream line_stream(manifest_entry_line); + std::string file_hash_type; + if (line_stream >> file_hash_type) { + if (file_hash_type == HASH_TYPE_SHA256_STR) { + manifest_check.sha256 = true; + } else if (file_hash_type == HASH_TYPE_SHA1_STR) { + manifest_check.sha1 = true; + } else if (file_hash_type == HASH_TYPE_XXH64_STR) { + manifest_check.xxh64 = true; + } else if (file_hash_type == HASH_TYPE_UUID_STR) { + manifest_check.uuid = true; + } + } + } + + return true; +} + +static hash_manifest_result_t manifest_verify(const std::string& manifest_file, const std::string& hash_type_str, const std::string& hash_str, const std::string& tensor_name) { + if (manifest_file.empty()) { + return HASH_MANIFEST_NOT_FOUND; + } + + std::ifstream file(manifest_file); + if (!file.is_open()) { + return HASH_MANIFEST_NOT_FOUND; + } + + std::string manifest_entry_line; + while (getline(file, manifest_entry_line)) { + std::istringstream line_stream(manifest_entry_line); + std::string file_hash_type; + std::string file_hash; + std::string file_tensor_name; + if (line_stream >> file_hash_type >> file_hash >> file_tensor_name) { + // Line parsed. Check hash validity + + if (file_hash_type != hash_type_str) { + continue; + } + + if (file_tensor_name != tensor_name) { + continue; + } + + return (file_hash == hash_str) ? HASH_MANIFEST_OK : HASH_MANIFEST_MISMATCH; + } + } + + return HASH_MANIFEST_NOT_FOUND; +} + +static void generate_uuidv5(const unsigned char sha1_digest[20], unsigned char uuid[16]) { + // Ref: https://www.rfc-editor.org/rfc/rfc9562.html#section-5.5 + // Assumes that digest was processed correctly with the expected namespace + for (int i = 0; i < 16; i++) { + uuid[i] = sha1_digest[i]; + } + + // Set bits corresponding to UUID ver 5 + uuid[ 6] &= ~(0xF << 4); + uuid[ 6] |= (5 << 4); + + // Set bits corresponding to UUID variant 0b10XX + uuid[ 8] &= ~(0xc << 4); + uuid[ 8] |= (0x8 << 4); +} + +static hash_exit_code_t gguf_hash(const hash_params & hash_params) { + const std::string & fname = hash_params.input; + struct ggml_context * ctx_data = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ &ctx_data, + }; + + // xxh64 init + XXH64_state_t* xxh64_model_hash_state = NULL; + if (hash_params.xxh64) { + xxh64_model_hash_state = XXH64_createState(); + if (xxh64_model_hash_state==NULL) { + abort(); + } + + XXH64_hash_t const seed = 0; + if (XXH64_reset(xxh64_model_hash_state, seed) == XXH_ERROR) { + abort(); + } + } + + // sha1 init + SHA1_CTX sha1_model_hash_ctx; + if (hash_params.sha1) { + SHA1Init(&sha1_model_hash_ctx); + } + + // sha256 init + sha256_t sha256_model_hash_ctx; + if (hash_params.sha256) { + sha256_init(&sha256_model_hash_ctx); + } + + // sha1 for uuid init + SHA1_CTX sha1_for_uuid_ctx; + if (hash_params.uuid) { + unsigned char const uuidv5_namespace[] = {UUID_NAMESPACE_LLAMA_CPP_HEX}; + SHA1Init(&sha1_for_uuid_ctx); + SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)uuidv5_namespace, sizeof(uuidv5_namespace)); + } + + struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); + const int n_tensors = gguf_get_n_tensors(ctx); + bool tensor_layer_in_manifest = false; + bool model_in_manifest = false; + bool tensor_layer_has_mismatch = false; + bool model_has_mismatch = false; + for (int i = 0; i < n_tensors; ++i) { + const char * name = gguf_get_tensor_name(ctx, i); + struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); + auto n_bytes = ggml_nbytes(cur); + auto *raw_data = cur->data; + const std::string tensor_layer_name = fname + ":" + name; + + if (hash_params.xxh64) { + + if (!hash_params.no_layer) { + // Per Layer Hash + XXH64_hash_t hash = XXH64(raw_data, n_bytes, 0); + + char hex_result[17]; + for (int offset = 0; offset < 8; offset++) { + unsigned int shift_bits_by = (8 * (8 - offset - 1)); + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_XXH64_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + if (XXH64_update(xxh64_model_hash_state, raw_data, n_bytes) == XXH_ERROR) abort(); + } + + if (hash_params.sha1) { + + if (!hash_params.no_layer) { + // Per Layer Hash + char result[21]; // sha1 outputs 20 bytes + SHA1( result, (const char *)raw_data, n_bytes); + + char hex_result[41] = {0}; + for (int offset = 0; offset < 20; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA1_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + SHA1Update( &sha1_model_hash_ctx, (unsigned char const *)raw_data, n_bytes); + } + + if (hash_params.sha256) { + + if (!hash_params.no_layer) { + // Per Layer Hash + unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes + sha256_hash((unsigned char*) result, (const unsigned char *)raw_data, n_bytes); + + char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0}; + for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + tensor_layer_in_manifest = true; + tensor_layer_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + tensor_layer_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA256_STR, hex_result, tensor_layer_name.c_str()); + } + } + + // Overall Model Hash + sha256_update( &sha256_model_hash_ctx, (unsigned char const *)raw_data, n_bytes); + } + + if (hash_params.uuid) { + SHA1Update( &sha1_for_uuid_ctx, (unsigned char const *)raw_data, n_bytes); + } + } + + if (hash_params.xxh64) { + XXH64_hash_t const hash = XXH64_digest(xxh64_model_hash_state); + + char hex_result[17]; + for (int offset = 0; offset < 8; offset++) { + unsigned int shift_bits_by = (8 * (8 - offset - 1)); + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_XXH64_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_XXH64_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.sha1) { + unsigned char result[21]; + SHA1Final(result, &sha1_model_hash_ctx); + + char hex_result[41]; + for (int offset = 0; offset < 20; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA1_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA1_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.sha256) { + unsigned char result[SHA256_DIGEST_SIZE]; // sha256 outputs 32 bytes + sha256_final( &sha256_model_hash_ctx, result); + + char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0}; + for (int offset = 0; offset < SHA256_DIGEST_SIZE; offset++) { + snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff); + } + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, hex_result, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_SHA256_STR, hex_result, fname.c_str()); + } + } + + if (hash_params.uuid) { + unsigned char result[21]; + SHA1Final(result, &sha1_for_uuid_ctx); + + unsigned char uuid[16]; + generate_uuidv5(result, uuid); + + char string_buffer[37] = {0}; + snprintf(string_buffer, sizeof(string_buffer), "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x", + uuid[0], uuid[1], uuid[2], uuid[3], + uuid[4], uuid[5], uuid[6], uuid[7], + uuid[8], uuid[9], uuid[10], uuid[11], + uuid[12], uuid[13], uuid[14], uuid[15]); + + if (hash_params.manifest_is_usable) { + hash_manifest_result_t verify_result = manifest_verify(hash_params.manifest_file, HASH_TYPE_SHA256_STR, string_buffer, fname); + + switch (verify_result) { + case HASH_MANIFEST_NOT_FOUND: + break; + case HASH_MANIFEST_MISMATCH: + model_in_manifest = true; + model_has_mismatch = true; + break; + case HASH_MANIFEST_OK: + model_in_manifest = true; + break; + } + + printf("%-8s %-s %s - %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str(), hash_manifest_result_to_str(verify_result)); + } else { + printf("%-8s %-s %s\n", HASH_TYPE_UUID_STR, string_buffer, fname.c_str()); + } + } + + + ggml_free(ctx_data); + gguf_free(ctx); + + + if (hash_params.manifest_is_usable) { + // In hash verification mode + + if (!model_in_manifest) { + // model missing in manifest? + + // Check tensor layer... + if (!tensor_layer_in_manifest) { + // Still missing? Maybe we are reading the wrong manifest. + return HASH_EXIT_MANIFEST_MISSING_ENTRY; + } + + if (tensor_layer_has_mismatch) { + // Per tensor check found error + return HASH_EXIT_FAILURE; + } + + // All per tensor layer checks passed? Sounds good enough. + return HASH_EXIT_SUCCESS; + } + + // Overall model check passed, but let's check per layer just in case + // If missing, we don't care too much as the overall model checked + if (tensor_layer_in_manifest && tensor_layer_has_mismatch) { + return HASH_EXIT_FAILURE; + } + + if (model_has_mismatch) { + // model has failed hash somewhere in the model + return HASH_EXIT_FAILURE; + } + + // All checks appears to be fine + return HASH_EXIT_SUCCESS; + } + + // In hash generation mode + return HASH_EXIT_SUCCESS; +} + +int main(int argc, const char ** argv) { + hash_params params; + manifest_check_params manifest_check; + hash_params_parse(argc, argv, params); + + if (!params.manifest_file.empty()) { + if (!manifest_type(params.manifest_file, manifest_check)) { + printf("ERROR cannot open manifest %s", params.manifest_file.c_str()); + return HASH_EXIT_MANIFEST_FILE_ERROR; + } + + if (!manifest_check.sha256 && !manifest_check.sha1 && !manifest_check.xxh64 && !manifest_check.uuid) { + printf("ERROR manifest does not have any known hash format in %s", params.manifest_file.c_str()); + return HASH_EXIT_MANIFEST_UNKNOWN_HASH; + } + + printf("manifest %s", params.manifest_file.c_str()); + + if (manifest_check.sha256) { + printf(" sha256"); + } + + if (manifest_check.sha1) { + printf(" sha1"); + } + + if (manifest_check.xxh64) { + printf(" xxh64"); + } + + if (manifest_check.uuid) { + printf(" uuid"); + } + + printf("\n"); + + // Autoselect the highest security hash if manifest is provided but + // the user has not specifically defined the hash they care about + if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) { + // User has not selected a specific value, pick most secure hash + if (manifest_check.sha256) { + params.sha256 = true; + } else if (manifest_check.sha1) { + params.sha1 = true; + } else if (manifest_check.xxh64) { + params.xxh64 = true; + } else if (manifest_check.uuid) { + params.uuid = true; + } + } + + params.manifest_is_usable = true; + } + + // By default if no swich argument provided, assume xxh64 + if (!params.xxh64 && !params.sha1 && !params.uuid && !params.sha256) { + params.xxh64 = true; + } + + hash_exit_code_t exit_code = gguf_hash(params); + + if (params.manifest_is_usable) { + printf("\nVerification results for %s - %s\n", params.manifest_file.c_str(), hash_exit_code_to_str(exit_code)); + } + + return exit_code; +} diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt new file mode 100644 index 000000000..c407e2f0a --- /dev/null +++ b/examples/gguf-split/CMakeLists.txt @@ -0,0 +1,5 @@ +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_17) diff --git a/examples/gguf-split/README.md b/examples/gguf-split/README.md new file mode 100644 index 000000000..ad1d86651 --- /dev/null +++ b/examples/gguf-split/README.md @@ -0,0 +1,10 @@ +## GGUF split Example + +CLI to split / merge GGUF files. + +**Command line options:** + +- `--split`: split GGUF to multiple GGUF, default operation. +- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`. +- `--split-max-tensors`: maximum tensors in each split: default(128) +- `--merge`: merge multiple GGUF to a single GGUF. diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp new file mode 100644 index 000000000..ef3ceb686 --- /dev/null +++ b/examples/gguf-split/gguf-split.cpp @@ -0,0 +1,584 @@ +#include "ggml.h" +#include "gguf.h" +#include "llama.h" +#include "common.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_WIN32) + #include + #ifndef PATH_MAX + #define PATH_MAX MAX_PATH + #endif + #include +#endif + +enum split_operation : uint8_t { + OP_NONE, + OP_SPLIT, + OP_MERGE, +}; + +enum split_mode : uint8_t { + MODE_NONE, + MODE_TENSOR, + MODE_SIZE, +}; + +struct split_params { + split_operation operation = OP_NONE; + split_mode mode = MODE_NONE; + size_t n_bytes_split = 0; + int n_split_tensors = 128; + std::string input; + std::string output; + bool no_tensor_first_split = false; + bool dry_run = false; +}; + +static void split_print_usage(const char * executable) { + const split_params default_params; + printf("\n"); + printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable); + printf("\n"); + printf("Apply a GGUF operation on IN to OUT."); + printf("\n"); + printf("options:\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" --version show version and build info\n"); + printf(" --split split GGUF to multiple GGUF (enabled by default)\n"); + printf(" --merge merge multiple GGUF to a single GGUF\n"); + printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors); + printf(" --split-max-size N(M|G) max size per split\n"); + printf(" --no-tensor-first-split do not add tensors to the first split (disabled by default)\n"); + printf(" --dry-run only print out a split plan and exit, without writing any new files\n"); + printf("\n"); +} + +// return convert string, for example "128M" or "4G" to number of bytes +static size_t split_str_to_n_bytes(std::string str) { + size_t n_bytes = 0; + int n; + if (str.back() == 'M') { + sscanf(str.c_str(), "%d", &n); + n_bytes = (size_t)n * 1000 * 1000; // megabytes + } else if (str.back() == 'G') { + sscanf(str.c_str(), "%d", &n); + n_bytes = (size_t)n * 1000 * 1000 * 1000; // gigabytes + } else { + throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back())); + } + if (n <= 0) { + throw std::invalid_argument("error: size must be a positive value"); + } + return n_bytes; +} + +static void split_params_parse_ex(int argc, const char ** argv, split_params & params) { + std::string arg; + const std::string arg_prefix = "--"; + bool invalid_param = false; + + int arg_idx = 1; + for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { + arg = argv[arg_idx]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + + bool arg_found = false; + if (arg == "-h" || arg == "--help") { + split_print_usage(argv[0]); + exit(0); + } else if (arg == "--version") { + fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); + exit(0); + } else if (arg == "--dry-run") { + arg_found = true; + params.dry_run = true; + } else if (arg == "--no-tensor-first-split") { + arg_found = true; + params.no_tensor_first_split = true; + } else if (arg == "--merge") { + arg_found = true; + if (params.operation != OP_NONE && params.operation != OP_MERGE) { + throw std::invalid_argument("error: either --split or --merge can be specified, but not both"); + } + params.operation = OP_MERGE; + } else if (arg == "--split") { + arg_found = true; + if (params.operation != OP_NONE && params.operation != OP_SPLIT) { + throw std::invalid_argument("error: either --split or --merge can be specified, but not both"); + } + params.operation = OP_SPLIT; + } else if (arg == "--split-max-tensors") { + if (++arg_idx >= argc) { + invalid_param = true; + break; + } + arg_found = true; + if (params.mode != MODE_NONE && params.mode != MODE_TENSOR) { + throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both"); + } + params.mode = MODE_TENSOR; + params.n_split_tensors = atoi(argv[arg_idx]); + } else if (arg == "--split-max-size") { + if (++arg_idx >= argc) { + invalid_param = true; + break; + } + arg_found = true; + if (params.mode != MODE_NONE && params.mode != MODE_SIZE) { + throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both"); + } + params.mode = MODE_SIZE; + params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]); + } + + if (!arg_found) { + throw std::invalid_argument("error: unknown argument: " + arg); + } + } + + // the operation is split if not specified + if (params.operation == OP_NONE) { + params.operation = OP_SPLIT; + } + // the split mode is by tensor if not specified + if (params.mode == MODE_NONE) { + params.mode = MODE_TENSOR; + } + + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument: " + arg); + } + + if (argc - arg_idx != 2) { + throw std::invalid_argument("error: bad arguments"); + } + + params.input = argv[arg_idx++]; + params.output = argv[arg_idx++]; +} + +static bool split_params_parse(int argc, const char ** argv, split_params & params) { + bool result = true; + try { + split_params_parse_ex(argc, argv, params); + } + catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + split_print_usage(argv[0]); + exit(EXIT_FAILURE); + } + return result; +} + +static void zeros(std::ofstream & file, size_t n) { + char zero = 0; + for (size_t i = 0; i < n; ++i) { + file.write(&zero, 1); + } +} + +struct split_strategy { + const split_params params; + std::ifstream & f_input; + struct gguf_context * ctx_gguf; + struct ggml_context * ctx_meta = NULL; + const int n_tensors; + + // one ctx_out per one output file + std::vector ctx_outs; + + // temporary buffer for reading in tensor data + std::vector read_buf; + + split_strategy(const split_params & params, + std::ifstream & f_input, + struct gguf_context * ctx_gguf, + struct ggml_context * ctx_meta) : + params(params), + f_input(f_input), + ctx_gguf(ctx_gguf), + ctx_meta(ctx_meta), + n_tensors(gguf_get_n_tensors(ctx_gguf)) { + + // because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits + int i_split = -1; + struct gguf_context * ctx_out = NULL; + auto new_ctx_out = [&](bool allow_no_tensors) { + i_split++; + if (ctx_out != NULL) { + if (gguf_get_n_tensors(ctx_out) == 0 && !allow_no_tensors) { + fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n"); + exit(EXIT_FAILURE); + } + ctx_outs.push_back(ctx_out); + } + ctx_out = gguf_init_empty(); + // Save all metadata in first split only + if (i_split == 0) { + gguf_set_kv(ctx_out, ctx_gguf); + } + gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split); + gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, 0); // placeholder + gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors); + }; + + // initialize ctx_out for the first split + new_ctx_out(false); + + // skip first split if no_tensor_first_split is set + if (params.no_tensor_first_split) { + new_ctx_out(true); + } + + // process tensors one by one + size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata) + for (int i = 0; i < n_tensors; ++i) { + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i)); + // calculate the "imaginary" size = the current size + next tensor size + size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT); + size_t next_tensors_size = curr_tensors_size + n_bytes; + if (should_split(i, next_tensors_size)) { + new_ctx_out(false); + curr_tensors_size = n_bytes; + } else { + curr_tensors_size = next_tensors_size; + } + gguf_add_tensor(ctx_out, t); + } + + // push the last ctx_out + ctx_outs.push_back(ctx_out); + + // set the correct n_split for all ctx_out + for (auto & ctx : ctx_outs) { + gguf_set_val_u16(ctx, LLM_KV_SPLIT_COUNT, ctx_outs.size()); + } + } + + ~split_strategy() { + for (auto & ctx_out : ctx_outs) { + gguf_free(ctx_out); + } + } + + bool should_split(int i_tensor, size_t next_size) { + if (params.mode == MODE_SIZE) { + // split by max size per file + return next_size > params.n_bytes_split; + } else if (params.mode == MODE_TENSOR) { + // split by number of tensors per file + return i_tensor > 0 && i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0; + } + // should never happen + GGML_ABORT("invalid mode"); + } + + void print_info() { + printf("n_split: %zu\n", ctx_outs.size()); + int i_split = 0; + for (auto & ctx_out : ctx_outs) { + // re-calculate the real gguf size for each split (= metadata size + total size of all tensors) + size_t total_size = gguf_get_meta_size(ctx_out); + for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) { + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i)); + total_size += ggml_nbytes(t); + } + total_size = total_size / 1000 / 1000; // convert to megabytes + printf("split %05d: n_tensors = %" PRIi64 ", total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size); + i_split++; + } + } + + void write() { + int i_split = 0; + int n_split = ctx_outs.size(); + for (auto & ctx_out : ctx_outs) { + // construct file path + char split_path[PATH_MAX] = {0}; + llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split); + + // open the output file + printf("Writing file %s ... ", split_path); + fflush(stdout); + std::ofstream fout = std::ofstream(split_path, std::ios::binary); + fout.exceptions(std::ofstream::failbit); // fail fast on write errors + + // write metadata + std::vector data(gguf_get_meta_size(ctx_out)); + gguf_get_meta_data(ctx_out, data.data()); + fout.write((const char *)data.data(), data.size()); + + // write tensors + for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) { + // read tensor meta and prepare buffer + const char * t_name = gguf_get_tensor_name(ctx_out, i); + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + auto n_bytes = ggml_nbytes(t); + read_buf.resize(n_bytes); + + // calculate offset + auto i_tensor_in = gguf_find_tensor(ctx_gguf, t_name); // idx of tensor in the input file + auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in); + + // copy tensor from input to output file + copy_file_to_file(f_input, fout, offset, n_bytes); + zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes); + } + + printf("done\n"); + // close the file + fout.close(); + i_split++; + } + } + + void copy_file_to_file(std::ifstream & f_in, std::ofstream & f_out, const size_t in_offset, const size_t len) { + // TODO: detect OS and use copy_file_range() here for better performance + if (read_buf.size() < len) { + read_buf.resize(len); + } + f_in.seekg(in_offset); + f_in.read((char *)read_buf.data(), len); + f_out.write((const char *)read_buf.data(), len); + } +}; + +static void gguf_split(const split_params & split_params) { + struct ggml_context * ctx_meta = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx_meta, + }; + + std::ifstream f_input(split_params.input.c_str(), std::ios::binary); + if (!f_input.is_open()) { + fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_params.input.c_str()); + exit(EXIT_FAILURE); + } + + auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params); + if (!ctx_gguf) { + fprintf(stderr, "%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str()); + exit(EXIT_FAILURE); + } + + // prepare the strategy + split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta); + int n_split = strategy.ctx_outs.size(); + strategy.print_info(); + + if (!split_params.dry_run) { + // write all output splits + strategy.write(); + } + + // done, clean up + gguf_free(ctx_gguf); + f_input.close(); + + fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n", + __func__, n_split, strategy.n_tensors); +} + +static void gguf_merge(const split_params & split_params) { + fprintf(stderr, "%s: %s -> %s\n", + __func__, split_params.input.c_str(), + split_params.output.c_str()); + int n_split = 1; + int total_tensors = 0; + + // avoid overwriting existing output file + if (std::ifstream(split_params.output.c_str())) { + fprintf(stderr, "%s: output file %s already exists\n", __func__, split_params.output.c_str()); + exit(EXIT_FAILURE); + } + + std::ofstream fout(split_params.output.c_str(), std::ios::binary); + fout.exceptions(std::ofstream::failbit); // fail fast on write errors + + auto * ctx_out = gguf_init_empty(); + + std::vector read_data; + std::vector ctx_metas; + std::vector ctx_ggufs; + + char split_path[PATH_MAX] = {0}; + strncpy(split_path, split_params.input.c_str(), sizeof(split_path) - 1); + char split_prefix[PATH_MAX] = {0}; + + // First pass to find KV and tensors metadata + for (int i_split = 0; i_split < n_split; i_split++) { + struct ggml_context * ctx_meta = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ true, + /*.ctx = */ &ctx_meta, + }; + + if (i_split > 0) { + llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split); + } + fprintf(stderr, "%s: reading metadata %s ...", __func__, split_path); + + auto * ctx_gguf = gguf_init_from_file(split_path, params); + if (!ctx_gguf) { + fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str()); + exit(EXIT_FAILURE); + } + ctx_ggufs.push_back(ctx_gguf); + ctx_metas.push_back(ctx_meta); + + if (i_split == 0) { + auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT); + if (key_n_split < 0) { + fprintf(stderr, + "\n%s: input file does not contain %s metadata\n", + __func__, + LLM_KV_SPLIT_COUNT); + gguf_free(ctx_gguf); + ggml_free(ctx_meta); + gguf_free(ctx_out); + fout.close(); + exit(EXIT_FAILURE); + } + + n_split = gguf_get_val_u16(ctx_gguf, key_n_split); + if (n_split < 1) { + fprintf(stderr, + "\n%s: input file does not contain a valid split count %d\n", + __func__, + n_split); + gguf_free(ctx_gguf); + ggml_free(ctx_meta); + gguf_free(ctx_out); + fout.close(); + exit(EXIT_FAILURE); + } + + // Verify the file naming and extract split_prefix + if (!llama_split_prefix(split_prefix, sizeof (split_prefix), split_path, i_split, n_split)) { + fprintf(stderr, "\n%s: unexpected input file name: %s" + " i_split=%d" + " n_split=%d\n", __func__, + split_path, i_split, n_split); + gguf_free(ctx_gguf); + ggml_free(ctx_meta); + gguf_free(ctx_out); + fout.close(); + exit(EXIT_FAILURE); + } + + // Do not trigger merge if we try to merge again the output + gguf_set_val_u16(ctx_gguf, LLM_KV_SPLIT_COUNT, 0); + + // Set metadata from the first split + gguf_set_kv(ctx_out, ctx_gguf); + } + + auto n_tensors = gguf_get_n_tensors(ctx_gguf); + for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) { + const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor); + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + gguf_add_tensor(ctx_out, t); + } + total_tensors += n_tensors; + + fprintf(stderr, "\033[3Ddone\n"); + } + + // placeholder for the meta data + { + auto meta_size = gguf_get_meta_size(ctx_out); + ::zeros(fout, meta_size); + } + + // Write tensors data + for (int i_split = 0; i_split < n_split; i_split++) { + llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split); + std::ifstream f_input(split_path, std::ios::binary); + if (!f_input.is_open()) { + fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_path); + for (uint32_t i = 0; i < ctx_ggufs.size(); i++) { + gguf_free(ctx_ggufs[i]); + ggml_free(ctx_metas[i]); + } + gguf_free(ctx_out); + fout.close(); + exit(EXIT_FAILURE); + } + fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path); + + auto * ctx_gguf = ctx_ggufs[i_split]; + auto * ctx_meta = ctx_metas[i_split]; + + auto n_tensors = gguf_get_n_tensors(ctx_gguf); + for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) { + const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor); + struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name); + + auto n_bytes = ggml_nbytes(t); + + if (read_data.size() < n_bytes) { + read_data.resize(n_bytes); + } + + auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor); + f_input.seekg(offset); + f_input.read((char *)read_data.data(), n_bytes); + + // write tensor data + padding + fout.write((const char *)read_data.data(), n_bytes); + zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes); + } + + gguf_free(ctx_gguf); + ggml_free(ctx_meta); + f_input.close(); + fprintf(stderr, "\033[3Ddone\n"); + } + + { + // go back to beginning of file and write the updated metadata + fout.seekp(0); + std::vector data(gguf_get_meta_size(ctx_out)); + gguf_get_meta_data(ctx_out, data.data()); + fout.write((const char *)data.data(), data.size()); + + fout.close(); + gguf_free(ctx_out); + } + + fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n", + __func__, split_params.output.c_str(), n_split, total_tensors); +} + +int main(int argc, const char ** argv) { + split_params params; + split_params_parse(argc, argv, params); + + switch (params.operation) { + case OP_SPLIT: gguf_split(params); + break; + case OP_MERGE: gguf_merge(params); + break; + default: split_print_usage(argv[0]); + exit(EXIT_FAILURE); + } + + return 0; +} diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh new file mode 100755 index 000000000..05a932227 --- /dev/null +++ b/examples/gguf-split/tests.sh @@ -0,0 +1,89 @@ +#!/bin/bash + +set -eu + +if [ $# -lt 1 ] +then + echo "usage: $0 path_to_build_binary [path_to_temp_folder]" + echo "example: $0 ../../build/bin ../../tmp" + exit 1 +fi + +if [ $# -gt 1 ] +then + TMP_DIR=$2 +else + TMP_DIR=/tmp +fi + +set -x + +SPLIT=$1/llama-gguf-split +MAIN=$1/llama-cli +WORK_PATH=$TMP_DIR/gguf-split +ROOT_DIR=$(realpath $(dirname $0)/../../) + +mkdir -p "$WORK_PATH" + +# Clean up in case of previously failed test +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf + +# 1. Get a model +( +cd $WORK_PATH +"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf +) +echo PASS + +# 2. Split with max tensors strategy +$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split +echo PASS +echo + +# 2b. Test the sharded model is loading properly +$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32 +echo PASS +echo + +# 3. Merge +$SPLIT --merge $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-merge.gguf +echo PASS +echo + +# 3b. Test the merged model is loading properly +$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32 +echo PASS +echo + +# 4. Split with no tensors in the first split +$SPLIT --split-max-tensors 32 --no-tensor-first-split $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors +echo PASS +echo + +# 4b. Test the sharded model is loading properly +$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32 +echo PASS +echo + +# 5. Merge +#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf +#echo PASS +#echo + +# 5b. Test the merged model is loading properly +#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32 +#echo PASS +#echo + +# 6. Split with size strategy +$SPLIT --split-max-size 2G $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-2G +echo PASS +echo + +# 6b. Test the sharded model is loading properly +$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32 +echo PASS +echo + +# Clean up +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt index 6481f087b..fb04eb83f 100644 --- a/examples/gguf/CMakeLists.txt +++ b/examples/gguf/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET gguf) +set(TARGET llama-gguf) add_executable(${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/gguf/gguf.cpp b/examples/gguf/gguf.cpp index e67be4fb2..f31989c8c 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -1,10 +1,9 @@ #include "ggml.h" +#include "gguf.h" #include -#include #include #include -#include #include #undef MIN @@ -92,6 +91,11 @@ static bool gguf_ex_read_0(const std::string & fname) { struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); + if (!ctx) { + fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname.c_str()); + return false; + } + printf("%s: version: %d\n", __func__, gguf_get_version(ctx)); printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); @@ -130,9 +134,10 @@ static bool gguf_ex_read_0(const std::string & fname) { for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); + const size_t size = gguf_get_tensor_size (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset); } } @@ -142,7 +147,7 @@ static bool gguf_ex_read_0(const std::string & fname) { } // read and create ggml_context containing the tensors and their data -static bool gguf_ex_read_1(const std::string & fname) { +static bool gguf_ex_read_1(const std::string & fname, bool check_data) { struct ggml_context * ctx_data = NULL; struct gguf_init_params params = { @@ -177,9 +182,10 @@ static bool gguf_ex_read_1(const std::string & fname) { for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); + const size_t size = gguf_get_tensor_size (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, size = %zu, offset = %zu\n", __func__, i, name, size, offset); } } @@ -194,7 +200,8 @@ static bool gguf_ex_read_1(const std::string & fname) { struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); - printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, ggml_n_dims(cur), cur->name, cur->data); + printf("%s: tensor[%d]: n_dims = %d, ne = (%d, %d, %d, %d), name = %s, data = %p\n", + __func__, i, ggml_n_dims(cur), int(cur->ne[0]), int(cur->ne[1]), int(cur->ne[2]), int(cur->ne[3]), cur->name, cur->data); // print first 10 elements const float * data = (const float *) cur->data; @@ -206,11 +213,12 @@ static bool gguf_ex_read_1(const std::string & fname) { printf("\n\n"); // check data - { + if (check_data) { const float * data = (const float *) cur->data; for (int j = 0; j < ggml_nelements(cur); ++j) { if (data[j] != 100 + i) { - fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]); + fprintf(stderr, "%s: tensor[%d], data[%d]: found %f, expected %f\n", __func__, i, j, data[j], float(100 + i)); + gguf_free(ctx); return false; } } @@ -228,9 +236,18 @@ static bool gguf_ex_read_1(const std::string & fname) { int main(int argc, char ** argv) { if (argc < 3) { - printf("usage: %s data.gguf r|w\n", argv[0]); + printf("usage: %s data.gguf r|w [n]\n", argv[0]); + printf("r: read data.gguf file\n"); + printf("w: write data.gguf file\n"); + printf("n: no check of tensor data\n"); return -1; } + bool check_data = true; + if (argc == 4) { + check_data = false; + } + + srand(123456); const std::string fname(argv[1]); const std::string mode (argv[2]); @@ -241,7 +258,7 @@ int main(int argc, char ** argv) { GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file"); } else if (mode == "r") { GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file"); - GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file"); + GGML_ASSERT(gguf_ex_read_1(fname, check_data) && "failed to read gguf file"); } return 0; diff --git a/examples/gpt4all.sh b/examples/gpt4all.sh deleted file mode 100755 index 5fd739e55..000000000 --- a/examples/gpt4all.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main --color --instruct --threads 4 \ - --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \ - --file ./prompts/alpaca.txt \ - --batch_size 8 --ctx_size 2048 -n -1 \ - --repeat_last_n 64 --repeat_penalty 1.3 \ - --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95 diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt new file mode 100644 index 000000000..fa1b4dc70 --- /dev/null +++ b/examples/gritlm/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-gritlm) +add_executable(${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_17) diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md new file mode 100644 index 000000000..786ba5736 --- /dev/null +++ b/examples/gritlm/README.md @@ -0,0 +1,62 @@ +## Generative Representational Instruction Tuning (GRIT) Example +[gritlm] a model which can generate embeddings as well as "normal" text +generation depending on the instructions in the prompt. + +* Paper: https://arxiv.org/pdf/2402.09906.pdf + +### Retrieval-Augmented Generation (RAG) use case +One use case for `gritlm` is to use it with RAG. If we recall how RAG works is +that we take documents that we want to use as context, to ground the large +language model (LLM), and we create token embeddings for them. We then store +these token embeddings in a vector database. + +When we perform a query, prompt the LLM, we will first create token embeddings +for the query and then search the vector database to retrieve the most +similar vectors, and return those documents so they can be passed to the LLM as +context. Then the query and the context will be passed to the LLM which will +have to _again_ create token embeddings for the query. But because gritlm is used +the first query can be cached and the second query tokenization generation does +not have to be performed at all. + +### Running the example +Download a Grit model: +```console +$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --outdir models +``` + +Run the example using the downloaded model: +```console +$ ./llama-gritlm -m models/gritlm-7b_q4_1.gguf + +Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605 +Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103 +Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112 +Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547 + +Oh, brave adventurer, who dared to climb +The lofty peak of Mt. Fuji in the night, +When shadows lurk and ghosts do roam, +And darkness reigns, a fearsome sight. + +Thou didst set out, with heart aglow, +To conquer this mountain, so high, +And reach the summit, where the stars do glow, +And the moon shines bright, up in the sky. + +Through the mist and fog, thou didst press on, +With steadfast courage, and a steadfast will, +Through the darkness, thou didst not be gone, +But didst climb on, with a steadfast skill. + +At last, thou didst reach the summit's crest, +And gazed upon the world below, +And saw the beauty of the night's best, +And felt the peace, that only nature knows. + +Oh, brave adventurer, who dared to climb +The lofty peak of Mt. Fuji in the night, +Thou art a hero, in the eyes of all, +For thou didst conquer this mountain, so bright. +``` + +[gritlm]: https://github.com/ContextualAI/gritlm diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp new file mode 100644 index 000000000..72eb46257 --- /dev/null +++ b/examples/gritlm/gritlm.cpp @@ -0,0 +1,229 @@ +#include "arg.h" +#include "common.h" +#include "llama.h" + +#include +#include + +// #define GRIT_DEBUG + +static std::vector> encode(llama_context * ctx, const std::vector & sentences, const std::string & instruction) { + std::vector> result; + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1); + + for (uint64_t i = 0; i < sentences.size(); i++) { + common_batch_clear(batch); + + const std::string input_string = instruction + sentences[i]; + + std::vector inputs = common_tokenize(vocab, input_string, true, false); + + const int32_t n_toks = inputs.size(); + + // GritLM seems to have EOS = "" + // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18 + // inputs.push_back(llama_vocab_eos(vocab)); + + // we want to ignore instruction tokens for mean pooling + const int32_t n_inst = common_tokenize(vocab, instruction, true, false).size(); + +#ifdef GRIT_DEBUG + // debug tokens - should be matching as referenced in the GritLM sample + std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) { + std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str()); + }); + std::printf("\n"); +#endif + + // add input to batch (this increments n_tokens) + for (int32_t j = 0; j < n_toks; j++) { + common_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst); + } + + // clear previous kv_cache values (irrelevant for embeddings) + llama_kv_cache_clear(ctx); + llama_set_embeddings(ctx, true); + llama_set_causal_attn(ctx, false); + + // run model + llama_decode(ctx, batch); + + // get embedding dimensions + uint64_t n_embd = llama_model_n_embd(model); + + // allocate embedding output + std::vector emb_unorm(n_embd, 0.0f); + + // sum up all token embeddings + for (int32_t k = n_inst; k < n_toks; k++) { + float * emb = llama_get_embeddings_ith(ctx, k); + for (uint64_t j = 0; j < n_embd; j++) { + emb_unorm[j] += emb[j]; + } + } + + // divide by number of tokens (mean pooling) + { + const uint64_t n_sent = n_toks - n_inst; + + for (uint64_t j = 0; j < n_embd; j++) { + emb_unorm[j] /= n_sent; + } + } + + std::vector emb_norm(emb_unorm.size()); + common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2); + result.push_back(emb_norm); + +#ifdef GRIT_DEBUG + // print out emb_norm + std::printf("embedding %ld: ", i); + for (uint64_t j = 0; j < n_embd; j++) { + std::printf("%.5f ", emb_norm[j]); + } + std::printf("\n\n"); +#endif + } + + llama_batch_free(batch); + + return result; +} + +static std::string generate(llama_context * ctx, llama_sampler * smpl, const std::string & prompt, bool stream) { + std::string result; + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + llama_token eos_token = llama_vocab_eos(vocab); + + llama_kv_cache_clear(ctx); + llama_set_embeddings(ctx, false); + llama_set_causal_attn(ctx, true); + + llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1); + + std::vector inputs = common_tokenize(vocab, prompt, false, true); + int32_t i_current_token = 0; + + while (true) { + common_batch_clear(bat); + { + const int32_t n_inputs = inputs.size(); + + for (int32_t i = 0; i < n_inputs; i++) { + common_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1); + } + } + inputs.clear(); + + llama_decode(ctx, bat); + + llama_token token = llama_sampler_sample(smpl, ctx, bat.n_tokens - 1); + + if (token == eos_token) { + break; + } + + std::string piece = common_token_to_piece(ctx, token); + if (stream) { + std::printf("%s", piece.c_str()); + std::fflush(stdout); + } + + inputs.push_back(token); + + result += piece; + } + + if (stream) { + std::printf("\n"); + } + + llama_batch_free(bat); + + return result; +} + +static std::string gritlm_instruction(const std::string & instruction) { + return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n"; +} + +int main(int argc, char * argv[]) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + + llama_model_params mparams = common_model_params_to_llama(params); + llama_context_params cparams = common_context_params_to_llama(params); + + llama_backend_init(); + + llama_model * model = llama_model_load_from_file(params.model.c_str(), mparams); + + // create generation context + llama_context * ctx = llama_init_from_model(model, cparams); + + auto sparams = llama_sampler_chain_default_params(); + + sparams.no_perf = false; + + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_greedy()); + + // ### Embedding/Representation ### + // samples taken from: https://github.com/ContextualAI/gritlm#basic + { + const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract"; + + const std::vector queries = { + "Bitcoin: A Peer-to-Peer Electronic Cash System", + "Generative Representational Instruction Tuning", + }; + + const std::vector documents = { + "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.", + "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.", + }; + + // No need to add instruction for retrieval documents + const std::vector> d_rep = encode(ctx, documents, gritlm_instruction("")); + const std::vector> q_rep = encode(ctx, queries, gritlm_instruction(instruction)); + + const int n_embd = llama_model_n_embd(model); + + const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd); + const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd); + const float cosine_sim_q1_d0 = common_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd); + const float cosine_sim_q1_d1 = common_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd); + + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0); + std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1); + } + + // ### Generation ### + // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction + { + const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n"; + std::string response = generate(ctx, smpl, prompt, true); + } + + llama_sampler_free(smpl); + llama_free(ctx); + llama_model_free(model); + llama_backend_free(); + + return 0; +} diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt index d688a1620..412696c47 100644 --- a/examples/imatrix/CMakeLists.txt +++ b/examples/imatrix/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET imatrix) +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 578e8fc27..9c056986b 100644 --- a/examples/imatrix/README.md +++ b/examples/imatrix/README.md @@ -1,32 +1,33 @@ # llama.cpp/examples/imatrix -Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantum models. +Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantized models. More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861 ## Usage ``` -./imatrix -m -f [-o ] [--verbosity ] - [-ofreq num_chunks] [-ow <0 or 1>] [other common params] +./llama-imatrix \ + -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \ + [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \ + [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...] ``` Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory. The parameters in square brackets are optional and have the following meaning: * `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used. * `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`. -* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks) -* `-ow` (or `--output-weight`) specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default. +* `--output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks) +* `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never) +* `--process-output` specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default. For faster computation, make sure to use GPU offloading via the `-ngl` argument ## Example ```bash -LLAMA_CUBLAS=1 make -j - # generate importance matrix (imatrix.dat) -./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 +./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 # use the imatrix to perform a Q4_K_M quantization -./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m +./llama-quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m ``` diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index f21bc48f3..b5f3feb9f 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -1,11 +1,12 @@ +#include "arg.h" #include "common.h" +#include "log.h" #include "llama.h" #include #include #include #include -#include #include #include #include @@ -17,52 +18,71 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +static void print_usage(int, char ** argv) { + LOG("\nexample usage:\n"); + LOG("\n %s \\\n" + " -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] \\\n" + " [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n" + " [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]); + LOG("\n"); +} + struct Stats { std::vector values; + std::vector counts; int ncall = 0; }; -struct StatParams { - std::string ofile = "imatrix.dat"; - int n_output_frequency = 10; - int verbosity = 1; - int keep_every = 0; - bool collect_output_weight = false; -}; - class IMatrixCollector { public: IMatrixCollector() = default; - void set_parameters(StatParams&& params) { m_params = std::move(params); } + void set_params(common_params params) { m_params = std::move(params); } bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data); - void save_imatrix() const; - bool load_imatrix(const char * file_name, bool add); - static bool load_imatrix(const char * file_name, std::unordered_map& imatrix); + void save_imatrix(int ncall = -1) const; + bool load_imatrix(const char * fname); private: std::unordered_map m_stats; - StatParams m_params; + common_params m_params; std::mutex m_mutex; int m_last_call = 0; std::vector m_src1_data; - std::vector m_ids; // the expert ids from ggml_mul_mat_id - // - void save_imatrix(const char * file_name) const; - void keep_imatrix(int ncall) const; + std::vector m_ids; // the expert ids from ggml_mul_mat_id }; +// remove any prefix and suffixes from the name +// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight +static std::string filter_tensor_name(const char * name) { + std::string wname; + const char * p = strchr(name, '#'); + if (p != NULL) { + p = p + 1; + const char * q = strchr(p, '#'); + if (q != NULL) { + wname = std::string(p, q - p); + } else { + wname = p; + } + } else { + wname = name; + } + return wname; +} + bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) { GGML_UNUSED(user_data); const struct ggml_tensor * src0 = t->src[0]; const struct ggml_tensor * src1 = t->src[1]; + std::string wname = filter_tensor_name(src0->name); // when ask is true, the scheduler wants to know if we are interested in data from this tensor // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection if (ask) { if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications if (t->op != GGML_OP_MUL_MAT) return false; + // why are small batches ignored (<16 tokens)? if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false; - if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return false; + if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false; return true; } @@ -78,82 +98,104 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const float * data = is_host ? (const float *) src1->data : m_src1_data.data(); + // this has been adapted to the new format of storing merged experts in a single 3d tensor + // ref: https://github.com/ggerganov/llama.cpp/pull/6387 if (t->op == GGML_OP_MUL_MAT_ID) { - const int idx = ((int32_t *) t->op_params)[0]; - const int n_as = ((int32_t *) t->op_params)[1]; + // ids -> [n_experts_used, n_tokens] + // src1 -> [cols, n_expert_used, n_tokens] + const ggml_tensor * ids = t->src[2]; + const int n_as = src0->ne[2]; + const int n_ids = ids->ne[0]; - // the top-k selected expert ids are stored in the src0 tensor - // for simplicity, always copy src0 to host, because it is small - // take into account that src0 is not contiguous! - GGML_ASSERT(src0->ne[1] == src1->ne[1]); - GGML_ASSERT(n_as*ggml_nrows(src0)*sizeof(int) == GGML_PAD(ggml_nbytes(src0), n_as*sizeof(int))); - m_ids.resize(ggml_nbytes(src0)/sizeof(int)); - ggml_backend_tensor_get(src0, m_ids.data(), 0, ggml_nbytes(src0)); + // the top-k selected expert ids are stored in the ids tensor + // for simplicity, always copy ids to host, because it is small + // take into account that ids is not contiguous! + GGML_ASSERT(ids->ne[1] == src1->ne[2]); + + m_ids.resize(ggml_nbytes(ids)); + ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids)); + + auto & e = m_stats[wname]; + + ++e.ncall; + + if (e.values.empty()) { + e.values.resize(src1->ne[0]*n_as, 0); + e.counts.resize(src1->ne[0]*n_as, 0); + } + else if (e.values.size() != (size_t)src1->ne[0]*n_as) { + LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as); + exit(1); //GGML_ABORT("fatal error"); + } + LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type); // loop over all possible experts, regardless if they are used or not in the batch - // this is necessary to guarantee equal number of "ncall" for each tensor for (int ex = 0; ex < n_as; ++ex) { - src0 = t->src[2 + ex]; - auto& e = m_stats[src0->name]; - if (e.values.empty()) { - e.values.resize(src1->ne[0], 0); - } - else if (e.values.size() != (size_t)src1->ne[0]) { - fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]); - exit(1); //GGML_ASSERT(false); - } - // NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger - // using the following line, we can correct for that if needed - //if (idx == t->src[0]->ne[0] - 1) ++e.ncall; - ++e.ncall; - if (m_params.verbosity > 1) { - printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); - } - for (int row = 0; row < (int)src1->ne[1]; ++row) { - const int excur = m_ids[row*n_as + idx]; - GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check - if (excur != ex) continue; - const float * x = data + row * src1->ne[0]; - for (int j = 0; j < (int)src1->ne[0]; ++j) { - e.values[j] += x[j]*x[j]; + size_t e_start = ex*src1->ne[0]; + + for (int idx = 0; idx < n_ids; ++idx) { + for (int row = 0; row < (int)src1->ne[2]; ++row) { + const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]); + + GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check + + if (excur != ex) continue; + + const int64_t i11 = idx % src1->ne[1]; + const int64_t i12 = row; + const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]); + + for (int j = 0; j < (int)src1->ne[0]; ++j) { + e.values[e_start + j] += x[j]*x[j]; + e.counts[e_start + j]++; + if (!std::isfinite(e.values[e_start + j])) { + LOG("\n"); + LOG_ERR("%f detected in %s\n", e.values[e_start + j], wname.c_str()); + exit(1); + } + } } } if (e.ncall > m_last_call) { m_last_call = e.ncall; - if (m_last_call % m_params.n_output_frequency == 0) { + if (m_last_call % m_params.n_out_freq == 0) { save_imatrix(); } - if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) { - keep_imatrix(m_last_call); + if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) { + save_imatrix(m_last_call); } } } } else { - auto& e = m_stats[src0->name]; + auto & e = m_stats[wname]; if (e.values.empty()) { e.values.resize(src1->ne[0], 0); + e.counts.resize(src1->ne[0], 0); } else if (e.values.size() != (size_t)src1->ne[0]) { - fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]); - exit(1); //GGML_ASSERT(false); + LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]); + exit(1); //GGML_ABORT("fatal error"); } ++e.ncall; - if (m_params.verbosity > 1) { - printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); - } + LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); for (int row = 0; row < (int)src1->ne[1]; ++row) { const float * x = data + row * src1->ne[0]; for (int j = 0; j < (int)src1->ne[0]; ++j) { e.values[j] += x[j]*x[j]; + e.counts[j]++; + if (!std::isfinite(e.values[j])) { + LOG_ERR("%f detected in %s\n", e.values[j], wname.c_str()); + exit(1); + } } } if (e.ncall > m_last_call) { m_last_call = e.ncall; - if (m_last_call % m_params.n_output_frequency == 0) { + if (m_last_call % m_params.n_out_freq == 0) { save_imatrix(); } - if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) { - keep_imatrix(m_last_call); + if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) { + save_imatrix(m_last_call); } } } @@ -161,46 +203,104 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * return true; } -void IMatrixCollector::save_imatrix() const { - save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str()); -} +void IMatrixCollector::save_imatrix(int ncall) const { + auto fname = m_params.out_file; + if (fname.empty()) { + fname = "imatrix.dat"; + } -void IMatrixCollector::keep_imatrix(int ncall) const { - auto file_name = m_params.ofile; - if (file_name.empty()) file_name = "imatrix.dat"; - file_name += ".at_"; - file_name += std::to_string(ncall); - save_imatrix(file_name.c_str()); -} + if (ncall > 0) { + fname += ".at_"; + fname += std::to_string(ncall); + } + + // avoid writing imatrix entries that do not have full data + // this can happen with MoE models where some of the experts end up not being exercised by the provided training data + + int n_entries = 0; + std::vector to_store; + + bool is_first = true; // for printing + for (const auto & kv : m_stats) { + const int n_all = kv.second.counts.size(); + + if (n_all == 0) { + continue; + } + + int n_zeros = 0; + for (const int c : kv.second.counts) { + if (c == 0) { + n_zeros++; + } + } + + if (n_zeros != 0 && is_first) { + LOG_INF("\n"); + is_first = false; + } + + if (n_zeros == n_all) { + LOG_WRN("%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str()); + continue; + } + + if (n_zeros > 0) { + LOG_WRN("%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all); + continue; + } + + n_entries++; + to_store.push_back(kv.first); + } + + if (to_store.size() < m_stats.size()) { + LOG_WRN("%s: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size()); + } -void IMatrixCollector::save_imatrix(const char * fname) const { std::ofstream out(fname, std::ios::binary); - int n_entries = m_stats.size(); - out.write((const char*)&n_entries, sizeof(n_entries)); - for (auto& p : m_stats) { - int len = p.first.size(); - out.write((const char*)&len, sizeof(len)); - out.write(p.first.c_str(), len); - out.write((const char*)&p.second.ncall, sizeof(p.second.ncall)); - int nval = p.second.values.size(); - out.write((const char*)&nval, sizeof(nval)); - if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float)); + out.write((const char *) &n_entries, sizeof(n_entries)); + for (const auto & name : to_store) { + const auto & stat = m_stats.at(name); + int len = name.size(); + out.write((const char *) &len, sizeof(len)); + out.write(name.c_str(), len); + out.write((const char *) &stat.ncall, sizeof(stat.ncall)); + int nval = stat.values.size(); + out.write((const char *) &nval, sizeof(nval)); + if (nval > 0) { + std::vector tmp(nval); + for (int i = 0; i < nval; i++) { + tmp[i] = (stat.values[i] / static_cast(stat.counts[i])) * static_cast(stat.ncall); + } + out.write((const char*)tmp.data(), nval*sizeof(float)); + } } - if (m_params.verbosity > 0) { - fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname); + + // Write the number of call the matrix was computed with + out.write((const char *) &m_last_call, sizeof(m_last_call)); + + // Write the input filename at the end of the file to later on specify it in quantize + { + int len = m_params.prompt_file.size(); + out.write((const char *) &len, sizeof(len)); + out.write(m_params.prompt_file.c_str(), len); } + + LOGV(1, "\n"); + LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str()); } -bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map& imatrix_data) { - std::ifstream in(imatrix_file, std::ios::binary); +bool IMatrixCollector::load_imatrix(const char * fname) { + std::ifstream in(fname, std::ios::binary); if (!in) { - printf("%s: failed to open %s\n",__func__,imatrix_file); + LOG_ERR("%s: failed to open %s\n",__func__, fname); return false; } int n_entries; in.read((char*)&n_entries, sizeof(n_entries)); if (in.fail() || n_entries < 1) { - printf("%s: no data in file %s\n", __func__, imatrix_file); + LOG_ERR("%s: no data in file %s\n", __func__, fname); return false; } for (int i = 0; i < n_entries; ++i) { @@ -208,40 +308,46 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma std::vector name_as_vec(len+1); in.read((char *)name_as_vec.data(), len); if (in.fail()) { - printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file); + LOG_ERR("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname); return false; } name_as_vec[len] = 0; std::string name{name_as_vec.data()}; - auto& e = imatrix_data[std::move(name)]; + auto & e = m_stats[std::move(name)]; int ncall; in.read((char*)&ncall, sizeof(ncall)); int nval; in.read((char *)&nval, sizeof(nval)); if (in.fail() || nval < 1) { - printf("%s: failed reading number of values for entry %d\n",__func__,i); - imatrix_data = {}; + LOG_ERR("%s: failed reading number of values for entry %d\n",__func__,i); + m_stats = {}; return false; } - e.values.resize(nval); - in.read((char*)e.values.data(), nval*sizeof(float)); + + if (e.values.empty()) { + e.values.resize(nval, 0); + e.counts.resize(nval, 0); + } + + std::vector tmp(nval); + in.read((char*)tmp.data(), nval*sizeof(float)); if (in.fail()) { - printf("%s: failed reading data for entry %d\n",__func__,i); - imatrix_data = {}; + LOG_ERR("%s: failed reading data for entry %d\n",__func__,i); + m_stats = {}; return false; } - e.ncall = ncall; + + // Recreate the state as expected by save_imatrix(), and corerct for weighted sum. + for (int i = 0; i < nval; i++) { + e.values[i] += tmp[i]; + e.counts[i] += ncall; + } + e.ncall += ncall; + } return true; } -bool IMatrixCollector::load_imatrix(const char * file_name, bool add) { - if (!add) { - m_stats.clear(); - } - return load_imatrix(file_name, m_stats); -} - static IMatrixCollector g_collector; static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) { @@ -255,7 +361,7 @@ struct results_log_softmax { float prob; }; -static std::vector softmax(const std::vector& logits) { +static std::vector softmax(const std::vector & logits) { std::vector probs(logits.size()); float max_logit = logits[0]; for (float v : logits) { @@ -289,8 +395,7 @@ static results_log_softmax log_softmax(int n_vocab, const float * logits, int to static void process_logits( int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, - double & nll, double & nll2, float * logit_history, float * prob_history -) { + double & nll, double & nll2, float * logit_history, float * prob_history) { std::mutex mutex; int counter = 0; auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () { @@ -322,39 +427,42 @@ static void process_logits( } } -static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) { +static bool compute_imatrix(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + const bool add_bos = llama_vocab_get_add_bos(vocab); const int n_ctx = llama_n_ctx(ctx); - auto tim1 = std::chrono::high_resolution_clock::now(); - fprintf(stderr, "%s: tokenizing the input ..\n", __func__); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); - std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + auto tim1 = std::chrono::high_resolution_clock::now(); + LOG_INF("%s: tokenizing the input ..\n", __func__); + + std::vector tokens = common_tokenize(ctx, params.prompt, true); auto tim2 = std::chrono::high_resolution_clock::now(); - fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); - if (from_chunk > 0) { - if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) { - fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk); + if (params.i_chunk > 0) { + if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) { + LOG_ERR("%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk); return false; } - fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx); - tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx); + LOG_INF("%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx); + tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx); } if (int(tokens.size()) < 2*n_ctx) { - fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx, - n_ctx); - fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + LOG_ERR("%s: you need at least %d tokens for a context of %d tokens\n", __func__, 2*n_ctx, n_ctx); + LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n", __func__, tokens.size()); return false; } std::vector logit_history; std::vector prob_history; - if (compute_ppl) { + if (params.compute_ppl) { logit_history.resize(tokens.size()); prob_history.resize(tokens.size()); } @@ -362,21 +470,21 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool const int n_chunk_max = tokens.size() / n_ctx; const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + const int n_vocab = llama_vocab_n_tokens(vocab); const int n_batch = params.n_batch; int count = 0; double nll = 0.0; double nll2 = 0.0; - fprintf(stderr, "%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch); + LOG_INF("%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch); std::vector workers(std::thread::hardware_concurrency() - 1); const int num_batches = (n_ctx + n_batch - 1) / n_batch; std::vector logits; - if (compute_ppl && num_batches > 1) { + if (params.compute_ppl && num_batches > 1) { logits.reserve((size_t)n_ctx * n_vocab); } @@ -391,6 +499,8 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -400,61 +510,69 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + tokens[batch_start] = llama_vocab_bos(vocab); } - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { - fprintf(stderr, "%s : failed to eval\n", __func__); + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { + LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return false; } // restore the original token in case it was set to BOS tokens[batch_start] = token_org; - if (compute_ppl && num_batches > 1) { + if (params.compute_ppl && num_batches > 1) { const auto * batch_logits = llama_get_logits(ctx); logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { const float t_total = std::chrono::duration(t_end - t_start).count(); - fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); + LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); int total_seconds = (int)(t_total * n_chunk); if (total_seconds >= 60*60) { - fprintf(stderr, "%d hours ", total_seconds / (60*60)); + LOG("%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); } - fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); + LOG("%.2f minutes\n", total_seconds / 60.0); } - if (compute_ppl) { + if (params.compute_ppl) { const int first = n_ctx/2; - const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); + const auto * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); count += n_ctx - first - 1; - printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); + LOG("[%d]%.4lf,", i + 1, std::exp(nll / count)); fflush(stdout); logits.clear(); } } - printf("\n"); + LOG("\n"); - if (compute_ppl) { + if (params.compute_ppl) { nll2 /= count; nll /= count; const double ppl = exp(nll); nll2 -= nll * nll; if (nll2 > 0) { nll2 = sqrt(nll2/(count-1)); - printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); + LOG("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); } else { - printf("Unexpected negative standard deviation of log(prob)\n"); + LOG("Unexpected negative standard deviation of log(prob)\n"); } } @@ -462,159 +580,84 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool } int main(int argc, char ** argv) { + common_params params; - StatParams sparams; - std::string prev_result_file; - std::string combine_files; - bool compute_ppl = true; - int from_chunk = 0; - std::vector args; - args.push_back(argv[0]); - int iarg = 1; - for (; iarg < argc-1; ++iarg) { - std::string arg{argv[iarg]}; - if (arg == "-o" || arg == "--output-file") { - sparams.ofile = argv[++iarg]; - } - else if (arg == "-ofreq" || arg == "--output-frequency") { - sparams.n_output_frequency = std::stoi(argv[++iarg]); - } - else if (arg == "-ow" || arg == "--output-weight") { - sparams.collect_output_weight = std::stoi(argv[++iarg]); - } - else if (arg == "--verbosity") { - sparams.verbosity = std::stoi(argv[++iarg]); - } else if (arg == "--no-ppl") { - compute_ppl = false; - } else if (arg == "--keep-imatrix") { - sparams.keep_every = std::stoi(argv[++iarg]); - } else if (arg == "--continue-from") { - prev_result_file = argv[++iarg]; - } else if (arg == "--combine") { - combine_files = argv[++iarg]; - } - else if (arg == "--from-chunk") { - from_chunk = std::stoi(argv[++iarg]); - } else { - args.push_back(argv[iarg]); - } - } - if (iarg < argc) { - std::string arg{argv[iarg]}; - if (arg == "--no-ppl") { - compute_ppl = false; - } else { - args.push_back(argv[iarg]); - } - } + params.n_ctx = 512; + params.logits_all = true; + params.escape = false; - g_collector.set_parameters(std::move(sparams)); - - if (!combine_files.empty()) { - std::vector files; - size_t pos = 0; - while (true) { - auto new_pos = combine_files.find(',', pos); - if (new_pos != std::string::npos) { - files.emplace_back(combine_files.substr(pos, new_pos - pos)); - pos = new_pos + 1; - } else { - files.emplace_back(combine_files.substr(pos)); - break; - } - } - if (files.size() < 2) { - fprintf(stderr, "You must provide at least two comma separated files to use --combine\n"); - return 1; - } - printf("Combining the following %d files\n", int(files.size())); - for (auto& file : files) { - printf(" %s\n", file.c_str()); - if (!g_collector.load_imatrix(file.c_str(), true)) { - fprintf(stderr, "Failed to load %s\n", file.c_str()); - return 1; - } - } - g_collector.save_imatrix(); - return 0; - } - - if (!prev_result_file.empty()) { - if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) { - fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str()); - return 1; - } - } - - gpt_params params; - params.n_batch = 512; - if (!gpt_params_parse(args.size(), args.data(), params)) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) { return 1; } - params.logits_all = true; + common_init(); + params.n_batch = std::min(params.n_batch, params.n_ctx); - print_build_info(); + g_collector.set_params(params); - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); + for (const auto & in_file : params.in_files) { + LOG_INF("%s : loading imatrix from '%s'\n", __func__, in_file.c_str()); + if (!g_collector.load_imatrix(in_file.c_str())) { + LOG_ERR("%s : failed to load %s\n", __func__, in_file.c_str()); + return 1; + } } - fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + if (params.in_files.size() > 1) { + LOG_INF("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str()); + g_collector.save_imatrix(); } llama_backend_init(); llama_numa_init(params.numa); - llama_model_params mparams = llama_model_params_from_gpt_params(params); - - llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); - if (model == NULL) { - fprintf(stderr, "%s: error: unable to load model\n", __func__); - return 1; - } - - llama_context_params cparams = llama_context_params_from_gpt_params(params); - // pass the callback to the backend scheduler // it will be executed for each node during the graph computation - cparams.cb_eval = ik_collect_imatrix; - cparams.cb_eval_user_data = NULL; + params.cb_eval = ik_collect_imatrix; + params.cb_eval_user_data = NULL; + params.warmup = false; - llama_context * ctx = llama_new_context_with_model(model, cparams); - if (ctx == NULL) { - fprintf(stderr, "%s: error: unable to create context\n", __func__); + // init + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + if (model == nullptr || ctx == nullptr) { + LOG_ERR("%s : failed to init\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const int n_ctx_train = llama_model_n_ctx_train(model); if (params.n_ctx > n_ctx_train) { - fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", + LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, params.n_ctx); } // print system information { - fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); - if (!OK) { - 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(); - llama_print_timings(ctx); - - llama_free(ctx); - llama_free_model(model); + LOG("\n"); + llama_perf_context_print(ctx); llama_backend_free(); diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index e4e8028da..fb26628d8 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET infill) +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 8c97f719b..df4d976f2 100644 --- a/examples/infill/README.md +++ b/examples/infill/README.md @@ -14,7 +14,8 @@ 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 @@ -36,6 +37,11 @@ The `infill` program offers a seamless way to interact with LLaMA models, allowi ### Example -```bash -./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n " +Download a model that supports infill, for example CodeLlama: +```console +scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models +``` + +```bash +./llama-infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n " ``` diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index d4b8729dd..489a208b6 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -1,8 +1,9 @@ +#include "arg.h" #include "common.h" - #include "console.h" +#include "sampling.h" +#include "log.h" #include "llama.h" -#include "grammar-parser.h" #include #include @@ -34,57 +35,14 @@ static llama_context ** g_ctx; static llama_model ** g_model; -static gpt_params * g_params; +static common_sampler ** g_smpl; +static common_params * g_params; static std::vector * g_input_tokens; static std::ostringstream * g_output_ss; static std::vector * g_output_tokens; static bool is_interacting = false; -static void write_logfile( - const llama_context * ctx, const gpt_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 = get_sortable_timestamp(); - - const bool success = create_directory_with_parents(params.logdir); - if (!success) { - fprintf(stderr, "%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) { - fprintf(stderr, "%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)); - dump_non_result_info_yaml(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"); - - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); - - llama_dump_timing_info_yaml(logfile, ctx); - fclose(logfile); -} - #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) static void sigint_handler(int signo) { if (signo == SIGINT) { @@ -92,9 +50,13 @@ static void sigint_handler(int signo) { is_interacting = true; } else { console::cleanup(); - printf("\n"); - llama_print_timings(*g_ctx); - write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); + LOG("\n"); + common_perf_print(*g_ctx, *g_smpl); + + // make sure all logs are flushed + LOG("Interrupted by user\n"); + common_log_pause(common_log_main()); + _exit(130); } } @@ -102,197 +64,135 @@ static void sigint_handler(int signo) { #endif int main(int argc, char ** argv) { - gpt_params params; - llama_sampling_params & sparams = params.sparams; + common_params params; g_params = ¶ms; - if (!gpt_params_parse(argc, argv, params)) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) { return 1; } -#ifndef LOG_DISABLE_LOGS - log_set_target(log_filename_generator("infill", "log")); - LOG_TEE("Log start\n"); - log_dump_cmdline(argc, argv); -#endif // LOG_DISABLE_LOGS + common_init(); + + auto & sparams = params.sampling; console::init(params.simple_io, params.use_color); atexit([]() { console::cleanup(); }); if (params.logits_all) { - printf("\n************\n"); - printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); - printf("************\n\n"); + LOG_ERR("\n************\n"); + LOG_ERR("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); + LOG_ERR("************\n\n"); return 0; } if (params.embedding) { - printf("\n************\n"); - printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__); - printf("************\n\n"); + LOG_ERR("\n************\n"); + LOG_ERR("%s: please use the 'embedding' tool for embedding calculations\n", __func__); + LOG_ERR("************\n\n"); return 0; } if (params.n_ctx != 0 && params.n_ctx < 8) { - LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); + LOG_WRN("%s: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; } - if (params.instruct) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for instruct mode\n", __func__); - printf("************\n\n"); - return 0; - } - if (params.chatml) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for chatml mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (!params.antiprompt.empty()) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for antiprompt mode\n", __func__); - printf("************\n\n"); - - return 0; - } if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) { - printf("\n************\n"); - printf("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__); - printf("************\n\n"); - - return 0; - } - if (params.random_prompt) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for random prompt mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (!params.path_prompt_cache.empty()) { - printf("\n************\n"); - printf("%s: infill does not support prompt caching\n", __func__); - printf("************\n\n"); + LOG_ERR("\n************\n"); + LOG_ERR("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__); + LOG_ERR("************\n\n"); return 0; } if (params.rope_freq_base != 0.0) { - LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); + LOG_WRN("%s: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); } if (params.rope_freq_scale != 0.0) { - LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); + LOG_WRN("%s: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); } - LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); - LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); - - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); - } - - LOG_TEE("%s: seed = %u\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - - LOG("%s: llama backend init\n", __func__); + LOG_INF("%s: llama backend init\n", __func__); llama_backend_init(); llama_numa_init(params.numa); - llama_model * model; - llama_context * ctx; - llama_context * ctx_guidance = NULL; + llama_model * model = nullptr; + llama_context * ctx = nullptr; + common_sampler * smpl = nullptr; + g_model = &model; g_ctx = &ctx; + g_smpl = &smpl; // load the model and apply lora adapter, if any - LOG("%s: load the model and apply lora adapter, if any\n", __func__); - std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (sparams.cfg_scale > 1.f) { - struct llama_context_params lparams = llama_context_params_from_gpt_params(params); - ctx_guidance = llama_new_context_with_model(model, lparams); - } + LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); + common_init_result llama_init = common_init_from_params(params); + + model = llama_init.model.get(); + ctx = llama_init.context.get(); if (model == NULL) { - LOG_TEE("%s: error: unable to load model\n", __func__); + LOG_ERR("%s: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_ctx_train = llama_model_n_ctx_train(model); const int n_ctx = llama_n_ctx(ctx); - LOG("n_ctx: %d\n", n_ctx); + LOG_DBG("n_ctx: %d\n", n_ctx); if (n_ctx > n_ctx_train) { - LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n", - __func__, n_ctx_train, n_ctx); + LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx); } // print system information { - LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - const bool add_bos = llama_should_add_bos_token(model); - LOG("add_bos: %d\n", add_bos); + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); - bool suff_rm_leading_spc = params.escape; - if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { - params.input_suffix.erase(0, 1); - suff_rm_leading_spc = false; - } std::vector embd_inp; - std::vector inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false); - std::vector inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false); - const int space_token = 29871; - if (suff_rm_leading_spc && inp_sfx[0] == space_token) { - inp_sfx.erase(inp_sfx.begin()); - } - inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model)); - if (add_bos) { - inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model)); - } - inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model)); - embd_inp = inp_pfx; - embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - embd_inp.push_back(llama_token_middle(model)); + std::vector embd_end; + std::vector inp_pfx = common_tokenize(ctx, params.input_prefix, false); + std::vector inp_sfx = common_tokenize(ctx, params.input_suffix, false); - LOG("prefix: \"%s\"\n", log_tostr(params.input_prefix)); - LOG("suffix: \"%s\"\n", log_tostr(params.input_suffix)); - LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); + GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0); + GGML_ASSERT(llama_vocab_fim_suf(vocab) >= 0); + + inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab)); + inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab)); + + embd_inp = params.spm_infill ? inp_sfx : inp_pfx; + embd_end = params.spm_infill ? inp_pfx : inp_sfx; + if (add_bos) { + embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); + } + embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); + + const llama_token middle_token = llama_vocab_fim_mid(vocab); + if (middle_token >= 0) { + embd_inp.push_back(middle_token); + } + + LOG_DBG("add_bos: %d\n", add_bos); + LOG_DBG("prefix: \"%s\"\n", params.input_prefix.c_str()); + LOG_DBG("suffix: \"%s\"\n", params.input_suffix.c_str()); + LOG_DBG("tokens: %s\n", string_from(ctx, embd_inp).c_str()); // Should not run without any tokens if (embd_inp.empty()) { - embd_inp.push_back(llama_token_bos(model)); - LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); - } - - // Tokenize negative prompt - std::vector guidance_inp; - int guidance_offset = 0; - int original_prompt_len = 0; - if (ctx_guidance) { - LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); - - guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos); - LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); - - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos); - LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); - - original_prompt_len = original_inp.size(); - guidance_offset = (int)guidance_inp.size() - original_prompt_len; - LOG("original_prompt_len: %s", log_tostr(original_prompt_len)); - LOG("guidance_offset: %s", log_tostr(guidance_offset)); + embd_inp.push_back(llama_vocab_bos(vocab)); + LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str()); } if ((int) embd_inp.size() > n_ctx - 4) { - LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); + LOG_ERR("%s: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); return 1; } @@ -301,9 +201,8 @@ int main(int argc, char ** argv) { params.n_keep = (int)embd_inp.size(); } - LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str()); - LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str()); - + LOG_INF("inp_pfx: %s\n", string_from(ctx, inp_pfx).c_str()); + LOG_INF("inp_sfx: %s\n", string_from(ctx, inp_sfx).c_str()); // enable interactive mode if interactive start is specified if (params.interactive_first) { @@ -311,30 +210,21 @@ int main(int argc, char ** argv) { } if (params.verbose_prompt) { - LOG_TEE("\n"); - LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); - LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); + LOG_INF("\n"); + LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); + LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); for (int i = 0; i < (int) embd_inp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); - } - - if (ctx_guidance) { - LOG_TEE("\n"); - LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str()); - LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); - for (int i = 0; i < (int) guidance_inp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str()); - } + LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str()); } if (params.n_keep > 0) { - LOG_TEE("%s: static prompt based on n_keep: '", __func__); + LOG_INF("%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { - LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); + LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str()); } - LOG_TEE("'\n"); + LOG_CNT("'\n"); } - LOG_TEE("\n"); + LOG_INF("\n"); } if (params.interactive) { @@ -351,55 +241,54 @@ int main(int argc, char ** argv) { SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif - LOG_TEE("%s: interactive mode on.\n", __func__); + LOG_INF("%s: interactive mode on.\n", __func__); if (params.input_prefix_bos) { - LOG_TEE("Input prefix with BOS\n"); + LOG_INF("Input prefix with BOS\n"); } if (!params.input_prefix.empty()) { - LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str()); + LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str()); } if (!params.input_suffix.empty()) { - LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str()); + LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str()); } } - LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str()); - LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); - LOG_TEE("\n\n"); + smpl = common_sampler_init(model, sparams); - LOG_TEE("\n##### Infill mode #####\n\n"); - if (params.infill) { - printf("\n************\n"); - printf("no need to specify '--infill', always running infill\n"); - printf("************\n\n"); - } + LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl)); + LOG_INF("sampler params: \n%s\n", sparams.print().c_str()); + LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str()); + + LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); + + LOG_INF("\n"); + LOG_INF("\n##### Infill mode #####\n\n"); if (params.interactive) { const char *control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to LLaMA, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to LLaMA.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } - LOG_TEE("== Running in interactive mode. ==\n"); + LOG_INF("== Running in interactive mode. ==\n"); #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) - LOG_TEE( " - Press Ctrl+C to interject at any time.\n"); + LOG_INF( " - Press Ctrl+C to interject at any time.\n"); #endif - LOG_TEE( "%s\n", control_message); + LOG_INF( "%s\n", control_message); is_interacting = params.interactive_first; } - bool input_echo = true; + bool input_echo = true; - int n_past = 0; - int n_remain = params.n_predict; - int n_consumed = 0; - int n_past_guidance = 0; + int n_past = 0; + int n_remain = params.n_predict; + int n_consumed = 0; std::vector input_tokens; g_input_tokens = &input_tokens; std::vector output_tokens; g_output_tokens = &output_tokens; @@ -409,9 +298,6 @@ int main(int argc, char ** argv) { console::set_display(console::prompt); std::vector embd; - std::vector embd_guidance; - - struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); while (n_remain != 0 || params.interactive) { // predict @@ -426,25 +312,24 @@ int main(int argc, char ** argv) { embd.resize(max_embd_size); console::set_display(console::error); - printf("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); + LOG_WRN("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); console::set_display(console::reset); - fflush(stdout); } // infinite text generation via context swapping // if we run out of context: // - take the n_keep first tokens from the original prompt (via n_past) // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches - if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) { + if (n_past + (int) embd.size() > n_ctx) { if (params.n_predict == -2) { - LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); + LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); break; } const int n_left = n_past - params.n_keep - 1; const int n_discard = n_left/2; - LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", + LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", n_past, n_left, n_ctx, params.n_keep, n_discard); llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); @@ -452,87 +337,42 @@ int main(int argc, char ** argv) { n_past -= n_discard; - if (ctx_guidance) { - n_past_guidance -= n_discard; - } + LOG_DBG("after swap: n_past = %d\n", n_past); - LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); - - LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); + LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); } // evaluate tokens in batches // embd is typically prepared beforehand to fit within a batch, but not always - - if (ctx_guidance) { - int input_size = 0; - llama_token * input_buf = NULL; - - if (n_past_guidance < (int) guidance_inp.size()) { - // Guidance context should have the same data with these modifications: - // - // * Replace the initial prompt - // * Shift everything by guidance_offset - embd_guidance = guidance_inp; - if (embd.begin() + original_prompt_len < embd.end()) { - embd_guidance.insert( - embd_guidance.end(), - embd.begin() + original_prompt_len, - embd.end() - ); - } - - input_buf = embd_guidance.data(); - input_size = embd_guidance.size(); - - LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str()); - } else { - input_buf = embd.data(); - input_size = embd.size(); - } - - for (int i = 0; i < input_size; i += params.n_batch) { - int n_eval = std::min(input_size - i, params.n_batch); - if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) { - LOG_TEE("%s : failed to eval\n", __func__); - return 1; - } - - n_past_guidance += n_eval; - } - } - for (int i = 0; i < (int) embd.size(); i += params.n_batch) { int n_eval = (int) embd.size() - i; if (n_eval > params.n_batch) { n_eval = params.n_batch; } - LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); + LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { - LOG_TEE("%s : failed to eval\n", __func__); + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { + LOG_ERR("%s : failed to eval\n", __func__); return 1; } n_past += n_eval; - LOG("n_past = %d\n", n_past); + LOG_DBG("n_past = %d\n", n_past); } } embd.clear(); - embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { + const llama_token id = common_sampler_sample(smpl, ctx, -1); - const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); + common_sampler_accept(smpl, id, true); - llama_sampling_accept(ctx_sampling, ctx, id, true); - - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); + // LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str()); embd.push_back(id); @@ -542,16 +382,16 @@ int main(int argc, char ** argv) { // decrement remaining sampling budget --n_remain; - LOG("n_remain: %d\n", n_remain); + LOG_DBG("n_remain: %d\n", n_remain); } else { // some user input remains from prompt or interaction, forward it to processing - LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); + LOG_DBG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); // push the prompt in the sampling context in order to apply repetition penalties later // for the prompt, we don't apply grammar rules - llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false); + common_sampler_accept(smpl, embd_inp[n_consumed], false); ++n_consumed; if ((int) embd.size() >= params.n_batch) { @@ -563,8 +403,8 @@ int main(int argc, char ** argv) { // display text if (input_echo) { for (auto id : embd) { - const std::string token_str = llama_token_to_piece(ctx, id); - printf("%s", token_str.c_str()); + const std::string token_str = common_token_to_piece(ctx, id); + LOG("%s", token_str.c_str()); if (embd.size() > 1) { input_tokens.push_back(id); @@ -573,7 +413,6 @@ int main(int argc, char ** argv) { output_ss << token_str; } } - fflush(stdout); } // reset color to default if we there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { @@ -582,15 +421,13 @@ int main(int argc, char ** argv) { // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { - // deal with eot token in infill mode - if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){ - if(is_interacting && !params.interactive_first) { + if ((common_sampler_last(smpl) == llama_vocab_eot(vocab) || is_interacting) && params.interactive){ + if (is_interacting && !params.interactive_first) { // print an eot token - printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str()); + LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str()); } - fflush(stdout); - printf("\n"); + LOG("\n"); console::set_display(console::user_input); std::string buffer; std::string line; @@ -620,62 +457,59 @@ int main(int argc, char ** argv) { if (params.escape) { //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); - } - suff_rm_leading_spc = params.escape; - if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { - params.input_suffix.erase(0, 1); - suff_rm_leading_spc = false; + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); } + // tokenize new prefix and suffix - std::vector inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false); - std::vector inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false); - if (suff_rm_leading_spc && inp_sfx[0] == space_token) { - inp_sfx.erase(inp_sfx.begin()); - } - inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model)); + std::vector inp_pfx = common_tokenize(ctx, params.input_prefix, false); + std::vector inp_sfx = common_tokenize(ctx, params.input_suffix, false); + + inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab)); + inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab)); + + embd_inp = params.spm_infill ? inp_sfx : inp_pfx; + embd_end = params.spm_infill ? inp_pfx : inp_sfx; if (add_bos) { - inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model)); + embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); } - inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model)); - embd_inp = inp_pfx; - embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - embd_inp.push_back(llama_token_middle(model)); + embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); + + if (middle_token >= 0) { + embd_inp.push_back(middle_token); + } + embd.clear(); - embd_guidance.clear(); n_remain = params.n_predict; n_past = 0; n_consumed = 0; - // LOG_TEE("took new input\n"); is_interacting = false; } - // deal with end of text token in interactive mode - else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) { - LOG("found EOS token\n"); + // deal with end of generation tokens in interactive mode + else if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) { + LOG_DBG("found EOS token\n"); if (params.interactive) { is_interacting = true; - printf("\n"); + LOG("\n"); console::set_display(console::user_input); - fflush(stdout); } } if (n_past > 0 && is_interacting && !params.interactive) { - LOG("waiting for user input\n"); + LOG_DBG("waiting for user input\n"); if (params.input_prefix_bos) { - LOG("adding input prefix BOS token\n"); - embd_inp.push_back(llama_token_bos(model)); + LOG_DBG("adding input prefix BOS token\n"); + embd_inp.push_back(llama_vocab_bos(vocab)); } std::string buffer; if (!params.input_prefix.empty()) { - LOG("appending input prefix: '%s'\n", params.input_prefix.c_str()); + LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str()); buffer += params.input_prefix; - printf("%s", buffer.c_str()); + LOG("%s", buffer.c_str()); } std::string line; @@ -693,30 +527,30 @@ int main(int argc, char ** argv) { if (buffer.length() > 1) { // append input suffix if any if (!params.input_suffix.empty()) { - LOG("appending input suffix: '%s'\n", params.input_suffix.c_str()); + LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str()); buffer += params.input_suffix; - printf("%s", params.input_suffix.c_str()); + LOG("%s", params.input_suffix.c_str()); } - LOG("buffer: '%s'\n", buffer.c_str()); + LOG_DBG("buffer: '%s'\n", buffer.c_str()); const size_t original_size = embd_inp.size(); - const auto line_inp = ::llama_tokenize(ctx, buffer, false); - LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); + const auto line_inp = common_tokenize(ctx, buffer, false); + LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str()); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); for (size_t i = original_size; i < embd_inp.size(); ++i) { const llama_token token = embd_inp[i]; output_tokens.push_back(token); - output_ss << llama_token_to_piece(ctx, token); + output_ss << common_token_to_piece(ctx, token); } n_remain -= line_inp.size(); - LOG("n_remain: %d\n", n_remain); + LOG_DBG("n_remain: %d\n", n_remain); } else { - LOG("empty line, passing control back\n"); + LOG_DBG("empty line, passing control back\n"); } input_echo = false; // do not echo this again @@ -724,14 +558,14 @@ int main(int argc, char ** argv) { if (n_past > 0) { if (is_interacting) { - llama_sampling_reset(ctx_sampling); + common_sampler_reset(smpl); } is_interacting = false; } } - // end of text token - if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) { + // end of generation + if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !params.interactive) { break; } @@ -743,24 +577,14 @@ int main(int argc, char ** argv) { } } if (!params.interactive && n_remain <= 0) { - printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str()); - fflush(stdout); + LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str()); } - llama_print_timings(ctx); - write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); + LOG("\n"); + common_perf_print(ctx, smpl); - if (ctx_guidance) { llama_free(ctx_guidance); } - llama_free(ctx); - llama_free_model(model); - - llama_sampling_free(ctx_sampling); + common_sampler_free(smpl); llama_backend_free(); -#ifndef LOG_DISABLE_LOGS - LOG_TEE("Log end\n"); -#endif // LOG_DISABLE_LOGS - return 0; } - diff --git a/examples/jeopardy/jeopardy.sh b/examples/jeopardy/jeopardy.sh index 9bdbc755c..07bcb3b8d 100755 --- a/examples/jeopardy/jeopardy.sh +++ b/examples/jeopardy/jeopardy.sh @@ -21,7 +21,7 @@ counter=1 echo 'Running' while IFS= read -r question do - exe_cmd="./main -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\"" + exe_cmd="./llama-cli -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\"" echo $counter echo "Current Question: $question" eval "$exe_cmd" diff --git a/examples/json-schema-to-grammar.py b/examples/json-schema-to-grammar.py deleted file mode 100755 index 6a977f031..000000000 --- a/examples/json-schema-to-grammar.py +++ /dev/null @@ -1,147 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import json -import re -import sys - -# whitespace is constrained to a single space char to prevent model "running away" in -# whitespace. Also maybe improves generation quality? -SPACE_RULE = '" "?' - -PRIMITIVE_RULES = { - 'boolean': '("true" | "false") space', - 'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', - 'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space', - 'string': r''' "\"" ( - [^"\\] | - "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\"" space ''', - 'null': '"null" space', -} - -INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+') -GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') -GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'} - - -class SchemaConverter: - def __init__(self, prop_order): - self._prop_order = prop_order - self._rules = {'space': SPACE_RULE} - - def _format_literal(self, literal): - escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub( - lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), json.dumps(literal) - ) - return f'"{escaped}"' - - def _add_rule(self, name, rule): - esc_name = INVALID_RULE_CHARS_RE.sub('-', name) - if esc_name not in self._rules or self._rules[esc_name] == rule: - key = esc_name - else: - i = 0 - while f'{esc_name}{i}' in self._rules: - i += 1 - key = f'{esc_name}{i}' - self._rules[key] = rule - return key - - def visit(self, schema, name): - schema_type = schema.get('type') - rule_name = name or 'root' - - if 'oneOf' in schema or 'anyOf' in schema: - rule = ' | '.join(( - self.visit(alt_schema, f'{name}{"-" if name else ""}{i}') - for i, alt_schema in enumerate(schema.get('oneOf') or schema['anyOf']) - )) - return self._add_rule(rule_name, rule) - - elif 'const' in schema: - return self._add_rule(rule_name, self._format_literal(schema['const'])) - - elif 'enum' in schema: - rule = ' | '.join((self._format_literal(v) for v in schema['enum'])) - return self._add_rule(rule_name, rule) - - elif schema_type == 'object' and 'properties' in schema: - # TODO: `required` keyword - prop_order = self._prop_order - prop_pairs = sorted( - schema['properties'].items(), - # sort by position in prop_order (if specified) then by key - key=lambda kv: (prop_order.get(kv[0], len(prop_order)), kv[0]), - ) - - rule = '"{" space' - for i, (prop_name, prop_schema) in enumerate(prop_pairs): - prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}') - if i > 0: - rule += ' "," space' - rule += fr' {self._format_literal(prop_name)} space ":" space {prop_rule_name}' - rule += ' "}" space' - - return self._add_rule(rule_name, rule) - - elif schema_type == 'array' and 'items' in schema: - # TODO `prefixItems` keyword - item_rule_name = self.visit(schema['items'], f'{name}{"-" if name else ""}item') - list_item_operator = f'("," space {item_rule_name})' - successive_items = "" - min_items = schema.get("minItems", 0) - if min_items > 0: - first_item = f"({item_rule_name})" - successive_items = list_item_operator * (min_items - 1) - min_items -= 1 - else: - first_item = f"({item_rule_name})?" - max_items = schema.get("maxItems") - if max_items is not None and max_items > min_items: - successive_items += (list_item_operator + "?") * (max_items - min_items - 1) - else: - successive_items += list_item_operator + "*" - rule = f'"[" space {first_item} {successive_items} "]" space' - return self._add_rule(rule_name, rule) - - else: - assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}' - return self._add_rule( - 'root' if rule_name == 'root' else schema_type, - PRIMITIVE_RULES[schema_type] - ) - - def format_grammar(self): - return '\n'.join((f'{name} ::= {rule}' for name, rule in self._rules.items())) - - -def main(args_in = None): - parser = argparse.ArgumentParser( - description=''' - Generates a grammar (suitable for use in ./main) that produces JSON conforming to a - given JSON schema. Only a subset of JSON schema features are supported; more may be - added in the future. - ''', - ) - parser.add_argument( - '--prop-order', - default=[], - type=lambda s: s.split(','), - help=''' - comma-separated property names defining the order of precedence for object properties; - properties not specified here are given lower precedence than those that are, and are - sorted alphabetically - ''' - ) - parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)') - args = parser.parse_args(args_in) - - schema = json.load(sys.stdin if args.schema == '-' else open(args.schema)) - prop_order = {name: idx for idx, name in enumerate(args.prop_order)} - converter = SchemaConverter(prop_order) - converter.visit(schema, '') - print(converter.format_grammar()) - - -if __name__ == '__main__': - main() diff --git a/examples/json_schema_pydantic_example.py b/examples/json_schema_pydantic_example.py new file mode 100644 index 000000000..19c0bdb5b --- /dev/null +++ b/examples/json_schema_pydantic_example.py @@ -0,0 +1,82 @@ +# Usage: +#! ./llama-server -m some-model.gguf & +#! pip install pydantic +#! python json_schema_pydantic_example.py + +from pydantic import BaseModel, Field, TypeAdapter +from annotated_types import MinLen +from typing import Annotated, List, Optional +import json, requests + +if True: + + def create_completion(*, response_model=None, endpoint="http://localhost:8080/v1/chat/completions", messages, **kwargs): + ''' + Creates a chat completion using an OpenAI-compatible endpoint w/ JSON schema support + (llama.cpp server, llama-cpp-python, Anyscale / Together...) + + The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below) + ''' + response_format = None + type_adapter = None + + if response_model: + type_adapter = TypeAdapter(response_model) + schema = type_adapter.json_schema() + messages = [{ + "role": "system", + "content": f"You respond in JSON format with the following schema: {json.dumps(schema, indent=2)}" + }] + messages + response_format={"type": "json_object", "schema": schema} + + data = requests.post(endpoint, headers={"Content-Type": "application/json"}, + json=dict(messages=messages, response_format=response_format, **kwargs)).json() + if 'error' in data: + raise Exception(data['error']['message']) + + content = data["choices"][0]["message"]["content"] + return type_adapter.validate_json(content) if type_adapter else content + +else: + + # This alternative branch uses Instructor + OpenAI client lib. + # Instructor support streamed iterable responses, retry & more. + # (see https://python.useinstructor.com/) + #! pip install instructor openai + import instructor, openai + client = instructor.patch( + openai.OpenAI(api_key="123", base_url="http://localhost:8080"), + mode=instructor.Mode.JSON_SCHEMA) + create_completion = client.chat.completions.create + + +if __name__ == '__main__': + + class QAPair(BaseModel): + class Config: + extra = 'forbid' # triggers additionalProperties: false in the JSON schema + question: str + concise_answer: str + justification: str + stars: Annotated[int, Field(ge=1, le=5)] + + class PyramidalSummary(BaseModel): + class Config: + extra = 'forbid' # triggers additionalProperties: false in the JSON schema + title: str + summary: str + question_answers: Annotated[List[QAPair], MinLen(2)] + sub_sections: Optional[Annotated[List['PyramidalSummary'], MinLen(2)]] + + print("# Summary\n", create_completion( + model="...", + response_model=PyramidalSummary, + messages=[{ + "role": "user", + "content": f""" + You are a highly efficient corporate document summarizer. + Create a pyramidal summary of an imaginary internal document about our company processes + (starting high-level, going down to each sub sections). + Keep questions short, and answers even shorter (trivia / quizz style). + """ + }])) diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py new file mode 100755 index 000000000..fc9f0097f --- /dev/null +++ b/examples/json_schema_to_grammar.py @@ -0,0 +1,811 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import itertools +import json +import re +import sys +from typing import Any, List, Optional, Set, Tuple, Union + +def _build_repetition(item_rule, min_items, max_items, separator_rule=None): + + if min_items == 0 and max_items == 1: + return f'{item_rule}?' + + if not separator_rule: + if min_items == 1 and max_items is None: + return f'{item_rule}+' + elif min_items == 0 and max_items is None: + return f'{item_rule}*' + else: + return f'{item_rule}{{{min_items},{max_items if max_items is not None else ""}}}' + + result = item_rule + ' ' + _build_repetition(f'({separator_rule} {item_rule})', min_items - 1 if min_items > 0 else 0, max_items - 1 if max_items is not None else None) + return f'({result})?' if min_items == 0 else result + +def _generate_min_max_int(min_value: Optional[int], max_value: Optional[int], out: list, decimals_left: int = 16, top_level: bool = True): + has_min = min_value != None + has_max = max_value != None + + def digit_range(from_char: str, to_char: str): + out.append("[") + if from_char == to_char: + out.append(from_char) + else: + out.append(from_char) + out.append("-") + out.append(to_char) + out.append("]") + + def more_digits(min_digits: int, max_digits: int): + out.append("[0-9]") + if min_digits == max_digits and min_digits == 1: + return + out.append("{") + out.append(str(min_digits)) + if max_digits != min_digits: + out.append(",") + if max_digits != sys.maxsize: + out.append(str(max_digits)) + out.append("}") + + def uniform_range(from_str: str, to_str: str): + i = 0 + while i < len(from_str) and from_str[i] == to_str[i]: + i += 1 + if i > 0: + out.append("\"") + out.append(from_str[:i]) + out.append("\"") + if i < len(from_str): + if i > 0: + out.append(" ") + sub_len = len(from_str) - i - 1 + if sub_len > 0: + from_sub = from_str[i+1:] + to_sub = to_str[i+1:] + sub_zeros = "0" * sub_len + sub_nines = "9" * sub_len + + to_reached = False + out.append("(") + if from_sub == sub_zeros: + digit_range(from_str[i], chr(ord(to_str[i]) - 1)) + out.append(" ") + more_digits(sub_len, sub_len) + else: + out.append("[") + out.append(from_str[i]) + out.append("] ") + out.append("(") + uniform_range(from_sub, sub_nines) + out.append(")") + if ord(from_str[i]) < ord(to_str[i]) - 1: + out.append(" | ") + if to_sub == sub_nines: + digit_range(chr(ord(from_str[i]) + 1), to_str[i]) + to_reached = True + else: + digit_range(chr(ord(from_str[i]) + 1), chr(ord(to_str[i]) - 1)) + out.append(" ") + more_digits(sub_len, sub_len) + if not to_reached: + out.append(" | ") + digit_range(to_str[i], to_str[i]) + out.append(" ") + uniform_range(sub_zeros, to_sub) + out.append(")") + else: + out.append("[") + out.append(from_str[i]) + out.append("-") + out.append(to_str[i]) + out.append("]") + + if has_min and has_max: + if min_value < 0 and max_value < 0: + out.append("\"-\" (") + _generate_min_max_int(-max_value, -min_value, out, decimals_left, top_level=True) + out.append(")") + return + + if min_value < 0: + out.append("\"-\" (") + _generate_min_max_int(0, -min_value, out, decimals_left, top_level=True) + out.append(") | ") + min_value = 0 + + min_s = str(min_value) + max_s = str(max_value) + min_digits = len(min_s) + max_digits = len(max_s) + + for digits in range(min_digits, max_digits): + uniform_range(min_s, "9" * digits) + min_s = "1" + "0" * digits + out.append(" | ") + uniform_range(min_s, max_s) + return + + less_decimals = max(decimals_left - 1, 1) + + if has_min: + if min_value < 0: + out.append("\"-\" (") + _generate_min_max_int(None, -min_value, out, decimals_left, top_level=False) + out.append(") | [0] | [1-9] ") + more_digits(0, decimals_left - 1) + elif min_value == 0: + if top_level: + out.append("[0] | [1-9] ") + more_digits(0, less_decimals) + else: + more_digits(1, decimals_left) + elif min_value <= 9: + c = str(min_value) + range_start = '1' if top_level else '0' + if c > range_start: + digit_range(range_start, chr(ord(c) - 1)) + out.append(" ") + more_digits(1, less_decimals) + out.append(" | ") + digit_range(c, "9") + out.append(" ") + more_digits(0, less_decimals) + else: + min_s = str(min_value) + length = len(min_s) + c = min_s[0] + + if c > "1": + digit_range("1" if top_level else "0", chr(ord(c) - 1)) + out.append(" ") + more_digits(length, less_decimals) + out.append(" | ") + digit_range(c, c) + out.append(" (") + _generate_min_max_int(int(min_s[1:]), None, out, less_decimals, top_level=False) + out.append(")") + if c < "9": + out.append(" | ") + digit_range(chr(ord(c) + 1), "9") + out.append(" ") + more_digits(length - 1, less_decimals) + return + + if has_max: + if max_value >= 0: + if top_level: + out.append("\"-\" [1-9] ") + more_digits(0, less_decimals) + out.append(" | ") + _generate_min_max_int(0, max_value, out, decimals_left, top_level=True) + else: + out.append("\"-\" (") + _generate_min_max_int(-max_value, None, out, decimals_left, top_level=False) + out.append(")") + return + + raise RuntimeError("At least one of min_value or max_value must be set") + +class BuiltinRule: + def __init__(self, content: str, deps: list | None = None): + self.content = content + self.deps = deps or [] + +# Constraining spaces to prevent model "running away". +SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}' + +PRIMITIVE_RULES = { + 'boolean' : BuiltinRule('("true" | "false") space', []), + 'decimal-part' : BuiltinRule('[0-9]{1,16}', []), + 'integral-part': BuiltinRule('[0] | [1-9] [0-9]{0,15}', []), + 'number' : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), + 'integer' : BuiltinRule('("-"? integral-part) space', ['integral-part']), + 'value' : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), + 'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), + 'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), + 'uuid' : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []), + 'char' : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []), + 'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']), + 'null' : BuiltinRule('"null" space', []), +} + +# TODO: support "uri", "email" string formats +STRING_FORMAT_RULES = { + 'date' : BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), + 'time' : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), + 'date-time' : BuiltinRule('date "T" time', ['date', 'time']), + 'date-string' : BuiltinRule('"\\"" date "\\"" space', ['date']), + 'time-string' : BuiltinRule('"\\"" time "\\"" space', ['time']), + 'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']), +} + +DOTALL = '[\\U00000000-\\U0010FFFF]' +DOT = '[^\\x0A\\x0D]' + +RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()]) + +INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+') +GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') +GRAMMAR_RANGE_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\]\-\\]') +GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'} + +NON_LITERAL_SET = set('|.()[]{}*+?') +ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('^$.[]()|{}*+?') + + +class SchemaConverter: + def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern): + self._prop_order = prop_order + self._allow_fetch = allow_fetch + self._dotall = dotall + self._raw_pattern = raw_pattern + self._rules = { + 'space': SPACE_RULE, + } + self._refs = {} + self._refs_being_resolved = set() + + def _format_literal(self, literal): + escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub( + lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)) or m.group(0), literal + ) + return f'"{escaped}"' + + def not_literal(self, literal: str, dotall: bool = True, maybe_escaped_underscores = False) -> str: + ''' + not_literal('a') -> '[^a]' + not_literal('abc') -> '([^a] | "a" ([^b] | "b" ([^c])?)?)?' + ''' + assert len(literal) > 0, 'Empty literal not supported' + def recurse(i: int): + c = literal[i] + if maybe_escaped_underscores and c == '_': + yield f'[^{c}\\\\]' + yield ' | ' + yield f'"\\\\"? "{c}"' + else: + yield f'[^{c}]' + if i < len(literal) - 1: + yield ' | ' + yield self._format_literal(c) + yield ' (' + yield from recurse(i + 1) + yield ')?' + + return ''.join(('(', *recurse(0), ')')) + + def _not_strings(self, strings): + class TrieNode: + def __init__(self): + self.children = {} + self.is_end_of_string = False + + def insert(self, string): + node = self + for c in string: + node = node.children.setdefault(c, TrieNode()) + node.is_end_of_string = True + + trie = TrieNode() + for s in strings: + trie.insert(s) + + char_rule = self._add_primitive('char', PRIMITIVE_RULES['char']) + out = ['["] ( '] + + def visit(node): + rejects = [] + first = True + for c in sorted(node.children.keys()): + child = node.children[c] + rejects.append(c) + if first: + first = False + else: + out.append(' | ') + out.append(f'[{c}]') + if child.children: + out.append(f' (') + visit(child) + out.append(')') + elif child.is_end_of_string: + out.append(f' {char_rule}+') + if node.children: + if not first: + out.append(' | ') + out.append(f'[^"{"".join(rejects)}] {char_rule}*') + visit(trie) + + out.append(f' ){"" if trie.is_end_of_string else "?"} ["] space') + return ''.join(out) + + def _add_rule(self, name, rule): + esc_name = INVALID_RULE_CHARS_RE.sub('-', name) + if esc_name not in self._rules or self._rules[esc_name] == rule: + key = esc_name + else: + i = 0 + while f'{esc_name}{i}' in self._rules and self._rules[f'{esc_name}{i}'] != rule: + i += 1 + key = f'{esc_name}{i}' + self._rules[key] = rule + return key + + def resolve_refs(self, schema: dict, url: str): + ''' + Resolves all $ref fields in the given schema, fetching any remote schemas, + replacing $ref with absolute reference URL and populating self._refs with the + respective referenced (sub)schema dictionaries. + ''' + def visit(n: dict): + if isinstance(n, list): + return [visit(x) for x in n] + elif isinstance(n, dict): + ref = n.get('$ref') + if ref is not None and ref not in self._refs: + if ref.startswith('https://'): + assert self._allow_fetch, 'Fetching remote schemas is not allowed (use --allow-fetch for force)' + import requests + + frag_split = ref.split('#') + base_url = frag_split[0] + + target = self._refs.get(base_url) + if target is None: + target = self.resolve_refs(requests.get(ref).json(), base_url) + self._refs[base_url] = target + + if len(frag_split) == 1 or frag_split[-1] == '': + return target + elif ref.startswith('#/'): + target = schema + ref = f'{url}{ref}' + n['$ref'] = ref + else: + raise ValueError(f'Unsupported ref {ref}') + + for sel in ref.split('#')[-1].split('/')[1:]: + assert target is not None and sel in target, f'Error resolving ref {ref}: {sel} not in {target}' + target = target[sel] + + self._refs[ref] = target + else: + for v in n.values(): + visit(v) + + return n + return visit(schema) + + def _generate_union_rule(self, name, alt_schemas): + return ' | '.join(( + self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}') + for i, alt_schema in enumerate(alt_schemas) + )) + + def _visit_pattern(self, pattern, name): + ''' + Transforms a regular expression pattern into a GBNF rule. + + Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions + Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md + + Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers. + + Mostly a 1:1 translation, except for {x} / {x,} / {x,y} quantifiers for which + we define sub-rules to keep the output lean. + ''' + + assert pattern.startswith('^') and pattern.endswith('$'), 'Pattern must start with "^" and end with "$"' + pattern = pattern[1:-1] + sub_rule_ids = {} + + i = 0 + length = len(pattern) + + def to_rule(s: tuple[str, bool]) -> str: + (txt, is_literal) = s + return "\"" + txt + "\"" if is_literal else txt + + def transform() -> tuple[str, bool]: + ''' + Parse a unit at index i (advancing it), and return its string representation + whether it's a literal. + ''' + nonlocal i + nonlocal pattern + nonlocal sub_rule_ids + + start = i + # For each component of this sequence, store its string representation and whether it's a literal. + # We only need a flat structure here to apply repetition operators to the last item, and + # to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially + # (GBNF's syntax is luckily very close to regular expressions!) + seq: list[tuple[str, bool]] = [] + + def get_dot(): + if self._dotall: + rule = DOTALL + else: + # Accept any character... except \n and \r line break chars (\x0A and \xOD) + rule = DOT + return self._add_rule(f'dot', rule) + + def join_seq(): + nonlocal seq + ret = [] + for is_literal, g in itertools.groupby(seq, lambda x: x[1]): + if is_literal: + ret.append((''.join(x[0] for x in g), True)) + else: + ret.extend(g) + if len(ret) == 1: + return ret[0] + return (' '.join(to_rule(x) for x in seq), False) + + while i < length: + c = pattern[i] + if c == '.': + seq.append((get_dot(), False)) + i += 1 + elif c == '(': + i += 1 + if i < length: + assert pattern[i] != '?', f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/' + seq.append((f'({to_rule(transform())})', False)) + elif c == ')': + i += 1 + assert start > 0 and pattern[start-1] == '(', f'Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}' + return join_seq() + elif c == '[': + square_brackets = c + i += 1 + while i < length and pattern[i] != ']': + if pattern[i] == '\\': + square_brackets += pattern[i:i+2] + i += 2 + else: + square_brackets += pattern[i] + i += 1 + assert i < length, f'Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}' + square_brackets += ']' + i += 1 + seq.append((square_brackets, False)) + elif c == '|': + seq.append(('|', False)) + i += 1 + elif c in ('*', '+', '?'): + seq[-1] = (to_rule(seq[-1]) + c, False) + i += 1 + elif c == '{': + curly_brackets = c + i += 1 + while i < length and pattern[i] != '}': + curly_brackets += pattern[i] + i += 1 + assert i < length, f'Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}' + curly_brackets += '}' + i += 1 + nums = [s.strip() for s in curly_brackets[1:-1].split(',')] + min_times = 0 + max_times = None + try: + if len(nums) == 1: + min_times = int(nums[0]) + max_times = min_times + else: + assert len(nums) == 2 + min_times = int(nums[0]) if nums[0] else 0 + max_times = int(nums[1]) if nums[1] else None + except ValueError: + raise ValueError(f'Invalid quantifier {curly_brackets} in /{pattern}/') + + (sub, sub_is_literal) = seq[-1] + + if not sub_is_literal: + id = sub_rule_ids.get(sub) + if id is None: + id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub) + sub_rule_ids[sub] = id + sub = id + + seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times), False) + else: + literal = '' + while i < length: + if pattern[i] == '\\' and i < length - 1: + next = pattern[i + 1] + if next in ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS: + i += 1 + literal += pattern[i] + i += 1 + else: + literal += pattern[i:i+2] + i += 2 + elif pattern[i] == '"' and not self._raw_pattern: + literal += '\\"' + i += 1 + elif pattern[i] not in NON_LITERAL_SET and \ + (i == length - 1 or literal == '' or pattern[i+1] == '.' or pattern[i+1] not in NON_LITERAL_SET): + literal += pattern[i] + i += 1 + else: + break + if literal: + seq.append((literal, True)) + + return join_seq() + + return self._add_rule( + name, + to_rule(transform()) if self._raw_pattern \ + else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space") + + + def _resolve_ref(self, ref): + ref_name = ref.split('/')[-1] + if ref_name not in self._rules and ref not in self._refs_being_resolved: + self._refs_being_resolved.add(ref) + resolved = self._refs[ref] + ref_name = self.visit(resolved, ref_name) + self._refs_being_resolved.remove(ref) + return ref_name + + def _generate_constant_rule(self, value): + return self._format_literal(json.dumps(value)) + + def visit(self, schema, name): + schema_type = schema.get('type') + schema_format = schema.get('format') + rule_name = name + '-' if name in RESERVED_NAMES else name or 'root' + + if (ref := schema.get('$ref')) is not None: + return self._add_rule(rule_name, self._resolve_ref(ref)) + + elif 'oneOf' in schema or 'anyOf' in schema: + return self._add_rule(rule_name, self._generate_union_rule(name, schema.get('oneOf') or schema['anyOf'])) + + elif isinstance(schema_type, list): + return self._add_rule(rule_name, self._generate_union_rule(name, [{**schema, 'type': t} for t in schema_type])) + + elif 'const' in schema: + return self._add_rule(rule_name, self._generate_constant_rule(schema['const']) + ' space') + + elif 'enum' in schema: + rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ') space' + return self._add_rule(rule_name, rule) + + elif schema_type in (None, 'object') and \ + ('properties' in schema or \ + ('additionalProperties' in schema and schema['additionalProperties'] is not True)): + required = set(schema.get('required', [])) + properties = list(schema.get('properties', {}).items()) + return self._add_rule(rule_name, self._build_object_rule(properties, required, name, schema.get('additionalProperties'))) + + elif schema_type in (None, 'object') and 'allOf' in schema: + required = set() + properties = [] + hybrid_name = name + def add_component(comp_schema, is_required): + if (ref := comp_schema.get('$ref')) is not None: + comp_schema = self._refs[ref] + + if 'properties' in comp_schema: + for prop_name, prop_schema in comp_schema['properties'].items(): + properties.append((prop_name, prop_schema)) + if is_required: + required.add(prop_name) + + for t in schema['allOf']: + if 'anyOf' in t: + for tt in t['anyOf']: + add_component(tt, is_required=False) + else: + add_component(t, is_required=True) + + return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None)) + + elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema): + items = schema.get('items') or schema['prefixItems'] + if isinstance(items, list): + return self._add_rule( + rule_name, + '"[" space ' + + ' "," space '.join( + self.visit(item, f'{name}{"-" if name else ""}tuple-{i}') + for i, item in enumerate(items)) + + ' "]" space') + else: + item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item') + min_items = schema.get("minItems", 0) + max_items = schema.get("maxItems") + return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space') + + elif schema_type in (None, 'string') and 'pattern' in schema: + return self._visit_pattern(schema['pattern'], rule_name) + + elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''): + return self._add_primitive( + 'root' if rule_name == 'root' else schema_format, + PRIMITIVE_RULES['uuid'] + ) + + elif schema_type in (None, 'string') and f'{schema_format}-string' in STRING_FORMAT_RULES: + prim_name = f'{schema_format}-string' + return self._add_rule(rule_name, self._add_primitive(prim_name, STRING_FORMAT_RULES[prim_name])) + + elif schema_type == 'string' and ('minLength' in schema or 'maxLength' in schema): + char_rule = self._add_primitive('char', PRIMITIVE_RULES['char']) + min_len = schema.get('minLength', 0) + max_len = schema.get('maxLength') + + return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space') + + elif schema_type in (None, 'integer') and \ + ('minimum' in schema or 'exclusiveMinimum' in schema or 'maximum' in schema or 'exclusiveMaximum' in schema): + min_value = None + max_value = None + if 'minimum' in schema: + min_value = schema['minimum'] + elif 'exclusiveMinimum' in schema: + min_value = schema['exclusiveMinimum'] + 1 + if 'maximum' in schema: + max_value = schema['maximum'] + elif 'exclusiveMaximum' in schema: + max_value = schema['exclusiveMaximum'] - 1 + + out = ["("] + _generate_min_max_int(min_value, max_value, out) + out.append(") space") + return self._add_rule(rule_name, ''.join(out)) + + elif (schema_type == 'object') or (len(schema) == 0): + return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object'])) + + else: + assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}' + # TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return self._add_primitive('root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type]) + + def _add_primitive(self, name: str, rule: BuiltinRule): + n = self._add_rule(name, rule.content) + + for dep in rule.deps: + dep_rule = PRIMITIVE_RULES.get(dep) or STRING_FORMAT_RULES.get(dep) + assert dep_rule, f'Rule {dep} not known' + if dep not in self._rules: + self._add_primitive(dep, dep_rule) + return n + + def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Optional[Union[bool, Any]]): + prop_order = self._prop_order + # sort by position in prop_order (if specified) then by original order + sorted_props = [kv[0] for _, kv in sorted(enumerate(properties), key=lambda ikv: (prop_order.get(ikv[1][0], len(prop_order)), ikv[0]))] + + prop_kv_rule_names = {} + for prop_name, prop_schema in properties: + prop_rule_name = self.visit(prop_schema, f'{name}{"-" if name else ""}{prop_name}') + prop_kv_rule_names[prop_name] = self._add_rule( + f'{name}{"-" if name else ""}{prop_name}-kv', + fr'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}' + ) + required_props = [k for k in sorted_props if k in required] + optional_props = [k for k in sorted_props if k not in required] + + if additional_properties is not None and additional_properties != False: + sub_name = f'{name}{"-" if name else ""}additional' + value_rule = self.visit(additional_properties, f'{sub_name}-value') if isinstance(additional_properties, dict) else \ + self._add_primitive('value', PRIMITIVE_RULES['value']) + key_rule = self._add_primitive('string', PRIMITIVE_RULES['string']) if not sorted_props \ + else self._add_rule(f'{sub_name}-k', self._not_strings(sorted_props)) + + prop_kv_rule_names["*"] = self._add_rule( + f'{sub_name}-kv', + f'{key_rule} ":" space {value_rule}' + ) + optional_props.append("*") + + rule = '"{" space ' + rule += ' "," space '.join(prop_kv_rule_names[k] for k in required_props) + + if optional_props: + rule += ' (' + if required_props: + rule += ' "," space ( ' + + def get_recursive_refs(ks, first_is_optional): + [k, *rest] = ks + kv_rule_name = prop_kv_rule_names[k] + comma_ref = f'( "," space {kv_rule_name} )' + if first_is_optional: + res = comma_ref + ('*' if k == '*' else '?') + else: + res = kv_rule_name + (' ' + comma_ref + "*" if k == '*' else '') + if len(rest) > 0: + res += ' ' + self._add_rule( + f'{name}{"-" if name else ""}{k}-rest', + get_recursive_refs(rest, first_is_optional=True) + ) + return res + + rule += ' | '.join( + get_recursive_refs(optional_props[i:], first_is_optional=False) + for i in range(len(optional_props)) + ) + if required_props: + rule += ' )' + rule += ' )?' + + rule += ' "}" space' + + return rule + + def format_grammar(self): + return '\n'.join( + f'{name} ::= {rule}' + for name, rule in sorted(self._rules.items(), key=lambda kv: kv[0]) + ) + + +def main(args_in = None): + parser = argparse.ArgumentParser( + description=''' + Generates a grammar (suitable for use in ./llama-cli) that produces JSON conforming to a + given JSON schema. Only a subset of JSON schema features are supported; more may be + added in the future. + ''', + ) + parser.add_argument( + '--prop-order', + default=[], + type=lambda s: s.split(','), + help=''' + comma-separated property names defining the order of precedence for object properties; + properties not specified here are given lower precedence than those that are, and + are kept in their original order from the schema. Required properties are always + given precedence over optional properties. + ''' + ) + parser.add_argument( + '--allow-fetch', + action='store_true', + default=False, + help='Whether to allow fetching referenced schemas over HTTPS') + parser.add_argument( + '--dotall', + action='store_true', + default=False, + help='Whether to treat dot (".") as matching all chars including line breaks in regular expression patterns') + parser.add_argument( + '--raw-pattern', + action='store_true', + default=False, + help='Treats string patterns as raw patterns w/o quotes (or quote escapes)') + + parser.add_argument('schema', help='file containing JSON schema ("-" for stdin)') + args = parser.parse_args(args_in) + + if args.schema.startswith('https://'): + url = args.schema + import requests + schema = requests.get(url).json() + elif args.schema == '-': + url = 'stdin' + schema = json.load(sys.stdin) + else: + url = f'file://{args.schema}' + with open(args.schema) as f: + schema = json.load(f) + converter = SchemaConverter( + prop_order={name: idx for idx, name in enumerate(args.prop_order)}, + allow_fetch=args.allow_fetch, + dotall=args.dotall, + raw_pattern=args.raw_pattern) + schema = converter.resolve_refs(schema, url) + converter.visit(schema, '') + print(converter.format_grammar()) + + +if __name__ == '__main__': + main() 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/README.md b/examples/llama-bench/README.md index 10f37b441..6bbe4bb75 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -1,4 +1,4 @@ -# llama.cpp/example/llama-bench +# llama.cpp/examples/llama-bench Performance testing tool for llama.cpp. @@ -14,7 +14,8 @@ Performance testing tool for llama.cpp. 1. [Markdown](#markdown) 2. [CSV](#csv) 3. [JSON](#json) - 4. [SQL](#sql) + 4. [JSONL](#jsonl) + 5. [SQL](#sql) ## Syntax @@ -23,30 +24,43 @@ usage: ./llama-bench [options] options: -h, --help - -m, --model (default: models/7B/ggml-model-q4_0.gguf) - -p, --n-prompt (default: 512) - -n, --n-gen (default: 128) - -b, --batch-size (default: 512) - -ctk , --cache-type-k (default: f16) - -ctv , --cache-type-v (default: f16) - -t, --threads (default: 112) - -ngl, --n-gpu-layers (default: 99) - -sm, --split-mode (default: layer) - -mg, --main-gpu (default: 0) - -nkvo, --no-kv-offload <0|1> (default: 0) - -mmp, --mmap <0|1> (default: 1) - -ts, --tensor_split (default: 0) - -r, --repetitions (default: 5) - -o, --output (default: md) - -v, --verbose (default: 0) + -m, --model (default: models/7B/ggml-model-q4_0.gguf) + -p, --n-prompt (default: 512) + -n, --n-gen (default: 128) + -pg (default: ) + -b, --batch-size (default: 2048) + -ub, --ubatch-size (default: 512) + -ctk, --cache-type-k (default: f16) + -ctv, --cache-type-v (default: f16) + -t, --threads (default: 8) + -C, --cpu-mask (default: 0x0) + --cpu-strict <0|1> (default: 0) + --poll <0...100> (default: 50) + -ngl, --n-gpu-layers (default: 99) + -rpc, --rpc (default: ) + -sm, --split-mode (default: layer) + -mg, --main-gpu (default: 0) + -nkvo, --no-kv-offload <0|1> (default: 0) + -fa, --flash-attn <0|1> (default: 0) + -mmp, --mmap <0|1> (default: 1) + --numa (default: disabled) + -embd, --embeddings <0|1> (default: 0) + -ts, --tensor-split (default: 0) + -r, --repetitions (default: 5) + --prio <0|1|2|3> (default: 0) + --delay <0...N> (seconds) (default: 0) + -o, --output (default: md) + -oe, --output-err (default: none) + -v, --verbose (default: 0) Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. ``` -llama-bench can perform two types of tests: +llama-bench can perform three types of tests: - Prompt processing (pp): processing a prompt in batches (`-p`) - Text generation (tg): generating a sequence of tokens (`-n`) +- Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (`-pg`) With the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`). @@ -156,7 +170,7 @@ $ ./llama-bench -o csv ``` ```csv -build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts +build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961" "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342" ``` @@ -173,7 +187,6 @@ $ ./llama-bench -o json "build_commit": "3469684", "build_number": 1275, "cuda": true, - "opencl": false, "metal": false, "gpu_blas": true, "blas": true, @@ -204,7 +217,6 @@ $ ./llama-bench -o json "build_commit": "3469684", "build_number": 1275, "cuda": true, - "opencl": false, "metal": false, "gpu_blas": true, "blas": true, @@ -234,6 +246,19 @@ $ ./llama-bench -o json ] ``` + +### JSONL + +```sh +$ ./llama-bench -o jsonl +``` + +```json lines +{"build_commit":"3469684","build_number":1275,"cuda":true,"metal":false,"gpu_blas":true,"blas":true,"cpu_info":"13th Gen Intel(R) Core(TM) i9-13900K","gpu_info":"NVIDIA GeForce RTX 3090 Ti","model_filename":"models/7B/ggml-model-q4_0.gguf","model_type":"llama 7B mostly Q4_0","model_size":3825065984,"model_n_params":6738415616,"n_batch":512,"n_threads":16,"f16_kv":true,"n_gpu_layers":99,"main_gpu":0,"mul_mat_q":true,"tensor_split":"0.00","n_prompt":512,"n_gen":0,"test_time":"2023-09-23T12:09:57Z","avg_ns":212365953,"stddev_ns":985423,"avg_ts":2410.974041,"stddev_ts":11.163766,"samples_ns":[213837238,211635853,212328053,211329715,212698907],"samples_ts":[2394.34,2419.25,2411.36,2422.75,2407.16]} +{"build_commit":"3469684","build_number":1275,"cuda":true,"metal":false,"gpu_blas":true,"blas":true,"cpu_info":"13th Gen Intel(R) Core(TM) i9-13900K","gpu_info":"NVIDIA GeForce RTX 3090 Ti","model_filename":"models/7B/ggml-model-q4_0.gguf","model_type":"llama 7B mostly Q4_0","model_size":3825065984,"model_n_params":6738415616,"n_batch":512,"n_threads":16,"f16_kv":true,"n_gpu_layers":99,"main_gpu":0,"mul_mat_q":true,"tensor_split":"0.00","n_prompt":0,"n_gen":128,"test_time":"2023-09-23T12:09:59Z","avg_ns":977425219,"stddev_ns":9268593,"avg_ts":130.965708,"stddev_ts":1.238924,"samples_ns":[984472709,974901233,989474741,970729355,967548060],"samples_ts":[130.019,131.295,129.362,131.86,132.293]} +``` + + ### SQL SQL output is suitable for importing into a SQLite database. The output can be piped into the `sqlite3` command line tool to add the results to a database. @@ -247,7 +272,6 @@ CREATE TABLE IF NOT EXISTS test ( build_commit TEXT, build_number INTEGER, cuda INTEGER, - opencl INTEGER, metal INTEGER, gpu_blas INTEGER, blas INTEGER, @@ -273,6 +297,6 @@ CREATE TABLE IF NOT EXISTS test ( stddev_ts REAL ); -INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634'); -INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692'); +INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634'); +INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692'); ``` diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c2155b2ac..4ac19ca86 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -6,6 +6,7 @@ #include #include #include +#include #include #include #include @@ -14,13 +15,20 @@ #include #include #include +#include #include +#include "common.h" #include "ggml.h" #include "llama.h" -#include "common.h" -#include "ggml-cuda.h" -#include "ggml-sycl.h" + +#ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +#endif // utils static uint64_t get_time_ns() { @@ -28,8 +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]; @@ -40,166 +47,176 @@ static std::string join(const std::vector & values, const std::string & delim return str.str(); } -template -static std::vector split(const std::string & str, char delim) { - std::vector values; - std::istringstream str_stream(str); - std::string token; - while (std::getline(str_stream, token, delim)) { - T value; - std::istringstream token_stream(token); - token_stream >> value; - values.push_back(value); - } - return values; -} - -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::string id; -#ifdef __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'; - } - id = p; - break; - } - } + 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); + if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU || dev_type == GGML_BACKEND_DEVICE_TYPE_ACCEL) { + cpu_list.push_back(ggml_backend_dev_description(dev)); } } -#endif - // TODO: other platforms - return id; + return join(cpu_list, ", "); } static std::string get_gpu_info() { - std::string id; -#ifdef GGML_USE_CUBLAS - int count = ggml_cuda_get_device_count(); - for (int i = 0; i < count; i++) { - char buf[128]; - ggml_cuda_get_device_description(i, buf, sizeof(buf)); - id += buf; - if (i < count - 1) { - id += "/"; + 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); + if (dev_type == GGML_BACKEND_DEVICE_TYPE_GPU) { + gpu_list.push_back(ggml_backend_dev_description(dev)); } } -#endif -#ifdef GGML_USE_SYCL - int device_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES); - - for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { - if (device_list[i] >0 ){ - char buf[128]; - ggml_sycl_get_device_description(i, buf, sizeof(buf)); - id += buf; - id += "/"; - } - } - if (id.length() >2 ) { - id.pop_back(); - } -#endif - // TODO: other backends - return id; + return join(gpu_list, ", "); } // command line params -enum output_formats {CSV, JSON, MARKDOWN, SQL}; +enum output_formats { NONE, CSV, JSON, JSONL, MARKDOWN, SQL }; static const char * output_format_str(output_formats format) { switch (format) { - case CSV: return "csv"; - case JSON: return "json"; - case MARKDOWN: return "md"; - case SQL: return "sql"; - default: GGML_ASSERT(!"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"); } } +static bool output_format_from_str(const std::string & s, output_formats & format) { + if (s == "none") { + format = NONE; + } else if (s == "csv") { + format = CSV; + } else if (s == "json") { + format = JSON; + } else if (s == "jsonl") { + format = JSONL; + } else if (s == "md") { + format = MARKDOWN; + } else if (s == "sql") { + format = SQL; + } else { + return false; + } + return true; +} + 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_ASSERT(!"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"); } } +static std::string pair_str(const std::pair & p) { + static char buf[32]; + snprintf(buf, sizeof(buf), "%d,%d", p.first, p.second); + return buf; +} + struct cmd_params { - std::vector model; - std::vector n_prompt; - std::vector n_gen; - std::vector n_batch; - std::vector type_k; - std::vector type_v; - std::vector n_threads; - std::vector n_gpu_layers; - std::vector split_mode; - std::vector main_gpu; - std::vector no_kv_offload; - std::vector> tensor_split; - std::vector use_mmap; - int reps; - bool verbose; - output_formats output_format; + 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; }; static const cmd_params cmd_params_defaults = { - /* model */ {"models/7B/ggml-model-q4_0.gguf"}, - /* n_prompt */ {512}, - /* n_gen */ {128}, - /* n_batch */ {512}, - /* type_k */ {GGML_TYPE_F16}, - /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {get_num_physical_cores()}, - /* n_gpu_layers */ {99}, - /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, - /* main_gpu */ {0}, - /* no_kv_offload */ {false}, - /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, - /* use_mmap */ {true}, - /* reps */ 5, - /* verbose */ false, - /* output_format */ MARKDOWN + /* 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 }, + /* numa */ GGML_NUMA_STRATEGY_DISABLED, + /* reps */ 5, + /* prio */ GGML_SCHED_PRIO_NORMAL, + /* delay */ 0, + /* verbose */ false, + /* progress */ false, + /* output_format */ MARKDOWN, + /* output_format_stderr */ NONE, }; static void print_usage(int /* argc */, char ** argv) { @@ -207,30 +224,69 @@ static void print_usage(int /* argc */, char ** argv) { printf("\n"); 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(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").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(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").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(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -ts, --tensor_split (default: 0)\n"); - printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); - printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); - printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); + 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(" -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(" --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()); + if (llama_supports_rpc()) { + 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(" --numa (default: disabled)\n"); + 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(" -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; } @@ -246,21 +302,28 @@ static ggml_type ggml_type_from_name(const std::string & s) { if (s == "q5_1") { return GGML_TYPE_Q5_1; } + if (s == "iq4_nl") { + return GGML_TYPE_IQ4_NL; + } 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.reps = cmd_params_defaults.reps; + 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; for (int i = 1; i < argc; i++) { arg = argv[i]; @@ -276,35 +339,53 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.model.insert(params.model.end(), p.begin(), p.end()); } else if (arg == "-p" || arg == "--n-prompt") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end()); } else if (arg == "-n" || arg == "--n-gen") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_gen.insert(params.n_gen.end(), p.begin(), p.end()); + } else if (arg == "-pg") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], ','); + if (p.size() != 2) { + invalid_param = true; + break; + } + 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; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_batch.insert(params.n_batch.end(), p.begin(), p.end()); + } else if (arg == "-ub" || arg == "--ubatch-size") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end()); } else if (arg == "-ctk" || arg == "--cache-type-k") { if (++i >= argc) { invalid_param = true; break; } - auto p = 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); @@ -314,13 +395,16 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } types.push_back(gt); } + if (invalid_param) { + break; + } params.type_k.insert(params.type_k.end(), types.begin(), types.end()); } else if (arg == "-ctv" || arg == "--cache-type-v") { if (++i >= argc) { invalid_param = true; break; } - auto p = 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); @@ -330,27 +414,57 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } types.push_back(gt); } + if (invalid_param) { + break; + } params.type_v.insert(params.type_v.end(), types.begin(), types.end()); } else if (arg == "-t" || arg == "--threads") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_threads.insert(params.n_threads.end(), p.begin(), p.end()); + } else if (arg == "-C" || arg == "--cpu-mask") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.cpu_mask.insert(params.cpu_mask.end(), p.begin(), p.end()); + } else if (arg == "--cpu-strict") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.cpu_strict.insert(params.cpu_strict.end(), p.begin(), p.end()); + } else if (arg == "--poll") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.poll.insert(params.poll.end(), p.begin(), p.end()); } else if (arg == "-ngl" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); + } else if (llama_supports_rpc() && (arg == "-rpc" || arg == "--rpc")) { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers.push_back(argv[i]); } else if (arg == "-sm" || arg == "--split-mode") { if (++i >= argc) { invalid_param = true; break; } - auto p = 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; @@ -366,37 +480,71 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } modes.push_back(mode); } + if (invalid_param) { + break; + } params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end()); } else if (arg == "-mg" || arg == "--main-gpu") { if (++i >= argc) { invalid_param = true; break; } - params.main_gpu = split(argv[i], split_delim); + params.main_gpu = string_split(argv[i], split_delim); } else if (arg == "-nkvo" || arg == "--no-kv-offload") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); + } else if (arg == "--numa") { + if (++i >= argc) { + invalid_param = true; + 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; + } + } + } else if (arg == "-fa" || arg == "--flash-attn") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end()); } else if (arg == "-mmp" || arg == "--mmap") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); + } else if (arg == "-embd" || arg == "--embeddings") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; break; } - for (auto ts : split(argv[i], split_delim)) { + 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()); @@ -415,25 +563,34 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } params.reps = std::stoi(argv[i]); + } else if (arg == "--prio") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.prio = (enum ggml_sched_priority) std::stoi(argv[i]); + } else if (arg == "--delay") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.delay = std::stoi(argv[i]); } else if (arg == "-o" || arg == "--output") { if (++i >= argc) { invalid_param = true; break; } - if (argv[i] == std::string("csv")) { - params.output_format = CSV; - } else if (argv[i] == std::string("json")) { - params.output_format = JSON; - } else if (argv[i] == std::string("md")) { - params.output_format = MARKDOWN; - } else if (argv[i] == std::string("sql")) { - params.output_format = SQL; - } else { + invalid_param = !output_format_from_str(argv[i], params.output_format); + } else if (arg == "-oe" || arg == "--output-err") { + if (++i >= argc) { invalid_param = true; break; } + invalid_param = !output_format_from_str(argv[i], params.output_format_stderr); } else if (arg == "-v" || arg == "--verbose") { params.verbose = true; + } else if (arg == "--progress") { + params.progress = true; } else { invalid_param = true; break; @@ -446,67 +603,156 @@ 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_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; } - 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.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.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.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } + 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; - ggml_type type_k; - ggml_type type_v; - int n_threads; - int n_gpu_layers; - llama_split_mode split_mode; - int main_gpu; - bool no_kv_offload; + 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_str; + llama_split_mode split_mode; + int main_gpu; + bool no_kv_offload; + bool flash_attn; std::vector tensor_split; - bool use_mmap; + bool use_mmap; + bool embeddings; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); mparams.n_gpu_layers = n_gpu_layers; - mparams.split_mode = split_mode; - mparams.main_gpu = main_gpu; + if (!rpc_servers_str.empty()) { + auto rpc_servers = string_split(rpc_servers_str, ','); + + // add RPC devices + if (!rpc_servers.empty()) { + ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC"); + if (!rpc_reg) { + fprintf(stderr, "%s: failed to find RPC backend\n", __func__); + exit(1); + } + + typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint); + ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device"); + if (!ggml_backend_rpc_add_device_fn) { + fprintf(stderr, "%s: failed to find RPC device add function\n", __func__); + exit(1); + } + static std::vector devices; + devices.clear(); + for (const std::string & server : rpc_servers) { + ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str()); + if (dev) { + devices.push_back(dev); + } else { + fprintf(stderr, "%s: failed to add RPC device for server '%s'\n", __func__, server.c_str()); + exit(1); + } + } + devices.push_back(nullptr); + mparams.devices = devices.data(); + } + } + 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 && - 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_str == other.rpc_servers_str && + 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.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; return cparams; } @@ -516,17 +762,25 @@ 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) for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) for (const auto & mmp : params.use_mmap) + for (const auto & embd : params.embeddings) for (const auto & nb : params.n_batch) + for (const auto & nub : params.n_ubatch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) for (const auto & nkvo : params.no_kv_offload) - for (const auto & nt : params.n_threads) { + for (const auto & fa : params.flash_attn) + for (const auto & nt : params.n_threads) + for (const auto & cm : params.cpu_mask) + for (const auto & cs : params.cpu_strict) + for (const auto & pl : params.poll) { for (const auto & n_prompt : params.n_prompt) { if (n_prompt == 0) { continue; @@ -536,15 +790,22 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_prompt = */ n_prompt, /* .n_gen = */ 0, /* .n_batch = */ nb, + /* .n_ubatch = */ nub, /* .type_k = */ tk, /* .type_v = */ tv, /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, + /* .flash_attn = */ fa, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, + /* .embeddings = */ embd, }; instances.push_back(instance); } @@ -558,164 +819,174 @@ static std::vector get_cmd_params_instances(const cmd_param /* .n_prompt = */ 0, /* .n_gen = */ n_gen, /* .n_batch = */ nb, + /* .n_ubatch = */ nub, /* .type_k = */ tk, /* .type_v = */ tv, /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, + /* .flash_attn = */ fa, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, + /* .embeddings = */ embd, + }; + instances.push_back(instance); + } + + for (const auto & n_pg : params.n_pg) { + if (n_pg.first == 0 && n_pg.second == 0) { + continue; + } + cmd_params_instance instance = { + /* .model = */ m, + /* .n_prompt = */ n_pg.first, + /* .n_gen = */ n_pg.second, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, + /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, + /* .split_mode = */ sm, + /* .main_gpu = */ mg, + /* .no_kv_offload= */ nkvo, + /* .flash_attn = */ fa, + /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, + /* .embeddings = */ embd, }; 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 opencl; - 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_threads; - ggml_type type_k; - ggml_type type_v; - int n_gpu_layers; - llama_split_mode split_mode; - int main_gpu; - bool no_kv_offload; - std::vector tensor_split; - bool use_mmap; - 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_threads = inst.n_threads; - 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; - tensor_split = inst.tensor_split; - use_mmap = inst.use_mmap; - 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() { - if (cuda) { - return GGML_CUDA_NAME; + std::vector backends; + for (size_t i = 0; i < ggml_backend_reg_count(); 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)); + } } - if (opencl) { - return "OpenCL"; - } - if (vulkan) { - return "Vulkan"; - } - if (kompute) { - return "Kompute"; - } - if (metal) { - return "Metal"; - } - if (sycl) { - return GGML_SYCL_NAME; - } - if (gpu_blas) { - return "GPU BLAS"; - } - if (blas) { - return "BLAS"; - } - - return "CPU"; + return backends.empty() ? "CPU" : join(backends, ","); } static const std::vector & get_fields() { static const std::vector fields = { - "build_commit", "build_number", - "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", - "cpu_info", "gpu_info", - "model_filename", "model_type", "model_size", "model_n_params", - "n_batch", "n_threads", "type_k", "type_v", - "n_gpu_layers", "split_mode", - "main_gpu", "no_kv_offload", - "tensor_split", "use_mmap", - "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_threads" || - 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 == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || - field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "use_mmap") { + 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") { @@ -726,7 +997,7 @@ 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; @@ -740,43 +1011,53 @@ struct test { tensor_split_str += "/"; } } - std::vector values = { - build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(sycl), 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_threads), 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), - tensor_split_str, std::to_string(use_mmap), - 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_cublas(); -const bool test::opencl = !!ggml_cpu_has_clblast(); -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(); @@ -784,9 +1065,12 @@ 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 { @@ -802,7 +1086,7 @@ 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; @@ -815,38 +1099,38 @@ struct csv_printer : public printer { } }; +static std::string escape_json(const std::string & value) { + std::string escaped; + for (auto c : value) { + if (c == '"') { + escaped += "\\\""; + } else if (c == '\\') { + escaped += "\\\\"; + } else if (c <= 0x1f) { + char buf[8]; + snprintf(buf, sizeof(buf), "\\u%04x", c); + escaped += buf; + } else { + escaped += c; + } + } + return escaped; +} + +static std::string format_json_value(const std::string & field, const std::string & value) { + switch (test::get_field_type(field)) { + case test::STRING: + return "\"" + escape_json(value) + "\""; + case test::BOOL: + return value == "0" ? "false" : "true"; + default: + return value; + } +} + struct json_printer : public printer { bool first = true; - static std::string escape_json(const std::string & value) { - std::string escaped; - for (auto c : value) { - if (c == '"') { - escaped += "\\\""; - } else if (c == '\\') { - escaped += "\\\\"; - } else if (c <= 0x1f) { - char buf[8]; - snprintf(buf, sizeof(buf), "\\u%04x", c); - escaped += buf; - } else { - escaped += c; - } - } - return escaped; - } - - static std::string format_value(const std::string & field, const std::string & value) { - switch (test::get_field_type(field)) { - case test::STRING: - return "\"" + escape_json(value) + "\""; - case test::BOOL: - return value == "0" ? "false" : "true"; - default: - return value; - } - } - void print_header(const cmd_params & params) override { fprintf(fout, "[\n"); (void) params; @@ -855,7 +1139,8 @@ 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_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()); } } @@ -873,8 +1158,24 @@ 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()); + for (size_t i = 0; i < fields.size(); i++) { + fprintf(fout, "\"%s\": %s, ", fields.at(i).c_str(), format_json_value(fields.at(i), values.at(i)).c_str()); + } + } + + void print_test(const test & t) override { + fprintf(fout, "{"); + print_fields(test::get_fields(), t.get_values()); + fprintf(fout, "\"samples_ns\": [ %s ],", join(t.samples_ns, ", ").c_str()); + fprintf(fout, "\"samples_ts\": [ %s ]", join(t.get_ts(), ", ").c_str()); + fprintf(fout, "}\n"); + fflush(fout); } }; @@ -886,7 +1187,7 @@ struct markdown_printer : public printer { return -30; } if (field == "t/s") { - return 16; + return 20; } if (field == "size" || field == "params") { return 10; @@ -894,8 +1195,32 @@ struct markdown_printer : public printer { if (field == "n_gpu_layers") { return 3; } + if (field == "n_threads") { + return 7; + } + if (field == "n_batch") { + return 7; + } + if (field == "n_ubatch") { + return 8; + } + if (field == "type_k" || field == "type_v") { + return 6; + } + if (field == "split_mode") { + return 5; + } + if (field == "flash_attn") { + return 2; + } + if (field == "use_mmap") { + return 4; + } + if (field == "test") { + 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; @@ -916,9 +1241,15 @@ struct markdown_printer : public printer { if (field == "no_kv_offload") { return "nkvo"; } + if (field == "flash_attn") { + return "fa"; + } if (field == "use_mmap") { return "mmap"; } + if (field == "embeddings") { + return "embd"; + } if (field == "tensor_split") { return "ts"; } @@ -931,16 +1262,29 @@ 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"); } if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) { fields.emplace_back("n_threads"); } + if (params.cpu_mask.size() > 1 || params.cpu_mask != cmd_params_defaults.cpu_mask) { + fields.emplace_back("cpu_mask"); + } + if (params.cpu_strict.size() > 1 || params.cpu_strict != cmd_params_defaults.cpu_strict) { + fields.emplace_back("cpu_strict"); + } + if (params.poll.size() > 1 || params.poll != cmd_params_defaults.poll) { + fields.emplace_back("poll"); + } if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) { fields.emplace_back("n_batch"); } + if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) { + fields.emplace_back("n_ubatch"); + } if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) { fields.emplace_back("type_k"); } @@ -956,12 +1300,18 @@ struct markdown_printer : public printer { if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { fields.emplace_back("no_kv_offload"); } + if (params.flash_attn.size() > 1 || params.flash_attn != cmd_params_defaults.flash_attn) { + fields.emplace_back("flash_attn"); + } if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { fields.emplace_back("tensor_split"); } if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { fields.emplace_back("use_mmap"); } + if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) { + fields.emplace_back("embeddings"); + } fields.emplace_back("test"); fields.emplace_back("t/s"); @@ -984,18 +1334,18 @@ 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); @@ -1005,12 +1355,11 @@ struct markdown_printer : public printer { value = test::get_backend(); } else if (field == "test") { if (t.n_prompt > 0 && t.n_gen == 0) { - snprintf(buf, sizeof(buf), "pp %d", t.n_prompt); + snprintf(buf, sizeof(buf), "pp%d", t.n_prompt); } else if (t.n_gen > 0 && t.n_prompt == 0) { - snprintf(buf, sizeof(buf), "tg %d", t.n_gen); + snprintf(buf, sizeof(buf), "tg%d", t.n_gen); } else { - assert(false); - exit(1); + snprintf(buf, sizeof(buf), "pp%d+tg%d", t.n_prompt, t.n_gen); } value = buf; } else if (field == "t/s") { @@ -1058,7 +1407,8 @@ 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"); @@ -1076,26 +1426,43 @@ struct sql_printer : public printer { } }; -static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) { - std::vector tokens(n_batch, llama_token_bos(llama_get_model(ctx))); - int n_processed = 0; - +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 llama_vocab * vocab = llama_model_get_vocab(model); + const int32_t n_vocab = llama_vocab_n_tokens(vocab); + + std::vector tokens(n_batch); + + int n_processed = 0; + while (n_processed < n_prompt) { int n_tokens = std::min(n_prompt - n_processed, n_batch); - llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0)); + tokens[0] = n_processed == 0 && llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab; + for (int i = 1; i < n_tokens; i++) { + tokens[i] = std::rand() % n_vocab; + } + llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens)); n_processed += n_tokens; } + + llama_synchronize(ctx); } -static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) { - llama_token token = llama_token_bos(llama_get_model(ctx)); - +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 llama_vocab * vocab = llama_model_get_vocab(model); + const int32_t n_vocab = llama_vocab_n_tokens(vocab); + + llama_token token = llama_vocab_get_add_bos(vocab) ? llama_vocab_bos(vocab) : std::rand() % n_vocab; + for (int i = 0; i < n_gen; i++) { - llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0)); + llama_decode(ctx, llama_batch_get_one(&token, 1)); + llama_synchronize(ctx); + token = std::rand() % n_vocab; } } @@ -1105,6 +1472,24 @@ static void llama_null_log_callback(enum ggml_log_level level, const char * text (void) user_data; } +static std::unique_ptr create_printer(output_formats format) { + switch (format) { + case NONE: + return nullptr; + case CSV: + return std::unique_ptr(new csv_printer()); + case JSON: + return std::unique_ptr(new json_printer()); + case JSONL: + return std::unique_ptr(new jsonl_printer()); + case MARKDOWN: + return std::unique_ptr(new markdown_printer()); + case SQL: + return std::unique_ptr(new sql_printer()); + } + GGML_ABORT("fatal error"); +} + int main(int argc, char ** argv) { // try to set locale for unicode characters in markdown setlocale(LC_CTYPE, ".UTF-8"); @@ -1123,47 +1508,59 @@ 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); } llama_backend_init(); + llama_numa_init(params.numa); + + set_process_priority(params.prio); // initialize printer - std::unique_ptr p; - switch (params.output_format) { - case CSV: - p.reset(new csv_printer()); - break; - case JSON: - p.reset(new json_printer()); - break; - case MARKDOWN: - p.reset(new markdown_printer()); - break; - case SQL: - p.reset(new sql_printer()); - break; - default: - assert(false); - exit(1); + std::unique_ptr p = create_printer(params.output_format); + std::unique_ptr p_err = create_printer(params.output_format_stderr); + + if (p) { + p->fout = stdout; + p->print_header(params); + } + + if (p_err) { + p_err->fout = stderr; + p_err->print_header(params); } - p->fout = stdout; - p->print_header(params); 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; + auto params_count = params_instances.size(); for (const auto & inst : params_instances) { + params_idx++; + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: starting\n", params_idx, params_count); + } // keep the same model between tests when possible if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) { if (lmodel) { - llama_free_model(lmodel); + llama_model_free(lmodel); } - lmodel = llama_load_model_from_file(inst.model.c_str(), inst.to_llama_mparams()); + lmodel = llama_model_load_from_file(inst.model.c_str(), inst.to_llama_mparams()); if (lmodel == NULL) { fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, inst.model.c_str()); return 1; @@ -1171,10 +1568,10 @@ int main(int argc, char ** argv) { prev_inst = &inst; } - llama_context * ctx = llama_new_context_with_model(lmodel, inst.to_llama_cparams()); + llama_context * ctx = llama_init_from_model(lmodel, inst.to_llama_cparams()); if (ctx == NULL) { fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, inst.model.c_str()); - llama_free_model(lmodel); + llama_model_free(lmodel); return 1; } @@ -1182,38 +1579,93 @@ int main(int argc, char ** argv) { llama_kv_cache_clear(ctx); + // cool off before the test + if (params.delay) { + std::this_thread::sleep_for(std::chrono::seconds(params.delay)); + } + + struct ggml_threadpool_params tpp = ggml_threadpool_params_default(t.n_threads); + if (!parse_cpu_mask(t.cpu_mask, tpp.cpumask)) { + fprintf(stderr, "%s: failed to parse cpu-mask: %s\n", __func__, t.cpu_mask.c_str()); + exit(1); + } + tpp.strict_cpu = t.cpu_strict; + tpp.poll = t.poll; + tpp.prio = params.prio; + + 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); + } + + llama_attach_threadpool(ctx, threadpool, NULL); + // warmup run if (t.n_prompt > 0) { - test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads); + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count); + } + //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads); + test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { - test_gen(ctx, 1, 0, t.n_threads); + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count); + } + test_gen(ctx, 1, t.n_threads); } for (int i = 0; i < params.reps; i++) { llama_kv_cache_clear(ctx); uint64_t t_start = get_time_ns(); + if (t.n_prompt > 0) { - test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: 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) { - test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads); + if (params.progress) { + fprintf(stderr, "llama-bench: benchmark %d/%zu: generation run %d/%d\n", params_idx, params_count, + i + 1, params.reps); + } + test_gen(ctx, t.n_gen, t.n_threads); } + uint64_t t_ns = get_time_ns() - t_start; t.samples_ns.push_back(t_ns); } - p->print_test(t); + if (p) { + p->print_test(t); + fflush(p->fout); + } - llama_print_timings(ctx); + if (p_err) { + p_err->print_test(t); + fflush(p_err->fout); + } + + llama_perf_context_print(ctx); llama_free(ctx); + + ggml_threadpool_free_fn(threadpool); } - llama_free_model(lmodel); + llama_model_free(lmodel); - p->print_footer(); + if (p) { + p->print_footer(); + } + + if (p_err) { + p_err->print_footer(); + } llama_backend_free(); diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts index d42140efe..8d1b37195 100644 --- a/examples/llama.android/app/build.gradle.kts +++ b/examples/llama.android/app/build.gradle.kts @@ -7,8 +7,6 @@ android { namespace = "com.example.llama" compileSdk = 34 - ndkVersion = "26.1.10909125" - defaultConfig { applicationId = "com.example.llama" minSdk = 33 @@ -20,17 +18,6 @@ android { vectorDrawables { useSupportLibrary = true } - ndk { - // Add NDK properties if wanted, e.g. - // abiFilters += listOf("arm64-v8a") - } - externalNativeBuild { - cmake { - arguments += "-DCMAKE_BUILD_TYPE=Release" - cppFlags += listOf() - arguments += listOf() - } - } } buildTypes { @@ -55,17 +42,6 @@ android { composeOptions { kotlinCompilerExtensionVersion = "1.5.1" } - packaging { - resources { - excludes += "/META-INF/{AL2.0,LGPL2.1}" - } - } - externalNativeBuild { - cmake { - path = file("src/main/cpp/CMakeLists.txt") - version = "3.22.1" - } - } } dependencies { @@ -78,6 +54,7 @@ dependencies { implementation("androidx.compose.ui:ui-graphics") implementation("androidx.compose.ui:ui-tooling-preview") implementation("androidx.compose.material3:material3") + implementation(project(":llama")) testImplementation("junit:junit:4.13.2") androidTestImplementation("androidx.test.ext:junit:1.1.5") androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt index be95e2221..45ac29938 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt @@ -1,5 +1,6 @@ package com.example.llama +import android.llama.cpp.LLamaAndroid import android.util.Log import androidx.compose.runtime.getValue import androidx.compose.runtime.mutableStateOf @@ -9,7 +10,7 @@ import androidx.lifecycle.viewModelScope import kotlinx.coroutines.flow.catch import kotlinx.coroutines.launch -class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { +class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() { companion object { @JvmStatic private val NanosPerSecond = 1_000_000_000.0 @@ -28,7 +29,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { - llm.unload() + llamaAndroid.unload() } catch (exc: IllegalStateException) { messages += exc.message!! } @@ -44,7 +45,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { messages += "" viewModelScope.launch { - llm.send(text) + llamaAndroid.send(text) .catch { Log.e(tag, "send() failed", it) messages += it.message!! @@ -57,7 +58,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { val start = System.nanoTime() - val warmupResult = llm.bench(pp, tg, pl, nr) + val warmupResult = llamaAndroid.bench(pp, tg, pl, nr) val end = System.nanoTime() messages += warmupResult @@ -70,7 +71,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { return@launch } - messages += llm.bench(512, 128, 1, 3) + messages += llamaAndroid.bench(512, 128, 1, 3) } catch (exc: IllegalStateException) { Log.e(tag, "bench() failed", exc) messages += exc.message!! @@ -81,7 +82,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { fun load(pathToModel: String) { viewModelScope.launch { try { - llm.load(pathToModel) + llamaAndroid.load(pathToModel) messages += "Loaded $pathToModel" } catch (exc: IllegalStateException) { Log.e(tag, "load() failed", exc) diff --git a/examples/llama.android/build.gradle.kts b/examples/llama.android/build.gradle.kts index 50ebc8211..acd1ada7d 100644 --- a/examples/llama.android/build.gradle.kts +++ b/examples/llama.android/build.gradle.kts @@ -2,4 +2,5 @@ plugins { id("com.android.application") version "8.2.0" apply false id("org.jetbrains.kotlin.android") version "1.9.0" apply false + id("com.android.library") version "8.2.0" apply false } diff --git a/examples/llama.android/llama/.gitignore b/examples/llama.android/llama/.gitignore new file mode 100644 index 000000000..796b96d1c --- /dev/null +++ b/examples/llama.android/llama/.gitignore @@ -0,0 +1 @@ +/build diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts new file mode 100644 index 000000000..28dbc1904 --- /dev/null +++ b/examples/llama.android/llama/build.gradle.kts @@ -0,0 +1,70 @@ +plugins { + id("com.android.library") + id("org.jetbrains.kotlin.android") +} + +android { + namespace = "android.llama.cpp" + compileSdk = 34 + + defaultConfig { + minSdk = 33 + + testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner" + consumerProguardFiles("consumer-rules.pro") + ndk { + // Add NDK properties if wanted, e.g. + // abiFilters += listOf("arm64-v8a") + } + externalNativeBuild { + cmake { + arguments += "-DLLAMA_BUILD_COMMON=ON" + arguments += "-DGGML_LLAMAFILE=OFF" + arguments += "-DCMAKE_BUILD_TYPE=Release" + cppFlags += listOf() + arguments += listOf() + + cppFlags("") + } + } + } + + buildTypes { + release { + isMinifyEnabled = false + proguardFiles( + getDefaultProguardFile("proguard-android-optimize.txt"), + "proguard-rules.pro" + ) + } + } + externalNativeBuild { + cmake { + path("src/main/cpp/CMakeLists.txt") + version = "3.22.1" + } + } + compileOptions { + sourceCompatibility = JavaVersion.VERSION_1_8 + targetCompatibility = JavaVersion.VERSION_1_8 + } + kotlinOptions { + jvmTarget = "1.8" + } + + packaging { + resources { + excludes += "/META-INF/{AL2.0,LGPL2.1}" + } + } +} + +dependencies { + + implementation("androidx.core:core-ktx:1.12.0") + implementation("androidx.appcompat:appcompat:1.6.1") + implementation("com.google.android.material:material:1.11.0") + testImplementation("junit:junit:4.13.2") + androidTestImplementation("androidx.test.ext:junit:1.1.5") + androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") +} diff --git a/examples/llama.android/llama/consumer-rules.pro b/examples/llama.android/llama/consumer-rules.pro new file mode 100644 index 000000000..e69de29bb diff --git a/examples/llama.android/llama/proguard-rules.pro b/examples/llama.android/llama/proguard-rules.pro new file mode 100644 index 000000000..f1b424510 --- /dev/null +++ b/examples/llama.android/llama/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt new file mode 100644 index 000000000..05d6ab5d2 --- /dev/null +++ b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt @@ -0,0 +1,24 @@ +package android.llama.cpp + +import androidx.test.platform.app.InstrumentationRegistry +import androidx.test.ext.junit.runners.AndroidJUnit4 + +import org.junit.Test +import org.junit.runner.RunWith + +import org.junit.Assert.* + +/** + * Instrumented test, which will execute on an Android device. + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +@RunWith(AndroidJUnit4::class) +class ExampleInstrumentedTest { + @Test + fun useAppContext() { + // Context of the app under test. + val appContext = InstrumentationRegistry.getInstrumentation().targetContext + assertEquals("android.llama.cpp.test", appContext.packageName) + } +} diff --git a/examples/llama.android/llama/src/main/AndroidManifest.xml b/examples/llama.android/llama/src/main/AndroidManifest.xml new file mode 100644 index 000000000..8bdb7e14b --- /dev/null +++ b/examples/llama.android/llama/src/main/AndroidManifest.xml @@ -0,0 +1,4 @@ + + + + diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt similarity index 78% rename from examples/llama.android/app/src/main/cpp/CMakeLists.txt rename to examples/llama.android/llama/src/main/cpp/CMakeLists.txt index 85139329a..2de496574 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt @@ -1,4 +1,3 @@ - # For more information about using CMake with Android Studio, read the # documentation: https://d.android.com/studio/projects/add-native-code.html. # For more examples on how to use CMake, see https://github.com/android/ndk-samples. @@ -12,15 +11,15 @@ cmake_minimum_required(VERSION 3.22.1) # build script scope). project("llama-android") -include(FetchContent) -FetchContent_Declare( - llama - GIT_REPOSITORY https://github.com/ggerganov/llama.cpp - GIT_TAG master -) +#include(FetchContent) +#FetchContent_Declare( +# llama +# GIT_REPOSITORY https://github.com/ggerganov/llama.cpp +# GIT_TAG master +#) # Also provides "common" -FetchContent_MakeAvailable(llama) +#FetchContent_MakeAvailable(llama) # Creates and names a library, sets it as either STATIC # or SHARED, and provides the relative paths to its source code. @@ -31,20 +30,24 @@ FetchContent_MakeAvailable(llama) # the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME} # is preferred for the same purpose. # + +#load local llama.cpp +add_subdirectory(../../../../../../ build-llama) + # In order to load a library into your app from Java/Kotlin, you must call # System.loadLibrary() and pass the name of the library defined here; # for GameActivity/NativeActivity derived applications, the same library name must be # used in the AndroidManifest.xml file. add_library(${CMAKE_PROJECT_NAME} SHARED - # List C/C++ source files with relative paths to this CMakeLists.txt. - llama-android.cpp) + # List C/C++ source files with relative paths to this CMakeLists.txt. + llama-android.cpp) # Specifies libraries CMake should link to your target library. You # can link libraries from various origins, such as libraries defined in this # build script, prebuilt third-party libraries, or Android system libraries. target_link_libraries(${CMAKE_PROJECT_NAME} - # List libraries link to the target library - llama - common - android - log) + # List libraries link to the target library + llama + common + android + log) diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp similarity index 68% rename from examples/llama.android/app/src/main/cpp/llama-android.cpp rename to examples/llama.android/llama/src/main/cpp/llama-android.cpp index 2beb1e0d5..2a73983a9 100644 --- a/examples/llama.android/app/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -5,7 +5,7 @@ #include #include #include "llama.h" -#include "common/common.h" +#include "common.h" // Write C++ code here. // @@ -33,6 +33,45 @@ jclass la_int_var; jmethodID la_int_var_value; jmethodID la_int_var_inc; +std::string cached_token_chars; + +bool is_valid_utf8(const char * string) { + if (!string) { + return true; + } + + const unsigned char * bytes = (const unsigned char *)string; + int num; + + while (*bytes != 0x00) { + if ((*bytes & 0x80) == 0x00) { + // U+0000 to U+007F + num = 1; + } else if ((*bytes & 0xE0) == 0xC0) { + // U+0080 to U+07FF + num = 2; + } else if ((*bytes & 0xF0) == 0xE0) { + // U+0800 to U+FFFF + num = 3; + } else if ((*bytes & 0xF8) == 0xF0) { + // U+10000 to U+10FFFF + num = 4; + } else { + return false; + } + + bytes += 1; + for (int i = 1; i < num; ++i) { + if ((*bytes & 0xC0) != 0x80) { + return false; + } + bytes += 1; + } + } + + return true; +} + static void log_callback(ggml_log_level level, const char * fmt, void * data) { if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data); else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data); @@ -42,13 +81,13 @@ static void log_callback(ggml_log_level level, const char * fmt, void * data) { extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { +Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) { llama_model_params model_params = llama_model_default_params(); auto path_to_model = env->GetStringUTFChars(filename, 0); LOGi("Loading model from %s", path_to_model); - auto model = llama_load_model_from_file(path_to_model, model_params); + auto model = llama_model_load_from_file(path_to_model, model_params); env->ReleaseStringUTFChars(filename, path_to_model); if (!model) { @@ -62,13 +101,13 @@ Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) { - llama_free_model(reinterpret_cast(model)); +Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) { + llama_model_free(reinterpret_cast(model)); } extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { +Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) { auto model = reinterpret_cast(jmodel); if (!model) { @@ -81,8 +120,8 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { LOGi("Using %d threads", n_threads); llama_context_params ctx_params = llama_context_default_params(); - ctx_params.seed = 1234; - ctx_params.n_ctx = 2048; + + ctx_params.n_ctx = 2048; ctx_params.n_threads = n_threads; ctx_params.n_threads_batch = n_threads; @@ -100,25 +139,25 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) { llama_free(reinterpret_cast(context)); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) { llama_backend_free(); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) { llama_log_set(log_callback, NULL); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_bench_1model( +Java_android_llama_cpp_LLamaAndroid_bench_1model( JNIEnv *env, jobject, jlong context_pointer, @@ -147,11 +186,11 @@ Java_com_example_llama_Llm_bench_1model( for (nri = 0; nri < nr; nri++) { LOGi("Benchmark prompt processing (pp)"); - llama_batch_clear(*batch); + common_batch_clear(*batch); const int n_tokens = pp; for (i = 0; i < n_tokens; i++) { - llama_batch_add(*batch, 0, i, { 0 }, false); + common_batch_add(*batch, 0, i, { 0 }, false); } batch->logits[batch->n_tokens - 1] = true; @@ -171,9 +210,9 @@ Java_com_example_llama_Llm_bench_1model( const auto t_tg_start = ggml_time_us(); for (i = 0; i < tg; i++) { - llama_batch_clear(*batch); + common_batch_clear(*batch); for (j = 0; j < pl; j++) { - llama_batch_add(*batch, 0, i, { j }, true); + common_batch_add(*batch, 0, i, { j }, true); } LOGi("llama_decode() text generation: %d", i); @@ -230,15 +269,9 @@ Java_com_example_llama_Llm_bench_1model( return env->NewStringUTF(result.str().c_str()); } -extern "C" -JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { - llama_batch_free(*reinterpret_cast(batch_pointer)); -} - extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { +Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { // Source: Copy of llama.cpp:llama_batch_init but heap-allocated. @@ -250,9 +283,6 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb nullptr, nullptr, nullptr, - 0, - 0, - 0, }; if (embd) { @@ -274,32 +304,61 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { + //llama_batch_free(*reinterpret_cast(batch_pointer)); + const auto batch = reinterpret_cast(batch_pointer); + delete batch; +} + +extern "C" +JNIEXPORT jlong JNICALL +Java_android_llama_cpp_LLamaAndroid_new_1sampler(JNIEnv *, jobject) { + auto sparams = llama_sampler_chain_default_params(); + sparams.no_perf = true; + llama_sampler * smpl = llama_sampler_chain_init(sparams); + llama_sampler_chain_add(smpl, llama_sampler_init_greedy()); + + return reinterpret_cast(smpl); +} + +extern "C" +JNIEXPORT void JNICALL +Java_android_llama_cpp_LLamaAndroid_free_1sampler(JNIEnv *, jobject, jlong sampler_pointer) { + llama_sampler_free(reinterpret_cast(sampler_pointer)); +} + +extern "C" +JNIEXPORT void JNICALL +Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) { llama_backend_init(); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) { +Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) { return env->NewStringUTF(llama_print_system_info()); } extern "C" JNIEXPORT jint JNICALL -Java_com_example_llama_Llm_completion_1init( +Java_android_llama_cpp_LLamaAndroid_completion_1init( JNIEnv *env, jobject, jlong context_pointer, jlong batch_pointer, jstring jtext, + jboolean format_chat, jint n_len ) { + cached_token_chars.clear(); + const auto text = env->GetStringUTFChars(jtext, 0); const auto context = reinterpret_cast(context_pointer); const auto batch = reinterpret_cast(batch_pointer); - const auto tokens_list = llama_tokenize(context, text, 1); + bool parse_special = (format_chat == JNI_TRUE); + const auto tokens_list = common_tokenize(context, text, true, parse_special); auto n_ctx = llama_n_ctx(context); auto n_kv_req = tokens_list.size() + (n_len - tokens_list.size()); @@ -311,14 +370,14 @@ Java_com_example_llama_Llm_completion_1init( } for (auto id : tokens_list) { - LOGi("%s", llama_token_to_piece(context, id).c_str()); + LOGi("token: `%s`-> %d ", common_token_to_piece(context, id).c_str(), id); } - llama_batch_clear(*batch); + common_batch_clear(*batch); // evaluate the initial prompt for (auto i = 0; i < tokens_list.size(); i++) { - llama_batch_add(*batch, tokens_list[i], i, { 0 }, false); + common_batch_add(*batch, tokens_list[i], i, { 0 }, false); } // llama_decode will output logits only for the last token of the prompt @@ -335,48 +394,47 @@ Java_com_example_llama_Llm_completion_1init( extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_completion_1loop( +Java_android_llama_cpp_LLamaAndroid_completion_1loop( JNIEnv * env, jobject, jlong context_pointer, jlong batch_pointer, + jlong sampler_pointer, jint n_len, jobject intvar_ncur ) { const auto context = reinterpret_cast(context_pointer); - const auto batch = reinterpret_cast(batch_pointer); + const auto batch = reinterpret_cast(batch_pointer); + const auto sampler = reinterpret_cast(sampler_pointer); const auto model = llama_get_model(context); + const auto vocab = llama_model_get_vocab(model); if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur); if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I"); if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V"); - auto n_vocab = llama_n_vocab(model); - auto logits = llama_get_logits_ith(context, batch->n_tokens - 1); - - std::vector candidates; - candidates.reserve(n_vocab); - - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - // sample the most likely token - const auto new_token_id = llama_sample_token_greedy(context, &candidates_p); + const auto new_token_id = llama_sampler_sample(sampler, context, -1); const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value); - if (new_token_id == llama_token_eos(model) || n_cur == n_len) { - return env->NewStringUTF(""); + if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) { + return nullptr; } - auto new_token_chars = llama_token_to_piece(context, new_token_id); - LOGi("new_token_chars: `%s`", new_token_chars.c_str()); - auto new_token = env->NewStringUTF(new_token_chars.c_str()); + auto new_token_chars = common_token_to_piece(context, new_token_id); + cached_token_chars += new_token_chars; - llama_batch_clear(*batch); - llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true); + jstring new_token = nullptr; + if (is_valid_utf8(cached_token_chars.c_str())) { + new_token = env->NewStringUTF(cached_token_chars.c_str()); + LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id); + cached_token_chars.clear(); + } else { + new_token = env->NewStringUTF(""); + } + + common_batch_clear(*batch); + common_batch_add(*batch, new_token_id, n_cur, { 0 }, true); env->CallVoidMethod(intvar_ncur, la_int_var_inc); @@ -389,6 +447,6 @@ Java_com_example_llama_Llm_completion_1loop( extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { llama_kv_cache_clear(reinterpret_cast(context)); } diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt similarity index 86% rename from examples/llama.android/app/src/main/java/com/example/llama/Llm.kt rename to examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt index 5f3270372..b964d93e3 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt +++ b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt @@ -1,4 +1,4 @@ -package com.example.llama +package android.llama.cpp import android.util.Log import kotlinx.coroutines.CoroutineDispatcher @@ -10,7 +10,7 @@ import kotlinx.coroutines.withContext import java.util.concurrent.Executors import kotlin.concurrent.thread -class Llm { +class LLamaAndroid { private val tag: String? = this::class.simpleName private val threadLocalState: ThreadLocal = ThreadLocal.withInitial { State.Idle } @@ -45,8 +45,10 @@ class Llm { private external fun free_context(context: Long) private external fun backend_init(numa: Boolean) private external fun backend_free() - private external fun free_batch(batch: Long) private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long + private external fun free_batch(batch: Long) + private external fun new_sampler(): Long + private external fun free_sampler(sampler: Long) private external fun bench_model( context: Long, model: Long, @@ -63,15 +65,17 @@ class Llm { context: Long, batch: Long, text: String, + formatChat: Boolean, nLen: Int ): Int private external fun completion_loop( context: Long, batch: Long, + sampler: Long, nLen: Int, ncur: IntVar - ): String + ): String? private external fun kv_cache_clear(context: Long) @@ -101,21 +105,24 @@ class Llm { val batch = new_batch(512, 0, 1) if (batch == 0L) throw IllegalStateException("new_batch() failed") + val sampler = new_sampler() + if (sampler == 0L) throw IllegalStateException("new_sampler() failed") + Log.i(tag, "Loaded model $pathToModel") - threadLocalState.set(State.Loaded(model, context, batch)) + threadLocalState.set(State.Loaded(model, context, batch, sampler)) } else -> throw IllegalStateException("Model already loaded") } } } - fun send(message: String): Flow = flow { + fun send(message: String, formatChat: Boolean = false): Flow = flow { when (val state = threadLocalState.get()) { is State.Loaded -> { - val ncur = IntVar(completion_init(state.context, state.batch, message, nlen)) + val ncur = IntVar(completion_init(state.context, state.batch, message, formatChat, nlen)) while (ncur.value <= nlen) { - val str = completion_loop(state.context, state.batch, nlen, ncur) - if (str.isEmpty()) { + val str = completion_loop(state.context, state.batch, state.sampler, nlen, ncur) + if (str == null) { break } emit(str) @@ -138,6 +145,7 @@ class Llm { free_context(state.context) free_model(state.model) free_batch(state.batch) + free_sampler(state.sampler); threadLocalState.set(State.Idle) } @@ -161,12 +169,12 @@ class Llm { private sealed interface State { data object Idle: State - data class Loaded(val model: Long, val context: Long, val batch: Long): State + data class Loaded(val model: Long, val context: Long, val batch: Long, val sampler: Long): State } // Enforce only one instance of Llm. - private val _instance: Llm = Llm() + private val _instance: LLamaAndroid = LLamaAndroid() - fun instance(): Llm = _instance + fun instance(): LLamaAndroid = _instance } } diff --git a/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt new file mode 100644 index 000000000..cbbb974d3 --- /dev/null +++ b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt @@ -0,0 +1,17 @@ +package android.llama.cpp + +import org.junit.Test + +import org.junit.Assert.* + +/** + * Example local unit test, which will execute on the development machine (host). + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +class ExampleUnitTest { + @Test + fun addition_isCorrect() { + assertEquals(4, 2 + 2) + } +} diff --git a/examples/llama.android/settings.gradle.kts b/examples/llama.android/settings.gradle.kts index 2ba32c4fa..c7c1a034a 100644 --- a/examples/llama.android/settings.gradle.kts +++ b/examples/llama.android/settings.gradle.kts @@ -15,3 +15,4 @@ dependencyResolutionManagement { rootProject.name = "LlamaAndroid" include(":app") +include(":llama") diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index 58fcf40c6..ee7141a66 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -24,13 +24,16 @@ func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama actor LlamaContext { private var model: OpaquePointer private var context: OpaquePointer + private var vocab: OpaquePointer + private var sampling: UnsafeMutablePointer private var batch: llama_batch private var tokens_list: [llama_token] + var is_done: Bool = false /// This variable is used to store temporarily invalid cchars private var temporary_invalid_cchars: [CChar] - var n_len: Int32 = 64 + var n_len: Int32 = 1024 var n_cur: Int32 = 0 var n_decode: Int32 = 0 @@ -41,12 +44,18 @@ actor LlamaContext { self.tokens_list = [] self.batch = llama_batch_init(512, 0, 1) self.temporary_invalid_cchars = [] + let sparams = llama_sampler_chain_default_params() + self.sampling = llama_sampler_chain_init(sparams) + llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4)) + llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234)) + vocab = llama_model_get_vocab(model) } deinit { + llama_sampler_free(sampling) llama_batch_free(batch) + llama_model_free(model) llama_free(context) - llama_free_model(model) llama_backend_free() } @@ -58,7 +67,7 @@ actor LlamaContext { model_params.n_gpu_layers = 0 print("Running on simulator, force use n_gpu_layers = 0") #endif - let model = llama_load_model_from_file(path, model_params) + let model = llama_model_load_from_file(path, model_params) guard let model else { print("Could not load model at \(path)") throw LlamaError.couldNotInitializeContext @@ -68,12 +77,11 @@ actor LlamaContext { print("Using \(n_threads) threads") var ctx_params = llama_context_default_params() - ctx_params.seed = 1234 ctx_params.n_ctx = 2048 - ctx_params.n_threads = UInt32(n_threads) - ctx_params.n_threads_batch = UInt32(n_threads) + ctx_params.n_threads = Int32(n_threads) + ctx_params.n_threads_batch = Int32(n_threads) - let context = llama_new_context_with_model(model, ctx_params) + let context = llama_init_from_model(model, ctx_params) guard let context else { print("Could not load context!") throw LlamaError.couldNotInitializeContext @@ -143,23 +151,11 @@ actor LlamaContext { func completion_loop() -> String { var new_token_id: llama_token = 0 - let n_vocab = llama_n_vocab(model) - let logits = llama_get_logits_ith(context, batch.n_tokens - 1) + new_token_id = llama_sampler_sample(sampling, context, batch.n_tokens - 1) - var candidates = Array() - candidates.reserveCapacity(Int(n_vocab)) - - for token_id in 0...allocate(capacity: n_tokens) - let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false) + let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false) var swiftTokens: [llama_token] = [] for i in 0...allocate(capacity: Int(-nTokens)) @@ -328,7 +326,7 @@ actor LlamaContext { defer { newResult.deallocate() } - let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens) + let nNewTokens = llama_token_to_piece(vocab, token, newResult, -nTokens, 0, false) let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens)) return Array(bufferPointer) } else { diff --git a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj index 3950b9e9d..ff3d108b2 100644 --- a/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj +++ b/examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj @@ -7,6 +7,7 @@ objects = { /* Begin PBXBuildFile section */ + 1809696D2D05A39F00400EE8 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = 1809696C2D05A39F00400EE8 /* llama */; }; 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; }; 79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */; }; 7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; }; @@ -17,7 +18,6 @@ 8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; }; 8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; }; 8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; }; - DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; }; F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; }; /* End PBXBuildFile section */ @@ -42,7 +42,7 @@ isa = PBXFrameworksBuildPhase; buildActionMask = 2147483647; files = ( - DF810E132B4A5BA200301144 /* llama in Frameworks */, + 1809696D2D05A39F00400EE8 /* llama in Frameworks */, 549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */, 8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */, ); @@ -151,7 +151,7 @@ ); name = llama.swiftui; packageProductDependencies = ( - DF810E122B4A5BA200301144 /* llama */, + 1809696C2D05A39F00400EE8 /* llama */, ); productName = llama.swiftui; productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */; @@ -429,7 +429,7 @@ /* End XCConfigurationList section */ /* Begin XCSwiftPackageProductDependency section */ - DF810E122B4A5BA200301144 /* llama */ = { + 1809696C2D05A39F00400EE8 /* llama */ = { isa = XCSwiftPackageProductDependency; productName = llama; }; diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift index 5bde18917..b8f6a31d5 100644 --- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift +++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift @@ -131,22 +131,29 @@ class LlamaState: ObservableObject { messageLog += "\(text)" - while await llamaContext.n_cur < llamaContext.n_len { - let result = await llamaContext.completion_loop() - messageLog += "\(result)" + Task.detached { + while await !llamaContext.is_done { + let result = await llamaContext.completion_loop() + await MainActor.run { + self.messageLog += "\(result)" + } + } + + let t_end = DispatchTime.now().uptimeNanoseconds + let t_generation = Double(t_end - t_heat_end) / self.NS_PER_S + let tokens_per_second = Double(await llamaContext.n_len) / t_generation + + await llamaContext.clear() + + await MainActor.run { + self.messageLog += """ + \n + Done + Heat up took \(t_heat)s + Generated \(tokens_per_second) t/s\n + """ + } } - - let t_end = DispatchTime.now().uptimeNanoseconds - let t_generation = Double(t_end - t_heat_end) / NS_PER_S - let tokens_per_second = Double(await llamaContext.n_len) / t_generation - - await llamaContext.clear() - messageLog += """ - \n - Done - Heat up took \(t_heat)s - Generated \(tokens_per_second) t/s\n - """ } func bench() async { diff --git a/examples/llama.vim b/examples/llama.vim index 1b5ad6ba0..57eb2a977 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -1,135 +1,783 @@ -" Requires an already running llama.cpp server -" To install either copy or symlink to ~/.vim/autoload/llama.vim -" Then start with either :call llama#doLlamaGen(), -" or add a keybind to your vimrc such as -" nnoremap Z :call llama#doLlamaGen() -" Similarly, you could add an insert mode keybind with -" inoremap call llama#doLlamaGen() +" LLM-based text completion using llama.cpp " -" g:llama_api_url, g:llama_api_key and g:llama_overrides can be configured in your .vimrc -" let g:llama_api_url = "192.168.1.10:8080" -" llama_overrides can also be set through buffer/window scopes. For instance -" autocmd filetype python let b:llama_overrides = {"temp": 0.2} -" Could be added to your .vimrc to automatically set a lower temperature when -" editing a python script -" Additionally, an override dict can be stored at the top of a file -" !*{"stop": ["User:"]} -" Could be added to the start of your chatlog.txt to set the stopping token -" These parameter dicts are merged together from lowest to highest priority: -" server default -> g:llama_overrides -> w:llama_overrides -> -" b:llama_overrides -> in file (!*) overrides +" requires: +" +" - neovim or vim +" - curl +" - llama.cpp server instance +" - FIM-compatible model +" +" sample config: +" +" - Tab - accept the current suggestion +" - Shift+Tab - accept just the first line of the suggestion +" - Ctrl+F - toggle FIM completion manually +" +" make symlink or copy this file to ~/.config/nvim/autoload/llama.vim +" +" start the llama.cpp server with a FIM-compatible model. for example: +" +" $ llama-server -m {model.gguf} --port 8012 -ngl 99 -fa -dt 0.1 --ubatch-size 512 --batch-size 1024 --cache-reuse 256 +" +" --batch-size [512, model max context] +" +" adjust the batch size to control how much of the provided local context will be used during the inference +" lower values will use smaller part of the context around the cursor, which will result in faster processing +" +" --ubatch-size [64, 2048] +" +" chunks the batch into smaller chunks for faster processing +" depends on the specific hardware. use llama-bench to profile and determine the best size +" +" --cache-reuse (ge:llama_config.n_predict, 1024] +" +" this should be either 0 (disabled) or strictly larger than g:llama_config.n_predict +" using non-zero value enables context reuse on the server side which dramatically improves the performance at +" large contexts. a value of 256 should be good for all cases +" +" run this once to initialise llama.vim: +" +" :call llama#init() +" +" more info: https://github.com/ggerganov/llama.cpp/pull/9787 " -" Sublists (like logit_bias and stop) are overridden, not merged -" Example override: -" !*{"logit_bias": [[13, -5], [2, false]], "temperature": 1, "top_k": 5, "top_p": 0.5, "n_predict": 256, "repeat_last_n": 256, "repeat_penalty": 1.17647} -if !exists("g:llama_api_url") - let g:llama_api_url= "127.0.0.1:8080" -endif -if !exists("g:llama_overrides") - let g:llama_overrides = {} -endif -const s:querydata = {"n_predict": 256, "stop": [ "\n" ], "stream": v:true } -const s:curlcommand = ['curl','--data-raw', "{\"prompt\":\"### System:\"}", '--silent', '--no-buffer', '--request', 'POST', '--url', g:llama_api_url .. '/completion', '--header', "Content-Type: application/json"] -let s:linedict = {} -func s:callbackHandler(bufn, channel, msg) - if len(a:msg) < 3 - return - elseif a:msg[0] == "d" - let l:msg = a:msg[6:-1] - else - let l:msg = a:msg - endif - let l:decoded_msg = json_decode(l:msg) - let l:newtext = split(l:decoded_msg['content'], "\n", 1) - if len(l:newtext) > 0 - call setbufline(a:bufn, s:linedict[a:bufn], getbufline(a:bufn, s:linedict[a:bufn])[0] .. newtext[0]) - else - echo "nothing genned" - endif - if len(newtext) > 1 - let l:failed = appendbufline(a:bufn, s:linedict[a:bufn], newtext[1:-1]) - let s:linedict[a:bufn] = s:linedict[a:bufn] + len(newtext)-1 - endif - if has_key(l:decoded_msg, "stop") && l:decoded_msg.stop - echo "Finished generation" - endif -endfunction +" colors (adjust to your liking) +highlight llama_hl_hint guifg=#ff772f ctermfg=202 +highlight llama_hl_info guifg=#77ff2f ctermfg=119 -func llama#doLlamaGen() - if exists("b:job") - if job_status(b:job) == "run" - call job_stop(b:job) - return - endif - endif +" general parameters: +" +" endpoint: llama.cpp server endpoint +" n_prefix: number of lines before the cursor location to include in the local prefix +" n_suffix: number of lines after the cursor location to include in the local suffix +" n_predict: max number of tokens to predict +" t_max_prompt_ms: max alloted time for the prompt processing (TODO: not yet supported) +" t_max_predict_ms: max alloted time for the prediction +" show_info: show extra info about the inference (0 - disabled, 1 - statusline, 2 - inline) +" auto_fim: trigger FIM completion automatically on cursor movement +" max_line_suffix: do not auto-trigger FIM completion if there are more than this number of characters to the right of the cursor +" +" ring buffer of chunks, accumulated with time upon: +" +" - completion request +" - yank +" - entering a buffer +" - leaving a buffer +" - writing a file +" +" parameters for the ring-buffer with extra context: +" +" ring_n_chunks: max number of chunks to pass as extra context to the server (0 to disable) +" ring_chunk_size: max size of the chunks (in number of lines) +" note: adjust these numbers so that you don't overrun your context +" at ring_n_chunks = 64 and ring_chunk_size = 64 you need ~32k context +" ring_scope: the range around the cursor position (in number of lines) for gathering chunks after FIM +" ring_update_ms: how often to process queued chunks in normal mode +" +let s:default_config = { + \ 'endpoint': 'http://127.0.0.1:8012/infill', + \ 'n_prefix': 256, + \ 'n_suffix': 64, + \ 'n_predict': 128, + \ 't_max_prompt_ms': 500, + \ 't_max_predict_ms': 3000, + \ 'show_info': 2, + \ 'auto_fim': v:true, + \ 'max_line_suffix': 8, + \ 'ring_n_chunks': 64, + \ 'ring_chunk_size': 64, + \ 'ring_scope': 1024, + \ 'ring_update_ms': 1000, + \ } - let l:cbuffer = bufnr("%") - let s:linedict[l:cbuffer] = line('$') - let l:buflines = getbufline(l:cbuffer, 1, 1000) - let l:querydata = copy(s:querydata) - call extend(l:querydata, g:llama_overrides) - if exists("w:llama_overrides") - call extend(l:querydata, w:llama_overrides) - endif - if exists("b:llama_overrides") - call extend(l:querydata, b:llama_overrides) - endif - if l:buflines[0][0:1] == '!*' - let l:userdata = json_decode(l:buflines[0][2:-1]) - call extend(l:querydata, l:userdata) - let l:buflines = l:buflines[1:-1] - endif - let l:querydata.prompt = join(l:buflines, "\n") - let l:curlcommand = copy(s:curlcommand) - if exists("g:llama_api_key") - call extend(l:curlcommand, ['--header', 'Authorization: Bearer ' .. g:llama_api_key]) - endif - let l:curlcommand[2] = json_encode(l:querydata) - let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])}) -endfunction +let g:llama_config = get(g:, 'llama_config', s:default_config) -" Echos the tokkenization of the provided string , or cursor to end of word -" Onus is placed on the user to include the preceding space -func llama#tokenizeWord(...) - if (a:0 > 0) - let l:input = a:1 - else - exe "normal \"*ye" - let l:input = @* - endif - let l:querydata = {"content": l:input} - let l:curlcommand = copy(s:curlcommand) - let l:curlcommand[2] = json_encode(l:querydata) - let l:curlcommand[8] = g:llama_api_url .. "/tokenize" - let s:token_job = job_start(l:curlcommand, {"callback": function("s:tokenizeWordCallback", [l:input])}) -endfunction - -func s:tokenizeWordCallback(plaintext, channel, msg) - echo '"' .. a:plaintext ..'" - ' .. string(json_decode(a:msg).tokens) -endfunction - - -" Echos the token count of the entire buffer (or provided string) -" Example usage :echo llama#tokenCount() -func llama#tokenCount(...) - if (a:0 > 0) - let l:buflines = a:1 - else - let l:buflines = getline(1,1000) - if l:buflines[0][0:1] == '!*' - let l:buflines = l:buflines[1:-1] +function! s:get_indent(str) + let l:count = 0 + for i in range(len(a:str)) + if a:str[i] == "\t" + let l:count += &tabstop - 1 + else + break endif - let l:buflines = join(l:buflines, "\n") - endif - let l:querydata = {"content": l:buflines} - let l:curlcommand = copy(s:curlcommand) - let l:curlcommand[2] = json_encode(l:querydata) - let l:curlcommand[8] = g:llama_api_url .. "/tokenize" - let s:token_job = job_start(l:curlcommand, {"callback": "s:tokenCountCallback"}) + endfor + return l:count endfunction -func s:tokenCountCallback(channel, msg) - let resp = json_decode(a:msg) - echo len(resp.tokens) +function! s:rand(i0, i1) abort + return a:i0 + rand() % (a:i1 - a:i0 + 1) +endfunction + +function! llama#init() + if !executable('curl') + echohl WarningMsg + echo 'llama.vim requires the "curl" command to be available' + echohl None + return + endif + + let s:pos_x = 0 " cursor position upon start of completion + let s:pos_y = 0 + + let s:line_cur = '' + + let s:line_cur_prefix = '' + let s:line_cur_suffix = '' + + let s:ring_chunks = [] " current set of chunks used as extra context + let s:ring_queued = [] " chunks that are queued to be sent for processing + let s:ring_n_evict = 0 + + let s:hint_shown = v:false + let s:pos_y_pick = -9999 " last y where we picked a chunk + let s:pos_dx = 0 + let s:content = [] + let s:can_accept = v:false + + let s:timer_fim = -1 + let s:t_fim_start = reltime() " used to measure total FIM time + let s:t_last_move = reltime() " last time the cursor moved + + let s:current_job = v:null + + let s:ghost_text_nvim = exists('*nvim_buf_get_mark') + let s:ghost_text_vim = has('textprop') + + if s:ghost_text_vim + let s:hlgroup_hint = 'llama_hl_hint' + let s:hlgroup_info = 'llama_hl_info' + + if empty(prop_type_get(s:hlgroup_hint)) + call prop_type_add(s:hlgroup_hint, {'highlight': s:hlgroup_hint}) + endif + if empty(prop_type_get(s:hlgroup_info)) + call prop_type_add(s:hlgroup_info, {'highlight': s:hlgroup_info}) + endif + endif + + augroup llama + autocmd! + autocmd InsertEnter * inoremap llama#fim_inline(v:false) + autocmd InsertLeavePre * call llama#fim_cancel() + + autocmd CursorMoved * call s:on_move() + autocmd CursorMovedI * call s:on_move() + autocmd CompleteChanged * call llama#fim_cancel() + + if g:llama_config.auto_fim + autocmd CursorMovedI * call llama#fim(v:true) + endif + + " gather chunks upon yanking + autocmd TextYankPost * if v:event.operator ==# 'y' | call s:pick_chunk(v:event.regcontents, v:false, v:true) | endif + + " gather chunks upon entering/leaving a buffer + autocmd BufEnter * call timer_start(100, {-> s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true)}) + autocmd BufLeave * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true) + + " gather chunk upon saving the file + autocmd BufWritePost * call s:pick_chunk(getline(max([1, line('.') - g:llama_config.ring_chunk_size/2]), min([line('.') + g:llama_config.ring_chunk_size/2, line('$')])), v:true, v:true) + augroup END + + silent! call llama#fim_cancel() + + " init background update of the ring buffer + if g:llama_config.ring_n_chunks > 0 + call s:ring_update() + endif +endfunction + +" compute how similar two chunks of text are +" 0 - no similarity, 1 - high similarity +" TODO: figure out something better +function! s:chunk_sim(c0, c1) + let l:lines0 = len(a:c0) + let l:lines1 = len(a:c1) + + let l:common = 0 + + for l:line0 in a:c0 + for l:line1 in a:c1 + if l:line0 == l:line1 + let l:common += 1 + break + endif + endfor + endfor + + return 2.0 * l:common / (l:lines0 + l:lines1) +endfunction + +" pick a random chunk of size g:llama_config.ring_chunk_size from the provided text and queue it for processing +" +" no_mod - do not pick chunks from buffers with pending changes +" do_evict - evict chunks that are very similar to the new one +" +function! s:pick_chunk(text, no_mod, do_evict) + " do not pick chunks from buffers with pending changes or buffers that are not files + if a:no_mod && (getbufvar(bufnr('%'), '&modified') || !buflisted(bufnr('%')) || !filereadable(expand('%'))) + return + endif + + " if the extra context option is disabled - do nothing + if g:llama_config.ring_n_chunks <= 0 + return + endif + + " don't pick very small chunks + if len(a:text) < 3 + return + endif + + if len(a:text) + 1 < g:llama_config.ring_chunk_size + let l:chunk = a:text + else + let l:l0 = s:rand(0, max([0, len(a:text) - g:llama_config.ring_chunk_size/2])) + let l:l1 = min([l:l0 + g:llama_config.ring_chunk_size/2, len(a:text)]) + + let l:chunk = a:text[l:l0:l:l1] + endif + + let l:chunk_str = join(l:chunk, "\n") . "\n" + + " check if this chunk is already added + let l:exist = v:false + + for i in range(len(s:ring_chunks)) + if s:ring_chunks[i].data == l:chunk + let l:exist = v:true + break + endif + endfor + + for i in range(len(s:ring_queued)) + if s:ring_queued[i].data == l:chunk + let l:exist = v:true + break + endif + endfor + + if l:exist + return + endif + + " evict queued chunks that are very similar to the new one + for i in range(len(s:ring_queued) - 1, 0, -1) + if s:chunk_sim(s:ring_queued[i].data, l:chunk) > 0.9 + if a:do_evict + call remove(s:ring_queued, i) + let s:ring_n_evict += 1 + else + return + endif + endif + endfor + + " also from s:ring_chunks + for i in range(len(s:ring_chunks) - 1, 0, -1) + if s:chunk_sim(s:ring_chunks[i].data, l:chunk) > 0.9 + if a:do_evict + call remove(s:ring_chunks, i) + let s:ring_n_evict += 1 + else + return + endif + endif + endfor + + " TODO: become parameter ? + if len(s:ring_queued) == 16 + call remove(s:ring_queued, 0) + endif + + call add(s:ring_queued, {'data': l:chunk, 'str': l:chunk_str, 'time': reltime(), 'filename': expand('%')}) + + "let &statusline = 'extra context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued) +endfunction + +" picks a queued chunk, sends it for processing and adds it to s:ring_chunks +" called every g:llama_config.ring_update_ms +function! s:ring_update() + call timer_start(g:llama_config.ring_update_ms, {-> s:ring_update()}) + + " update only if in normal mode or if the cursor hasn't moved for a while + if mode() !=# 'n' && reltimefloat(reltime(s:t_last_move)) < 3.0 + return + endif + + if len(s:ring_queued) == 0 + return + endif + + " move the first queued chunk to the ring buffer + if len(s:ring_chunks) == g:llama_config.ring_n_chunks + call remove(s:ring_chunks, 0) + endif + + call add(s:ring_chunks, remove(s:ring_queued, 0)) + + "let &statusline = 'updated context: ' . len(s:ring_chunks) . ' / ' . len(s:ring_queued) + + " send asynchronous job with the new extra context so that it is ready for the next FIM + let l:extra_context = [] + for l:chunk in s:ring_chunks + call add(l:extra_context, { + \ 'text': l:chunk.str, + \ 'time': l:chunk.time, + \ 'filename': l:chunk.filename + \ }) + endfor + + " no samplers needed here + let l:request = json_encode({ + \ 'input_prefix': "", + \ 'input_suffix': "", + \ 'input_extra': l:extra_context, + \ 'prompt': "", + \ 'n_predict': 1, + \ 'temperature': 0.0, + \ 'stream': v:false, + \ 'samplers': ["temperature"], + \ 'cache_prompt': v:true, + \ 't_max_prompt_ms': 1, + \ 't_max_predict_ms': 1 + \ }) + + let l:curl_command = [ + \ "curl", + \ "--silent", + \ "--no-buffer", + \ "--request", "POST", + \ "--url", g:llama_config.endpoint, + \ "--header", "Content-Type: application/json", + \ "--data", l:request + \ ] + + " no callbacks because we don't need to process the response + if s:ghost_text_nvim + call jobstart(l:curl_command, {}) + elseif s:ghost_text_vim + call job_start(l:curl_command, {}) + endif +endfunction + +" necessary for 'inoremap ' +function! llama#fim_inline(is_auto) abort + call llama#fim(a:is_auto) + return '' +endfunction + +" the main FIM call +" takes local context around the cursor and sends it together with the extra context to the server for completion +function! llama#fim(is_auto) abort + " we already have a suggestion for the current cursor position + if s:hint_shown && !a:is_auto + call llama#fim_cancel() + return + endif + + call llama#fim_cancel() + + " avoid sending repeated requests too fast + if reltimefloat(reltime(s:t_fim_start)) < 0.6 + if s:timer_fim != -1 + call timer_stop(s:timer_fim) + let s:timer_fim = -1 + endif + + let s:t_fim_start = reltime() + let s:timer_fim = timer_start(600, {-> llama#fim(v:true)}) + return + endif + + let s:t_fim_start = reltime() + + let s:content = [] + let s:can_accept = v:false + + let s:pos_x = col('.') - 1 + let s:pos_y = line('.') + let l:max_y = line('$') + + let l:lines_prefix = getline(max([1, s:pos_y - g:llama_config.n_prefix]), s:pos_y - 1) + let l:lines_suffix = getline(s:pos_y + 1, min([l:max_y, s:pos_y + g:llama_config.n_suffix])) + + let s:line_cur = getline('.') + + let s:line_cur_prefix = strpart(s:line_cur, 0, s:pos_x) + let s:line_cur_suffix = strpart(s:line_cur, s:pos_x) + + if a:is_auto && len(s:line_cur_suffix) > g:llama_config.max_line_suffix + return + endif + + let l:prefix = "" + \ . join(l:lines_prefix, "\n") + \ . "\n" + + let l:prompt = "" + \ . s:line_cur_prefix + + let l:suffix = "" + \ . s:line_cur_suffix + \ . "\n" + \ . join(l:lines_suffix, "\n") + \ . "\n" + + " prepare the extra context data + let l:extra_context = [] + for l:chunk in s:ring_chunks + call add(l:extra_context, { + \ 'text': l:chunk.str, + \ 'time': l:chunk.time, + \ 'filename': l:chunk.filename + \ }) + endfor + + " the indentation of the current line + let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*')) + + let l:request = json_encode({ + \ 'input_prefix': l:prefix, + \ 'input_suffix': l:suffix, + \ 'input_extra': l:extra_context, + \ 'prompt': l:prompt, + \ 'n_predict': g:llama_config.n_predict, + \ 'n_indent': l:indent, + \ 'top_k': 40, + \ 'top_p': 0.99, + \ 'stream': v:false, + \ 'samplers': ["top_k", "top_p", "infill"], + \ 'cache_prompt': v:true, + \ 't_max_prompt_ms': g:llama_config.t_max_prompt_ms, + \ 't_max_predict_ms': g:llama_config.t_max_predict_ms + \ }) + + let l:curl_command = [ + \ "curl", + \ "--silent", + \ "--no-buffer", + \ "--request", "POST", + \ "--url", g:llama_config.endpoint, + \ "--header", "Content-Type: application/json", + \ "--data", l:request + \ ] + + if s:current_job != v:null + if s:ghost_text_nvim + call jobstop(s:current_job) + elseif s:ghost_text_vim + call job_stop(s:current_job) + endif + endif + + " send the request asynchronously + if s:ghost_text_nvim + let s:current_job = jobstart(l:curl_command, { + \ 'on_stdout': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]), + \ 'on_exit': function('s:fim_on_exit'), + \ 'stdout_buffered': v:true + \ }) + elseif s:ghost_text_vim + let s:current_job = job_start(l:curl_command, { + \ 'out_cb': function('s:fim_on_stdout', [s:pos_x, s:pos_y, a:is_auto]), + \ 'exit_cb': function('s:fim_on_exit') + \ }) + endif + + " TODO: per-file location + let l:delta_y = abs(s:pos_y - s:pos_y_pick) + + " gather some extra context nearby and process it in the background + " only gather chunks if the cursor has moved a lot + " TODO: something more clever? reranking? + if a:is_auto && l:delta_y > 32 + " expand the prefix even further + call s:pick_chunk(getline(max([1, s:pos_y - g:llama_config.ring_scope]), max([1, s:pos_y - g:llama_config.n_prefix])), v:false, v:false) + + " pick a suffix chunk + call s:pick_chunk(getline(min([l:max_y, s:pos_y + g:llama_config.n_suffix]), min([l:max_y, s:pos_y + g:llama_config.n_suffix + g:llama_config.ring_chunk_size])), v:false, v:false) + + let s:pos_y_pick = s:pos_y + endif +endfunction + +" if first_line == v:true accept only the first line of the response +function! llama#fim_accept(first_line) + " insert the suggestion at the cursor location + if s:can_accept && len(s:content) > 0 + call setline(s:pos_y, s:line_cur[:(s:pos_x - 1)] . s:content[0]) + if len(s:content) > 1 + if !a:first_line + call append(s:pos_y, s:content[1:-1]) + endif + endif + + " move the cursor to the end of the accepted text + if !a:first_line && len(s:content) > 1 + call cursor(s:pos_y + len(s:content) - 1, s:pos_x + s:pos_dx + 1) + else + call cursor(s:pos_y, s:pos_x + len(s:content[0])) + endif + endif + + call llama#fim_cancel() +endfunction + +function! llama#fim_cancel() + let s:hint_shown = v:false + + " clear the virtual text + let l:bufnr = bufnr('%') + + if s:ghost_text_nvim + let l:id_vt_fim = nvim_create_namespace('vt_fim') + call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim, 0, -1) + elseif s:ghost_text_vim + call prop_remove({'type': s:hlgroup_hint, 'all': v:true}) + call prop_remove({'type': s:hlgroup_info, 'all': v:true}) + endif + + " remove the mappings + silent! iunmap + silent! iunmap + silent! iunmap +endfunction + +function! s:on_move() + let s:t_last_move = reltime() + + call llama#fim_cancel() +endfunction + +" callback that processes the FIM result from the server and displays the suggestion +function! s:fim_on_stdout(pos_x, pos_y, is_auto, job_id, data, event = v:null) + if s:ghost_text_nvim + let l:raw = join(a:data, "\n") + elseif s:ghost_text_vim + let l:raw = a:data + endif + + if len(l:raw) == 0 + return + endif + + if a:pos_x != col('.') - 1 || a:pos_y != line('.') + return + endif + + " show the suggestion only in insert mode + if mode() !=# 'i' + return + endif + + let s:pos_x = a:pos_x + let s:pos_y = a:pos_y + + let s:can_accept = v:true + let l:has_info = v:false + + if s:can_accept && v:shell_error + if !a:is_auto + call add(s:content, "<| curl error: is the server on? |>") + endif + let s:can_accept = v:false + endif + + let l:n_prompt = 0 + let l:t_prompt_ms = 1.0 + let l:s_prompt = 0 + + let l:n_predict = 0 + let l:t_predict_ms = 1.0 + let l:s_predict = 0 + + " get the generated suggestion + if s:can_accept + let l:response = json_decode(l:raw) + + for l:part in split(get(l:response, 'content', ''), "\n", 1) + call add(s:content, l:part) + endfor + + " remove trailing new lines + while len(s:content) > 0 && s:content[-1] == "" + call remove(s:content, -1) + endwhile + + let l:generation_settings = get(l:response, 'generation_settings', {}) + let l:n_ctx = get(l:generation_settings, 'n_ctx', 0) + + let l:n_cached = get(l:response, 'tokens_cached', 0) + let l:truncated = get(l:response, 'truncated', v:false) + + " if response.timings is available + if len(get(l:response, 'timings', {})) > 0 + let l:has_info = v:true + let l:timings = get(l:response, 'timings', {}) + + let l:n_prompt = get(l:timings, 'prompt_n', 0) + let l:t_prompt_ms = get(l:timings, 'prompt_ms', 1) + let l:s_prompt = get(l:timings, 'prompt_per_second', 0) + + let l:n_predict = get(l:timings, 'predicted_n', 0) + let l:t_predict_ms = get(l:timings, 'predicted_ms', 1) + let l:s_predict = get(l:timings, 'predicted_per_second', 0) + endif + endif + + if len(s:content) == 0 + call add(s:content, "") + let s:can_accept = v:false + endif + + if len(s:content) == 0 + return + endif + + " NOTE: the following is logic for discarding predictions that repeat existing text + " the code is quite ugly and there is very likely a simpler and more canonical way to implement this + " + " still, I wonder if there is some better way that avoids having to do these special hacks? + " on one hand, the LLM 'sees' the contents of the file before we start editing, so it is normal that it would + " start generating whatever we have given it via the extra context. but on the other hand, it's not very + " helpful to re-generate the same code that is already there + + " truncate the suggestion if the first line is empty + if len(s:content) == 1 && s:content[0] == "" + let s:content = [""] + endif + + " ... and the next lines are repeated + if len(s:content) > 1 && s:content[0] == "" && s:content[1:] == getline(s:pos_y + 1, s:pos_y + len(s:content) - 1) + let s:content = [""] + endif + + " truncate the suggestion if it repeats the suffix + if len(s:content) == 1 && s:content[0] == s:line_cur_suffix + let s:content = [""] + endif + + " find the first non-empty line (strip whitespace) + let l:cmp_y = s:pos_y + 1 + while l:cmp_y < line('$') && getline(l:cmp_y) =~? '^\s*$' + let l:cmp_y += 1 + endwhile + + if (s:line_cur_prefix . s:content[0]) == getline(l:cmp_y) + " truncate the suggestion if it repeats the next line + if len(s:content) == 1 + let s:content = [""] + endif + + " ... or if the second line of the suggestion is the prefix of line l:cmp_y + 1 + if len(s:content) == 2 && s:content[-1] == getline(l:cmp_y + 1)[:len(s:content[-1]) - 1] + let s:content = [""] + endif + + " ... or if the middle chunk of lines of the suggestion is the same as [l:cmp_y + 1, l:cmp_y + len(s:content) - 1) + if len(s:content) > 2 && join(s:content[1:-1], "\n") == join(getline(l:cmp_y + 1, l:cmp_y + len(s:content) - 1), "\n") + let s:content = [""] + endif + endif + + " keep only lines that have the same or larger whitespace prefix as s:line_cur_prefix + "let l:indent = strlen(matchstr(s:line_cur_prefix, '^\s*')) + "for i in range(1, len(s:content) - 1) + " if strlen(matchstr(s:content[i], '^\s*')) < l:indent + " let s:content = s:content[:i - 1] + " break + " endif + "endfor + + let s:pos_dx = len(s:content[-1]) + + let s:content[-1] .= s:line_cur_suffix + + call llama#fim_cancel() + + " display virtual text with the suggestion + let l:bufnr = bufnr('%') + + if s:ghost_text_nvim + let l:id_vt_fim = nvim_create_namespace('vt_fim') + endif + + " construct the info message + if g:llama_config.show_info > 0 && l:has_info + let l:prefix = ' ' + + if l:truncated + let l:info = printf("%s | WARNING: the context is full: %d / %d, increase the server context size or reduce g:llama_config.ring_n_chunks", + \ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim', + \ l:n_cached, l:n_ctx + \ ) + else + let l:info = printf("%s | c: %d / %d, r: %d / %d, e: %d, q: %d / 16 | p: %d (%.2f ms, %.2f t/s) | g: %d (%.2f ms, %.2f t/s) | t: %.2f ms", + \ g:llama_config.show_info == 2 ? l:prefix : 'llama.vim', + \ l:n_cached, l:n_ctx, len(s:ring_chunks), g:llama_config.ring_n_chunks, s:ring_n_evict, len(s:ring_queued), + \ l:n_prompt, l:t_prompt_ms, l:s_prompt, + \ l:n_predict, l:t_predict_ms, l:s_predict, + \ 1000.0 * reltimefloat(reltime(s:t_fim_start)) + \ ) + endif + + if g:llama_config.show_info == 1 + " display the info in the statusline + let &statusline = l:info + let l:info = '' + endif + endif + + " display the suggestion and append the info to the end of the first line + if s:ghost_text_nvim + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, { + \ 'virt_text': [[s:content[0], 'llama_hl_hint'], [l:info, 'llama_hl_info']], + \ 'virt_text_win_col': virtcol('.') - 1 + \ }) + + call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, 0, { + \ 'virt_lines': map(s:content[1:], {idx, val -> [[val, 'llama_hl_hint']]}), + \ 'virt_text_win_col': virtcol('.') + \ }) + elseif s:ghost_text_vim + let l:new_suffix = s:content[0] + if !empty(l:new_suffix) + call prop_add(s:pos_y, s:pos_x + 1, { + \ 'type': s:hlgroup_hint, + \ 'text': l:new_suffix + \ }) + endif + for line in s:content[1:] + call prop_add(s:pos_y, 0, { + \ 'type': s:hlgroup_hint, + \ 'text': line, + \ 'text_padding_left': s:get_indent(line), + \ 'text_align': 'below' + \ }) + endfor + if !empty(l:info) + call prop_add(s:pos_y, 0, { + \ 'type': s:hlgroup_info, + \ 'text': l:info, + \ 'text_padding_left': col('$'), + \ 'text_wrap': 'truncate' + \ }) + endif + endif + + " setup accept shortcuts + inoremap :call llama#fim_accept(v:false) + inoremap :call llama#fim_accept(v:true) + + let s:hint_shown = v:true +endfunction + +function! s:fim_on_exit(job_id, exit_code, event = v:null) + if a:exit_code != 0 + echom "Job failed with exit code: " . a:exit_code + endif + + let s:current_job = v:null endfunction diff --git a/examples/llama2-13b.sh b/examples/llama2-13b.sh deleted file mode 100755 index 92b3f6dd8..000000000 --- a/examples/llama2-13b.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \ - --color \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 8 diff --git a/examples/llama2.sh b/examples/llama2.sh deleted file mode 100755 index 221b37553..000000000 --- a/examples/llama2.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \ - --color \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 8 diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 2985caff8..319effd19 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) @@ -30,8 +30,30 @@ if(TARGET BUILD_INFO) add_dependencies(llava BUILD_INFO) endif() -set(TARGET llava-cli) -add_executable(llava-cli llava-cli.cpp) -install(TARGETS llava-cli RUNTIME) -target_link_libraries(llava-cli PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(llava PRIVATE cxx_std_11) +set(TARGET llama-llava-cli) +add_executable(${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_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_17) + +set(TARGET llama-qwen2vl-cli) +add_executable(${TARGET} qwen2vl-cli.cpp) +set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) + +set(TARGET llama-llava-clip-quantize-cli) +add_executable(${TARGET} clip-quantize-cli.cpp) +set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 9eba791da..4f783f3ce 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -1,18 +1,20 @@ # MobileVLM -Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-1.7B) variants. +Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants. for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM) The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava. -## Usage -Build with cmake or run `make llava-cli` to build it. +Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown. -After building, run: `./llava-cli` to see the usage. For example: +## Usage +Build with cmake or run `make llama-llava-cli` to build it. + +After building, run: `./llama-llava-cli` to see the usage. For example: ```sh -./llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \ +./llama-llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \ --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \ --image path/to/an/image.jpg \ -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:" @@ -20,7 +22,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## Model conversion -- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally: +1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally: ```sh git clone https://huggingface.co/mtgv/MobileVLM-1.7B @@ -28,31 +30,39 @@ git clone https://huggingface.co/mtgv/MobileVLM-1.7B git clone https://huggingface.co/openai/clip-vit-large-patch14-336 ``` -2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: +2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B +python ./examples/llava/llava_surgery.py -m path/to/MobileVLM-1.7B ``` -3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` to convert the LLaVA image encoder to GGUF: +3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert-image-encoder-to-gguf \ +python ./examples/llava/convert_image_encoder_to_gguf.py \ -m path/to/clip-vit-large-patch14-336 \ --llava-projector path/to/MobileVLM-1.7B/llava.projector \ --output-dir path/to/MobileVLM-1.7B \ --projector-type ldp ``` -4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: - ```sh -python ./convert.py path/to/MobileVLM-1.7B +python ./examples/llava/convert_image_encoder_to_gguf.py \ + -m path/to/clip-vit-large-patch14-336 \ + --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \ + --output-dir path/to/MobileVLM-1.7B_V2 \ + --projector-type ldpv2 ``` -5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k` +4. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: + ```sh -./quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s +python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B --skip-unknown +``` + +5. Use `quantize` to convert LLaMA part's DataType from `fp32` to `q4_k` +```sh +./llama-quantize path/to/MobileVLM-1.7B/ggml-model-F32.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s ``` Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory. @@ -68,11 +78,11 @@ cd examples/llava/android/build_64 ### run on Android refer to `android/adb_run.sh`, modify resources' `name` and `path` -## some result on Android with `Snapdragon 888` chip +## Some result on Android with `Snapdragon 888` chip ### case 1 **input** ```sh -/data/local/tmp/llava-cli \ +/data/local/tmp/llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ -t 4 \ @@ -92,14 +102,13 @@ llama_print_timings: total time = 34731.93 ms ### case 2 **input** ```sh -/data/local/tmp/llava-cli \ +/data/local/tmp/llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ -t 4 \ --image /data/local/tmp/cat.jpeg \ -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" ``` - **output** ```sh encode_image_with_clip: image encoded in 21149.51 ms by CLIP ( 146.87 ms per image patch) @@ -111,17 +120,87 @@ llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 m llama_print_timings: total time = 34570.79 ms ``` + +## Some result on Android with `Snapdragon 778G` chip +### MobileVLM-1.7B case +#### llava-cli release-b2005 +**input** +```sh +/data/local/tmp/llama-llava-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -t 4 \ + --image /data/local/tmp/many_llamas.jpeg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat's that? ASSISTANT:" +``` +**output** +```sh +encode_image_with_clip: image encoded in 18728.52 ms by CLIP ( 130.06 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + A group of llamas are standing in a green pasture. + +llama_print_timings: load time = 20357.33 ms +llama_print_timings: sample time = 2.96 ms / 14 runs ( 0.21 ms per token, 4734.53 tokens per second) +llama_print_timings: prompt eval time = 8119.49 ms / 191 tokens ( 42.51 ms per token, 23.52 tokens per second) +llama_print_timings: eval time = 1005.75 ms / 14 runs ( 71.84 ms per token, 13.92 tokens per second) +llama_print_timings: total time = 28038.34 ms / 205 tokens +``` +#### llava-cli latest-version +**input** + +Just the same as above. + +**output**(seems to be much slower) +```sh +encode_image_with_clip: image embedding created: 144 tokens + +encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + It is a group of sheep standing together in a grass field. + +llama_print_timings: load time = 818120.91 ms +llama_print_timings: sample time = 3.44 ms / 14 runs ( 0.25 ms per token, 4067.40 tokens per second) +llama_print_timings: prompt eval time = 529274.69 ms / 191 tokens ( 2771.07 ms per token, 0.36 tokens per second) +llama_print_timings: eval time = 43894.02 ms / 13 runs ( 3376.46 ms per token, 0.30 tokens per second) +llama_print_timings: total time = 865441.76 ms / 204 tokens +``` +### MobileVLM_V2-1.7B case +#### llava-cli release-2005b +**input** + +Just the same as above. + +**output** +```sh +encode_image_with_clip: image encoded in 20609.61 ms by CLIP ( 143.12 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + This image captures a lively scene of 20 llamas in motion on an expansive, grassy field. The llama is scattered across the landscape with some standing and others sitting down as if taking rest or observing their surroundings from different vantage points within this verdant setting. + +The background offers glimpses into a picturesque town nestled amidst hills under an overcast sky, adding depth to the scene while also emphasizing that distance between these llama and human-made structures like houses or roads in which they roam freely without any barriers around them. The image is framed by text at both right angles on white backgrounds against a contrasting blue backdrop with green foliage, further drawing attention to the llamas amidst their natural habitat while also inviting viewers into this picturesque landscape within town limits of Alta Llama + +llama_print_timings: load time = 22406.77 ms +llama_print_timings: sample time = 49.26 ms / 186 runs ( 0.26 ms per token, 3776.27 tokens per second) +llama_print_timings: prompt eval time = 9044.54 ms / 191 tokens ( 47.35 ms per token, 21.12 tokens per second) +llama_print_timings: eval time = 14497.49 ms / 186 runs ( 77.94 ms per token, 12.83 tokens per second) +llama_print_timings: total time = 44411.01 ms / 377 tokens +``` + ## Orin compile and run ### compile ```sh -make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32 +make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 GGML_CUDA_F16=1 -j 32 ``` - ### run on Orin ### case 1 **input** ```sh -./llava-cli \ +./llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ --image /data/local/tmp/demo.jpeg \ @@ -145,7 +224,7 @@ llama_print_timings: total time = 1352.63 ms / 252 tokens ### case 2 **input** ```sh -./llava-cli \ +./llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" \ @@ -165,8 +244,121 @@ llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 m llama_print_timings: total time = 1365.47 ms / 243 tokens ``` -## Minor shortcomings -The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost. +## Running on Intel(R) Core(TM) i7-10750H +### Operating system +Ubuntu22.04 +### compile +```sh +make -j32 +``` +### MobileVLM-1.7B case +**input** +```sh +-m /path/to/ggml-model-q4_k.gguf \ + --mmproj /path/to/mmproj-model-f16.gguf \ + --image /path/to/many_llamas.jpeg + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat's that? ASSISTANT:" \ +``` +**output** +```sh +encode_image_with_clip: image embedding created: 144 tokens + +encode_image_with_clip: image encoded in 2730.94 ms by CLIP ( 18.96 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that?ASSISTANT: + + A group of llamas are walking together in a field. + +llama_print_timings: load time = 5506.60 ms +llama_print_timings: sample time = 0.44 ms / 13 runs ( 0.03 ms per token, 29545.45 tokens per second) +llama_print_timings: prompt eval time = 2031.58 ms / 190 tokens ( 10.69 ms per token, 93.52 tokens per second) +llama_print_timings: eval time = 438.92 ms / 12 runs ( 36.58 ms per token, 27.34 tokens per second) +llama_print_timings: total time = 5990.25 ms / 202 tokens +``` + +### MobileVLM_V2-1.7B case +**input** + +Just the same as above. + +**ouput** +```sh +encode_image_with_clip: image embedding created: 144 tokens + +encode_image_with_clip: image encoded in 3223.89 ms by CLIP ( 22.39 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that?ASSISTANT: + + The image captures a tranquil scene in a park, where a group of approximately 20 llamas are gathered. The llamas, a mix of white and black, are standing in a line, their black and white patterns contrasting with the lush green grass of the park. The lamas are arranged in a line, suggesting a social order. + +The park itself is lush and green, with trees dotting the landscape in the background. A sign reading "Llamas Tico Ana" is also visible in the image, possibly indicating the location or the breed of the llamas. The image seems to be taken from a distance, providing a wide view of the scene and the surrounding environment. + +The llamas' positions relative to each other, the sign, and the trees create a harmonious composition. The image does not contain any discernible text. The overall scene is one of peace and natural beauty, with the llamas in their natural habitat, surrounded by the vibrant colors and lush greenery of the park. + +llama_print_timings: load time = 6642.61 ms +llama_print_timings: sample time = 8.15 ms / 223 runs ( 0.04 ms per token, 27358.61 tokens per second) +llama_print_timings: prompt eval time = 2475.07 ms / 190 tokens ( 13.03 ms per token, 76.77 tokens per second) +llama_print_timings: eval time = 8760.60 ms / 222 runs ( 39.46 ms per token, 25.34 tokens per second) +llama_print_timings: total time = 15513.95 ms / 412 tokens +``` + +## Run on Intel(R) Core(TM) Ultra7 115H +### operation system +Windows11 +### comiple +```sh +make -j32 +``` +### MobileVLM-1.7B case +**input** +```sh +-m /path/to/ggml-model-q4_k.gguf \ + --mmproj /path/to/tmp/mmproj-model-f16.gguf \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat's that? ASSISTANT:" \ +``` +**output** +```sh +encode_image_with_clip: image encoded in 4902.81 ms by CLIP ( 34.05 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + The image features a group of brown and white llamas standing in a grassy field. + +llama_print_timings: load time = 7441.06 ms +llama_print_timings: sample time = 0.72 ms / 19 runs ( 0.04 ms per token, 26279.39 tokens per second) +llama_print_timings: prompt eval time = 2090.71 ms / 191 tokens ( 10.95 ms per token, 91.36 tokens per second) +llama_print_timings: eval time = 512.35 ms / 18 runs ( 28.46 ms per token, 35.13 tokens per second) +llama_print_timings: total time = 7987.23 ms / 209 tokens +``` + +### MobileVLM_V2-1.7B case +**input** + +Just the same as above. + +**output** +```sh +encode_image_with_clip: image encoded in 4682.44 ms by CLIP ( 32.52 ms per image patch) +system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: +user_prompt: \nWhat's that? ASSISTANT: + + This image captures a lively scene of a group of 14 llamas in a grassy field. The llamas, with their distinctive black and white coats, are standing and walking in a line, seemingly engaged in a social activity. One + of them, possibly the first in the line, has its back turned, perhaps observing something in the distance. + +The llama in the front of the line stands out due to its black and white coloring, which is quite unusual for llama patterns. The llama in the front also seems to be more aware of its surroundings, as it faces the camera, giving a sense of engagement with the viewer. + +The image is taken from the side of the llama, providing a clear view of the llama in the front and its companions. The lameness in the llama in + front is not visible, indicating that it might not be the main focus of the photo. + +The background of the image features a grassy field, with a fence and a tree visible in the distance. The tree appears to be bare, suggesting that it might be during a time of year when most trees are dormant or have shed their leaves. + + +llama_print_timings: load time = 7015.35 ms +llama_print_timings: sample time = 10.61 ms / 256 runs ( 0.04 ms per token, 24119.09 tokens per second) +llama_print_timings: prompt eval time = 2052.45 ms / 191 tokens ( 10.75 ms per token, 93.06 tokens per second) +llama_print_timings: eval time = 7259.43 ms / 255 runs ( 28.47 ms per token, 35.13 tokens per second) +llama_print_timings: total time = 14371.19 ms / 446 tokens +``` ## TODO @@ -181,5 +373,5 @@ The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quic ## contributor ```sh -zhangjidong05, yangyang260, huyiming03, chenxiaotao03 +zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77 ``` diff --git a/examples/llava/README-glmedge.md b/examples/llava/README-glmedge.md new file mode 100644 index 000000000..603d01474 --- /dev/null +++ b/examples/llava/README-glmedge.md @@ -0,0 +1,43 @@ +# GLMV-EDGE + +Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b). + +## Usage +Build with cmake or run `make llama-llava-cli` to build it. + +After building, run: `./llama-llava-cli` to see the usage. For example: + +```sh +./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <|user|>\n prompt <|assistant|>\n" +``` + +**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. +**note**: For GPU offloading ensure to use the `-ngl` flag just like usual + +## GGUF conversion + +1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example: + +```sh +git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b +``` + +2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents: + +```sh +python ./examples/llava/glmedge-surgery.py -m ../model_path +``` + +4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF: + +```sh +python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path +``` + +5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF: + +```sh +python convert_hf_to_gguf.py ../model_path +``` + +Now both the LLM part and the image encoder are in the `model_path` directory. diff --git a/examples/llava/README-minicpmo2.6.md b/examples/llava/README-minicpmo2.6.md new file mode 100644 index 000000000..8713a43d6 --- /dev/null +++ b/examples/llava/README-minicpmo2.6.md @@ -0,0 +1,46 @@ +## MiniCPM-o 2.6 +Currently, this readme only supports minicpm-omni's image capabilities, and we will update the full-mode support as soon as possible. + +### Prepare models and code + +Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch model from huggingface to "MiniCPM-o-2_6" folder. + +Clone llama.cpp: +```bash +git clone git@github.com:OpenBMB/llama.cpp.git +cd llama.cpp +git checkout minicpm-omni +``` + +### Usage of MiniCPM-o 2.6 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us) + +```bash +python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6 +python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4 +python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model + +# quantize int4 version +./llama-quantize ../MiniCPM-o-2_6/model/ggml-model-f16.gguf ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + +Build llama.cpp using `CMake`: +https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md + +```bash +cmake -B build +cmake --build build --config Release +``` + +Inference on Linux or Mac +``` +# run f16 version +./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run quantized int4 version +./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# or run in interactive mode +./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i +``` diff --git a/examples/llava/README-minicpmv2.5.md b/examples/llava/README-minicpmv2.5.md new file mode 100644 index 000000000..1c8498ff9 --- /dev/null +++ b/examples/llava/README-minicpmv2.5.md @@ -0,0 +1,99 @@ +## MiniCPM-Llama3-V 2.5 + +### Prepare models and code + +Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5) PyTorch model from huggingface to "MiniCPM-Llama3-V-2_5" folder. + +Clone llama.cpp: +```bash +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +``` + +### Usage + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us) + +```bash +python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5 +python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2 +python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model + +# quantize int4 version +./llama-quantize ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + +Build for Linux or Mac + +```bash +make +make llama-minicpmv-cli +``` + +Inference on Linux or Mac +``` +# run f16 version +./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run quantized int4 version +./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# or run in interactive mode +./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i +``` + +### Android + +#### Build on Android device using Termux +We found that build on Android device would bring better runtime performance, so we recommend to build on device. + +[Termux](https://github.com/termux/termux-app#installation) is a terminal app on Android device (no root required). + +Install tools in Termux: +``` +apt update && apt upgrade -y +apt install git make cmake +``` + +It's recommended to move your model inside the `~/` directory for best performance: +``` +cd storage/downloads +mv model.gguf ~/ +``` + +#### Building the Project using Android NDK +Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake. + +Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux: + +```bash +mkdir build-android +cd build-android +export NDK=/your_ndk_path +cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod .. +make +``` + +Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice). + +Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission: + +(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`) +``` +$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/ +$cd /data/data/com.termux/files/home/bin +$chmod +x ./* +``` + +Download models and push them to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/` + +``` +$mv /sdcard/llama.cpp/ggml-model-Q4_K_M.gguf /data/data/com.termux/files/home/model/ +$mv /sdcard/llama.cpp/mmproj-model-f16.gguf /data/data/com.termux/files/home/model/ +``` + +Now, you can start chatting: +``` +$cd /data/data/com.termux/files/home/bin +$./llama-minicpmv-cli -m ../model/ggml-model-Q4_K_M.gguf --mmproj ../model/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" +``` diff --git a/examples/llava/README-minicpmv2.6.md b/examples/llava/README-minicpmv2.6.md new file mode 100644 index 000000000..c4be5e5dd --- /dev/null +++ b/examples/llava/README-minicpmv2.6.md @@ -0,0 +1,107 @@ +## MiniCPM-V 2.6 + +### Prepare models and code + +Download [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) PyTorch model from huggingface to "MiniCPM-V-2_6" folder. + +Clone llama.cpp: +```bash +git clone git@github.com:OpenBMB/llama.cpp.git +cd llama.cpp +git checkout minicpmv-main +``` + +### Usage of MiniCPM-V 2.6 + +Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us) + +```bash +python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-V-2_6 +python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3 +python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model + +# quantize int4 version +./llama-quantize ../MiniCPM-V-2_6/model/ggml-model-f16.gguf ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M +``` + +Build for Linux or Mac + +```bash +make +make llama-minicpmv-cli +``` + +Inference on Linux or Mac +``` +# run f16 version +./llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# run quantized int4 version +./llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" + +# or run in interactive mode +./llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i +``` + +### Video +Install FFmpeg +``` +brew install ffmpeg +brew install pkg-config +``` + +### Android + +#### Build on Android device using Termux +We found that build on Android device would bring better runtime performance, so we recommend to build on device. + +[Termux](https://github.com/termux/termux-app#installation) is a terminal app on Android device (no root required). + +Install tools in Termux: +``` +apt update && apt upgrade -y +apt install git make cmake +``` + +It's recommended to move your model inside the `~/` directory for best performance: +``` +cd storage/downloads +mv model.gguf ~/ +``` + +#### Building the Project using Android NDK +Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake. + +Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux: + +```bash +mkdir build-android +cd build-android +export NDK=/your_ndk_path +cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod .. +make +``` + +Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice). + +Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission: + +(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`) +``` +$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/ +$cd /data/data/com.termux/files/home/bin +$chmod +x ./* +``` + +Download models and push them to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/` + +``` +$mv /sdcard/llama.cpp/ggml-model-Q4_K_M.gguf /data/data/com.termux/files/home/model/ +$mv /sdcard/llama.cpp/mmproj-model-f16.gguf /data/data/com.termux/files/home/model/ +``` + +Now, you can start chatting: +``` +$cd /data/data/com.termux/files/home/bin +$./llama-minicpmv-cli -m ../model/ggml-model-Q4_K_M.gguf --mmproj ../model/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?" +``` diff --git a/examples/llava/README-quantize.md b/examples/llava/README-quantize.md new file mode 100644 index 000000000..b931513ab --- /dev/null +++ b/examples/llava/README-quantize.md @@ -0,0 +1,44 @@ +# Quantizing CLIP Visual Projector + +This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance. + +## Usage + +To quantize a CLIP visual projector model, use the following command: + +```sh +./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf +``` + +After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc). + +### Arguments + +- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format. +- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved. +- ``: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`. + +### Quantization Types + +The following quantization types are supported, based on the `enum ggml_type` definition: + +- `2` - `q4_0`: 4-bit quantization with a single scale value. +- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block. +- `6` - `q5_0`: 5-bit quantization with a single scale value. +- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block. +- `8` - `q8_0`: 8-bit quantization with a single scale value. + +### Example + +To quantize a model using the `q4_0` quantization type, you would run: + +```sh +./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2 +``` + +This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method. + +## Notes + +- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements. +- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments. diff --git a/examples/llava/README.md b/examples/llava/README.md index 35e6d9e5d..012451361 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -11,12 +11,12 @@ For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](h After API is confirmed, more models will be supported / uploaded. ## Usage -Build with cmake or run `make llava-cli` to build it. +Build with cmake or run `make llama-llava-cli` to build it. -After building, run: `./llava-cli` to see the usage. For example: +After building, run: `./llama-llava-cli` to see the usage. For example: ```sh -./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg +./llama-llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg ``` **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. @@ -24,7 +24,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## LLaVA 1.5 -- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: +1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: ```sh git clone https://huggingface.co/liuhaotian/llava-v1.5-7b @@ -38,37 +38,45 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336 pip install -r examples/llava/requirements.txt ``` -3. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: +3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents: ```sh -python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b +python ./examples/llava/llava_surgery.py -m ../llava-v1.5-7b ``` -4. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF: +4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF: ```sh -python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b +python ./examples/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` -5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py ../llava-v1.5-7b --skip-unknown +python ./examples/convert_legacy_llama.py ../llava-v1.5-7b --skip-unknown ``` -Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory. +Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory. ## LLaVA 1.6 gguf conversion 1) First clone a LLaVA 1.6 model: ```console git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b ``` -2) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: + +2) Install the required Python packages: + +```sh +pip install -r examples/llava/requirements.txt +``` + +3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models: ```console -python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/ +python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/ ``` - you will find a llava.projector and a llava.clip file in your model directory -3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: + +4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory: ```console mkdir vit cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin @@ -76,20 +84,20 @@ cp ../llava-v1.6-vicuna-7b/llava.projector vit/ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json ``` -4) Create the visual gguf model: +5) Create the visual gguf model: ```console -python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision +python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision ``` - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP -5) Then convert the model to gguf format: +6) Then convert the model to gguf format: ```console -python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown +python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown ``` -6) And finally we can run the llava-cli using the 1.6 model version: +7) And finally we can run the llava cli using the 1.6 model version: ```console -./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096 +./llama-llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096 ``` **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) diff --git a/examples/llava/android/adb_run.sh b/examples/llava/android/adb_run.sh index f73623ae3..45ccf8d70 100755 --- a/examples/llava/android/adb_run.sh +++ b/examples/llava/android/adb_run.sh @@ -10,7 +10,7 @@ prompt="A chat between a curious user and an artificial intelligence assistant. # prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" program_dir="build_64/bin" -binName="llava-cli" +binName="llama-llava-cli" n_threads=4 diff --git a/examples/llava/clip-quantize-cli.cpp b/examples/llava/clip-quantize-cli.cpp new file mode 100644 index 000000000..566506954 --- /dev/null +++ b/examples/llava/clip-quantize-cli.cpp @@ -0,0 +1,59 @@ +#include "arg.h" +#include "base64.hpp" +#include "log.h" +#include "common.h" +#include "sampling.h" +#include "clip.h" +#include "llava.h" +#include "llama.h" +#include "ggml.h" + +static void print_usage(int argc, char ** argv) { + (void) argc; + + fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]); + fprintf(stderr, " type = 2 - q4_0\n"); + fprintf(stderr, " type = 3 - q4_1\n"); + fprintf(stderr, " type = 6 - q5_0\n"); + fprintf(stderr, " type = 7 - q5_1\n"); + fprintf(stderr, " type = 8 - q8_0\n"); +} + +int main(int argc, char ** argv) { + if (argc != 4) { + print_usage(argc, argv); + return 1; + } + + const std::string fname_inp = argv[1]; + const std::string fname_out = argv[2]; + + const int itype = atoi(argv[3]); + + const int64_t t_main_start_us = ggml_time_us(); + + int64_t t_quantize_us = 0; + + // load the model + { + const int64_t t_start_us = ggml_time_us(); + + if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) { + fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str()); + return 1; + } + + t_quantize_us = ggml_time_us() - t_start_us; + } + + // report timing + { + const int64_t t_main_end_us = ggml_time_us(); + + printf("\n"); + printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f); + printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f); + } + + return 0; +} diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index ef9e4ba7a..271cf2a2a 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -4,16 +4,30 @@ // 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" +#include "gguf.h" -#ifdef GGML_USE_CUBLAS -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif +//#ifdef GGML_USE_CUDA +//#include "ggml-cuda.h" +//#endif +// +//#ifdef GGML_USE_SYCL +//#include "ggml-sycl.h" +//#endif +// +//#ifdef GGML_USE_METAL +//#include "ggml-metal.h" +//#endif +// +//#ifdef GGML_USE_CANN +//#include "ggml-cann.h" +//#endif +// +//#ifdef GGML_USE_VULKAN +//#include "ggml-vulkan.h" +//#endif #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" @@ -23,7 +37,6 @@ #include #include #include -#include #include #include #include @@ -32,6 +45,18 @@ #include #include +#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 // RGB uint8 image @@ -70,26 +95,31 @@ static std::string format(const char * fmt, ...) { // key constants // -#define KEY_FTYPE "general.file_type" -#define KEY_NAME "general.name" -#define KEY_DESCRIPTION "general.description" -#define KEY_HAS_TEXT_ENC "clip.has_text_encoder" -#define KEY_HAS_VIS_ENC "clip.has_vision_encoder" -#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector" -#define KEY_USE_GELU "clip.use_gelu" -#define KEY_N_EMBD "clip.%s.embedding_length" -#define KEY_N_FF "clip.%s.feed_forward_length" -#define KEY_N_BLOCK "clip.%s.block_count" -#define KEY_N_HEAD "clip.%s.attention.head_count" -#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" -#define KEY_PROJ_DIM "clip.%s.projection_dim" -#define KEY_TOKENS "tokenizer.ggml.tokens" -#define KEY_N_POSITIONS "clip.text.context_length" -#define KEY_IMAGE_SIZE "clip.vision.image_size" -#define KEY_PATCH_SIZE "clip.vision.patch_size" -#define KEY_IMAGE_MEAN "clip.vision.image_mean" -#define KEY_IMAGE_STD "clip.vision.image_std" -#define KEY_PROJ_TYPE "clip.projector_type" +#define KEY_FTYPE "general.file_type" +#define KEY_NAME "general.name" +#define KEY_DESCRIPTION "general.description" +#define KEY_HAS_TEXT_ENC "clip.has_text_encoder" +#define KEY_HAS_VIS_ENC "clip.has_vision_encoder" +#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector" +#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector" +#define KEY_HAS_GLM_PROJ "clip.has_glm_projector" +#define KEY_MINICPMV_VERSION "clip.minicpmv_version" +#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger" +#define KEY_USE_GELU "clip.use_gelu" +#define KEY_USE_SILU "clip.use_silu" +#define KEY_N_EMBD "clip.%s.embedding_length" +#define KEY_N_FF "clip.%s.feed_forward_length" +#define KEY_N_BLOCK "clip.%s.block_count" +#define KEY_N_HEAD "clip.%s.attention.head_count" +#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" +#define KEY_PROJ_DIM "clip.%s.projection_dim" +#define KEY_TOKENS "tokenizer.ggml.tokens" +#define KEY_N_POSITIONS "clip.text.context_length" +#define KEY_IMAGE_SIZE "clip.vision.image_size" +#define KEY_PATCH_SIZE "clip.vision.patch_size" +#define KEY_IMAGE_MEAN "clip.vision.image_mean" +#define KEY_IMAGE_STD "clip.vision.image_std" +#define KEY_PROJ_TYPE "clip.projector_type" #define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type" #define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints" @@ -103,7 +133,9 @@ static std::string format(const char * fmt, ...) { #define TN_TOKEN_EMBD "%s.token_embd.weight" #define TN_POS_EMBD "%s.position_embd.weight" #define TN_CLASS_EMBD "v.class_embd" -#define TN_PATCH_EMBD "v.patch_embd.weight" +#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat +#define TN_PATCH_EMBD_1 "v.patch_embd.weight.1" +#define TN_PATCH_BIAS "v.patch_embd.bias" #define TN_ATTN_K "%s.blk.%d.attn_k.%s" #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" #define TN_ATTN_V "%s.blk.%d.attn_v.%s" @@ -119,19 +151,44 @@ static std::string format(const char * fmt, ...) { #define TN_LLAVA_PROJ "mm.%d.%s" #define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s" #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s" +#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s" #define TN_IMAGE_NEWLINE "model.image_newline" +#define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k" +#define TN_MINICPMV_QUERY "resampler.query" +#define TN_MINICPMV_PROJ "resampler.proj.weight" +#define TN_MINICPMV_KV_PROJ "resampler.kv.weight" +#define TN_MINICPMV_ATTN "resampler.attn.%s.%s" +#define TN_MINICPMV_LN "resampler.ln_%s.%s" + +#define TN_GLM_ADAPER_CONV "adapter.conv.%s" +#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s" +#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s" +#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s" +#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s" +#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s" +#define TN_GLM_BOI_W "adapter.boi" +#define TN_GLM_EOI_W "adapter.eoi" + enum projector_type { PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP_NORM, PROJECTOR_TYPE_LDP, + PROJECTOR_TYPE_LDPV2, + PROJECTOR_TYPE_RESAMPLER, + PROJECTOR_TYPE_GLM_EDGE, + PROJECTOR_TYPE_MERGER, PROJECTOR_TYPE_UNKNOWN, }; static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_MLP, "mlp" }, { PROJECTOR_TYPE_LDP, "ldp" }, + { PROJECTOR_TYPE_LDPV2, "ldpv2"}, + { PROJECTOR_TYPE_RESAMPLER, "resampler"}, + { PROJECTOR_TYPE_GLM_EDGE, "adapter"}, + { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"}, }; @@ -142,7 +199,7 @@ static std::map PROJECTOR_TYPE_NAMES = { static int get_key_idx(const gguf_context * ctx, const char * key) { int i = gguf_find_key(ctx, key); if (i == -1) { - fprintf(stderr, "key %s not found in file\n", key); + LOG_ERR("key %s not found in file\n", key); throw std::runtime_error(format("Missing required key: %s", key)); } @@ -192,17 +249,20 @@ static std::string gguf_data_to_str(enum gguf_type type, const void * data, int } static void replace_all(std::string & s, const std::string & search, const std::string & replace) { - std::string result; - for (size_t pos = 0; ; pos += search.length()) { - auto new_pos = s.find(search, pos); - if (new_pos == std::string::npos) { - result += s.substr(pos, s.size() - pos); - break; - } - result += s.substr(pos, new_pos - pos) + replace; - pos = new_pos; + if (search.empty()) { + return; } - s = std::move(result); + std::string builder; + builder.reserve(s.length()); + size_t pos = 0; + size_t last_pos = 0; + while ((pos = s.find(search, last_pos)) != std::string::npos) { + builder.append(s, last_pos, pos - last_pos); + builder.append(replace); + last_pos = pos + search.length(); + } + builder.append(s, last_pos, std::string::npos); + s = std::move(builder); } static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { @@ -215,7 +275,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { { const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i); int arr_n = gguf_get_arr_n(ctx_gguf, i); - const void * data = gguf_get_arr_data(ctx_gguf, i); + const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i); std::stringstream ss; ss << "["; for (int j = 0; j < arr_n; j++) { @@ -244,7 +304,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") { size_t tensor_size = ggml_nbytes(tensor); - printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", + LOG_INF("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", prefix, ggml_n_dims(tensor), tensor->name, tensor_size, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type)); } @@ -262,7 +322,7 @@ static projector_type clip_projector_type_from_string(const std::string & name) static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) { std::ofstream file(filename, std::ios::binary); if (!file.is_open()) { - std::cerr << "Failed to open file for writing: " << filename << std::endl; + LOG_ERR("Failed to open file for writing: %s\n", filename.c_str()); return; } @@ -281,7 +341,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) { std::ofstream file(filename, std::ios::binary); if (!file.is_open()) { - std::cerr << "Failed to open file for writing: " << filename << std::endl; + LOG_ERR("Failed to open file for writing: %s\n", filename.c_str()); return; } @@ -421,7 +481,9 @@ struct clip_vision_model { // embeddings struct ggml_tensor * class_embedding; - struct ggml_tensor * patch_embeddings; + struct ggml_tensor * patch_embeddings_0; + struct ggml_tensor * patch_embeddings_1; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) + struct ggml_tensor * patch_bias; struct ggml_tensor * position_embeddings; struct ggml_tensor * pre_ln_w; @@ -450,6 +512,12 @@ struct clip_vision_model { struct ggml_tensor * mm_4_w = NULL; struct ggml_tensor * mm_4_b = NULL; + //GLMV-Edge projection + struct ggml_tensor * mm_model_adapter_conv_w; + struct ggml_tensor * mm_model_adapter_conv_b; + struct ggml_tensor * boi_w; + struct ggml_tensor * eoi_w; + // MobileVLM projection struct ggml_tensor * mm_model_mlp_1_w; struct ggml_tensor * mm_model_mlp_1_b; @@ -475,12 +543,44 @@ struct clip_vision_model { struct ggml_tensor * mm_model_block_2_block_2_0_w; struct ggml_tensor * mm_model_block_2_block_2_1_w; struct ggml_tensor * mm_model_block_2_block_2_1_b; + + // MobileVLM_V2 projection + struct ggml_tensor * mm_model_mlp_0_w; + struct ggml_tensor * mm_model_mlp_0_b; + struct ggml_tensor * mm_model_mlp_2_w; + struct ggml_tensor * mm_model_mlp_2_b; + struct ggml_tensor * mm_model_peg_0_w; + struct ggml_tensor * mm_model_peg_0_b; + + // MINICPMV projection + struct ggml_tensor * mm_model_pos_embed_k; + struct ggml_tensor * mm_model_query; + struct ggml_tensor * mm_model_proj; + struct ggml_tensor * mm_model_kv_proj; + struct ggml_tensor * mm_model_attn_q_w; + struct ggml_tensor * mm_model_attn_q_b; + struct ggml_tensor * mm_model_attn_k_w; + struct ggml_tensor * mm_model_attn_k_b; + struct ggml_tensor * mm_model_attn_v_w; + struct ggml_tensor * mm_model_attn_v_b; + struct ggml_tensor * mm_model_attn_o_w; + struct ggml_tensor * mm_model_attn_o_b; + struct ggml_tensor * mm_model_ln_q_w; + struct ggml_tensor * mm_model_ln_q_b; + struct ggml_tensor * mm_model_ln_kv_w; + struct ggml_tensor * mm_model_ln_kv_b; + struct ggml_tensor * mm_model_ln_post_w; + struct ggml_tensor * mm_model_ln_post_b; }; struct clip_ctx { bool has_text_encoder = false; bool has_vision_encoder = false; bool has_llava_projector = false; + bool has_minicpmv_projector = false; + bool has_glm_projector = false; + bool has_qwen2vl_merger = false; + int minicpmv_version = 2; struct clip_vision_model vision_model; projector_type proj_type = PROJECTOR_TYPE_MLP; @@ -488,8 +588,14 @@ struct clip_ctx { float image_mean[3]; float image_std[3]; bool use_gelu = false; + bool use_silu = false; int32_t ftype = 1; + bool has_class_embedding = true; + bool has_pre_norm = true; + bool has_post_norm = false; + bool has_patch_bias = false; + struct gguf_context * ctx_gguf; struct ggml_context * ctx_data; @@ -497,35 +603,61 @@ struct clip_ctx { // memory buffers to evaluate the model ggml_backend_buffer_t params_buffer = NULL; - ggml_backend_buffer_t compute_buffer = NULL; ggml_backend_t backend = NULL; ggml_gallocr_t compute_alloc = NULL; + + struct clip_image_size * load_image_size; }; -static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) { +static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_ERR("This gguf file seems to have no vision encoder\n"); return nullptr; } const auto & model = ctx->vision_model; const auto & hparams = model.hparams; - const int image_size = hparams.image_size; + const int image_size = hparams.image_size; + int image_size_width = image_size; + int image_size_height = image_size; + if (ctx->has_minicpmv_projector) { + if (load_image_size == nullptr) { + load_image_size = clip_image_size_init(); + } + LOG_DBG("%s: %d %d\n", __func__, load_image_size->width, load_image_size->height); + image_size_width = load_image_size->width; + image_size_height = load_image_size->height; + if (is_inf) { + image_size_width = imgs->data->nx; + image_size_height = imgs->data->ny; + } + } + else if (ctx->has_qwen2vl_merger) { + // use the image's native resolution when image is avaible + if (is_inf) { + // if (imgs->data->nx && imgs->data->ny) { + image_size_width = imgs->data->nx; + image_size_height = imgs->data->ny; + } + } const int patch_size = hparams.patch_size; - const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); - const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side); - const int num_positions = num_patches + 1; + const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); + const int patches_w = image_size_width / patch_size; + const int patches_h = image_size_height / patch_size; + const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); + const int num_position_ids = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions; const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; - const int n_layer = hparams.n_layer; + int n_layer = hparams.n_layer; const float eps = hparams.eps; + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; const int batch_size = imgs->size; - if (ctx->has_llava_projector) { + if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) { GGML_ASSERT(batch_size == 1); } @@ -538,35 +670,82 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 struct ggml_context * ctx0 = ggml_init(params); struct ggml_cgraph * gf = ggml_new_graph(ctx0); - struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size, image_size, 3, batch_size); + struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size); ggml_set_name(inp_raw, "inp_raw"); ggml_set_input(inp_raw); - struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); - inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); + if (ctx->has_qwen2vl_merger) { + GGML_ASSERT(image_size_width % (patch_size * 2) == 0); + GGML_ASSERT(image_size_height % (patch_size * 2) == 0); - // concat class_embeddings and patch_embeddings - struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - ggml_set_name(embeddings, "embeddings"); - ggml_set_input(embeddings); + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3)); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_reshape_4d( + ctx0, inp, + hidden_size * 2, patches_w / 2, patches_h, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2)); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3)); + inp = ggml_reshape_3d( + ctx0, inp, + hidden_size, patches_w * patches_h, batch_size); + } + else { + inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); + inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); + } - embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + if (ctx->has_patch_bias) { + // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); + inp = ggml_add(ctx0, inp, model.patch_bias); + } + struct ggml_tensor * embeddings = inp; + struct ggml_tensor * pos_embed = nullptr; - embeddings = ggml_acc(ctx0, embeddings, inp, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); + if (ctx->has_llava_projector) { + // concat class_embeddings and patch_embeddings + if (ctx->has_class_embedding) { + embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); + ggml_set_name(embeddings, "embeddings"); + ggml_set_input(embeddings); + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); + } + } - struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); + struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); ggml_set_name(positions, "positions"); ggml_set_input(positions); - embeddings = - ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); + if (!ctx->has_qwen2vl_merger) { // qwen2vl use rope position embedding + embeddings = + ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); + } + + if (ctx->has_minicpmv_projector) { + int pos_w = image_size_width/patch_size; + int pos_h = image_size_height/patch_size; + if (ctx->minicpmv_version == 2) { + pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 4096, pos_w * pos_h, 1); + } + else if (ctx->minicpmv_version == 3) { + pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1); + } + else if (ctx->minicpmv_version == 4) { + pos_embed = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 3584, pos_w * pos_h, 1); + } + ggml_set_name(pos_embed, "pos_embed"); + ggml_set_input(pos_embed); + } // pre-layernorm - { + if (ctx->has_pre_norm) { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "pre_ln"); @@ -574,6 +753,9 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 } // loop over layers + if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) { + n_layer += 1; + } for (int il = 0; il < n_layer - 1; il++) { struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states @@ -593,8 +775,13 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b); - Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size); + if (ctx->has_qwen2vl_merger) { + Q = ggml_rope_multi( + ctx0, Q, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + } + Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size); @@ -602,6 +789,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b); K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); + if (ctx->has_qwen2vl_merger) { + K = ggml_rope_multi( + ctx0, K, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + } K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); @@ -641,6 +833,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 if (ctx->use_gelu) { cur = ggml_gelu_inplace(ctx0, cur); + } else if (ctx->use_silu) { + cur = ggml_silu_inplace(ctx0, cur); } else { cur = ggml_gelu_quick_inplace(ctx0, cur); } @@ -652,10 +846,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 cur = ggml_add(ctx0, embeddings, cur); embeddings = cur; + + } + + // post-layernorm + if (ctx->has_post_norm) { + embeddings = ggml_norm(ctx0, embeddings, eps); + ggml_set_name(embeddings, "post_ln"); + + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); } // llava projector - { + if (ctx->has_llava_projector) { embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); struct ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_patches); @@ -676,8 +879,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = ggml_gelu(ctx0, embeddings); embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); - - } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { + } + else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); @@ -715,7 +918,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3)); mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); // stride = 1, padding = 1, bias is nullptr - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); // layer norm // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] @@ -763,7 +966,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // block_2 { // stride = 2 - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] // layer norm @@ -808,10 +1011,147 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 } embeddings = block_1; } + else if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) + { + int n_patch = 24; + struct ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); + mlp_0 = ggml_gelu(ctx0, mlp_0); + struct ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); + mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); + // mlp_2 ne = [2048, 576, 1, 1] + // // AVG Pool Layer 2*2, strides = 2 + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 0, 2, 3)); + // mlp_2 ne = [576, 2048, 1, 1] + mlp_2 = ggml_reshape_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); + // mlp_2 ne [24, 24, 2048, 1] + mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); + // weight ne = [3, 3, 2048, 1] + struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, mlp_2); + peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); + embeddings = peg_0; + } + else { + GGML_ABORT("fatal error"); + } + } + // minicpmv projector + else if (ctx->has_minicpmv_projector) + { + if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) { + struct ggml_tensor * q = model.mm_model_query; + { // layernorm + q = ggml_norm(ctx0, q, eps); + q = ggml_add(ctx0, ggml_mul(ctx0, q, model.mm_model_ln_q_w), model.mm_model_ln_q_b); + } + struct ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); + { // layernorm + v = ggml_norm(ctx0, v, eps); + v = ggml_add(ctx0, ggml_mul(ctx0, v, model.mm_model_ln_kv_w), model.mm_model_ln_kv_b); + } + struct ggml_tensor * k; + { // position + // q = ggml_add(ctx0, q, model.mm_model_pos_embed); + k = ggml_add(ctx0, v, pos_embed); + } + + { // attention + int hidden_size = 4096; + const int d_head = 128; + int n_head = hidden_size/d_head; + int num_query = 96; + if (ctx->minicpmv_version == 2) { + hidden_size = 4096; + n_head = hidden_size/d_head; + num_query = 96; + } + else if (ctx->minicpmv_version == 3) { + hidden_size = 3584; + n_head = hidden_size/d_head; + num_query = 64; + } + else if (ctx->minicpmv_version == 4) { + hidden_size = 3584; + n_head = hidden_size/d_head; + num_query = 64; + } + + struct ggml_tensor * Q = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), model.mm_model_attn_q_b); + Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head)); + struct ggml_tensor * K = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), model.mm_model_attn_k_b); + struct ggml_tensor * V = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), model.mm_model_attn_v_b); + // permute + Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_query, batch_size); + Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3)); + Q = ggml_reshape_3d(ctx0, Q, d_head, num_query, n_head * batch_size); + K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size); + K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3)); + K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size); + V = ggml_reshape_4d(ctx0, V, d_head, n_head, num_positions, batch_size); + V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3)); + V = ggml_reshape_3d(ctx0, V, num_positions, d_head, n_head * batch_size); + struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); + KQ = ggml_soft_max_inplace(ctx0, KQ); + struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ); + KQV = ggml_reshape_4d(ctx0, KQV, d_head, num_query, n_head, batch_size); + KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + KQV = ggml_cont_3d(ctx0, KQV, hidden_size, num_query, batch_size); + + embeddings = ggml_add(ctx0, ggml_mul_mat(ctx0, model.mm_model_attn_o_w, KQV), model.mm_model_attn_o_b); + } + { // layernorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_post_w), model.mm_model_ln_post_b); + } + embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); + } else { GGML_ASSERT(false); } } + // glm projector + else if (ctx->has_glm_projector) { + if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + size_t gridsz = (size_t)sqrt(embeddings->ne[1]); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3)); + embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); + embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); + embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); + embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); + //GLU + { + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); + embeddings = ggml_gelu_inplace(ctx0, embeddings); + struct ggml_tensor * x = embeddings; + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); + x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); + embeddings = ggml_silu_inplace(ctx0, embeddings); + embeddings = ggml_mul(ctx0, embeddings,x); + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); + } + } else { + GGML_ABORT("fatel error"); + } + } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size); + + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + + // GELU activation + embeddings = ggml_gelu(ctx0, embeddings); + + // Second linear layer + embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_1_b); + } // build the graph ggml_build_forward_expand(gf, embeddings); @@ -845,21 +1185,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { const int idx_name = gguf_find_key(ctx, KEY_NAME); if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug const std::string name = gguf_get_val_str(ctx, idx_name); - printf("%s: model name: %s\n", __func__, name.c_str()); + LOG_INF("%s: model name: %s\n", __func__, name.c_str()); } - printf("%s: description: %s\n", __func__, description.c_str()); - printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx)); - printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - printf("%s: n_tensors: %d\n", __func__, n_tensors); - printf("%s: n_kv: %d\n", __func__, n_kv); - printf("%s: ftype: %s\n", __func__, ftype_str.c_str()); - printf("\n"); + LOG_INF("%s: description: %s\n", __func__, description.c_str()); + LOG_INF("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx)); + LOG_INF("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + LOG_INF("%s: n_tensors: %d\n", __func__, n_tensors); + LOG_INF("%s: n_kv: %d\n", __func__, n_kv); + LOG_INF("%s: ftype: %s\n", __func__, ftype_str.c_str()); + LOG_INF("\n"); } const int n_tensors = gguf_get_n_tensors(ctx); // kv const int n_kv = gguf_get_n_kv(ctx); - printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n", + LOG_INF("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n", __func__, n_kv, n_tensors, fname); { std::map n_type; @@ -870,7 +1210,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { n_type[type]++; } - printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); + LOG_INF("%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(ctx, i); const enum gguf_type type = gguf_get_kv_type(ctx, i); @@ -886,7 +1226,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } replace_all(value, "\n", "\\n"); - printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); + LOG_INF("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); } // print type counts @@ -895,7 +1235,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { continue; } - printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second); + LOG_INF("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second); } } @@ -910,13 +1250,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { size_t tensor_size = ggml_nbytes(cur); model_size += tensor_size; if (verbosity >= 3) { - printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", + LOG_INF("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type)); } } } - clip_ctx * new_clip = new clip_ctx; + clip_ctx * new_clip = new clip_ctx{}; // update projector type { @@ -935,20 +1275,34 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } } -#ifdef GGML_USE_CUBLAS - new_clip->backend = ggml_backend_cuda_init(0); - printf("%s: CLIP using CUDA backend\n", __func__); -#endif - -#ifdef GGML_USE_METAL - new_clip->backend = ggml_backend_metal_init(); - printf("%s: CLIP using Metal backend\n", __func__); -#endif - +//#ifdef GGML_USE_CUDA +// new_clip->backend = ggml_backend_cuda_init(0); +// LOG_INF("%s: CLIP using CUDA backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_METAL +// new_clip->backend = ggml_backend_metal_init(); +// LOG_INF("%s: CLIP using Metal backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_CANN +// new_clip->backend = ggml_backend_cann_init(0); +// LOG_INF("%s: CLIP using CANN backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_VULKAN +// new_clip->backend = ggml_backend_vk_init(0); +// 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(); - printf("%s: CLIP using CPU backend\n", __func__); + LOG_INF("%s: CLIP using CPU backend\n", __func__); } // model size and capabilities @@ -964,23 +1318,52 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->has_llava_projector = gguf_get_val_bool(ctx, idx); } - GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search + idx = gguf_find_key(ctx, KEY_HAS_MINICPMV_PROJ); + if (idx != -1) { + new_clip->has_minicpmv_projector = gguf_get_val_bool(ctx, idx); + } + + idx = gguf_find_key(ctx, KEY_MINICPMV_VERSION); + if (idx != -1) { + new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx); + } + + idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ); + if (idx != -1) { + new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx); + } + + idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER); + if (idx != -1) { + new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx); + } + // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search + GGML_ASSERT(new_clip->has_vision_encoder); GGML_ASSERT(!new_clip->has_text_encoder); idx = get_key_idx(ctx, KEY_USE_GELU); new_clip->use_gelu = gguf_get_val_bool(ctx, idx); + try { + idx = get_key_idx(ctx, KEY_USE_SILU); + new_clip->use_silu = gguf_get_val_bool(ctx, idx); + } catch (std::runtime_error & /*e*/) { + new_clip->use_silu = false; + } + if (verbosity >= 1) { - printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); - printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); - printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); - printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); - printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); + LOG_INF("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); + LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); + LOG_INF("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); + LOG_INF("%s: minicpmv_projector: %d\n", __func__, new_clip->has_minicpmv_projector); + LOG_INF("%s: glm_projector: %d\n", __func__, new_clip->has_glm_projector); + LOG_INF("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); + LOG_INF("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); } } - printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); + LOG_INF("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); // load tensors { @@ -993,15 +1376,17 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->ctx_data = ggml_init(params); if (!new_clip->ctx_data) { - fprintf(stderr, "%s: ggml_init() failed\n", __func__); + LOG_ERR("%s: ggml_init() failed\n", __func__); clip_free(new_clip); + gguf_free(ctx); return nullptr; } auto fin = std::ifstream(fname, std::ios::binary); if (!fin) { - printf("cannot open model file for loading tensors\n"); + LOG_ERR("cannot open model file for loading tensors\n"); clip_free(new_clip); + gguf_free(ctx); return nullptr; } @@ -1021,8 +1406,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); if (!fin) { - printf("%s: failed to seek for tensor %s\n", __func__, name); + LOG_ERR("%s: failed to seek for tensor %s\n", __func__, name); clip_free(new_clip); + gguf_free(ctx); return nullptr; } int num_bytes = ggml_nbytes(cur); @@ -1062,20 +1448,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } if (n < 32) hparams.image_grid_pinpoints[n] = 0; - } catch (std::runtime_error & e) { + } catch (std::runtime_error & /*e*/) { hparams.image_grid_pinpoints[0]=0; } try { int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE); strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx)); - } catch (std::runtime_error & e) { + } catch (std::runtime_error & /*e*/) { strcpy(hparams.mm_patch_merge_type, "flat"); } try { hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6 - } catch(const std::exception& e) { + } catch(const std::exception& /*e*/) { hparams.image_crop_resolution = hparams.image_size; } @@ -1091,34 +1477,66 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } if (verbosity >= 2) { - printf("\n%s: vision model hparams\n", __func__); - printf("image_size %d\n", hparams.image_size); - printf("patch_size %d\n", hparams.patch_size); - printf("v_hidden_size %d\n", hparams.hidden_size); - printf("v_n_intermediate %d\n", hparams.n_intermediate); - printf("v_projection_dim %d\n", hparams.projection_dim); - printf("v_n_head %d\n", hparams.n_head); - printf("v_n_layer %d\n", hparams.n_layer); - printf("v_eps %f\n", hparams.eps); - printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); - printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); - printf("v_image_grid_pinpoints: "); + LOG_INF("\n%s: vision model hparams\n", __func__); + LOG_INF("image_size %d\n", hparams.image_size); + LOG_INF("patch_size %d\n", hparams.patch_size); + LOG_INF("v_hidden_size %d\n", hparams.hidden_size); + LOG_INF("v_n_intermediate %d\n", hparams.n_intermediate); + LOG_INF("v_projection_dim %d\n", hparams.projection_dim); + LOG_INF("v_n_head %d\n", hparams.n_head); + LOG_INF("v_n_layer %d\n", hparams.n_layer); + LOG_INF("v_eps %f\n", hparams.eps); + LOG_INF("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); + LOG_INF("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); + LOG_INF("v_image_grid_pinpoints: "); for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) { - printf("%d ", hparams.image_grid_pinpoints[i]); + LOG_INF("%d ", hparams.image_grid_pinpoints[i]); } - printf("\n"); - printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); + LOG_INF("\n"); + LOG_INF("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); } try { - vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); - vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + new_clip->has_class_embedding = true; + } catch (const std::exception& /*e*/) { + new_clip->has_class_embedding = false; + } + + try { + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + new_clip->has_pre_norm = true; + } catch (std::exception & /*e*/) { + new_clip->has_pre_norm = false; + } + + try { + vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight")); + vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias")); + new_clip->has_post_norm = true; + } catch (std::exception & /*e*/) { + new_clip->has_post_norm = false; + } + + try { + vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS); + new_clip->has_patch_bias = true; + } catch (std::exception & /*e*/) { + new_clip->has_patch_bias = false; + } + + try { + vision_model.patch_embeddings_0 = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); - vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); - } catch(const std::exception& e) { - fprintf(stderr, "%s: failed to load vision model tensors\n", __func__); + } catch(const std::exception& /*e*/) { + LOG_ERR("%s: failed to load vision model tensors\n", __func__); + } + try { + vision_model.patch_embeddings_1 = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD_1); + } catch(const std::exception& /*e*/) { + new_clip->has_qwen2vl_merger = false; } // LLaVA projection @@ -1129,26 +1547,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // Yi-type llava vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight")); vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias")); - } catch (std::runtime_error & e) { } + } catch (std::runtime_error & /*e*/) { } try { // missing in Yi-type llava vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); - } catch (std::runtime_error & e) { } + } catch (std::runtime_error & /*e*/) { } try { // Yi-type llava vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight")); vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias")); - } catch (std::runtime_error & e) { } + } catch (std::runtime_error & /*e*/) { } try { // Yi-type llava vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight")); vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias")); - } catch (std::runtime_error & e) { } + } catch (std::runtime_error & /*e*/) { } try { vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE); - // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__); - } catch (std::runtime_error & e) { } + // LOG_INF("%s: image_newline tensor (llava-1.6) found\n", __func__); + } catch (std::runtime_error & /*e*/) { } } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { // MobileVLM projection vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight")); @@ -1175,7 +1593,57 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.mm_model_block_2_block_2_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "0.weight")); vision_model.mm_model_block_2_block_2_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.weight")); vision_model.mm_model_block_2_block_2_1_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_BLOCK, 2, 2, "1.bias")); - } else { + } + else if (new_clip->proj_type == PROJECTOR_TYPE_LDPV2) + { + // MobilVLM_V2 projection + vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "weight")); + vision_model.mm_model_mlp_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 0, "bias")); + vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "weight")); + vision_model.mm_model_mlp_2_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 2, "bias")); + vision_model.mm_model_peg_0_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "weight")); + vision_model.mm_model_peg_0_b = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_PEG, 0, "bias")); + } + else if (new_clip->proj_type == PROJECTOR_TYPE_RESAMPLER) { + // vision_model.mm_model_pos_embed = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD); + vision_model.mm_model_pos_embed_k = get_tensor(new_clip->ctx_data, TN_MINICPMV_POS_EMBD_K); + vision_model.mm_model_query = get_tensor(new_clip->ctx_data, TN_MINICPMV_QUERY); + vision_model.mm_model_proj = get_tensor(new_clip->ctx_data, TN_MINICPMV_PROJ); + vision_model.mm_model_kv_proj = get_tensor(new_clip->ctx_data, TN_MINICPMV_KV_PROJ); + vision_model.mm_model_attn_q_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "q", "weight")); + vision_model.mm_model_attn_k_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "k", "weight")); + vision_model.mm_model_attn_v_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "v", "weight")); + vision_model.mm_model_attn_q_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "q", "bias")); + vision_model.mm_model_attn_k_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "k", "bias")); + vision_model.mm_model_attn_v_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "v", "bias")); + vision_model.mm_model_attn_o_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "out", "weight")); + vision_model.mm_model_attn_o_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_ATTN, "out", "bias")); + vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "q", "weight")); + vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "q", "bias")); + vision_model.mm_model_ln_kv_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "kv", "weight")); + vision_model.mm_model_ln_kv_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "kv", "bias")); + vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight")); + vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias")); + } + else if (new_clip->proj_type == PROJECTOR_TYPE_GLM_EDGE) { + vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight")); + vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias")); + vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight")); + vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight")); + vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias")); + vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight")); + vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight")); + vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight")); + vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W); + vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W); + } + else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) { + vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); + vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); + vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); + vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); + } + else { std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type]; throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } @@ -1213,15 +1681,31 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend)); clip_image_f32_batch batch; batch.size = 1; - ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch); + batch.data = nullptr; + ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false); ggml_gallocr_reserve(new_clip->compute_alloc, gf); size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0); - printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); + LOG_INF("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); } return new_clip; } +void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size) { + ctx_clip->load_image_size = load_image_size; +} + +struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip) { + return ctx_clip->load_image_size; +} + +struct clip_image_size * clip_image_size_init() { + struct clip_image_size * load_image_size = new struct clip_image_size(); + load_image_size->width = 448; + load_image_size->height = 448; + return load_image_size; +} + struct clip_image_u8 * clip_image_u8_init() { return new clip_image_u8(); } @@ -1232,16 +1716,16 @@ struct clip_image_f32 * clip_image_f32_init() { void clip_image_u8_free(struct clip_image_u8 * img) { delete img; } void clip_image_f32_free(struct clip_image_f32 * img) { delete img; } -void clip_image_u8_batch_free(struct clip_image_u8_batch & batch) { - if (batch.size > 0) { - delete[] batch.data; - batch.size = 0; +void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) { + if (batch->size > 0) { + delete[] batch->data; + batch->size = 0; } } -void clip_image_f32_batch_free(struct clip_image_f32_batch & batch) { - if (batch.size > 0) { - delete[] batch.data; - batch.size = 0; +void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) { + if (batch->size > 0) { + delete[] batch->data; + batch->size = 0; } } @@ -1256,7 +1740,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { int nx, ny, nc; auto * data = stbi_load(fname, &nx, &ny, &nc, 3); if (!data) { - fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname); + LOG_ERR("%s: failed to load image '%s'\n", __func__, fname); return false; } build_clip_img_from_data(data, nx, ny, img); @@ -1268,7 +1752,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length int nx, ny, nc; auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); if (!data) { - fprintf(stderr, "%s: failed to decode image bytes\n", __func__); + LOG_ERR("%s: failed to decode image bytes\n", __func__); return false; } build_clip_img_from_data(data, nx, ny, img); @@ -1277,7 +1761,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length } // Linear interpolation between two points -inline float lerp(float s, float e, float t) { +inline float clip_lerp(float s, float e, float t) { return s + (e - s) * t; } // Bilinear resize function @@ -1299,17 +1783,17 @@ static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int ta float y_lerp = py - y_floor; for (int c = 0; c < 3; c++) { - float top = lerp( + float top = clip_lerp( static_cast(src.buf[3 * (y_floor * src.nx + x_floor) + c]), static_cast(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]), x_lerp ); - float bottom = lerp( + float bottom = clip_lerp( static_cast(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]), static_cast(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]), x_lerp ); - dst.buf[3 * (y * target_width + x) + c] = static_cast(lerp(top, bottom, y_lerp)); + dst.buf[3 * (y * target_width + x) + c] = static_cast(clip_lerp(top, bottom, y_lerp)); } } } @@ -1327,7 +1811,7 @@ static void normalize_image_u8_to_f32(const clip_image_u8* src, clip_image_f32* } } -inline float clip(float x, float lower, float upper) { +inline int clip(int x, int lower, int upper) { return std::max(lower, std::min(x, upper)); } @@ -1458,7 +1942,7 @@ static std::pair select_best_resolution(const std::pair & or int downscaled_height = static_cast(original_height * scale); int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); int wasted_resolution = (width * height) - effective_resolution; - // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + // LOG_INF("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { max_effective_resolution = effective_resolution; min_wasted_resolution = wasted_resolution; @@ -1492,12 +1976,224 @@ static std::vector divide_to_patches_u8(const clip_image_u8 & im return patches; } +static int ensure_divide(int length, int patch_size) { + return std::max(static_cast(std::round(static_cast(length) / patch_size) * patch_size), patch_size); +} + +static std::pair uhd_find_best_resize(std::pair original_size, int scale_resolution, int patch_size, bool allow_upscale = false) { + int width = original_size.first; + int height = original_size.second; + if ((width * height > scale_resolution * scale_resolution) || allow_upscale) { + float r = static_cast(width) / height; + height = static_cast(scale_resolution / std::sqrt(r)); + width = static_cast(height * r); + } + int best_width = ensure_divide(width, patch_size); + int best_height = ensure_divide(height, patch_size); + return std::make_pair(best_width, best_height); +} + +static std::pair uhd_get_refine_size(std::pair original_size, std::pair grid, int scale_resolution, int patch_size, bool allow_upscale = false) { + int width, height; + std::tie(width, height) = original_size; + int grid_x, grid_y; + std::tie(grid_x, grid_y) = grid; + + int refine_width = ensure_divide(width, grid_x); + int refine_height = ensure_divide(height, grid_y); + + int grid_width = refine_width / grid_x; + int grid_height = refine_height / grid_y; + + // auto best_grid_size = find_best_resize(std::make_tuple(grid_width, grid_height), scale_resolution, patch_size, allow_upscale); (old line) + auto best_grid_size = uhd_find_best_resize(std::make_pair(grid_width, grid_height), scale_resolution, patch_size, allow_upscale); // (new line) => fixes conversion for make_tuple to make_pair + int best_grid_width, best_grid_height; + std::tie(best_grid_width, best_grid_height) = best_grid_size; + + // std::pair refine_size = std::make_tuple(best_grid_width * grid_x, best_grid_height * grid_y); (old line) + std::pair refine_size = std::make_pair(best_grid_width * grid_x, best_grid_height * grid_y); // (new line) + return refine_size; +} + +static std::pair uhd_best_grid(const int max_slice_nums, const int multiple, const float log_ratio) { + std::vector candidate_split_grids_nums; + for (int i : {multiple - 1, multiple, multiple + 1}) { + if (i == 1 || i > max_slice_nums) { + continue; + } + candidate_split_grids_nums.push_back(i); + } + + std::vector> candidate_grids; + for (int split_grids_nums : candidate_split_grids_nums) { + int m = 1; + while (m <= split_grids_nums) { + if (split_grids_nums % m == 0) { + candidate_grids.emplace_back(m, split_grids_nums / m); + } + ++m; + } + } + + std::pair best_grid{1, 1}; + float min_error = std::numeric_limits::infinity(); + for (const auto& grid : candidate_grids) { + float error = std::abs(log_ratio - std::log(1.0 * grid.first / grid.second)); + if (error < min_error) { + best_grid = grid; + min_error = error; + } + } + return best_grid; +} + +// inspired from LLaVA-UHD: +// -> https://arxiv.org/pdf/2403.11703 +// -> https://github.com/thunlp/LLaVA-UHD +// -> https://github.com/thunlp/LLaVA-UHD/blob/302301bc2175f7e717fb8548516188e89f649753/llava_uhd/train/llava-uhd/slice_logic.py#L118 +static std::vector> uhd_slice_image(const clip_image_u8 * img, const int max_slice_nums=9, const int scale_resolution=448, const int patch_size=14) { + const std::pair original_size={img->nx,img->ny}; + const int original_width = img->nx; + const int original_height = img->ny; + const float log_ratio = log(1.0*original_width/original_height); + const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution); + const int multiple = fmin(ceil(ratio), max_slice_nums); + + std::vector> images; + LOG_INF("%s: multiple %d\n", __func__, multiple); + images.push_back(std::vector()); + + if (multiple <= 1) { + auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size, true); + clip_image_u8 * source_image = clip_image_u8_init(); + bicubic_resize(*img, *source_image, best_size.first, best_size.second); + // source_image = image.resize(best_size, Image.Resampling.BICUBIC) + images[images.size()-1].push_back(source_image); + } + else if (multiple > 1) { + auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size); + clip_image_u8 * source_image = clip_image_u8_init(); + bicubic_resize(*img, *source_image, best_size.first, best_size.second); + // source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC) + LOG_INF("%s: image_size: %d %d; source_image size: %d %d\n", __func__, img->nx, img->ny, best_size.first, best_size.second); + images[images.size()-1].push_back(source_image); + + std::pair best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio); + LOG_INF("%s: image_size: %d %d; best_grid: %d %d\n", __func__, img->nx, img->ny, best_grid.first, best_grid.second); + + auto refine_size = uhd_get_refine_size(original_size, best_grid, scale_resolution, patch_size, true); + clip_image_u8 * refine_image = clip_image_u8_init(); + bicubic_resize(*img, *refine_image, refine_size.first, refine_size.second); + + LOG_INF("%s: refine_image_size: %d %d; refine_size: %d %d\n", __func__, refine_image->nx, refine_image->ny, refine_size.first, refine_size.second); + + // split_to_patches + int width = refine_image->nx; + int height = refine_image->ny; + int grid_x = int(width / best_grid.first); + int grid_y = int(height / best_grid.second); + for (int patches_i = 0, ic = 0; patches_i < height && ic < best_grid.second; patches_i += grid_y, ic += 1){ + images.push_back(std::vector()); + for(int patches_j = 0, jc = 0; patches_j < width && jc < best_grid.first; patches_j += grid_x, jc += 1){ + clip_image_u8 * patch = clip_image_u8_init(); + patch->nx = grid_x; + patch->ny = grid_y; + patch->buf.resize(3 * patch->nx * patch->ny); + for (int y = patches_i; y < patches_i + grid_y; ++y) { + for (int x = patches_j; x < patches_j + grid_x; ++x) { + const int i = 3 * (y * refine_image->nx + x); + const int j = 3 * ((y-patches_i) * patch->nx + (x-patches_j)); + patch->buf[j] = refine_image->buf[i]; + patch->buf[j+1] = refine_image->buf[i+1]; + patch->buf[j+2] = refine_image->buf[i+2]; + } + } + images[images.size()-1].push_back(patch); + } + } + clip_image_u8_free(refine_image); + } + return images; +} + +int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip) { + const int max_slice_nums=9; + const int scale_resolution=448; + const int original_width = ctx_clip->load_image_size->width; + const int original_height = ctx_clip->load_image_size->height; + const float log_ratio = log(1.0*original_width/original_height); + const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution); + const int multiple = fmin(ceil(ratio), max_slice_nums); + std::pair best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio); + return best_grid.first; +} + // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector // res_imgs memory is being allocated here, previous allocations will be freed if found -bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) { +bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) { + + if(clip_is_minicpmv(ctx)){ + int max_slice_nums = 9; + std::vector> imgs = uhd_slice_image(img, max_slice_nums); + res_imgs->size = 0; + for (size_t i = 0; i < imgs.size(); ++i){ + res_imgs->size += imgs[i].size(); + } + res_imgs->data = new clip_image_f32[res_imgs->size]; + int idx = 0; + for (size_t i = 0; i < imgs.size(); ++i) { + for (size_t j = 0; j < imgs[i].size(); ++j) { + LOG_DBG("%s: %d %d\n", __func__,imgs[i][j]->nx,imgs[i][j]->ny); + clip_image_f32 * res = clip_image_f32_init(); + normalize_image_u8_to_f32(imgs[i][j], res, ctx->image_mean, ctx->image_std); + res_imgs->data[idx++] = *res; + clip_image_f32_free(res); + } + } + for (size_t i = 0; i < imgs.size(); ++i) { + for (size_t j = 0; j < imgs[i].size(); ++j) { + if (imgs[i][j] != nullptr) { + clip_image_u8_free(imgs[i][j]); + } + } + } + return true; + } + else if (ctx->has_qwen2vl_merger) { + clip_image_u8 * resized = clip_image_u8_init(); + auto patch_size = clip_patch_size(ctx) * 2; + int nx = ceil((float)img->nx / patch_size) * patch_size; + int ny = ceil((float)img->ny / patch_size) * patch_size; + bicubic_resize(*img, *resized, nx, ny); + + res_imgs->data = new clip_image_f32[1]; + // clip_image_f32 * res = clip_image_f32_init(); + normalize_image_u8_to_f32(resized, res_imgs->data, ctx->image_mean, ctx->image_std); + // res_imgs->data[0] = *res; + res_imgs->size = 1; + + // clip_image_f32_free(res); + clip_image_u8_free(resized); + return true; + } + + if (ctx->has_glm_projector) { + res_imgs->size = 1; + res_imgs->data = new clip_image_f32[res_imgs->size]; + clip_image_u8 resized_image; + int32_t sz=ctx->vision_model.hparams.image_size; + bicubic_resize(*img, resized_image,sz,sz); + clip_image_f32 * res = clip_image_f32_init(); + //clip_image_save_to_bmp(resized_image, "resized.bmp"); + normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std); + res_imgs->data[0] = *res; + clip_image_f32_free(res); + return true; + } + bool pad_to_square = true; if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_ERR("This gguf file seems to have no vision encoder\n"); return false; } auto & params = ctx->vision_model.hparams; @@ -1506,11 +2202,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli pad_to_square = false; } // free the previous res_imgs if any set - if (res_imgs.size > 0) { + if (res_imgs->size > 0) { clip_image_f32_batch_free(res_imgs); } - res_imgs.data = nullptr; - res_imgs.size = 0; + res_imgs->data = nullptr; + res_imgs->size = 0; // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104) // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156 @@ -1565,16 +2261,16 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square patches.insert(patches.begin(), image_original_resize); // clip_image_f32_batch_init(patches.size()); - res_imgs.size = patches.size(); - res_imgs.data = new clip_image_f32[res_imgs.size]; + res_imgs->size = patches.size(); + res_imgs->data = new clip_image_f32[res_imgs->size]; int num=0; for (auto& patch : patches) { - normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std); + normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std); num++; } for (size_t i = 0; i < patches.size(); i++) { - // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny); + // LOG_DBG("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny); clip_image_u8_free(patches[i]); } @@ -1657,9 +2353,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli // } // res_imgs.push_back(res); - res_imgs.size = 1; - res_imgs.data = new clip_image_f32[res_imgs.size]; - res_imgs.data[0] = *res; + res_imgs->size = 1; + res_imgs->data = new clip_image_f32[res_imgs->size]; + res_imgs->data[0] = *res; clip_image_f32_free(res); return true; @@ -1673,11 +2369,22 @@ void clip_free(clip_ctx * ctx) { ggml_free(ctx->ctx_data); gguf_free(ctx->ctx_gguf); + ggml_backend_buffer_free(ctx->params_buffer); + ggml_backend_free(ctx->backend); + ggml_gallocr_free(ctx->compute_alloc); delete ctx; } size_t clip_embd_nbytes(const struct clip_ctx * ctx) { - return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float); + int extra_tokens = ctx->has_glm_projector ? 2 : 0; + return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float); +} + +size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) { + clip_image_f32 img; + img.nx = img_w; + img.ny = img_h; + return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float); } int32_t clip_image_size(const struct clip_ctx * ctx) { @@ -1701,20 +2408,128 @@ const int32_t * clip_image_grid(const struct clip_ctx * ctx) { } int clip_n_patches(const struct clip_ctx * ctx) { + clip_image_f32 img; + img.nx = ctx->vision_model.hparams.image_size; + img.ny = ctx->vision_model.hparams.image_size; + return clip_n_patches_by_img(ctx, &img); +} + +int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) { const auto & params = ctx->vision_model.hparams; int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size); - if (ctx->proj_type == PROJECTOR_TYPE_LDP) { + if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) { n_patches /= 4; + } else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) { + if (ctx->minicpmv_version == 2) { + n_patches = 96; + } + else if (ctx->minicpmv_version == 3) { + n_patches = 64; + } + else if (ctx->minicpmv_version == 4) { + n_patches = 64; + } + } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + int patch_size = params.patch_size * 2; + int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0); + int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0); + n_patches = x_patch * y_patch; } return n_patches; } +static std::vector>> get_1d_sincos_pos_embed_from_grid_new(int embed_dim, const std::vector> & pos) { + assert(embed_dim % 2 == 0); + int H = pos.size(); + int W = pos[0].size(); + + std::vector omega(embed_dim / 2); + for (int i = 0; i < embed_dim / 2; ++i) { + omega[i] = 1.0 / pow(10000.0, static_cast(i) / (embed_dim / 2)); + } + + std::vector>> emb(H, std::vector>(W, std::vector(embed_dim))); + for (int h = 0; h < H; ++h) { + for (int w = 0; w < W; ++w) { + for (int d = 0; d < embed_dim / 2; ++d) { + float out_value = pos[h][w] * omega[d]; + emb[h][w][d] = sin(out_value); + emb[h][w][d + embed_dim / 2] = cos(out_value); + } + } + } + + return emb; +} + +static std::vector>> get_2d_sincos_pos_embed_from_grid(int embed_dim, const std::vector>> & grid) { + assert(embed_dim % 2 == 0); + std::vector>> emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim / 2, grid[0]); // (H, W, D/2) + std::vector>> emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim / 2, grid[1]); // (H, W, D/2) + + int H = emb_h.size(); + int W = emb_h[0].size(); + std::vector>> emb(H, std::vector>(W, std::vector(embed_dim))); + + for (int h = 0; h < H; ++h) { + for (int w = 0; w < W; ++w) { + for (int d = 0; d < embed_dim / 2; ++d) { + emb[h][w][d] = emb_h[h][w][d]; + emb[h][w][d + embed_dim / 2] = emb_w[h][w][d]; + } + } + } + return emb; +} + +static std::vector> get_2d_sincos_pos_embed(int embed_dim, const std::pair image_size) { + int grid_h_size = image_size.first; + int grid_w_size = image_size.second; + + std::vector grid_h(grid_h_size); + std::vector grid_w(grid_w_size); + + for (int i = 0; i < grid_h_size; ++i) { + grid_h[i] = static_cast(i); + } + for (int i = 0; i < grid_w_size; ++i) { + grid_w[i] = static_cast(i); + } + + std::vector> grid(grid_h_size, std::vector(grid_w_size)); + for (int h = 0; h < grid_h_size; ++h) { + for (int w = 0; w < grid_w_size; ++w) { + grid[h][w] = grid_w[w]; + } + } + std::vector>> grid_2d = {grid, grid}; + for (int h = 0; h < grid_h_size; ++h) { + for (int w = 0; w < grid_w_size; ++w) { + grid_2d[0][h][w] = grid_h[h]; + grid_2d[1][h][w] = grid_w[w]; + } + } + + std::vector>> pos_embed_3d = get_2d_sincos_pos_embed_from_grid(embed_dim, grid_2d); + + int H = image_size.first; + int W = image_size.second; + std::vector> pos_embed_2d(H * W, std::vector(embed_dim)); + for (int h = 0; h < H; ++h) { + for (int w = 0; w < W; ++w) { + pos_embed_2d[w * H + h] = pos_embed_3d[h][w]; + } + } + + return pos_embed_2d; +} + bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_ERR("This gguf file seems to have no vision encoder\n"); return false; } @@ -1726,7 +2541,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_ERR("This gguf file seems to have no vision encoder\n"); return false; } @@ -1734,19 +2549,39 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima if (ctx->has_llava_projector) { GGML_ASSERT(batch_size == 1); // TODO: support multiple images } + if (ctx->has_minicpmv_projector) { + GGML_ASSERT(batch_size == 1); + } + if (ctx->has_glm_projector) { + GGML_ASSERT(batch_size == 1); + ggml_tensor * boi = ctx->vision_model.boi_w; + ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi)); + vec = (float*)(vec+ggml_nelements(boi)); //offset for boi + } // build the inference graph - ggml_cgraph * gf = clip_image_build_graph(ctx, imgs); + ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true); ggml_gallocr_alloc_graph(ctx->compute_alloc, gf); // set inputs const auto & model = ctx->vision_model; const auto & hparams = model.hparams; - const int image_size = hparams.image_size; + const int image_size = hparams.image_size; + int image_size_width = image_size; + int image_size_height = image_size; + if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) { + image_size_width = imgs->data[0].nx; + image_size_height = imgs->data[0].ny; + } const int patch_size = hparams.patch_size; - const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); - const int num_positions = num_patches + 1; + const int num_patches = ((image_size_width / patch_size) * (image_size_height / patch_size)); + const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); + if(ctx->load_image_size==nullptr){ + ctx->load_image_size= clip_image_size_init(); + } + const int pos_w = ctx->load_image_size->width/patch_size; + const int pos_h = ctx->load_image_size->height/patch_size; { struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); @@ -1755,7 +2590,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima for (size_t i = 0; i < imgs->size; i++) { const int nx = imgs->data[i].nx; const int ny = imgs->data[i].ny; - GGML_ASSERT(nx == image_size && ny == image_size); + if (!(ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger)) { + GGML_ASSERT(nx == image_size && ny == image_size); + } const int n = nx * ny; @@ -1772,63 +2609,144 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima ggml_backend_tensor_set(inp_raw, data, 0, ggml_nbytes(inp_raw)); free(data); } - - { - struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); - - void* zero_mem = malloc(ggml_nbytes(embeddings)); - memset(zero_mem, 0, ggml_nbytes(embeddings)); - ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); - free(zero_mem); - } - - { - struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); - - int* positions_data = (int*)malloc(ggml_nbytes(positions)); - for (int i = 0; i < num_positions; i++) { - positions_data[i] = i; + if (ctx->has_minicpmv_projector) { + { + // inspired from siglip: + // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit + // -> https://huggingface.co/HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit/blob/d66538faeba44480d0bfaa42145eef26f9423199/modeling_siglip.py#L316 + struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); + int* positions_data = (int*)malloc(ggml_nbytes(positions)); + int bucket_coords_h[1024]; + int bucket_coords_w[1024]; + for (int i = 0; i < pos_h; i++){ + bucket_coords_h[i] = std::floor(70.0*i/pos_h); + } + for (int i = 0; i < pos_w; i++){ + bucket_coords_w[i] = std::floor(70.0*i/pos_w); + } + for (int i = 0, id = 0; i < pos_h; i++){ + for (int j = 0; j < pos_w; j++){ + positions_data[id++] = bucket_coords_h[i]*70 + bucket_coords_w[j]; + } + } + ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); + free(positions_data); } - ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); - free(positions_data); - } - { - struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches"); - int* patches_data = (int*)malloc(ggml_nbytes(patches)); - for (int i = 0; i < num_patches; i++) { - patches_data[i] = i + 1; + { + // inspired from resampler of Qwen-VL: + // -> https://huggingface.co/Qwen/Qwen-VL/tree/main + // -> https://huggingface.co/Qwen/Qwen-VL/blob/0547ed36a86561e2e42fecec8fd0c4f6953e33c4/visual.py#L23 + struct ggml_tensor * pos_embed = ggml_graph_get_tensor(gf, "pos_embed"); + int embed_dim = 4096; + if (ctx->minicpmv_version == 2) { + embed_dim = 4096; + } + else if (ctx->minicpmv_version == 3) { + embed_dim = 3584; + } + else if (ctx->minicpmv_version == 4) { + embed_dim = 3584; + } + auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h)); + + float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed)); + for(int i=0;i < pos_w * pos_h; ++i){ + for(int j=0; j < embed_dim; ++j){ + pos_embed_data[i * embed_dim + j] = pos_embed_t[i][j]; + } + } + + ggml_backend_tensor_set(pos_embed, pos_embed_data, 0, ggml_nbytes(pos_embed)); + free(pos_embed_data); + } + } + else{ + { + if (ctx->has_class_embedding) { + struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); + + void* zero_mem = malloc(ggml_nbytes(embeddings)); + memset(zero_mem, 0, ggml_nbytes(embeddings)); + ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); + free(zero_mem); + } + } + + if (ctx->has_qwen2vl_merger) { + struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); + + const int pw = image_size_width / patch_size; + const int ph = image_size_height / patch_size; + int* positions_data = (int*)malloc(ggml_nbytes(positions)); + + int ptr = 0; + for (int y = 0; y < ph; y+=2) + { + for (int x = 0; x < pw; x+=2) + { + for (int dy = 0; dy < 2; dy++) { + for (int dx = 0; dx < 2; dx++) { + positions_data[ptr] = y + dy; + positions_data[num_patches + ptr] = x + dx; + positions_data[num_patches * 2 + ptr] = y + dy; + positions_data[num_patches * 3 + ptr] = x + dx; + ptr++; + } + } + } + } + + ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); + free(positions_data); + } + else { + struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions"); + + int* positions_data = (int*)malloc(ggml_nbytes(positions)); + for (int i = 0; i < num_positions; i++) { + positions_data[i] = i; + } + ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions)); + free(positions_data); + + if (!ctx->has_glm_projector) { + struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches"); + int* patches_data = (int*)malloc(ggml_nbytes(patches)); + for (int i = 0; i < num_patches; i++) { + patches_data[i] = i + 1; + } + ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); + free(patches_data); + } } - ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches)); - free(patches_data); } if (ggml_backend_is_cpu(ctx->backend)) { ggml_backend_cpu_set_n_threads(ctx->backend, n_threads); } -#ifdef GGML_USE_METAL - if (ggml_backend_is_metal(ctx->backend)) { - ggml_backend_metal_set_n_cb(ctx->backend, n_threads); - } -#endif - ggml_backend_graph_compute(ctx->backend, gf); // the last node is the embedding tensor - struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * embeddings = ggml_graph_node(gf, -1); // copy the embeddings to the location passed by the user ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings)); + if (ctx->has_glm_projector) { + //eoi + ggml_tensor * eoi = ctx->vision_model.eoi_w; + int offset = ggml_nelements(embeddings); + ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi)); + } + return true; } bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) { - ggml_type type = GGML_TYPE_Q4_1; - assert(itype < GGML_TYPE_COUNT); - type = static_cast(itype); + ggml_type type = static_cast(itype); auto * ctx_clip = clip_model_load(fname_inp, 2); @@ -1862,7 +2780,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i std::vector work(512); std::vector conv_buf(512); - std::vector hist_all(1 << 4, 0); size_t total_size_org = 0; size_t total_size_new = 0; @@ -1882,14 +2799,14 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i } } - // quantize only 2D tensors - quantize &= (ggml_n_dims(cur) == 2); + // quantize only 2D tensors and bigger than block size + quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type); if (quantize) { new_type = type; if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) { new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type - // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); + // LOG_ERR("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); } const size_t n_elms = ggml_nelements(cur); float * f32_data; @@ -1908,7 +2825,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i f32_data = (float *)conv_buf.data(); break; default: - printf("Please use an input file in f32 or f16\n"); + LOG_ERR("Please use an input file in f32 or f16\n"); + gguf_free(ctx_out); return false; } @@ -1917,48 +2835,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i } new_data = work.data(); - std::vector hist_cur(1 << 4, 0); - - switch (new_type) { - case GGML_TYPE_Q4_0: { - new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q4_1: { - new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_0: { - new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_1: { - new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q8_0: { - new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q2_K: { - new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q3_K: { - new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q4_K: { - new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q5_K: { - new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - case GGML_TYPE_Q6_K: { - new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data()); - } break; - default: { - fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type); - return false; - } - } - - for (size_t j = 0; j < hist_cur.size(); ++j) { - hist_all[j] += hist_cur[j]; - } + new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr); } else { new_type = cur->type; new_data = cur->data; @@ -1968,14 +2845,15 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i total_size_org += orig_size; total_size_new += new_size; gguf_set_tensor_type(ctx_out, name.c_str(), new_type); - gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size); + GGML_ASSERT(gguf_get_tensor_size(ctx_out, gguf_find_tensor(ctx_out, name.c_str())) == new_size); + gguf_set_tensor_data(ctx_out, name.c_str(), new_data); fout.write((const char *)new_data, new_size); size_t pad = GGML_PAD(new_size, gguf_get_alignment(ctx_out)) - new_size; for (size_t j = 0; j < pad; ++j) { fout.put(0); } - printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize, + LOG_INF("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize, orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); } @@ -1991,19 +2869,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i gguf_free(ctx_out); { - printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); - printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); - - int64_t sum_all = 0; - for (size_t i = 0; i < hist_all.size(); ++i) { - sum_all += hist_all[i]; - } - - printf("%s: hist: ", __func__); - for (size_t i = 0; i < hist_all.size(); ++i) { - printf("%5.3f ", hist_all[i] / (float)sum_all); - } - printf("\n"); + LOG_INF("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); + LOG_INF("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); } return true; @@ -2013,13 +2880,61 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { if (ctx->proj_type == PROJECTOR_TYPE_LDP) { return ctx->vision_model.mm_model_block_1_block_2_1_b->ne[0]; } + if (ctx->proj_type == PROJECTOR_TYPE_LDPV2) { + return ctx->vision_model.mm_model_peg_0_b->ne[0]; + } if (ctx->proj_type == PROJECTOR_TYPE_MLP) { return ctx->vision_model.mm_2_b->ne[0]; } if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { return ctx->vision_model.mm_3_b->ne[0]; } + if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) { + if (ctx->minicpmv_version == 2) { + return 4096; + } + else if (ctx->minicpmv_version == 3) { + return 3584; + } + else if (ctx->minicpmv_version == 4) { + return 3584; + } + } + if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE){ + return ctx->vision_model.mm_model_mlp_3_w->ne[1]; + } + if (ctx->proj_type == PROJECTOR_TYPE_MERGER) { + return ctx->vision_model.mm_1_b->ne[0]; + } std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str())); } + +int clip_is_minicpmv(const struct clip_ctx * ctx) { + if (ctx->has_minicpmv_projector) { + return ctx->minicpmv_version; + } + return 0; +} + +bool clip_is_glm(const struct clip_ctx * ctx) { + return ctx->has_glm_projector; +} +bool clip_is_qwen2vl(const struct clip_ctx * ctx) { + return ctx->has_qwen2vl_merger; +} + + +bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) { + clip_image_f32 clip_img; + clip_img.buf.resize(h * w * 3); + for (int i = 0; i < h*w*3; i++) + { + clip_img.buf[i] = img[i]; + } + clip_img.nx = w; + clip_img.ny = h; + clip_image_encode(ctx, n_threads, &clip_img, vec); + return true; +} diff --git a/examples/llava/clip.h b/examples/llava/clip.h index e5bd54924..841b4f6f9 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -18,14 +18,17 @@ # define CLIP_API #endif -struct clip_ctx; - #ifdef __cplusplus extern "C" { #endif struct clip_ctx; +struct clip_image_size { + int width; + int height; +}; + struct clip_image_u8_batch { struct clip_image_u8 * data; size_t size; @@ -42,6 +45,7 @@ CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity CLIP_API void clip_free(struct clip_ctx * ctx); CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx); +CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w); CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx); CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx); @@ -52,24 +56,30 @@ CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx); CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx); -CLIP_API int clip_n_patches (const struct clip_ctx * ctx); -CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx); +CLIP_API int clip_n_patches (const struct clip_ctx * ctx); +CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img); +CLIP_API int clip_n_mmproj_embd (const struct clip_ctx * ctx); +CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip); +CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size); +CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip); + +CLIP_API struct clip_image_size * clip_image_size_init(); CLIP_API struct clip_image_u8 * clip_image_u8_init (); CLIP_API struct clip_image_f32 * clip_image_f32_init(); CLIP_API void clip_image_u8_free (struct clip_image_u8 * img); CLIP_API void clip_image_f32_free(struct clip_image_f32 * img); -CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch & batch); -CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch); +CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch * batch); +CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch); CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img); /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); -/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */ -CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs ); +/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */ +CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); @@ -78,6 +88,13 @@ CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, cons CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype); +CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx); +CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx); + +CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec); + +CLIP_API bool clip_is_glm(const struct clip_ctx * ctx); + #ifdef __cplusplus } #endif diff --git a/examples/llava/convert-image-encoder-to-gguf.py b/examples/llava/convert_image_encoder_to_gguf.py similarity index 96% rename from examples/llava/convert-image-encoder-to-gguf.py rename to examples/llava/convert_image_encoder_to_gguf.py index c69f89ac2..4fa1d6cea 100644 --- a/examples/llava/convert-image-encoder-to-gguf.py +++ b/examples/llava/convert_image_encoder_to_gguf.py @@ -1,6 +1,7 @@ import argparse import os import json +import re import torch import numpy as np @@ -38,9 +39,11 @@ def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: b def get_tensor_name(name: str) -> str: if "projection" in name: return name - if "mm_projector" in name: - return name.replace("model.mm_projector", "mm") + name = name.replace("model.mm_projector", "mm") + name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1) + name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1) + return name return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln") @@ -83,7 +86,7 @@ ap.add_argument("--clip-model-is-vision", action="store_true", required=False, ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, help="The clip model is from openclip (for ViT-SO400M type))") ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") -ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp", choices=["mlp", "ldp"], default="mlp") +ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp") ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) # Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 # Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 @@ -182,6 +185,8 @@ else: fout.add_description("two-tower CLIP model") if has_text_encoder: + assert t_hparams is not None + assert tokens is not None # text_model hparams fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"]) fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"]) @@ -256,8 +261,8 @@ if has_vision_encoder: if processor is not None: - image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean - image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std + image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean # pyright: ignore[reportAttributeAccessIssue] + image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std # pyright: ignore[reportAttributeAccessIssue] else: image_mean = args.image_mean if args.image_mean is not None else default_image_mean image_std = args.image_std if args.image_std is not None else default_image_std diff --git a/examples/llava/glmedge-convert-image-encoder-to-gguf.py b/examples/llava/glmedge-convert-image-encoder-to-gguf.py new file mode 100644 index 000000000..848ef1cf3 --- /dev/null +++ b/examples/llava/glmedge-convert-image-encoder-to-gguf.py @@ -0,0 +1,280 @@ +import argparse +import os +import json +import re + +import torch +import numpy as np +from gguf import * + +TEXT = "clip.text" +VISION = "clip.vision" +from transformers import SiglipVisionModel, SiglipVisionConfig + +def k(raw_key: str, arch: str) -> str: + return raw_key.format(arch=arch) + + +def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool: + if name in ( + "logit_scale", + "text_model.embeddings.position_ids", + "vision_model.embeddings.position_ids", + ): + return True + + if name in ( + "vision_model.head.probe", + "vision_model.head.attention.in_proj_weight", + "vision_model.head.attention.in_proj_bias", + "vision_model.head.attention.out_proj.weight", + "vision_model.head.attention.out_proj.bias", + "vision_model.head.layernorm.weight", + "vision_model.head.layernorm.bias", + "vision_model.head.mlp.fc1.weight", + "vision_model.head.mlp.fc1.bias", + "vision_model.head.mlp.fc2.weight", + "vision_model.head.mlp.fc2.bias" + ): + return True + + if name.startswith("v") and not has_vision: + return True + + if name.startswith("t") and not has_text: + return True + + return False + + +def get_tensor_name(name: str) -> str: + if "projection" in name: + return name + if "mm_projector" in name: + name = name.replace("model.mm_projector", "mm") + name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1) + name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1) + return name + + return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln") + + +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = ( + list(range(ord("!"), ord("~") + 1)) + + list(range(ord("¡"), ord("¬") + 1)) + + list(range(ord("®"), ord("ÿ") + 1)) + ) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +ap = argparse.ArgumentParser() +ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True) +ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16") +ap.add_argument("--text-only", action="store_true", required=False, + help="Save a text-only model. It can't be used to encode images") +ap.add_argument("--vision-only", action="store_true", required=False, + help="Save a vision-only model. It can't be used to encode texts") +ap.add_argument("--clip-model-is-vision", action="store_true", required=False, + help="The clip model is a pure vision model (ShareGPT4V vision extract for example)") +ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, + help="The clip model is from openclip (for ViT-SO400M type))") +ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.") +ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter") +ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) +# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 +# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 +default_image_mean = [0.5, 0.5, 0.5] +default_image_std = [0.5, 0.5, 0.5] +ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None) +ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None) + +# with proper +args = ap.parse_args() + + +if args.text_only and args.vision_only: + print("--text-only and --image-only arguments cannot be specified at the same time.") + exit(1) + +if args.use_f32: + print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.") + +# output in the same directory as the model if output_dir is None +dir_model = args.model_dir + +if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip: + vocab = None + tokens = None +else: + with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f: + vocab = json.load(f) + tokens = [key for key in vocab] + +with open(dir_model + "/config.json", "r", encoding="utf-8") as f: + config = json.load(f) + if args.clip_model_is_vision: + v_hparams = config + t_hparams = None + else: + v_hparams = config["vision_config"] + t_hparams = None + +# possible data types +# ftype == 0 -> float32 +# ftype == 1 -> float16 +# +# map from ftype to string +ftype_str = ["f32", "f16"] + +ftype = 1 +if args.use_f32: + ftype = 0 + +vision_config = SiglipVisionConfig(**v_hparams) +model = SiglipVisionModel(vision_config) +model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip"))) + +fname_middle = None +has_text_encoder = False +has_vision_encoder = True +has_glm_projector = True +if args.text_only: + fname_middle = "text-" + has_vision_encoder = False +elif args.llava_projector is not None: + fname_middle = "mmproj-" + has_text_encoder = False + has_glm_projector = True +elif args.vision_only: + fname_middle = "vision-" + has_text_encoder = False +else: + fname_middle = "" + +output_dir = args.output_dir if args.output_dir is not None else dir_model +os.makedirs(output_dir, exist_ok=True) +output_prefix = os.path.basename(output_dir).replace("ggml_", "") +fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf") +fout = GGUFWriter(path=fname_out, arch="clip") + +fout.add_bool("clip.has_text_encoder", has_text_encoder) +fout.add_bool("clip.has_vision_encoder", has_vision_encoder) +fout.add_bool("clip.has_glm_projector", has_glm_projector) +fout.add_file_type(ftype) +model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model) +fout.add_name(model_name) +if has_glm_projector: + fout.add_description("image encoder for glm4v") + fout.add_string("clip.projector_type", "adapter") +else: + fout.add_description("two-tower CLIP model") + +if has_text_encoder: + assert t_hparams is not None + assert tokens is not None + # text_model hparams + fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"]) + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"]) + fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"]) + fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"])) + fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"]) + fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"]) + fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"]) + fout.add_token_list(tokens) + +if has_vision_encoder: + # vision_model hparams + fout.add_uint32("clip.vision.image_size", v_hparams["image_size"]) + fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"]) + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"]) + fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"]) + fout.add_uint32("clip.vision.projection_dim", 0) + fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"]) + fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) + fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"]) + + image_mean = args.image_mean if args.image_mean is not None else default_image_mean + image_std = args.image_std if args.image_std is not None else default_image_std + fout.add_array("clip.vision.image_mean", image_mean) + fout.add_array("clip.vision.image_std", image_std) + +fout.add_bool("clip.use_gelu", True) + + +if has_glm_projector: + # model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue] + projector = torch.load(args.llava_projector) + for name, data in projector.items(): + name = get_tensor_name(name) + # pw and dw conv ndim==4 + if data.ndim == 2 or data.ndim == 4: + data = data.squeeze().numpy().astype(np.float16) + else: + data = data.squeeze().numpy().astype(np.float32) + if name.startswith("vision."): + name=name.replace("vision.","") + fout.add_tensor(name, data) + print(f"Projector {name} - {data.dtype} - shape = {data.shape}") + # print(f"Projector {name} tensors added\n") + +state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue] +for name, data in state_dict.items(): + if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector): + # we don't need this + print(f"skipping parameter: {name}") + continue + + name = get_tensor_name(name) + data = data.squeeze().numpy() + + n_dims = len(data.shape) + + # ftype == 0 -> float32, ftype == 1 -> float16 + ftype_cur = 0 + if n_dims == 4: + print(f"tensor {name} is always saved in f16") + data = data.astype(np.float16) + ftype_cur = 1 + elif ftype == 1: + if name[-7:] == ".weight" and n_dims == 2: + # print(" Converting to float16") + data = data.astype(np.float16) + ftype_cur = 1 + else: + # print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + else: + if data.dtype != np.float32: + # print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + print(f"siglip {name} - {data.dtype} - shape = {data.shape}") + # print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}") + fout.add_tensor(name, data) + + +fout.write_header_to_file() +fout.write_kv_data_to_file() +fout.write_tensors_to_file() +fout.close() + +print("Done. Output file: " + fname_out) diff --git a/examples/llava/glmedge-surgery.py b/examples/llava/glmedge-surgery.py new file mode 100644 index 000000000..16bb915d0 --- /dev/null +++ b/examples/llava/glmedge-surgery.py @@ -0,0 +1,33 @@ +import argparse +import os +import torch +from transformers import AutoModel + +ap = argparse.ArgumentParser() +ap.add_argument("-m", "--model", help="Path to GLM model") +args = ap.parse_args() + +# find the model part that includes the the multimodal projector weights +model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True) +checkpoint = model.state_dict() + +# get a list of mm tensor names +mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")] + +# store these tensors in a new dictionary and torch.save them +projector = {name: checkpoint[name].float() for name in mm_tensors} +torch.save(projector, f"{args.model}/glm.projector") + +clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")] +if len(clip_tensors) > 0: + clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors} + torch.save(clip, f"{args.model}/glm.clip") + + # added tokens should be removed to be able to convert Mistral models + if os.path.exists(f"{args.model}/added_tokens.json"): + with open(f"{args.model}/added_tokens.json", "w") as f: + f.write("{}\n") + +print("Done!") +print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") +print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.") diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index e29da6cb2..40aa0876f 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -1,13 +1,16 @@ -#include "ggml.h" +#include "arg.h" +#include "base64.hpp" +#include "log.h" #include "common.h" +#include "sampling.h" #include "clip.h" #include "llava.h" #include "llama.h" - -#include "base64.hpp" +#include "ggml.h" #include #include +#include #include static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past) { @@ -17,8 +20,8 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector n_batch) { n_eval = n_batch; } - if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) { - fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { + LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); return false; } *n_past += n_eval; @@ -34,21 +37,25 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ std::string str2 = str; - std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true); + std::vector embd_inp = common_tokenize(ctx_llama, str2, add_bos, true); eval_tokens(ctx_llama, embd_inp, n_batch, n_past); return true; } -static const char * sample(struct llama_sampling_context * ctx_sampling, +static const char * sample(struct common_sampler * smpl, struct llama_context * ctx_llama, int * n_past) { - const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL); - llama_sampling_accept(ctx_sampling, ctx_llama, id, true); + const llama_token id = common_sampler_sample(smpl, ctx_llama, -1); + common_sampler_accept(smpl, id, true); + + const llama_model * model = llama_get_model(ctx_llama); + const llama_vocab * vocab = llama_model_get_vocab(model); + static std::string ret; - if (id == llama_token_eos(llama_get_model(ctx_llama))) { + if (llama_vocab_is_eog(vocab, id)) { ret = ""; } else { - ret = llama_token_to_piece(ctx_llama, id); + ret = common_token_to_piece(ctx_llama, id); } eval_id(ctx_llama, id, n_past); return ret.c_str(); @@ -73,7 +80,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip size_t img_base64_str_start, img_base64_str_end; find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end); if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) { - fprintf(stderr, "%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); + LOG_ERR("%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); return NULL; } @@ -87,7 +94,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); if (!embed) { - fprintf(stderr, "%s: could not load image from base64 string.\n", __func__); + LOG_ERR("%s: could not load image from base64 string.\n", __func__); return NULL; } @@ -111,30 +118,31 @@ struct llava_context { struct llama_model * model = NULL; }; -static void show_additional_info(int /*argc*/, char ** argv) { - fprintf(stderr, "\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n"); +static void print_usage(int, char ** argv) { + LOG("\n example usage:\n"); + LOG("\n %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n"); } -static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { +static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) { // load and preprocess the image llava_image_embed * embed = NULL; auto prompt = params->prompt; if (prompt_contains_image(prompt)) { if (!params->image.empty()) { - fprintf(stderr, "using base64 encoded image instead of command line image path\n"); + LOG_INF("using base64 encoded image instead of command line image path\n"); } - embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt); + embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt); if (!embed) { - fprintf(stderr, "%s: can't load image from prompt\n", __func__); + LOG_ERR("%s: can't load image from prompt\n", __func__); return NULL; } params->prompt = remove_image_from_prompt(prompt); } else { - embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str()); + embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str()); if (!embed) { - fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str()); + fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str()); return NULL; } } @@ -142,11 +150,10 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para return embed; } -static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, gpt_params * params, const std::string & prompt) { +static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) { int n_past = 0; const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama)); std::string system_prompt, user_prompt; size_t image_pos = prompt.find(""); @@ -154,18 +161,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ // new templating mode: Provide the full prompt including system message and use as a placeholder for the image system_prompt = prompt.substr(0, image_pos); user_prompt = prompt.substr(image_pos + std::string("").length()); - printf("system_prompt: %s\n", system_prompt.c_str()); + LOG_INF("system_prompt: %s\n", system_prompt.c_str()); if (params->verbose_prompt) { - auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); + auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } - printf("user_prompt: %s\n", user_prompt.c_str()); + LOG_INF("user_prompt: %s\n", user_prompt.c_str()); if (params->verbose_prompt) { - auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } } else { @@ -173,29 +180,34 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:"; user_prompt = prompt + "\nASSISTANT:"; if (params->verbose_prompt) { - auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } } - eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos); + eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true); llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past); eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false); // generate the response - fprintf(stderr, "\n"); + LOG("\n"); + + 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); + } - struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); std::string response = ""; for (int i = 0; i < max_tgt_len; i++) { - const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); + const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past); response += tmp; if (strcmp(tmp, "") == 0) break; if (strstr(tmp, "###")) break; // Yi-VL behavior - printf("%s", tmp); + LOG("%s", tmp); if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works) if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6 if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6 @@ -203,12 +215,25 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ fflush(stdout); } - llama_sampling_free(ctx_sampling); - printf("\n"); + common_sampler_free(smpl); + LOG("\n"); } +static struct llama_model * llava_init(common_params * params) { + llama_backend_init(); + llama_numa_init(params->numa); -static struct llava_context * llava_init(gpt_params * params) { + llama_model_params model_params = common_model_params_to_llama(*params); + + llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params); + if (model == NULL) { + LOG_ERR("%s: unable to load model\n" , __func__); + return NULL; + } + return model; +} + +static struct llava_context * llava_init_context(common_params * params, llama_model * model) { const char * clip_path = params->mmproj.c_str(); auto prompt = params->prompt; @@ -218,28 +243,17 @@ static struct llava_context * llava_init(gpt_params * params) { auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); - llama_backend_init(); - llama_numa_init(params->numa); - - llama_model_params model_params = llama_model_params_from_gpt_params(*params); - - llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); - if (model == NULL) { - fprintf(stderr , "%s: error: unable to load model\n" , __func__); - return NULL; - } - - llama_context_params ctx_params = llama_context_params_from_gpt_params(*params); + llama_context_params ctx_params = common_context_params_to_llama(*params); ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings - llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); + llama_context * ctx_llama = llama_init_from_model(model, ctx_params); if (ctx_llama == NULL) { - fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + LOG_ERR("%s: failed to create the llama_context\n" , __func__); return NULL; } - auto ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); + auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); ctx_llava->ctx_llama = ctx_llama; ctx_llava->ctx_clip = ctx_clip; @@ -254,42 +268,65 @@ static void llava_free(struct llava_context * ctx_llava) { } llama_free(ctx_llava->ctx_llama); - llama_free_model(ctx_llava->model); + llama_model_free(ctx_llava->model); llama_backend_free(); } int main(int argc, char ** argv) { ggml_time_init(); - gpt_params params; + common_params params; - if (!gpt_params_parse(argc, argv, params)) { - show_additional_info(argc, argv); + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) { return 1; } + + common_init(); + if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { - gpt_print_usage(argc, argv, params); - show_additional_info(argc, argv); + print_usage(argc, argv); return 1; } - auto ctx_llava = llava_init(¶ms); - if (ctx_llava == NULL) { - fprintf(stderr, "%s: error: failed to init llava\n", __func__); + auto * model = llava_init(¶ms); + if (model == NULL) { + fprintf(stderr, "%s: error: failed to init llava model\n", __func__); return 1; } - auto image_embed = load_image(ctx_llava, ¶ms); - if (!image_embed) { - return 1; + if (prompt_contains_image(params.prompt)) { + auto * ctx_llava = llava_init_context(¶ms, model); + + auto * image_embed = load_image(ctx_llava, ¶ms, ""); + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_perf_context_print(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); + } else { + for (auto & image : params.image) { + auto * ctx_llava = llava_init_context(¶ms, model); + + auto * image_embed = load_image(ctx_llava, ¶ms, image); + if (!image_embed) { + LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str()); + return 1; + } + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_perf_context_print(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); + } } - // process the prompt - process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + llama_model_free(model); - llama_print_timings(ctx_llava->ctx_llama); - - llava_image_embed_free(image_embed); - llava_free(ctx_llava); return 0; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 980128166..300714045 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -1,13 +1,27 @@ #include "clip.h" -#include "common.h" -#include "llama.h" #include "llava.h" -#include "base64.hpp" +#include "llama.h" + +#include +#include #include #include +#include +#include #include -#include + +#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 { @@ -54,7 +68,7 @@ static std::pair select_best_resolution(const std::pair& ori int downscaled_height = static_cast(original_height * scale); int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); int wasted_resolution = (width * height) - effective_resolution; - // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + // LOG_DBG("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { max_effective_resolution = effective_resolution; min_wasted_resolution = wasted_resolution; @@ -88,7 +102,6 @@ static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair< // Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out) static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) { struct { - struct ggml_tensor * newline; struct ggml_context * ctx; } model; @@ -150,20 +163,6 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector model.ctx = ggml_init(params); - ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip); - model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]); - if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) { - if (newline_tmp->buffer == NULL) { - printf("newline_tmp tensor buffer is NULL\n"); - } - ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp)); - } else { - model.newline->data = newline_tmp->data; - if (model.newline->data == NULL) { - printf("newline_tmp tensor data is NULL\n"); - } - } - struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4 // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false); // fill it with the image embeddings, ignoring the base @@ -199,7 +198,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false); ggml_build_forward_expand(gf, flatten); ggml_graph_compute_with_ctx(model.ctx, gf, 1); - struct ggml_tensor* result = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor* result = ggml_graph_node(gf, -1); memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context // append without newline tokens (default behavior in llava_arch when not using unpad ): @@ -217,14 +216,41 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector return true; } +static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) { + int width = image->nx; + int height = image->ny; + int num_patches = (height / patch_size) * (width / patch_size); + clip_image_f32 * patch = clip_image_f32_init(); + patch->nx = patch_size * num_patches; + patch->ny = patch_size; + patch->buf.resize(3 * patch->nx * patch->ny); + + int patch_index = 0; + + for (int i = 0; i < height; i += patch_size) { + for (int j = 0; j < width; j += patch_size) { + for (int pi = 0; pi < patch_size; ++pi) { + for (int pj = 0; pj < patch_size; ++pj) { + int input_index = ((i + pi) * width + (j + pj)) * 3; + int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3; + patch->buf[output_index] = image->buf[input_index]; + patch->buf[output_index+1] = image->buf[input_index+1]; + patch->buf[output_index+2] = image->buf[input_index+2]; + } + } + patch_index++; + } + } + return patch; +} static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) { // std::vector img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336 clip_image_f32_batch img_res_v; img_res_v.size = 0; img_res_v.data = nullptr; - if (!clip_image_preprocess(ctx_clip, img, img_res_v)) { - fprintf(stderr, "%s: unable to preprocess image\n", __func__); + if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) { + LOG_ERR("%s: unable to preprocess image\n", __func__); delete[] img_res_v.data; return false; } @@ -233,17 +259,84 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip); - if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) { + if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) { + std::vector image_embd_v; + image_embd_v.resize(img_res_v.size); + struct clip_image_size * load_image_size = clip_image_size_init(); + + for (size_t i = 0; i < img_res_v.size; i++) { + const int64_t t_img_enc_step_start_us = ggml_time_us(); + image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny)); + int patch_size=14; + load_image_size->width = img_res_v.data[i].nx; + load_image_size->height = img_res_v.data[i].ny; + clip_add_load_image_size(ctx_clip, load_image_size); + + bool encoded = false; + if (clip_is_qwen2vl(ctx_clip)) { + encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); + } + else { + encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]); + } + + if (!encoded) { + LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); + return false; + } + const int64_t t_img_enc_steop_batch_us = ggml_time_us(); + LOG_INF("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)img_res_v.size, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0); + } + const int64_t t_img_enc_batch_us = ggml_time_us(); + LOG_INF("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); + + int n_img_pos_out = 0; + for (size_t i = 0; i < image_embd_v.size(); i++) { + std::memcpy( + image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), + image_embd_v[i], + clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny)); + n_img_pos_out += clip_n_patches_by_img(ctx_clip, &img_res_v.data[i]); + } + *n_img_pos = n_img_pos_out; + for (size_t i = 0; i < image_embd_v.size(); i++) { + free(image_embd_v[i]); + } + image_embd_v.clear(); + load_image_size->width = img->nx; + load_image_size->height = img->ny; + clip_add_load_image_size(ctx_clip, load_image_size); + LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height); + delete[] img_res_v.data; + img_res_v.size = 0; + img_res_v.data = nullptr; + } + else if (clip_is_glm(ctx_clip)){ + struct clip_image_size * load_image_size = clip_image_size_init(); + load_image_size->width = img_res_v.data[0].nx; + load_image_size->height = img_res_v.data[0].ny; + clip_add_load_image_size(ctx_clip, load_image_size); + + bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); + int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2); + *n_img_pos = (pos * pos + 2); + if (!encoded){ + LOG_ERR("Unable to encode image \n"); + return false; + } + } + else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) { // flat / default llava-1.5 type embedding *n_img_pos = clip_n_patches(ctx_clip); bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096 delete[] img_res_v.data; if (!encoded) { - fprintf(stderr, "Unable to encode image\n"); + LOG_ERR("Unable to encode image\n"); return false; } - } else { + } + else { // spatial_unpad llava-1.6 type embedding // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working std::vector image_embd_v; @@ -252,12 +345,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184 const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside if (!encoded) { - fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); + LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); return false; } } const int64_t t_img_enc_batch_us = ggml_time_us(); - printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); + LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); const int32_t * image_grid = clip_image_grid(ctx_clip); @@ -290,37 +383,50 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli // clip_image_save_to_bmp(*tmp, "image_feature.bmp"); } - printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); + LOG_INF("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); const int64_t t_img_enc_end_us = ggml_time_us(); float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos); + LOG_INF("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos); return true; } bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) { // make sure that the correct mmproj was used, i.e., compare apples to apples - int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); + int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama)); auto n_image_embd = clip_n_mmproj_embd(ctx_clip); if (n_image_embd != n_llama_embd) { - printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); + LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); return false; } return true; } bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { - float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model + int num_max_patches = 6; + if (clip_is_minicpmv(ctx_clip)) { + num_max_patches = 10; + } + if (clip_is_glm(ctx_clip)) { + num_max_patches = 1; + } + float * image_embd; + if (clip_is_qwen2vl(ctx_clip)) { + // qwen2vl don't split image into chunks, so `num_max_patches` is not needed. + image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny)); + } else { + image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model + } if (!image_embd) { - fprintf(stderr, "Unable to allocate memory for image embeddings\n"); + LOG_ERR("Unable to allocate memory for image embeddings\n"); return false; } int n_img_pos; if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) { - fprintf(stderr, "%s: cannot encode image, aborting\n", __func__); + LOG_ERR("%s: cannot encode image, aborting\n", __func__); free(image_embd); return false; } @@ -330,17 +436,51 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co return true; } +struct llava_embd_batch { + std::vector pos; + std::vector n_seq_id; + std::vector seq_id_0; + std::vector seq_ids; + std::vector logits; + llama_batch batch; + llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) { + pos .resize(n_tokens); + n_seq_id.resize(n_tokens); + seq_ids .resize(n_tokens + 1); + logits .resize(n_tokens); + seq_id_0.resize(1); + seq_id_0[0] = seq_id; + seq_ids [n_tokens] = nullptr; + batch = { + /*n_tokens =*/ n_tokens, + /*tokens =*/ nullptr, + /*embd =*/ embd, + /*pos =*/ pos.data(), + /*n_seq_id =*/ n_seq_id.data(), + /*seq_id =*/ seq_ids.data(), + /*logits =*/ logits.data(), + }; + for (int i = 0; i < n_tokens; i++) { + batch.pos [i] = pos_0 + i; + batch.n_seq_id[i] = 1; + batch.seq_id [i] = seq_id_0.data(); + batch.logits [i] = false; + } + } +}; + bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) { - int n_embd = llama_n_embd(llama_get_model(ctx_llama)); + int n_embd = llama_model_n_embd(llama_get_model(ctx_llama)); for (int i = 0; i < image_embed->n_image_pos; i += n_batch) { int n_eval = image_embed->n_image_pos - i; if (n_eval > n_batch) { n_eval = n_batch; } - llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; - if (llama_decode(ctx_llama, batch)) { - fprintf(stderr, "%s : failed to eval\n", __func__); + float * embd = image_embed->embed+i*n_embd; + llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0); + if (llama_decode(ctx_llama, llava_batch.batch)) { + LOG_ERR("%s : failed to eval\n", __func__); return false; } *n_past += n_eval; @@ -352,7 +492,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c clip_image_u8 * img = clip_image_u8_init(); if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { clip_image_u8_free(img); - fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__); + LOG_ERR("%s: can't load image from bytes, is it a valid image?", __func__); return NULL; } @@ -361,7 +501,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); if (!image_embed_result) { clip_image_u8_free(img); - fprintf(stderr, "%s: coulnd't embed the image\n", __func__); + LOG_ERR("%s: couldn't embed the image\n", __func__); return NULL; } @@ -375,7 +515,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) { auto file = fopen(path, "rb"); if (file == NULL) { - fprintf(stderr, "%s: can't read file %s\n", __func__, path); + LOG_ERR("%s: can't read file %s\n", __func__, path); return false; } @@ -385,7 +525,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data if (buffer == NULL) { - fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); + LOG_ERR("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); perror("Memory allocation error"); fclose(file); return false; @@ -393,10 +533,16 @@ 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 @@ -410,7 +556,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx long image_bytes_length; auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length); if (!loaded) { - fprintf(stderr, "%s: failed to load %s\n", __func__, image_path); + LOG_ERR("%s: failed to load %s\n", __func__, image_path); return NULL; } diff --git a/examples/llava/llava.h b/examples/llava/llava.h index 2d40f3f1d..b6feb3027 100644 --- a/examples/llava/llava.h +++ b/examples/llava/llava.h @@ -17,28 +17,27 @@ # define LLAVA_API #endif -struct clip_ctx; - #ifdef __cplusplus extern "C" { #endif +struct clip_ctx; struct llava_image_embed { float * embed; int n_image_pos; }; /** sanity check for clip <-> llava embed size match */ -LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip); +LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip); -LLAVA_API bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out); +LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out); /** build an image embed from image file bytes */ LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length); /** build an image embed from a path to an image filename */ LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path); -LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); /** free an embedding made with llava_image_embed_make_* */ +LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */ LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past); diff --git a/examples/llava/llava-surgery.py b/examples/llava/llava_surgery.py similarity index 100% rename from examples/llava/llava-surgery.py rename to examples/llava/llava_surgery.py diff --git a/examples/llava/llava-surgery-v2.py b/examples/llava/llava_surgery_v2.py similarity index 94% rename from examples/llava/llava-surgery-v2.py rename to examples/llava/llava_surgery_v2.py index eb56d6988..2d5b32fe6 100644 --- a/examples/llava/llava-surgery-v2.py +++ b/examples/llava/llava_surgery_v2.py @@ -2,7 +2,9 @@ import argparse import glob import os import torch -from safetensors.torch import load as safe_load, save as safe_save, safe_open, save_file +from safetensors import safe_open +from safetensors.torch import save_file +from typing import Any, ContextManager, cast # Function to determine if file is a SafeTensor file def is_safetensor_file(file_path): @@ -13,7 +15,7 @@ def is_safetensor_file(file_path): def load_model(file_path): if is_safetensor_file(file_path): tensors = {} - with safe_open(file_path, framework="pt", device="cpu") as f: + with cast(ContextManager[Any], safe_open(file_path, framework="pt", device="cpu")) as f: for key in f.keys(): tensors[key] = f.get_tensor(key).clone() # output shape @@ -134,7 +136,7 @@ if len(mm_tensors) == 0: if last_checkpoint is not None: for k, v in last_checkpoint.items(): print(k) - print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint)} tensors.") + print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint) if last_checkpoint is not None else 0} tensors.") print("No tensors found. Is this a LLaVA model?") exit() @@ -143,8 +145,10 @@ print(f"Found additional {len(first_mm_tensors)} tensors to extract.") # projector = {name: checkpoint.[name].float() for name in mm_tensors} projector = {} for name in mm_tensors: + assert last_checkpoint is not None projector[name] = last_checkpoint[name].float() for name in first_mm_tensors: + assert first_checkpoint is not None projector[name] = first_checkpoint[name].float() if len(projector) > 0: diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp new file mode 100644 index 000000000..53d902d61 --- /dev/null +++ b/examples/llava/minicpmv-cli.cpp @@ -0,0 +1,335 @@ +#include "arg.h" +#include "log.h" +#include "common.h" +#include "sampling.h" +#include "clip.h" +#include "llava.h" +#include "llama.h" +#include "ggml.h" + +#include +#include +#include +#include +#include +#include // TODO: remove me + +struct llava_context { + struct clip_ctx * ctx_clip = NULL; + struct llama_context * ctx_llama = NULL; + struct llama_model * model = NULL; +}; + +static void show_additional_info(int /*argc*/, char ** argv) { + LOG("\nexample usage:\n\n%s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n"); +} + +static struct llama_model * llava_init(common_params * params) { + llama_backend_init(); + llama_numa_init(params->numa); + + llama_model_params model_params = common_model_params_to_llama(*params); + + llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params); + if (model == NULL) { + LOG_ERR("%s: unable to load model\n" , __func__); + return NULL; + } + return model; +} + +static struct llava_context * llava_init_context(common_params * params, llama_model * model) { + auto prompt = params->prompt; + if (prompt.empty()) { + prompt = "describe the image in detail."; + } + + llama_context_params ctx_params = common_context_params_to_llama(*params); + if (params->n_ctx < 2048) { + // warn user here, "Image processing requires at least 2048 context, setting context to 2048" + LOG_WRN("%s: Image processing requires at least 2048 context, setting context to 2048\n" , __func__); + ctx_params.n_ctx = 2048; + } else { + ctx_params.n_ctx = params->n_ctx; + } + + llama_context * ctx_llama = llama_init_from_model(model, ctx_params); + + if (ctx_llama == NULL) { + LOG_ERR("%s: failed to create the llama_context\n" , __func__); + return NULL; + } + + auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); + + ctx_llava->ctx_llama = ctx_llama; + ctx_llava->model = model; + return ctx_llava; +} + +static void llava_free(struct llava_context * ctx_llava) { + if (ctx_llava->ctx_clip) { + clip_free(ctx_llava->ctx_clip); + ctx_llava->ctx_clip = NULL; + } + + llama_free(ctx_llava->ctx_llama); + llama_model_free(ctx_llava->model); + llama_backend_free(); +} + +static struct clip_ctx * clip_init_context(common_params * params) { + const char * clip_path = params->mmproj.c_str(); + + auto prompt = params->prompt; + if (prompt.empty()) { + prompt = "describe the image in detail."; + } + auto * ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + return ctx_clip; +} + +static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past) { + int N = (int) tokens.size(); + for (int i = 0; i < N; i += n_batch) { + int n_eval = (int) tokens.size() - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) { + LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); + return false; + } + *n_past += n_eval; + } + return true; +} + +static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { + std::vector tokens; + tokens.push_back(id); + return eval_tokens(ctx_llama, tokens, 1, n_past); +} + +static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ + std::string str2 = str; + std::vector embd_inp = common_tokenize(ctx_llama, str2, add_bos, true); + return eval_tokens(ctx_llama, embd_inp, n_batch, n_past); +} + +static void process_eval_image_embed(struct llava_context * ctx_llava, const struct llava_image_embed * embeds, int n_batch, int * n_past, int idx) { + float * image_embed = (float *)malloc(clip_embd_nbytes(ctx_llava->ctx_clip)); + std::memcpy(image_embed, embeds->embed + idx * clip_n_patches(ctx_llava->ctx_clip) * clip_n_mmproj_embd(ctx_llava->ctx_clip), clip_embd_nbytes(ctx_llava->ctx_clip)); + + auto * slice_embed = (llava_image_embed*)malloc(sizeof(llava_image_embed)); + slice_embed->embed = image_embed; + slice_embed->n_image_pos = clip_n_patches(ctx_llava->ctx_clip); + llava_eval_image_embed(ctx_llava->ctx_llama, slice_embed, n_batch, n_past); + llava_image_embed_free(slice_embed); +} + +static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, common_params * params, int &n_past) { + std::string system_prompt; + int idx = 0; + int num_image_embeds = embeds->n_image_pos / clip_n_patches(ctx_llava->ctx_clip); + int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip); + if (has_minicpmv_projector == 2) { + system_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"; + } + else if (has_minicpmv_projector == 3) { + system_prompt = "<|im_start|>user\n"; + } + else if (has_minicpmv_projector == 4) { + system_prompt = "<|im_start|>user\n"; + } + LOG_INF("%s: image token past: %d\n", __func__, n_past); + eval_string(ctx_llava->ctx_llama, (system_prompt+"").c_str(), params->n_batch, &n_past, false); + process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++); + eval_string(ctx_llava->ctx_llama, std::string("").c_str(), params->n_batch, &n_past, false); + if (num_image_embeds > 1) { + size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip); + eval_string(ctx_llava->ctx_llama, std::string("").c_str(), params->n_batch, &n_past, false); + for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) { + for (size_t j = 0; j < num_image_embeds_col; ++j) { + eval_string(ctx_llava->ctx_llama, std::string("").c_str(), params->n_batch, &n_past, false); + process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++); + eval_string(ctx_llava->ctx_llama, std::string("").c_str(), params->n_batch, &n_past, false); + if (j == num_image_embeds_col - 1) { + eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false); + } + } + } + eval_string(ctx_llava->ctx_llama, std::string("").c_str(), params->n_batch, &n_past, false); + } + LOG_INF("%s: image token past: %d\n", __func__, n_past); +} + +static const char * sample(struct common_sampler * smpl, + struct llama_context * ctx_llama, + int * n_past) { + const llama_token id = common_sampler_sample(smpl, ctx_llama, -1); + common_sampler_accept(smpl, id, true); + + const llama_model * model = llama_get_model(ctx_llama); + const llama_vocab * vocab = llama_model_get_vocab(model); + + static std::string ret; + if (llama_vocab_is_eog(vocab, id)) { + ret = ""; + } else { + ret = common_token_to_piece(ctx_llama, id); + } + eval_id(ctx_llama, id, n_past); + return ret.c_str(); +} + +static struct llava_context * minicpmv_init(common_params * params, const std::string & fname, int &n_past){ + auto * ctx_clip = clip_init_context(params); + auto * embeds = llava_image_embed_make_with_filename(ctx_clip, params->cpuparams.n_threads, fname.c_str()); + if (!embeds) { + LOG_ERR("failed to load image %s. Terminating\n\n", fname.c_str()); + return NULL; + } + + // process the prompt + if (params->prompt.empty() && params->interactive == false) { + LOG_ERR("prompt should be given or interactive mode should be on"); + return NULL; + } + + auto * model = llava_init(params); + if (model == NULL) { + fprintf(stderr, "%s: error: failed to init minicpmv model\n", __func__); + return NULL; + } + const int64_t t_llava_init_start_us = ggml_time_us(); + auto * ctx_llava = llava_init_context(params, model); + ctx_llava->ctx_clip = ctx_clip; + const int64_t t_llava_init_end_us = ggml_time_us(); + float t_llava_init_ms = (t_llava_init_end_us - t_llava_init_start_us) / 1000.0; + LOG_INF("%s: llava init in %8.2f ms.\n", __func__, t_llava_init_ms); + + const int64_t t_process_image_start_us = ggml_time_us(); + process_image(ctx_llava, embeds, params, n_past); + const int64_t t_process_image_end_us = ggml_time_us(); + float t_process_image_ms = (t_process_image_end_us - t_process_image_start_us) / 1000.0; + LOG_INF("%s: llama process image in %8.2f ms.\n", __func__, t_process_image_ms); + + llava_image_embed_free(embeds); + return ctx_llava; +} + +static struct common_sampler * llama_init(struct llava_context * ctx_llava, common_params * params, const std::string & prompt, int & n_past, bool is_first = false){ + std::string user_prompt = prompt; + int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip); + if (!is_first) { + if (has_minicpmv_projector == 2) { + user_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" + prompt; + } + else if (has_minicpmv_projector == 3) { + user_prompt = "<|im_start|>user\n" + prompt; + } + else if (has_minicpmv_projector == 4) { + user_prompt = "<|im_start|>user\n" + prompt; + } + } + + eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false); + if (has_minicpmv_projector == 2) { + eval_string(ctx_llava->ctx_llama, "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", params->n_batch, &n_past, false); + } + else if (has_minicpmv_projector == 3) { + eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false); + } + else if (has_minicpmv_projector == 4) { + eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false); + } + + // generate the response + + LOG_INF("\n"); + + struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling); + return smpl; +} + +static const char * llama_loop(struct llava_context * ctx_llava,struct common_sampler * smpl, int &n_past){ + + const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past); + return tmp; +} + +int main(int argc, char ** argv) { + ggml_time_init(); + + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) { + return 1; + } + + common_init(); + + if (params.mmproj.empty() || (params.image.empty())) { + show_additional_info(argc, argv); + return 1; + } + + for (auto & image : params.image) { + int n_past = 0; + auto * ctx_llava = minicpmv_init(¶ms, image, n_past); + + if (!params.prompt.empty()) { + LOG("%s\n", params.prompt.c_str()); + LOG(""); + auto * smpl = llama_init(ctx_llava, ¶ms, params.prompt, n_past, true); + const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict; + std::string response; + bool have_tmp = false; + for (int i = 0; i < max_tgt_len; i++) { + const auto * tmp = llama_loop(ctx_llava, smpl, n_past); + response += tmp; + if (strcmp(tmp, "") == 0){ + if (!have_tmp) { + continue; + } + break; + } + if (strstr(tmp, "###")) break; // Yi-VL behavior + have_tmp = true; + printf("%s", tmp); + if (strstr(response.c_str(), "")) break; // minicpm-v + + fflush(stdout); + } + common_sampler_free(smpl); + }else { + while (true) { + LOG(""); + std::string prompt; + std::getline(std::cin, prompt); + LOG(""); + auto * smpl = llama_init(ctx_llava, ¶ms, prompt, n_past, true); + const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict; + std::string response; + for (int i = 0; i < max_tgt_len; i++) { + const auto * tmp = llama_loop(ctx_llava, smpl, n_past); + response += tmp; + if (strcmp(tmp, "") == 0) break; + printf("%s", tmp);// mistral llava-1.6 + if (strstr(response.c_str(), "")) break; // minicpm-v + fflush(stdout); + } + common_sampler_free(smpl); + } + } + printf("\n"); + llama_perf_context_print(ctx_llava->ctx_llama); + + ctx_llava->model = NULL; + llava_free(ctx_llava); + } + + return 0; +} diff --git a/examples/llava/minicpmv-convert-image-encoder-to-gguf.py b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py new file mode 100644 index 000000000..9b196757f --- /dev/null +++ b/examples/llava/minicpmv-convert-image-encoder-to-gguf.py @@ -0,0 +1,815 @@ +# coding=utf-8 +# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Siglip model. """ +# Copied from HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes + + +import os +import math +import warnings + +import numpy as np +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn.init import _calculate_fan_in_and_fan_out + +from transformers.activations import ACT2FN +from transformers.modeling_utils import PreTrainedModel +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import ( + logging, +) +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +class SiglipVisionConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a + Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip + [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + Args: + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + num_channels (`int`, *optional*, defaults to 3): + Number of channels in the input images. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 16): + The size (resolution) of each patch. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. + layer_norm_eps (`float`, *optional*, defaults to 1e-06): + The epsilon used by the layer normalization layers. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + Example: + ```python + >>> from transformers import SiglipVisionConfig, SiglipVisionModel + >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipVisionConfig() + >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipVisionModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip_vision_model" + + def __init__( + self, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + num_channels=3, + image_size=224, + patch_size=16, + hidden_act="gelu_pytorch_tanh", + layer_norm_eps=1e-6, + attention_dropout=0.0, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_channels = num_channels + self.patch_size = patch_size + self.image_size = image_size + self.attention_dropout = attention_dropout + self.layer_norm_eps = layer_norm_eps + self.hidden_act = hidden_act + +_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224" + +SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "google/siglip-base-patch16-224", + # See all SigLIP models at https://huggingface.co/models?filter=siglip +] + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +def _trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + if tensor.dtype in [torch.float16, torch.bfloat16]: + # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu + og_dtype = tensor.dtype + tensor = tensor.to(torch.float32) + tensor.erfinv_() + tensor = tensor.to(og_dtype) + else: + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + if tensor.dtype == torch.float16: + # The `clamp_` op is not (yet?) defined in float16+cpu + tensor = tensor.to(torch.float32) + tensor.clamp_(min=a, max=b) + tensor = tensor.to(torch.float16) + else: + tensor.clamp_(min=a, max=b) + + +def trunc_normal_tf_( + tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0 +): + """Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \\leq \text{mean} \\leq b`. + NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the + bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 + and the result is subsquently scaled and shifted by the mean and std args. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + """ + with torch.no_grad(): + _trunc_normal_(tensor, 0, 1.0, a, b) + tensor.mul_(std).add_(mean) + + +def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + denom = fan_in + if mode == "fan_in": + denom = fan_in + elif mode == "fan_out": + denom = fan_out + elif mode == "fan_avg": + denom = (fan_in + fan_out) / 2 + + variance = scale / denom + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) + elif distribution == "normal": + with torch.no_grad(): + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + with torch.no_grad(): + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") + + +def default_flax_embed_init(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="normal") + +class SiglipVisionEmbeddings(nn.Module): + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches_per_side = self.image_size // self.patch_size + self.num_patches = self.num_patches_per_side**2 + self.num_positions = self.num_patches + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + +class SiglipAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__ + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip +class SiglipMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip +class SiglipEncoderLayer(nn.Module): + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self.self_attn = ( + SiglipAttention(config) + ) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + +class SiglipPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = SiglipVisionConfig + base_model_prefix = "siglip" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + """Initialize the weights""" + + if isinstance(module, SiglipVisionEmbeddings): + width = self.config.hidden_size + nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + elif isinstance(module, nn.Embedding): + default_flax_embed_init(module.weight) + elif isinstance(module, SiglipAttention): + nn.init.normal_(module.q_proj.weight) + nn.init.normal_(module.k_proj.weight) + nn.init.normal_(module.v_proj.weight) + nn.init.normal_(module.out_proj.weight) + nn.init.zeros_(module.q_proj.bias) + nn.init.zeros_(module.k_proj.bias) + nn.init.zeros_(module.v_proj.bias) + nn.init.zeros_(module.out_proj.bias) + elif isinstance(module, SiglipMLP): + nn.init.normal_(module.fc1.weight) + nn.init.normal_(module.fc2.weight) + nn.init.normal_(module.fc1.bias, std=1e-6) + nn.init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + lecun_normal_(module.weight) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +SIGLIP_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + Parameters: + config ([`SiglipVisionConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +SIGLIP_VISION_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip +class SiglipEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`SiglipEncoderLayer`]. + Args: + config: SiglipConfig + """ + + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + +class SiglipVisionTransformer(SiglipPreTrainedModel): + config_class = SiglipVisionConfig + main_input_name = "pixel_values" + _supports_flash_attn_2 = True + + def __init__(self, config: SiglipVisionConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + + self.embeddings = SiglipVisionEmbeddings(config) + self.encoder = SiglipEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.embeddings.patch_embedding + +import argparse +import json +import re + +import numpy as np +from gguf import * +from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer, Idefics2VisionConfig + +TEXT = "clip.text" +VISION = "clip.vision" + + +def add_key_str(raw_key: str, arch: str) -> str: + return raw_key.format(arch=arch) + + +def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_minicpmv: bool) -> bool: + if name in ( + "logit_scale", + "text_model.embeddings.position_ids", + "vision_model.embeddings.position_ids", + ): + return True + + if has_minicpmv and name in ["visual_projection.weight"]: + return True + + if name.startswith("v") and not has_vision: + return True + + if name.startswith("t") and not has_text: + return True + + return False + + +def get_tensor_name(name: str) -> str: + if "projection" in name: + return name + if "mm_projector" in name: + name = name.replace("model.mm_projector", "mm") + name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1) + name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1) + return name + + return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln") + + +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = ( + list(range(ord("!"), ord("~") + 1)) + + list(range(ord("¡"), ord("¬") + 1)) + + list(range(ord("®"), ord("ÿ") + 1)) + ) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +ap = argparse.ArgumentParser() +ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True) +ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16") +ap.add_argument("--text-only", action="store_true", required=False, + help="Save a text-only model. It can't be used to encode images") +ap.add_argument("--vision-only", action="store_true", required=False, + help="Save a vision-only model. It can't be used to encode texts") +ap.add_argument("--clip-model-is-vision", action="store_true", required=False, + help="The clip model is a pure vision model (ShareGPT4V vision extract for example)") +ap.add_argument("--clip-model-is-openclip", action="store_true", required=False, + help="The clip model is from openclip (for ViT-SO400M type))") +ap.add_argument("--minicpmv-projector", help="Path to minicpmv.projector file. If specified, save an image encoder for MiniCPM-V models.") +ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp") +ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None) +# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711 +# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5 +default_image_mean = [0.48145466, 0.4578275, 0.40821073] +default_image_std = [0.26862954, 0.26130258, 0.27577711] +ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None) +ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None) +ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4', default=2) + +# with proper +args = ap.parse_args() + + +if args.text_only and args.vision_only: + print("--text-only and --image-only arguments cannot be specified at the same time.") + exit(1) + +if args.use_f32: + print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.") + +# output in the same directory as the model if output_dir is None +dir_model = args.model_dir + +if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip: + vocab = None + tokens = None +else: + with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f: + vocab = json.load(f) + tokens = [key for key in vocab] + +# possible data types +# ftype == 0 -> float32 +# ftype == 1 -> float16 +# +# map from ftype to string +ftype_str = ["f32", "f16"] + +ftype = 1 +if args.use_f32: + ftype = 0 + +# if args.clip_model_is_vision or args.clip_model_is_openclip: +# model = CLIPVisionModel.from_pretrained(dir_model) +# processor = None +# else: +# model = CLIPModel.from_pretrained(dir_model) +# processor = CLIPProcessor.from_pretrained(dir_model) + +minicpmv_version = args.minicpmv_version +emb_dim = 4096 +block_count = 26 +if minicpmv_version == 1: + emb_dim = 2304 + block_count = 26 +elif minicpmv_version == 2: + emb_dim = 4096 + block_count = 27 +elif minicpmv_version == 3: + emb_dim = 3584 + block_count = 27 +elif minicpmv_version == 4: + emb_dim = 3584 + block_count = 27 + +default_vision_config = { + "hidden_size": 1152, + "image_size": 980, + "intermediate_size": 4304, + "model_type": "idefics2", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14, + } + +vision_config = Idefics2VisionConfig(**default_vision_config) +model = Idefics2VisionTransformer(vision_config) +if minicpmv_version == 3: + vision_config = SiglipVisionConfig(**default_vision_config) + model = SiglipVisionTransformer(vision_config) +elif minicpmv_version == 4: + vision_config = SiglipVisionConfig(**default_vision_config) + model = SiglipVisionTransformer(vision_config) + +processor = None +# if model.attn_pool is not None: +# model.attn_pool = torch.nn.Identity() + +# model.blocks = model.blocks[:-1] +model.load_state_dict(torch.load(os.path.join(dir_model, "minicpmv.clip"))) + +fname_middle = None +has_text_encoder = True +has_vision_encoder = True +has_minicpmv_projector = False + +if args.text_only: + fname_middle = "text-" + has_vision_encoder = False +elif args.minicpmv_projector is not None: + fname_middle = "mmproj-" + has_text_encoder = False + has_minicpmv_projector = True + minicpmv_version = 4 +elif args.vision_only: + fname_middle = "vision-" + has_text_encoder = False +else: + fname_middle = "" + +output_dir = args.output_dir if args.output_dir is not None else dir_model +os.makedirs(output_dir, exist_ok=True) +output_prefix = os.path.basename(output_dir).replace("ggml_", "") +fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf") +fout = GGUFWriter(path=fname_out, arch="clip") + +fout.add_bool("clip.has_text_encoder", has_text_encoder) +fout.add_bool("clip.has_vision_encoder", has_vision_encoder) +fout.add_bool("clip.has_minicpmv_projector", has_minicpmv_projector) +fout.add_file_type(ftype) +if args.text_only: + fout.add_description("text-only CLIP model") +elif args.vision_only and not has_minicpmv_projector: + fout.add_description("vision-only CLIP model") +elif has_minicpmv_projector: + fout.add_description("image encoder for MiniCPM-V") + # add projector type + fout.add_string("clip.projector_type", "resampler") + fout.add_int32("clip.minicpmv_version", minicpmv_version) +else: + fout.add_description("two-tower CLIP model") + +if has_vision_encoder: + # vision_model hparams + fout.add_uint32("clip.vision.image_size", 448) + fout.add_uint32("clip.vision.patch_size", 14) + fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152) + fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304) + fout.add_uint32("clip.vision.projection_dim", 0) + fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16) + fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) + fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count) + + if processor is not None: + image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean + image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std + else: + image_mean = args.image_mean if args.image_mean is not None else default_image_mean + image_std = args.image_std if args.image_std is not None else default_image_std + fout.add_array("clip.vision.image_mean", image_mean) + fout.add_array("clip.vision.image_std", image_std) + +use_gelu = True +fout.add_bool("clip.use_gelu", use_gelu) + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float32) + omega /= embed_dim / 2. + omega = 1. / 10000 ** omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20 +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + if isinstance(grid_size, int): + grid_h_size, grid_w_size = grid_size, grid_size + else: + grid_h_size, grid_w_size = grid_size[0], grid_size[1] + + grid_h = np.arange(grid_h_size, dtype=np.float32) + grid_w = np.arange(grid_w_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_h_size, grid_w_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token: + pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) + return pos_embed + +def _replace_name_resampler(s, v): + if re.match("resampler.pos_embed", s): + return { + s: v, + re.sub("pos_embed", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(emb_dim, (70, 70))), + } + if re.match("resampler.proj", s): + return { + re.sub("proj", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(emb_dim, (70, 70))), + re.sub("proj", "proj.weight", s): v.transpose(-1, -2).contiguous(), + } + if re.match("resampler.attn.in_proj_.*", s): + return { + re.sub("attn.in_proj_", "attn.q.", s): v.chunk(3, dim=0)[0], + re.sub("attn.in_proj_", "attn.k.", s): v.chunk(3, dim=0)[1], + re.sub("attn.in_proj_", "attn.v.", s): v.chunk(3, dim=0)[2], + } + return {s: v} + +if has_minicpmv_projector: + projector = torch.load(args.minicpmv_projector) + new_state_dict = {} + for k, v in projector.items(): + kvs = _replace_name_resampler(k, v) + for nk, nv in kvs.items(): + new_state_dict[nk] = nv + projector = new_state_dict + ftype_cur = 0 + for name, data in projector.items(): + name = get_tensor_name(name) + data = data.squeeze().numpy() + + n_dims = len(data.shape) + if ftype == 1: + if name[-7:] == ".weight" and n_dims == 2: + print(" Converting to float16") + data = data.astype(np.float16) + ftype_cur = 1 + else: + print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + else: + if data.dtype != np.float32: + print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + + fout.add_tensor(name, data) + print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}") + + print("Projector tensors added\n") + +def _replace_name(s, v): + s = "vision_model." + s + if re.match("vision_model.embeddings.position_embedding", s): + v = v.unsqueeze(0) + return {s: v} + + return {s: v} + +state_dict = model.state_dict() +new_state_dict = {} +for k, v in state_dict.items(): + kvs = _replace_name(k, v) + for nk, nv in kvs.items(): + new_state_dict[nk] = nv +state_dict = new_state_dict +for name, data in state_dict.items(): + if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_minicpmv_projector): + # we don't need this + print(f"skipping parameter: {name}") + continue + + name = get_tensor_name(name) + data = data.squeeze().numpy() + + n_dims = len(data.shape) + + # ftype == 0 -> float32, ftype == 1 -> float16 + ftype_cur = 0 + if n_dims == 4: + print(f"tensor {name} is always saved in f16") + data = data.astype(np.float16) + ftype_cur = 1 + elif ftype == 1: + if name[-7:] == ".weight" and n_dims == 2: + print(" Converting to float16") + data = data.astype(np.float16) + ftype_cur = 1 + else: + print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + else: + if data.dtype != np.float32: + print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + + print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}") + fout.add_tensor(name, data) + + +fout.write_header_to_file() +fout.write_kv_data_to_file() +fout.write_tensors_to_file() +fout.close() + +print("Done. Output file: " + fname_out) diff --git a/examples/llava/minicpmv-surgery.py b/examples/llava/minicpmv-surgery.py new file mode 100644 index 000000000..ba8211658 --- /dev/null +++ b/examples/llava/minicpmv-surgery.py @@ -0,0 +1,45 @@ +import argparse +import os +import torch +from transformers import AutoModel, AutoTokenizer + +ap = argparse.ArgumentParser() +ap.add_argument("-m", "--model", help="Path to MiniCPM-V model") +args = ap.parse_args() + +# find the model part that includes the the multimodal projector weights +model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True, torch_dtype=torch.bfloat16) +checkpoint = model.state_dict() + +# get a list of mm tensor names +mm_tensors = [k for k, v in checkpoint.items() if k.startswith("resampler")] + +# store these tensors in a new dictionary and torch.save them +projector = {name: checkpoint[name].float() for name in mm_tensors} +torch.save(projector, f"{args.model}/minicpmv.projector") + +clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vpm")] +if len(clip_tensors) > 0: + clip = {name.replace("vpm.", ""): checkpoint[name].float() for name in clip_tensors} + torch.save(clip, f"{args.model}/minicpmv.clip") + + # added tokens should be removed to be able to convert Mistral models + if os.path.exists(f"{args.model}/added_tokens.json"): + with open(f"{args.model}/added_tokens.json", "w") as f: + f.write("{}\n") + +config = model.llm.config +config.auto_map = { + "AutoConfig": "configuration_minicpm.MiniCPMConfig", + "AutoModel": "modeling_minicpm.MiniCPMModel", + "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM", + "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM", + "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification" +} +model.llm.save_pretrained(f"{args.model}/model") +tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True) +tok.save_pretrained(f"{args.model}/model") + +print("Done!") +print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") +print(f"Also, use {args.model}/minicpmv.projector to prepare a minicpmv-encoder.gguf file.") diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py new file mode 100644 index 000000000..c87606b4f --- /dev/null +++ b/examples/llava/qwen2_vl_surgery.py @@ -0,0 +1,165 @@ +import argparse +from typing import Dict + +import torch +import numpy as np +from gguf import * +from transformers import ( + Qwen2VLForConditionalGeneration, + Qwen2VLProcessor, + AutoProcessor, + Qwen2VLConfig +) + + +VISION = "clip.vision" + + +def k(raw_key: str, arch: str) -> str: + return raw_key.format(arch=arch) + + +def to_gguf_name(name: str) -> str: + og = name + name = name.replace("text_model", "t").replace("vision_model", "v") + name = name.replace("blocks", "blk").replace("embeddings.", "") + name = name.replace("attn.", "attn_") + name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.") + # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln") + name = name.replace("norm1", "ln1").replace("norm2", "ln2") + name = name.replace("merger.mlp", 'mm') + print(f"[to_gguf_name] {og} --> {name}") + return name + + +def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]: + vision_model = qwen2vl.visual + tensor_map = {} + for name, ten in vision_model.state_dict().items(): + ten = ten.numpy() + if 'qkv' in name: + if ten.ndim == 2: # weight + c3, _ = ten.shape + else: # bias + c3 = ten.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = ten[:c] + wk = ten[c: c * 2] + wv = ten[c * 2:] + tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq + tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk + tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv + elif 'merger' in name: + if name.endswith("ln_q.weight"): + tensor_map['v.post_ln.weight'] = ten + elif name.endswith("ln_q.bias"): + tensor_map['v.post_ln.bias'] = ten + else: + # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias" + tensor_map[to_gguf_name(name)] = ten + elif 'patch_embed.proj.weight' in name: + # NOTE: split Conv3D into Conv2Ds + c1, c2, kt, kh, kw = ten.shape + assert kt == 2, "Current implmentation only support temporal_patch_size of 2" + tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...] + tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...] + else: + tensor_map[to_gguf_name(f"vision_model.{name}")] = ten + + for new_name, ten in tensor_map.items(): + if ten.ndim <= 1 or new_name.endswith("_norm.weight"): + tensor_map[new_name] = ten.astype(np.float32) + else: + tensor_map[new_name] = ten.astype(dtype) + tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder + return tensor_map + + +def main(args): + if args.data_type == 'fp32': + dtype = torch.float32 + np_dtype = np.float32 + ftype = 0 + elif args.data_type == 'fp16': + dtype = torch.float32 + np_dtype = np.float16 + ftype = 1 + else: + raise ValueError() + + local_model = False + model_path = "" + model_name = args.model_name + print("model_name: ", model_name) + qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained( + model_name, torch_dtype=dtype, device_map="cpu" + ) + cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] + vcfg = cfg.vision_config + + if os.path.isdir(model_name): + local_model = True + if model_name.endswith(os.sep): + model_name = model_name[:-1] + model_path = model_name + model_name = os.path.basename(model_name) + fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf" + + fout = GGUFWriter(path=fname_out, arch="clip") + fout.add_description("image encoder for Qwen2VL") + + fout.add_file_type(ftype) + fout.add_bool("clip.has_text_encoder", False) + fout.add_bool("clip.has_vision_encoder", True) + fout.add_bool("clip.has_qwen2vl_merger", True) + fout.add_string("clip.projector_type", "qwen2vl_merger") + + print(cfg.vision_config) + if 'silu' in cfg.vision_config.hidden_act.lower(): + fout.add_bool("clip.use_silu", True) + fout.add_bool("clip.use_gelu", False) + elif 'gelu' in cfg.vision_config.hidden_act.lower(): + fout.add_bool("clip.use_silu", False) + fout.add_bool("clip.use_gelu", 'quick' not in cfg.vision_config.hidden_act.lower()) + else: + raise ValueError() + + tensor_map = find_vision_tensors(qwen2vl, np_dtype) + for name, data in tensor_map.items(): + fout.add_tensor(name, data) + + fout.add_uint32("clip.vision.patch_size", vcfg.patch_size) + fout.add_uint32("clip.vision.image_size", 14 * 40) # some reasonable size that is divable by (14*2) + fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim) + fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size) + fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads) + fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) + fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth) + fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 0) # not sure what this does, put 0 here as a placeholder + fout.add_name(model_name) + """ + HACK: Since vision rope related parameter aren't stored in the `Qwen2VLConfig, + it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`. + """ + + if local_model: + processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path) + else: + processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name) + fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue] + fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue] + + fout.write_header_to_file() + fout.write_kv_data_to_file() + fout.write_tensors_to_file() + fout.close() + print("save model as: ", fname_out) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct") + parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32") + args = parser.parse_args() + main(args) diff --git a/examples/llava/qwen2vl-cli.cpp b/examples/llava/qwen2vl-cli.cpp new file mode 100644 index 000000000..132a7da54 --- /dev/null +++ b/examples/llava/qwen2vl-cli.cpp @@ -0,0 +1,584 @@ +#include "arg.h" +#include "base64.hpp" +#include "log.h" +#include "common.h" +#include "sampling.h" +#include "clip.h" +#include "llava.h" +#include "llama.h" +#include "ggml.h" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif +#ifdef NDEBUG +#include "ggml-alloc.h" +#include "ggml-backend.h" +#endif + +#include +#include +#include +#include +#include +#include +#include + + +static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, + int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) { + int n_embd = llama_model_n_embd(llama_get_model(ctx_llama)); + const int patch_size = 14 * 2; + const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0); + const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0); + auto img_tokens = image_embed->n_image_pos; + // llama_pos mrope_pos[img_tokens * 4]; + std::vector mrope_pos; + mrope_pos.resize(img_tokens * 4); + + for (int y = 0; y < ph; y++) + { + for (int x = 0; x < pw; x++) + { + int i = y * pw + x; + mrope_pos[i] = *st_pos_id; + mrope_pos[i + img_tokens] = *st_pos_id + y; + mrope_pos[i + img_tokens * 2] = *st_pos_id + x; + mrope_pos[i + img_tokens * 3] = 0; + } + } + *st_pos_id += std::max(pw, ph); + + int processed = 0; + std::vector batch_mrope_pos; + batch_mrope_pos.resize(img_tokens * 4); + + for (int i = 0; i < img_tokens; i += n_batch) { + int n_eval = img_tokens - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + + // llama_pos batch_mrope_pos[n_eval * 4]; + std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0); + memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos)); + memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos)); + memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos)); + memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos)); + + llama_batch batch = { + int32_t(n_eval), // n_tokens + nullptr, // token + (image_embed->embed+i*n_embd), // embed + batch_mrope_pos.data(), // pos + nullptr, // n_seq_id + nullptr, // seq_id + nullptr, // logits + }; + + if (llama_decode(ctx_llama, batch)) { + LOG_ERR("%s : failed to eval\n", __func__); + return false; + } + *n_past += n_eval; + processed += n_eval; + } + return true; +} + + +static bool eval_tokens(struct llama_context * ctx_llama, std::vector tokens, int n_batch, int * n_past, int * st_pos_id) { + int N = (int) tokens.size(); + std::vector pos; + for (int i = 0; i < N; i += n_batch) { + int n_eval = (int) tokens.size() - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + auto batch = llama_batch_get_one(&tokens[i], n_eval); + // TODO: add mrope pos ids somewhere else + pos.resize(batch.n_tokens * 4); + std::fill(pos.begin(), pos.end(), 0); + for (int j = 0; j < batch.n_tokens * 3; j ++) { + pos[j] = *st_pos_id + (j % batch.n_tokens); + } + batch.pos = pos.data(); + + if (llama_decode(ctx_llama, batch)) { + LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past); + return false; + } + *n_past += n_eval; + *st_pos_id += n_eval; + } + return true; +} + +static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) { + std::vector tokens; + tokens.push_back(id); + return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id); +} + +static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){ + std::string str2 = str; + std::vector embd_inp = common_tokenize(ctx_llama, str2, add_bos, true); + eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id); + return true; +} + +static const char * sample(struct common_sampler * smpl, + struct llama_context * ctx_llama, + int * n_past, int * st_pos_id) { + const llama_token id = common_sampler_sample(smpl, ctx_llama, -1); + common_sampler_accept(smpl, id, true); + + const llama_model * model = llama_get_model(ctx_llama); + const llama_vocab * vocab = llama_model_get_vocab(model); + + static std::string ret; + if (llama_vocab_is_eog(vocab, id)) { + ret = ""; + } else { + ret = common_token_to_piece(ctx_llama, id); + } + eval_id(ctx_llama, id, n_past, st_pos_id); + return ret.c_str(); +} + +static const char* IMG_BASE64_TAG_BEGIN = ""; + +static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) { + begin_out = prompt.find(IMG_BASE64_TAG_BEGIN); + end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out); +} + +static bool prompt_contains_image(const std::string& prompt) { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + return (begin != std::string::npos); +} + +// replaces the base64 image tag in the prompt with `replacement` +static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) { + size_t img_base64_str_start, img_base64_str_end; + find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end); + if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) { + LOG_ERR("%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); + return NULL; + } + + auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN); + auto base64_bytes_count = img_base64_str_end - base64_bytes_start; + auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count ); + + auto required_bytes = base64::required_encode_size(base64_str.size()); + auto img_bytes = std::vector(required_bytes); + base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin()); + + auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); + if (!embed) { + LOG_ERR("%s: could not load image from base64 string.\n", __func__); + return NULL; + } + + return embed; +} + +static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") { + size_t begin, end; + find_image_tag_in_prompt(prompt, begin, end); + if (begin == std::string::npos || end == std::string::npos) { + return prompt; + } + auto pre = prompt.substr(0, begin); + auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END)); + return pre + replacement + post; +} + +struct llava_context { + struct clip_ctx * ctx_clip = NULL; + struct llama_context * ctx_llama = NULL; + struct llama_model * model = NULL; +}; + +static void print_usage(int, char ** argv) { + LOG("\n example usage:\n"); + LOG("\n %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n"); +} + +static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) { + + // load and preprocess the image + llava_image_embed * embed = NULL; + auto prompt = params->prompt; + if (prompt_contains_image(prompt)) { + if (!params->image.empty()) { + LOG_INF("using base64 encoded image instead of command line image path\n"); + } + embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt); + if (!embed) { + LOG_ERR("%s: can't load image from prompt\n", __func__); + return NULL; + } + params->prompt = remove_image_from_prompt(prompt); + } else { + embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str()); + if (!embed) { + fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str()); + return NULL; + } + } + + return embed; +} + +static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) { + int n_past = 0; + int cur_pos_id = 0; + + const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; + + std::string system_prompt, user_prompt; + size_t image_pos = prompt.find("<|vision_start|>"); + if (image_pos != std::string::npos) { + // new templating mode: Provide the full prompt including system message and use as a placeholder for the image + system_prompt = prompt.substr(0, image_pos); + user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length()); + LOG_INF("system_prompt: %s\n", system_prompt.c_str()); + if (params->verbose_prompt) { + auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } + LOG_INF("user_prompt: %s\n", user_prompt.c_str()); + if (params->verbose_prompt) { + auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } + } else { + // llava-1.5 native mode + system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>"; + user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n"; + if (params->verbose_prompt) { + auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); + for (int i = 0; i < (int) tmp.size(); i++) { + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + } + } + } + + eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true); + if (image_embed != nullptr) { + auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip); + qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size); + } + eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false); + + // generate the response + + LOG("\n"); + + 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); + } + + std::string response = ""; + for (int i = 0; i < max_tgt_len; i++) { + const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id); + response += tmp; + if (strcmp(tmp, "") == 0) break; + if (strstr(tmp, "###")) break; // Yi-VL behavior + LOG("%s", tmp); + if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works) + if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6 + if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6 + + fflush(stdout); + } + + common_sampler_free(smpl); + LOG("\n"); +} + +static struct llama_model * llava_init(common_params * params) { + llama_backend_init(); + llama_numa_init(params->numa); + + llama_model_params model_params = common_model_params_to_llama(*params); + + llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params); + if (model == NULL) { + LOG_ERR("%s: unable to load model\n" , __func__); + return NULL; + } + return model; +} + +static struct llava_context * llava_init_context(common_params * params, llama_model * model) { + const char * clip_path = params->mmproj.c_str(); + + auto prompt = params->prompt; + if (prompt.empty()) { + prompt = "describe the image in detail."; + } + + auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + + llama_context_params ctx_params = common_context_params_to_llama(*params); + ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings + + llama_context * ctx_llama = llama_init_from_model(model, ctx_params); + + if (ctx_llama == NULL) { + LOG_ERR("%s: failed to create the llama_context\n" , __func__); + return NULL; + } + + auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context)); + + ctx_llava->ctx_llama = ctx_llama; + ctx_llava->ctx_clip = ctx_clip; + ctx_llava->model = model; + return ctx_llava; +} + +static void llava_free(struct llava_context * ctx_llava) { + if (ctx_llava->ctx_clip) { + clip_free(ctx_llava->ctx_clip); + ctx_llava->ctx_clip = NULL; + } + + llama_free(ctx_llava->ctx_llama); + llama_model_free(ctx_llava->model); + llama_backend_free(); +} + +#ifndef NDEBUG + +static void debug_test_mrope_2d() { + // 1. Initialize backend + ggml_backend_t backend = NULL; + std::string backend_name = ""; +#ifdef GGML_USE_CUDA + fprintf(stderr, "%s: using CUDA backend\n", __func__); + backend = ggml_backend_cuda_init(0); // init device 0 + backend_name = "cuda"; + if (!backend) { + fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); + } +#endif + // if there aren't GPU Backends fallback to CPU backend + if (!backend) { + backend = ggml_backend_cpu_init(); + backend_name = "cpu"; + } + + // Calculate the size needed to allocate + size_t ctx_size = 0; + ctx_size += 2 * ggml_tensor_overhead(); // tensors + // no need to allocate anything else! + + // 2. Allocate `ggml_context` to store tensor data + struct ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors() + }; + struct ggml_context * ctx = ggml_init(params); + + struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30); + ggml_set_name(inp_raw, "inp_raw"); + ggml_set_input(inp_raw); + + struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4); + ggml_set_name(pos, "pos"); + ggml_set_input(pos); + + std::vector dummy_q; + dummy_q.resize(128 * 12 * 30); + std::fill(dummy_q.begin(), dummy_q.end(), 0.1); + // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw)); + + std::vector pos_id; + pos_id.resize(30 * 4); + for (int i = 0; i < 30; i ++) { + pos_id[i] = i; + pos_id[i + 30] = i + 10; + pos_id[i + 60] = i + 20; + pos_id[i + 90] = i + 30; + } + int sections[4] = {32, 32, 0, 0}; + + // 4. Allocate a `ggml_backend_buffer` to store all tensors + ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend); + + // 5. Copy tensor data from main memory (RAM) to backend buffer + ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw)); + ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos)); + + // 6. Create a `ggml_cgraph` for mul_mat operation + struct ggml_cgraph * gf = NULL; + struct ggml_context * ctx_cgraph = NULL; + + // create a temporally context to build the graph + struct ggml_init_params params0 = { + /*.mem_size =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph() + }; + ctx_cgraph = ggml_init(params0); + gf = ggml_new_graph(ctx_cgraph); + + struct ggml_tensor * result0 = ggml_rope_multi( + ctx_cgraph, inp_raw, pos, nullptr, + 128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1, + 0, 1, 32, 1); + + // Add "result" tensor and all of its dependencies to the cgraph + ggml_build_forward_expand(gf, result0); + + // 7. Create a `ggml_gallocr` for cgraph computation + ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend)); + ggml_gallocr_alloc_graph(allocr, gf); + + // 9. Run the computation + int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading + if (ggml_backend_is_cpu(backend)) { + ggml_backend_cpu_set_n_threads(backend, n_threads); + } + ggml_backend_graph_compute(backend, gf); + + // 10. Retrieve results (output tensors) + // in this example, output tensor is always the last tensor in the graph + struct ggml_tensor * result = result0; + // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1]; + float * result_data = (float *)malloc(ggml_nbytes(result)); + // because the tensor data is stored in device buffer, we need to copy it back to RAM + ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result)); + const std::string bin_file = "mrope_2d_" + backend_name +".bin"; + std::ofstream outFile(bin_file, std::ios::binary); + + if (outFile.is_open()) { + outFile.write(reinterpret_cast(result_data), ggml_nbytes(result)); + outFile.close(); + std::cout << "Data successfully written to " + bin_file << std::endl; + } else { + std::cerr << "Error opening file!" << std::endl; + } + + free(result_data); + // 11. Free memory and exit + ggml_free(ctx_cgraph); + ggml_gallocr_free(allocr); + ggml_free(ctx); + ggml_backend_buffer_free(buffer); + ggml_backend_free(backend); +} + +static void debug_dump_img_embed(struct llava_context * ctx_llava) { + int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama)); + int ne = n_embd * 4; + float vals[56 * 56 * 3]; + // float embd[ne]; + std::vector embd; + embd.resize(ne); + + for (int i = 0; i < 56*56; i++) + { + for (int c = 0; c < 3; c++) + vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56); + } + + clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data()); + + std::ofstream outFile("img_embed.bin", std::ios::binary); + if (outFile.is_open()) { + outFile.write(reinterpret_cast(embd.data()), ne * sizeof(float)); + + outFile.close(); + std::cout << "Data successfully written to mrope.bin" << std::endl; + } else { + std::cerr << "Error opening file!" << std::endl; + } +} + +#endif + + +int main(int argc, char ** argv) { + ggml_time_init(); + + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) { + return 1; + } + + common_init(); + + if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { + print_usage(argc, argv); + return 1; + } + + auto * model = llava_init(¶ms); + if (model == NULL) { + fprintf(stderr, "%s: error: failed to init llava model\n", __func__); + return 1; + } + + if (prompt_contains_image(params.prompt)) { + auto * ctx_llava = llava_init_context(¶ms, model); + + auto * image_embed = load_image(ctx_llava, ¶ms, ""); + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_perf_context_print(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); +#ifndef NDEBUG + } else if (params.image[0].empty()) { + auto ctx_llava = llava_init_context(¶ms, model); + + debug_test_mrope_2d(); + debug_dump_img_embed(ctx_llava); + + llama_perf_context_print(ctx_llava->ctx_llama); + ctx_llava->model = NULL; + llava_free(ctx_llava); +#endif + } else { + for (auto & image : params.image) { + auto * ctx_llava = llava_init_context(¶ms, model); + + auto * image_embed = load_image(ctx_llava, ¶ms, image); + if (!image_embed) { + LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str()); + return 1; + } + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_perf_context_print(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); + } + } + + llama_model_free(model); + + return 0; +} diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt index f80f727a7..cbcbf26c9 100644 --- a/examples/llava/requirements.txt +++ b/examples/llava/requirements.txt @@ -1,3 +1,5 @@ --r ../../requirements/requirements-convert.txt +-r ../../requirements/requirements-convert_legacy_llama.txt +--extra-index-url https://download.pytorch.org/whl/cpu pillow~=10.2.0 -torch~=2.1.1 +torch~=2.2.1 +torchvision~=0.17.1 diff --git a/examples/lookahead/CMakeLists.txt b/examples/lookahead/CMakeLists.txt index 8827e3f11..346861314 100644 --- a/examples/lookahead/CMakeLists.txt +++ b/examples/lookahead/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET lookahead) +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 e2551e7a4..2f0898e62 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -1,7 +1,9 @@ +#include "arg.h" #include "common.h" +#include "sampling.h" +#include "log.h" #include "llama.h" -#include #include #include #include @@ -35,56 +37,51 @@ struct ngram_container { }; int main(int argc, char ** argv) { - gpt_params params; + common_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { return 1; } + common_init(); + const int W = 15; // lookahead window const int N = 5; // n-gram size const int G = 15; // max verification n-grams const bool dump_kv_cache = params.dump_kv_cache; -#ifndef LOG_DISABLE_LOGS - log_set_target(log_filename_generator("lookahead", "log")); - LOG_TEE("Log start\n"); - log_dump_cmdline(argc, argv); -#endif // LOG_DISABLE_LOGS - // init llama.cpp llama_backend_init(); llama_numa_init(params.numa); - llama_model * model = NULL; - llama_context * ctx = NULL; - // load the target model - std::tie(model, ctx) = llama_init_from_gpt_params(params); + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + const llama_vocab * vocab = llama_model_get_vocab(model); // Tokenize the prompt - const bool add_bos = llama_should_add_bos_token(model); - LOG("add_bos tgt: %d\n", add_bos); - std::vector inp; std::vector all; - inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + inp = common_tokenize(ctx, params.prompt, true, true); all = inp; const int max_context_size = llama_n_ctx(ctx); const int max_tokens_list_size = max_context_size - 4; if ((int) inp.size() > max_tokens_list_size) { - fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); + LOG_ERR("%s: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); return 1; } - fprintf(stderr, "\n\n"); + LOG("\n\n"); for (auto id : inp) { - fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str()); + LOG("%s", common_token_to_piece(ctx, id).c_str()); } fflush(stderr); @@ -94,8 +91,8 @@ int main(int argc, char ** argv) { const auto t_enc_start = ggml_time_us(); // eval the prompt - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); for (int s = 1; s < W + G + 1; ++s) { llama_kv_cache_seq_cp(ctx, 0, s, -1, -1); @@ -120,7 +117,7 @@ int main(int argc, char ** argv) { llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1); // target model sampling context - struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams); + struct common_sampler * smpl = common_sampler_init(model, params.sampling); // verification n-grams std::vector ngrams_cur(G); @@ -152,7 +149,7 @@ int main(int argc, char ** argv) { } // here we keep adding new n-grams as we go - ngram_container ngrams_observed(llama_n_vocab(model), N, G); + ngram_container ngrams_observed(llama_vocab_n_tokens(vocab), N, G); // debug struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1); @@ -161,14 +158,14 @@ int main(int argc, char ** argv) { // sample first token { - id = llama_sampling_sample(ctx_sampling, ctx, NULL, 0); + id = common_sampler_sample(smpl, ctx, 0); - llama_sampling_accept(ctx_sampling, ctx, id, true); + common_sampler_accept(smpl, id, true); { - const std::string token_str = llama_token_to_piece(ctx, id); + const std::string token_str = common_token_to_piece(ctx, id); - printf("%s", token_str.c_str()); + LOG("%s", token_str.c_str()); fflush(stdout); } } @@ -177,7 +174,7 @@ int main(int argc, char ** argv) { // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + common_kv_cache_dump_view_seqs(kvc_view, 40); } // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/ @@ -206,10 +203,10 @@ int main(int argc, char ** argv) { // V V V V V V // id { - llama_batch_clear(batch); + common_batch_clear(batch); // current token - first token of the first level - llama_batch_add(batch, id, n_past, seq_id_all, true); + common_batch_add(batch, id, n_past, seq_id_all, true); // verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation { @@ -234,7 +231,7 @@ int main(int argc, char ** argv) { ngrams_cur[g].tokens [j + 1] = t; ngrams_cur[g].i_batch[j + 1] = batch.n_tokens; - llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true); + common_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true); } } } @@ -246,19 +243,19 @@ int main(int argc, char ** argv) { seq_id_look[j] = i + j + 1; } - llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false); + common_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false); } // fill the rest of the levels for (int j = 1; j < N - 1; j++) { for (int i = 0; i < W; i++) { - llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2); + common_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2); } } } if (llama_decode(ctx, batch) != 0) { - fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__); + LOG_ERR("\n\n%s: llama_decode failed - increase KV cache size\n", __func__); return 1; } @@ -286,23 +283,23 @@ int main(int argc, char ** argv) { } // sample the next token - id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_batch); + id = common_sampler_sample(smpl, ctx, i_batch); - llama_sampling_accept(ctx_sampling, ctx, id, true); + common_sampler_accept(smpl, id, true); // print { - const std::string token_str = llama_token_to_piece(ctx, id); + const std::string token_str = common_token_to_piece(ctx, id); if (v == 0) { - printf("%s", token_str.c_str()); + LOG("%s", token_str.c_str()); } else { // print light cyan - printf("\033[0;96m%s\033[0m", token_str.c_str()); + LOG("\033[0;96m%s\033[0m", token_str.c_str()); } fflush(stdout); - if (id == llama_token_eos(model)) { + if (llama_vocab_is_eog(vocab, id)) { has_eos = true; } @@ -332,21 +329,21 @@ int main(int argc, char ** argv) { // print known n-grams starting with token id (debug) if (0 && v == 0) { if (ngrams_observed.cnt[id] > 0) { - printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str()); + LOG("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], common_token_to_piece(ctx, id).c_str()); } for (int i = 0; i < ngrams_observed.cnt[id]; i++) { - printf(" - ngram %2d: ", i); + LOG(" - ngram %2d: ", i); const int idx = id*(N - 1)*G + i*(N - 1); for (int j = 0; j < N - 1; j++) { - const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]); + const std::string token_str = common_token_to_piece(ctx, ngrams_observed.tokens[idx + j]); - printf("%s", token_str.c_str()); + LOG("%s", token_str.c_str()); } - printf("\n"); + LOG("\n"); } } @@ -363,7 +360,7 @@ int main(int argc, char ** argv) { if (v == 0) { // sample from the last level for (int i = 0; i < W; i++) { - tokens_j[N - 2][i] = llama_sampling_sample(ctx_sampling, ctx, NULL, ngrams_cur.size()*(N-1) + W*(N - 2) + i); + tokens_j[N - 2][i] = common_sampler_sample(smpl, ctx, ngrams_cur.size()*(N-1) + W*(N - 2) + i); } } else { for (int i = 0; i < W; i++) { @@ -457,32 +454,31 @@ int main(int argc, char ** argv) { auto t_dec_end = ggml_time_us(); - LOG_TEE("\n\n"); + LOG("\n\n"); - LOG_TEE("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_TEE("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("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_TEE("\n"); - LOG_TEE("W = %2d\n", W); - LOG_TEE("N = %2d\n", N); - LOG_TEE("G = %2d\n", G); - LOG_TEE("\n"); - LOG_TEE("n_predict = %d\n", n_predict); - LOG_TEE("n_accept = %d\n", n_accept); + LOG_INF("\n"); + LOG_INF("W = %2d\n", W); + LOG_INF("N = %2d\n", N); + LOG_INF("G = %2d\n", G); + LOG_INF("\n"); + LOG_INF("n_predict = %d\n", n_predict); + LOG_INF("n_accept = %d\n", n_accept); - llama_print_timings(ctx); + LOG_INF("\n"); + common_perf_print(ctx, smpl); + + common_sampler_free(smpl); llama_kv_cache_view_free(&kvc_view); - llama_sampling_free(ctx_sampling); llama_batch_free(batch); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); - fprintf(stderr, "\n\n"); + LOG("\n\n"); return 0; } diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt index c060b8f56..fba78ceda 100644 --- a/examples/lookup/CMakeLists.txt +++ b/examples/lookup/CMakeLists.txt @@ -1,5 +1,23 @@ -set(TARGET lookup) +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_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_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_17) diff --git a/examples/lookup/README.md b/examples/lookup/README.md index 5bfb0de93..71c345c03 100644 --- a/examples/lookup/README.md +++ b/examples/lookup/README.md @@ -10,4 +10,3 @@ More info: https://github.com/ggerganov/llama.cpp/pull/4484 https://github.com/ggerganov/llama.cpp/issues/4226 - diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp new file mode 100644 index 000000000..3da45ed9e --- /dev/null +++ b/examples/lookup/lookup-create.cpp @@ -0,0 +1,40 @@ +#include "arg.h" +#include "common.h" +#include "ngram-cache.h" +#include "llama.h" + +#include +#include + +int main(int argc, char ** argv){ + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { + return 1; + } + + // init llama.cpp + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model + common_init_result llama_init = common_init_from_params(params); + + llama_model_ptr & model = llama_init.model; + llama_context_ptr & ctx = llama_init.context; + + GGML_ASSERT(model != nullptr); + + // tokenize the prompt + std::vector inp; + inp = common_tokenize(ctx.get(), params.prompt, true, true); + fprintf(stderr, "%s: tokenization done\n", __func__); + + common_ngram_cache ngram_cache; + common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true); + fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str()); + + common_ngram_cache_save(ngram_cache, params.lookup_cache_static); + + return 0; +} diff --git a/examples/lookup/lookup-merge.cpp b/examples/lookup/lookup-merge.cpp new file mode 100644 index 000000000..6871c0f5f --- /dev/null +++ b/examples/lookup/lookup-merge.cpp @@ -0,0 +1,47 @@ +#include "ggml.h" +#include "llama.h" +#include "common.h" +#include "ngram-cache.h" + +#include +#include +#include +#include +#include +#include +#include + +static void print_usage(char* argv0) { + fprintf(stderr, "Merges multiple lookup cache files into a single one.\n"); + fprintf(stderr, "Usage: %s [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n", argv0); +} + +int main(int argc, char ** argv){ + if (argc < 3) { + print_usage(argv[0]); + exit(1); + } + + std::vector args; + args.resize(argc-1); + for (int i = 0; i < argc-1; ++i) { + args[i] = argv[i+1]; + if (args[i] == "-h" || args[i] == "--help") { + print_usage(argv[0]); + exit(0); + } + } + + fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str()); + common_ngram_cache ngram_cache_merged = common_ngram_cache_load(args[0]); + + for (size_t i = 1; i < args.size()-1; ++i) { + fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str()); + common_ngram_cache ngram_cache = common_ngram_cache_load(args[i]); + + common_ngram_cache_merge(ngram_cache_merged, ngram_cache); + } + + fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str()); + common_ngram_cache_save(ngram_cache_merged, args.back()); +} diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp new file mode 100644 index 000000000..fcb289abe --- /dev/null +++ b/examples/lookup/lookup-stats.cpp @@ -0,0 +1,157 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "ngram-cache.h" +#include "llama.h" +#include "ggml.h" + +#include +#include +#include +#include +#include +#include + +int main(int argc, char ** argv){ + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { + return 1; + } + + common_init(); + + const int n_draft = params.speculative.n_max; + + // init llama.cpp + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model + common_init_result llama_init = common_init_from_params(params); + + llama_context_ptr & ctx = llama_init.context; + + // tokenize the prompt + std::vector inp; + inp = common_tokenize(ctx.get(), params.prompt, true, true); + + 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; + + { + const int64_t t_start_draft_us = ggml_time_us(); + + if (!params.lookup_cache_static.empty()) { + try { + ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static); + } catch (std::ifstream::failure const &) { + LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str()); + exit(1); + } + } + + if (!params.lookup_cache_dynamic.empty()) { + try { + ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic); + } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program + } + + t_draft_flat_us += ggml_time_us() - t_start_draft_us; + } + + const int n_input = inp.size(); + const int n_ctx = llama_n_ctx(ctx.get()); + + int n_drafted = 0; + int n_accept = 0; + + const int64_t t_start_ms = ggml_time_ms(); + + // Iterate over input tokens in chunks of size n_ctx. + // Each chunk is treated as if a sequential generation but with pre-determined tokens to ensure reproducibility. + for (int i_start = 0; i_start + n_ctx < n_input; i_start += n_ctx) { + const std::vector inp_slice(inp.begin() + i_start, inp.begin() + i_start + n_ctx); + std::vector pseudo_output; + pseudo_output.push_back(inp_slice[0]); + + while ((int) pseudo_output.size() < n_ctx) { + // Simulate drafting and decoding from draft: + std::vector draft; + draft.push_back(pseudo_output.back()); + + { + const int64_t t_start_draft_us = ggml_time_us(); + common_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static); + t_draft_us += ggml_time_us() - t_start_draft_us; + } + + n_drafted += draft.size() - 1; + + for (size_t j = 1; j < draft.size() && (int) pseudo_output.size() < n_ctx; ++j) { + const llama_token ground_truth = inp_slice[pseudo_output.size()]; + const llama_token drafted = draft[j]; + + if (ground_truth != drafted) { + break; + } + + ++n_accept; + pseudo_output.push_back(ground_truth); + + { + const int64_t t_start_draft_us = ggml_time_us(); + common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } + } + + // After each simulated batch decoding simulate the sampling of a single token: + if ((int) pseudo_output.size() < n_ctx) { + pseudo_output.push_back(inp_slice[pseudo_output.size()]); + { + const int64_t t_start_draft_us = ggml_time_us(); + common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } + } + + draft.erase(draft.begin()); + + } + if (i_start > 0 && i_start / 100000 != (i_start - n_ctx) / 100000) { + const int64_t t_now_ms = ggml_time_ms(); + const int64_t eta_ms = (n_input - i_start) * (t_now_ms - t_start_ms) / i_start; + const int64_t eta_min = eta_ms / (60*1000); + const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000; + + LOG_INF("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s); + } + + // After each chunk, update the dynamic ngram cache with the context ngram cache: + common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context); + ngram_cache_context.clear(); + } + + LOG("\n"); + + LOG_INF("\n"); + LOG_INF("n_draft = %d\n", n_draft); + LOG_INF("n_predict = %d\n", n_input - n_input % n_ctx); + LOG_INF("n_drafted = %d\n", n_drafted); + LOG_INF("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3); + LOG_INF("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n", + t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us)); + LOG_INF("n_accept = %d\n", n_accept); + LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + + llama_backend_free(); + + LOG("\n\n"); + + return 0; +} diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index b53fae110..dbd0444ec 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -1,64 +1,88 @@ -#include "common.h" +#include "arg.h" #include "ggml.h" +#include "common.h" +#include "ngram-cache.h" +#include "sampling.h" +#include "log.h" #include "llama.h" -#include #include #include +#include #include #include int main(int argc, char ** argv){ - gpt_params params; + common_params params; - if (!gpt_params_parse(argc, argv, params)) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { return 1; } - // max/min n-grams size to search for in prompt - const int ngram_max = 4; - const int ngram_min = 1; + common_init(); - // length of the candidate / draft sequence, if match is found - const int n_draft = params.n_draft; + // max. number of additional tokens to draft if match is found + const int n_draft = params.speculative.n_max; const bool dump_kv_cache = params.dump_kv_cache; -#ifndef LOG_DISABLE_LOGS - log_set_target(log_filename_generator("lookup", "log")); - LOG_TEE("Log start\n"); - log_dump_cmdline(argc, argv); -#endif // LOG_DISABLE_LOGS - // init llama.cpp llama_backend_init(); llama_numa_init(params.numa); - llama_model * model = NULL; - llama_context * ctx = NULL; - // load the model - std::tie(model, ctx) = llama_init_from_gpt_params(params); + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + const llama_vocab * vocab = llama_model_get_vocab(model); // tokenize the prompt - const bool add_bos = llama_should_add_bos_token(model); - LOG("add_bos tgt: %d\n", add_bos); - std::vector inp; - inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); + inp = common_tokenize(ctx, params.prompt, true, true); + + 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; + + { + // Fill up context ngram cache with tokens from user input: + const int64_t t_start_draft_us = ggml_time_us(); + common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false); + + if (!params.lookup_cache_static.empty()) { + try { + ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static); + } catch (std::ifstream::failure const &) { + LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str()); + exit(1); + } + } + + if (!params.lookup_cache_dynamic.empty()) { + try { + ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic); + } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program + } + + t_draft_flat_us += ggml_time_us() - t_start_draft_us; + } const int max_context_size = llama_n_ctx(ctx); const int max_tokens_list_size = max_context_size - 4; if ((int) inp.size() > max_tokens_list_size) { - fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); + LOG_ERR("%s: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); return 1; } - fprintf(stderr, "\n\n"); + LOG("\n\n"); for (auto id : inp) { - fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str()); + LOG("%s", common_token_to_piece(ctx, id).c_str()); } fflush(stderr); @@ -67,8 +91,8 @@ int main(int argc, char ** argv){ const auto t_enc_start = ggml_time_us(); - llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0)); - llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0)); + llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1)); + llama_decode(ctx, llama_batch_get_one(&inp.back(), 1)); const auto t_enc_end = ggml_time_us(); @@ -76,13 +100,11 @@ int main(int argc, char ** argv){ int n_drafted = 0; int n_accept = 0; - int64_t t_draft_us = 0; - int n_past = inp.size(); bool has_eos = false; - struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams); + struct common_sampler * smpl = common_sampler_init(model, params.sampling); std::vector draft; @@ -97,26 +119,26 @@ int main(int argc, char ** argv){ // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + common_kv_cache_dump_view_seqs(kvc_view, 40); } // print current draft sequence - LOG("drafted %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, draft).c_str()); + LOG_DBG("drafted %s\n", string_from(ctx, draft).c_str()); int i_dft = 0; while (true) { // sample from the target model - llama_token id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_dft); + llama_token id = common_sampler_sample(smpl, ctx, i_dft); - llama_sampling_accept(ctx_sampling, ctx, id, true); + common_sampler_accept(smpl, id, true); - const std::string token_str = llama_token_to_piece(ctx, id); + const std::string token_str = common_token_to_piece(ctx, id); if (!params.use_color) { - printf("%s", token_str.c_str()); + LOG("%s", token_str.c_str()); } - if (id == llama_token_eos(model)) { + if (llama_vocab_is_eog(vocab, id)) { has_eos = true; } @@ -124,31 +146,43 @@ int main(int argc, char ** argv){ // check if the target token matches the draft if (i_dft < (int) draft.size() && id == draft[i_dft]) { - LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str()); + LOG_DBG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str()); ++n_accept; ++n_past; ++i_dft; inp.push_back(id); + { + // Update context ngram cache with the newly accepted token: + const int64_t t_start_draft_us = ggml_time_us(); + common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } if (params.use_color) { // color accepted draft token - printf("\033[34m%s\033[0m", token_str.c_str()); + LOG("\033[34m%s\033[0m", token_str.c_str()); fflush(stdout); } continue; } if (params.use_color) { - printf("%s", token_str.c_str()); + LOG("%s", token_str.c_str()); } fflush(stdout); - LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str()); + LOG_DBG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str()); draft.clear(); draft.push_back(id); inp.push_back(id); + { + // Update context ngram cache with the newly accepted token: + const int64_t t_start_draft_us = ggml_time_us(); + common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false); + t_draft_us += ggml_time_us() - t_start_draft_us; + } break; } @@ -160,47 +194,22 @@ int main(int argc, char ** argv){ // clean the cache of draft tokens that weren't accepted llama_kv_cache_seq_rm(ctx, 0, n_past, -1); - llama_batch_clear(batch_tgt); - llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true); - - // generate n_pred tokens through prompt lookup - auto prompt_lookup = [&]() -> void { - const int inp_size = inp.size(); - for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){ - const llama_token * ngram = &inp[inp_size - ngram_size]; - - for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) { - bool match = true; - for (int j = 0; j < ngram_size; ++j) { - if (inp[i + j] != ngram[j]) { - match = false; - break; - } - } - - if (match) { - const int startIdx = i + ngram_size; - const int endIdx = startIdx + n_draft; - if (endIdx < inp_size) { - for (int j = startIdx; j < endIdx; ++j) { - LOG(" - draft candidate %d: %d\n", j, inp[j]); - draft.push_back(inp[j]); - llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true); - ++n_drafted; - } - return; - } - } - } - } - return; - }; + common_batch_clear(batch_tgt); + common_batch_add(batch_tgt, draft[0], n_past, { 0 }, true); + // Draft already contains a single token sampled from the model: + GGML_ASSERT(draft.size() == 1); + GGML_ASSERT(draft[0] == inp.back()); const int64_t t_start_draft_us = ggml_time_us(); - prompt_lookup(); + common_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static); + + for (size_t i = 1; i < draft.size(); ++i) { + common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true); + } t_draft_us += ggml_time_us() - t_start_draft_us; + n_drafted += draft.size() - 1; llama_decode(ctx, batch_tgt); ++n_past; @@ -210,32 +219,35 @@ int main(int argc, char ** argv){ auto t_dec_end = ggml_time_us(); - LOG_TEE("\n\n"); + // Update dynamic ngram cache with context ngram cache and save it to disk: + common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context); + common_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic); - LOG_TEE("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_TEE("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("\n\n"); - LOG_TEE("\n"); - LOG_TEE("n_draft = %d\n", n_draft); - LOG_TEE("n_predict = %d\n", n_predict); - LOG_TEE("n_drafted = %d\n", n_drafted); - LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\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("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3); + LOG_INF("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n", t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us)); - LOG_TEE("n_accept = %d\n", n_accept); - LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + LOG_INF("n_accept = %d\n", n_accept); + LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); - LOG_TEE("\ntarget:\n"); - llama_print_timings(ctx); + LOG_INF("\ntarget:\n\n"); + common_perf_print(ctx, smpl); + + common_sampler_free(smpl); - llama_sampling_free(ctx_sampling); llama_batch_free(batch_tgt); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); - fprintf(stderr, "\n\n"); + LOG("\n\n"); return 0; } diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt deleted file mode 100644 index deb77d588..000000000 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ /dev/null @@ -1,33 +0,0 @@ -cmake_minimum_required(VERSION 3.12) -project("main-cmake-pkg" C CXX) -set(TARGET main-cmake-pkg) - -find_package(Llama 0.0.1 REQUIRED) - -# Bake common functionality in with target. Because applications -# using the relocatable Llama package should be outside of the -# source tree, main-cmake-pkg pretends the dependencies are built-in. -set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common") -add_library(common OBJECT) -file(GLOB _common_files - "${_common_path}/*.h" - "${_common_path}/*.cpp" -) -target_sources(common PRIVATE ${_common_files}) - -# If the common project was part of "main-cmake-pkg" the transient -# defines would automatically be attached. Because the common func- -# tionality is separate, but dependent upon the defines, it must be -# explicitly extracted from the "llama" target. -# -get_target_property(_llama_transient_defines llama - INTERFACE_COMPILE_DEFINITIONS) - -target_compile_definitions(common PRIVATE "${_llama_transient_defines}") - -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) - diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md deleted file mode 100644 index 6d665f28f..000000000 --- a/examples/main-cmake-pkg/README.md +++ /dev/null @@ -1,37 +0,0 @@ -# llama.cpp/example/main-cmake-pkg - -This program builds the [main](../main) application using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree. - -## Building - -Because this example is "outside of the source tree", it is important to first build/install llama.cpp using CMake. An example is provided here, but please see the [llama.cpp build instructions](../..) for more detailed build instructions. - -### Considerations - -When hardware acceleration libraries are used (e.g. CUBlas, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_. - -### Build llama.cpp and install to C:\LlamaCPP directory - -In this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`. - -```cmd -git clone https://github.com/ggerganov/llama.cpp -cd llama.cpp -mkdir build -cd build -cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64 -cmake --build . --config Release -cmake --install . --prefix C:/LlamaCPP -``` - -### Build main-cmake-pkg - - -```cmd -cd ..\examples\main-cmake-pkg -mkdir build -cd build -cmake .. -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64 -cmake --build . --config Release -cmake --install . --prefix C:/MyLlamaApp -``` diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index d532980b7..af3d9150f 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET main) +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 7f84e4262..ceaed42f6 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -1,6 +1,6 @@ -# llama.cpp/example/main +# llama.cpp/examples/main -This example program allows you to use various LLaMA language models in an easy and efficient way. It is specifically designed to work with the [llama.cpp](https://github.com/ggerganov/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. +This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggerganov/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. ## Table of Contents @@ -17,73 +17,71 @@ This example program allows you to use various LLaMA language models in an easy To get started right away, run the following command, making sure to use the correct path for the model you have: -#### Unix-based systems (Linux, macOS, etc.): +First, we will need to download a model. In these examples, we will use the Gemma model from the ggml-org repo on Hugging Face. +[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) + +Once downloaded, place your model in the models folder in llama.cpp. + +### Unix-based systems (Linux, macOS, etc.): + +##### Input prompt (One-and-done) ```bash -./main -m models/7B/ggml-model.bin --prompt "Once upon a time" +./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --prompt "Once upon a time" ``` - -#### Windows: - -```powershell -main.exe -m models\7B\ggml-model.bin --prompt "Once upon a time" -``` - -For an interactive experience, try this command: - -#### Unix-based systems (Linux, macOS, etc.): +##### Conversation mode (Allow for continuous interaction with the model) ```bash -./main -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -p \ -'User: Hi -AI: Hello. I am an AI chatbot. Would you like to talk? -User: Sure! -AI: What would you like to talk about? -User:' +./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf -cnv --chat-template gemma ``` -#### Windows: - -```powershell -main.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -e -p "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:" -``` - -The following command generates "infinite" text from a starting prompt (you can use `Ctrl-C` to stop it): - -#### Unix-based systems (Linux, macOS, etc.): - +##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it): ```bash -./main -m models/7B/ggml-model.bin --ignore-eos -n -1 --random-prompt +./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` -#### Windows: +### Windows: + +##### Input prompt (One-and-done) +```powershell +./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --prompt "Once upon a time" +``` +##### Conversation mode (Allow for continuous interaction with the model) ```powershell -main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt +./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -cnv --chat-template gemma +``` + +#### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it): + +```powershell +llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` ## Common Options -In this section, we cover the most commonly used options for running the `main` program with the LLaMA models: +In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models: -- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`). +- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/gemma-1.1-7b-it.Q4_K_M.gguf`; inferred from `--model-url` if set). +- `-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. -- `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models. - `-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. ## Input Prompts -The `main` program provides several ways to interact with the LLaMA models using input prompts: +The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts: - `--prompt PROMPT`: Provide a prompt directly as a command-line option. - `--file FNAME`: Provide a file containing a prompt or multiple prompts. - `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.) -- `--random-prompt`: Start with a randomized prompt. ## Interaction -The `main` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive`, `--interactive-first`, and `--instruct`. +The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`. In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing. @@ -91,7 +89,7 @@ In interactive mode, users can participate in text generation by injecting their - `-i, --interactive`: Run the program in interactive mode, allowing users to engage in real-time conversations or provide specific instructions to the model. - `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation. -- `-ins, --instruct`: Run the program in instruction mode, which is specifically designed to work with Alpaca models that excel in completing tasks based on user instructions. +- `-cnv, --conversation`: Run the program in conversation mode (does not print special tokens and suffix/prefix, use default chat template) (default: false) - `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text. By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs. @@ -109,7 +107,7 @@ To overcome this limitation, you can use the `--in-prefix` flag to add a space o The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./main -r "User:" --in-prefix " " +./llama-cli -r "User:" --in-prefix " " ``` ### In-Suffix @@ -117,18 +115,15 @@ The `--in-prefix` flag is used to add a prefix to your input, primarily, this is The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./main -r "User:" --in-prefix " " --in-suffix "Assistant:" +./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:" ``` +When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled -### Instruction Mode +### Chat templates -Instruction mode is particularly useful when working with Alpaca models, which are designed to follow user instructions for specific tasks: + `--chat-template JINJA_TEMPLATE`: This option sets a custom jinja chat template. It accepts a string, not a file name. Default: template taken from model's metadata. Llama.cpp only supports [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template). These include llama2, llama3, gemma, monarch, chatml, orion, vicuna, vicuna-orca, deepseek, command-r, zephyr. When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled. -- `-ins, --instruct`: Enable instruction mode to leverage the capabilities of Alpaca models in completing tasks based on user-provided instructions. - -Technical detail: the user's input is internally prefixed with the reverse prompt (or `### Instruction:` as the default), and followed by `### Response:` (except if you just press Return without any input, to keep generating a longer response). - -By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs. + Example usage: `--chat-template gemma` ## Context Management @@ -136,13 +131,11 @@ During text generation, LLaMA models have a limited context size, which means th ### Context Size -The `--ctx-size` option allows you to set the size of the prompt context used by the LLaMA models during text generation. A larger context size helps the model to better comprehend and generate responses for longer input or conversations. - -- `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results. +- `-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 -Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. +Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model has a context length (max sequence length) of 4096 (4k) and the fine-tuned model has 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. - `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. @@ -160,15 +153,17 @@ The following options allow you to control the text generation process and fine- ### Number of Tokens to Predict -- `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity, -2 = until context filled) +- `-n N, --predict N`: Set the number of tokens to predict when generating text (default: -1, -1 = infinity, -2 = until context filled) -The `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text. +The `--predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text. -A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--n-keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in significant pause in output. +A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in a significant pause in output. If the pause is undesirable, a value of -2 will stop generation immediately when the context is filled. -It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `n-predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter. +The `--no-context-shift` option allows you to stop the infinite text generation once the finite context window is full. + +It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode, text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `--predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter. ### Temperature @@ -176,21 +171,40 @@ It is important to note that the generated text may be shorter than the specifie Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run. -Example usage: `--temp 0.5` +Example usage: `--temp 0` ### Repeat Penalty -- `--repeat-penalty N`: Control the repetition of token sequences in the generated text (default: 1.1). +- `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled). - `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size). -- `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty. -The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.1. +The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1. The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`). -Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases. +### DRY Repetition Penalty -Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl` +DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)). + +- `--dry-multiplier N`: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled). +- `--dry-base N`: Set the DRY sampling base value (default: 1.75). +- `--dry-allowed-length N`: Set the 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 a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: `['\n', ':', '"', '*']`. If the string `"none"` is supplied, no sequence breakers are used. + +The `dry-multiplier` option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8. + +The `dry-base` option sets the base value for the exponential penalty calculation in DRY sampling. Higher values lead to more aggressive penalization of repetitions. + +The `dry-allowed-length` option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words. + +The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of -1 considers the entire context. Use a positive value to limit the consideration to a specific number of recent tokens. + +The `dry-sequence-breaker` option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied. + +DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence. + +Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n -1 --dry-sequence-breaker "—" --dry-sequence-breaker "##"` ### Top-K Sampling @@ -208,22 +222,14 @@ Top-p sampling, also known as nucleus sampling, is another text generation metho Example usage: `--top-p 0.95` -### Min P Sampling +### Min-P Sampling -- `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.05). +- `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.1). The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. Example usage: `--min-p 0.05` -### Tail Free Sampling (TFS) - -- `--tfs N`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled). - -Tail free sampling (TFS) is a text generation technique that aims to reduce the impact of less likely tokens, which may be less relevant, less coherent, or nonsensical, on the output. Similar to Top-P it tries to determine the bulk of the most likely tokens dynamically. But TFS filters out logits based on the second derivative of their probabilities. Adding tokens is stopped after the sum of the second derivatives reaches the parameter z. In short: TFS looks how quickly the probabilities of the tokens decrease and cuts off the tail of unlikely tokens using the parameter z. Typical values for z are in the range of 0.9 to 0.95. A value of 1.0 would include all tokens, and thus disables the effect of TFS. - -Example usage: `--tfs 0.95` - ### Locally Typical Sampling - `--typical N`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled). @@ -246,6 +252,19 @@ The `--mirostat-ent` option sets the Mirostat target entropy (tau), which repres Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0` +### XTC Sampling + +- `--xtc-probability N`: Sets the chance for token removal (checked once on sampler start) (default: 0.0). +- `--xtc-threshold N`: Sets a minimum probability threshold for tokens to be removed (default: 0.1). + +Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-probability` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one. + +By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models. + +Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 --xtc-probability 0.5`. + +Example usage: `--xtc-probability 0.5 --xtc-threshold 0.1` + ### Logit Bias - `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion. @@ -285,29 +304,38 @@ These options help improve the performance and memory usage of the LLaMA models. - `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes. - `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node. -- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitraty core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus. +- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitrary core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus. These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root. -### Memory Float 32 - -- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended. - ### Batch Size -- `-b N, --batch-size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations. +- `-ub N`, `--ubatch-size N`: Physical batch size. This is the maximum number of tokens that may be processed at a time. Increasing this value may improve performance during prompt processing, at the expense of higher memory usage. Default: `512`. + +- `-b N`, `--batch-size N`: Logical batch size. Increasing this value above the value of the physical batch size may improve prompt processing performance when using multiple GPUs with pipeline parallelism. Default: `2048`. ### Prompt Caching - `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation. -### Grammars +### Grammars & JSON schemas - `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax. +- `--json-schema SCHEMA`: Specify a [JSON schema](https://json-schema.org/) to constrain model output to (e.g. `{}` for any JSON object, or `{"items": {"type": "string", "minLength": 10, "maxLength": 100}, "minItems": 10}` for a JSON array of strings with size constraints). If a schema uses external `$ref`s, you should use `--grammar "$( python examples/json_schema_to_grammar.py myschema.json )"` instead. + ### Quantization -For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run). +For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize). + +## LoRA (Low-Rank Adaptation) adapters + +- `--lora FNAME`: Optional path to a LoRA adapter to use with scaling of 1.0. Can be mixed with `--lora-scaled` and can be repeated to use multiple adapters. +- `--lora-scaled FNAME`: Optional path to a LoRA adapter with user-defined scaling. Can be mixed with `--lora` and can repeated to use multiple adapters. + +You can add LoRA adapters using `--lora` or `--lora-scaled`. For example: `--lora my_adapter_1.gguf --lora my_adapter_2.gguf ...` or `--lora-scaled lora_task_A.gguf 0.5 --lora-scaled lora_task_B.gguf 0.5`. + +LoRA adapters should be in GGUF format. To convert from Hugging Face format use the `convert-lora-to-gguf.py` script. LoRA adapters are loaded separately and applied during inference - they are not merged with the main model. This means that mmap model loading is fully supported when using LoRA adapters. The old `--lora-base` flag has been removed now that merging is no longer performed. ## Additional Options @@ -315,8 +343,7 @@ 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. -- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. -- `-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. Requires cuBLAS. -- `-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. Requires cuBLAS. -- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. -- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. +- `--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 34e84d0d4..e654d3542 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -1,11 +1,11 @@ +#include "arg.h" #include "common.h" - #include "console.h" +#include "log.h" +#include "sampling.h" #include "llama.h" +#include "chat-template.hpp" -#include -#include -#include #include #include #include @@ -31,110 +31,70 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +static const char * DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant"; + static llama_context ** g_ctx; static llama_model ** g_model; -static gpt_params * g_params; +static common_sampler ** g_smpl; +static common_params * g_params; static std::vector * g_input_tokens; static std::ostringstream * g_output_ss; static std::vector * g_output_tokens; -static bool is_interacting = false; +static bool is_interacting = false; +static bool need_insert_eot = false; -static bool file_exists(const std::string &path) { +static void print_usage(int argc, char ** argv) { + (void) argc; + + LOG("\nexample usage:\n"); + LOG("\n text generation: %s -m your_model.gguf -p \"I believe the meaning of life is\" -n 128\n", argv[0]); + LOG("\n chat (conversation): %s -m your_model.gguf -p \"You are a helpful assistant\" -cnv\n", argv[0]); + LOG("\n"); +} + +static bool file_exists(const std::string & path) { std::ifstream f(path.c_str()); return f.good(); } -static bool file_is_empty(const std::string &path) { +static bool file_is_empty(const std::string & path) { std::ifstream f; f.exceptions(std::ifstream::failbit | std::ifstream::badbit); f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate); return f.tellg() == 0; } -static void write_logfile( - const llama_context * ctx, const gpt_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 = get_sortable_timestamp(); - - const bool success = create_directory_with_parents(params.logdir); - if (!success) { - fprintf(stderr, "%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) { - fprintf(stderr, "%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)); - dump_non_result_info_yaml(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"); - - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); - - llama_dump_timing_info_yaml(logfile, ctx); - fclose(logfile); -} - #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) static void sigint_handler(int signo) { if (signo == SIGINT) { if (!is_interacting && g_params->interactive) { - is_interacting = true; + is_interacting = true; + need_insert_eot = true; } else { console::cleanup(); - printf("\n"); - llama_print_timings(*g_ctx); - write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); + LOG("\n"); + common_perf_print(*g_ctx, *g_smpl); + + // make sure all logs are flushed + LOG("Interrupted by user\n"); + common_log_pause(common_log_main()); + _exit(130); } } } #endif -static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) { - (void) level; - (void) user_data; - LOG_TEE("%s", text); -} - int main(int argc, char ** argv) { - gpt_params params; + common_params params; g_params = ¶ms; - - if (!gpt_params_parse(argc, argv, params)) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) { return 1; } - llama_sampling_params & sparams = params.sparams; -#ifndef LOG_DISABLE_LOGS - log_set_target(log_filename_generator("main", "log")); - LOG_TEE("Log start\n"); - log_dump_cmdline(argc, argv); - llama_log_set(llama_log_callback_logTee, nullptr); -#endif // LOG_DISABLE_LOGS + common_init(); - // TODO: Dump params ? - //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity)); + auto & sparams = params.sampling; // save choice to use color for later // (note for later: this is a slightly awkward choice) @@ -142,155 +102,207 @@ int main(int argc, char ** argv) { atexit([]() { console::cleanup(); }); if (params.logits_all) { - printf("\n************\n"); - printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); - printf("************\n\n"); + LOG_ERR("************\n"); + LOG_ERR("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); + LOG_ERR("************\n\n"); return 0; } if (params.embedding) { - printf("\n************\n"); - printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__); - printf("************\n\n"); + LOG_ERR("************\n"); + LOG_ERR("%s: please use the 'embedding' tool for embedding calculations\n", __func__); + LOG_ERR("************\n\n"); return 0; } if (params.n_ctx != 0 && params.n_ctx < 8) { - LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); + LOG_WRN("%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; } if (params.rope_freq_base != 0.0) { - LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); + LOG_WRN("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); } if (params.rope_freq_scale != 0.0) { - LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); + LOG_WRN("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); } - LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); - LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); + LOG_INF("%s: llama backend init\n", __func__); - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); - } - - LOG_TEE("%s: seed = %u\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } - - LOG("%s: llama backend init\n", __func__); llama_backend_init(); llama_numa_init(params.numa); - llama_model * model; - llama_context * ctx; - llama_context * ctx_guidance = NULL; + llama_model * model = nullptr; + llama_context * ctx = nullptr; + common_sampler * smpl = nullptr; + g_model = &model; g_ctx = &ctx; + g_smpl = &smpl; + + std::vector chat_msgs; // load the model and apply lora adapter, if any - LOG("%s: load the model and apply lora adapter, if any\n", __func__); - std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (sparams.cfg_scale > 1.f) { - struct llama_context_params lparams = llama_context_params_from_gpt_params(params); - ctx_guidance = llama_new_context_with_model(model, lparams); - } + LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); + common_init_result llama_init = common_init_from_params(params); + + model = llama_init.model.get(); + ctx = llama_init.context.get(); if (model == NULL) { - LOG_TEE("%s: error: unable to load model\n", __func__); + LOG_ERR("%s: error: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const llama_vocab * vocab = llama_model_get_vocab(model); + auto chat_templates = common_chat_templates_from_model(model, params.chat_template); + + 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 = + ggml_threadpool_params_from_cpu_params(params.cpuparams); + + set_process_priority(params.cpuparams.priority); + + struct ggml_threadpool * threadpool_batch = NULL; + if (!ggml_threadpool_params_match(&tpp, &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; + } + + // Start the non-batch threadpool in the paused state + tpp.paused = true; + } + + 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; + } + + llama_attach_threadpool(ctx, threadpool, threadpool_batch); + + const int n_ctx_train = llama_model_n_ctx_train(model); const int n_ctx = llama_n_ctx(ctx); - LOG("n_ctx: %d\n", n_ctx); if (n_ctx > n_ctx_train) { - LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n", - __func__, n_ctx_train, n_ctx); + LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx); + } + + // auto enable conversation mode if chat template is available + const bool has_chat_template = chat_templates.has_explicit_template && chat_templates.template_default; + if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) { + if (has_chat_template) { + LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__); + params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED; + } else { + params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED; + } + } + + // in case user force-activate conversation mode (via -cnv) without proper chat template, we show a warning + if (params.conversation_mode && !has_chat_template) { + LOG_WRN("%s: chat template is not available or is not supported. This may cause the model to output suboptimal responses\n", __func__); + } + + // print chat template example in conversation mode + if (params.conversation_mode) { + if (params.enable_chat_template) { + LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(*chat_templates.template_default, params.use_jinja).c_str()); + } else { + LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__); + } } // print system information { - LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + LOG_INF("\n"); } std::string path_session = params.path_prompt_cache; std::vector session_tokens; if (!path_session.empty()) { - LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str()); + LOG_INF("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str()); if (!file_exists(path_session)) { - LOG_TEE("%s: session file does not exist, will create.\n", __func__); + LOG_INF("%s: session file does not exist, will create.\n", __func__); } else if (file_is_empty(path_session)) { - LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__); + LOG_INF("%s: The session file is empty. A new session will be initialized.\n", __func__); } else { // The file exists and is not empty session_tokens.resize(n_ctx); size_t n_token_count_out = 0; - if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) { - LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str()); + if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) { + LOG_ERR("%s: failed to load session file '%s'\n", __func__, path_session.c_str()); return 1; } session_tokens.resize(n_token_count_out); - llama_set_rng_seed(ctx, params.seed); - LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size()); + LOG_INF("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size()); } } - const bool add_bos = llama_should_add_bos_token(model); - LOG("add_bos: %d\n", add_bos); + const bool add_bos = llama_vocab_get_add_bos(vocab) && !params.use_jinja; + if (!llama_model_has_encoder(model)) { + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); + } + + LOG_DBG("n_ctx: %d, add_bos: %d\n", n_ctx, add_bos); std::vector embd_inp; - if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) { - LOG("tokenize the prompt\n"); - if (params.chatml) { - params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>"; - } - embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); - } else { - LOG("use session tokens\n"); - embd_inp = session_tokens; - } + auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) { + common_chat_msg new_msg{role, content, {}}; + auto formatted = common_chat_format_single(*chat_templates.template_default, chat_msgs, new_msg, role == "user", g_params->use_jinja); + chat_msgs.push_back({role, content, {}}); + LOG_DBG("formatted: '%s'\n", formatted.c_str()); + return formatted; + }; - LOG("prompt: \"%s\"\n", log_tostr(params.prompt)); - LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); + { + auto prompt = (params.conversation_mode && params.enable_chat_template) + // format the system prompt in conversation mode (fallback to default if empty) + ? chat_add_and_format("system", params.prompt.empty() ? DEFAULT_SYSTEM_MESSAGE : params.prompt) + // otherwise use the prompt as is + : params.prompt; + if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) { + LOG_DBG("tokenize the prompt\n"); + embd_inp = common_tokenize(ctx, prompt, true, true); + } else { + LOG_DBG("use session tokens\n"); + embd_inp = session_tokens; + } + + LOG_DBG("prompt: \"%s\"\n", prompt.c_str()); + LOG_DBG("tokens: %s\n", string_from(ctx, embd_inp).c_str()); + } // Should not run without any tokens if (embd_inp.empty()) { - embd_inp.push_back(llama_token_bos(model)); - LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); + if (add_bos) { + embd_inp.push_back(llama_vocab_bos(vocab)); + LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str()); + } else { + LOG_ERR("input is empty\n"); + return -1; + } } // Tokenize negative prompt - std::vector guidance_inp; - int guidance_offset = 0; - int original_prompt_len = 0; - if (ctx_guidance) { - LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); - - guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true); - LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); - - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); - LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); - - original_prompt_len = original_inp.size(); - guidance_offset = (int)guidance_inp.size() - original_prompt_len; - LOG("original_prompt_len: %s", log_tostr(original_prompt_len)); - LOG("guidance_offset: %s", log_tostr(guidance_offset)); - } - if ((int) embd_inp.size() > n_ctx - 4) { - LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); + LOG_ERR("%s: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); return 1; } @@ -304,63 +316,41 @@ int main(int argc, char ** argv) { n_matching_session_tokens++; } if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) { - LOG_TEE("%s: using full prompt from session file\n", __func__); + LOG_INF("%s: using full prompt from session file\n", __func__); } else if (n_matching_session_tokens >= embd_inp.size()) { - LOG_TEE("%s: session file has exact match for prompt!\n", __func__); + LOG_INF("%s: session file has exact match for prompt!\n", __func__); } else if (n_matching_session_tokens < (embd_inp.size() / 2)) { - LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", - __func__, n_matching_session_tokens, embd_inp.size()); + LOG_WRN("%s: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", + __func__, n_matching_session_tokens, embd_inp.size()); } else { - LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n", - __func__, n_matching_session_tokens, embd_inp.size()); + LOG_INF("%s: session file matches %zu / %zu tokens of prompt\n", + __func__, n_matching_session_tokens, embd_inp.size()); } // remove any "future" tokens that we might have inherited from the previous session llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1); } - LOGLN( - "recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu", - log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size()); + LOG_DBG("recalculate the cached logits (check): embd_inp.size() %zu, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu\n", + embd_inp.size(), n_matching_session_tokens, embd_inp.size(), session_tokens.size()); // if we will use the cache for the full prompt without reaching the end of the cache, force - // reevaluation of the last token token to recalculate the cached logits + // reevaluation of the last token to recalculate the cached logits if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) { - LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1); + LOG_DBG("recalculate the cached logits (do): session_tokens.resize( %zu )\n", embd_inp.size() - 1); session_tokens.resize(embd_inp.size() - 1); } // number of tokens to keep when resetting context - if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) { + if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) { params.n_keep = (int)embd_inp.size(); } else { params.n_keep += add_bos; // always keep the BOS token } - // prefix & suffix for instruct mode - const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true); - const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true); - - LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str()); - LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str()); - - // chatml prefix & suffix - const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true); - const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true); - - LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str()); - LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str()); - - // in instruct mode, we inject a prefix and a suffix to each input by the user - if (params.instruct) { + if (params.conversation_mode) { params.interactive_first = true; - params.antiprompt.emplace_back("### Instruction:\n\n"); - } - // similar for chatml mode - else if (params.chatml) { - params.interactive_first = true; - params.antiprompt.emplace_back("<|im_start|>user\n"); } // enable interactive mode if interactive start is specified @@ -369,30 +359,20 @@ int main(int argc, char ** argv) { } if (params.verbose_prompt) { - LOG_TEE("\n"); - LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); - LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); + LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); + LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); for (int i = 0; i < (int) embd_inp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); - } - - if (ctx_guidance) { - LOG_TEE("\n"); - LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str()); - LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); - for (int i = 0; i < (int) guidance_inp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str()); - } + LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str()); } if (params.n_keep > add_bos) { - LOG_TEE("%s: static prompt based on n_keep: '", __func__); + LOG_INF("%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { - LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); + LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str()); } - LOG_TEE("'\n"); + LOG_CNT("'\n"); } - LOG_TEE("\n"); + LOG_INF("\n"); } // ctrl+C handling @@ -412,47 +392,56 @@ int main(int argc, char ** argv) { } if (params.interactive) { - LOG_TEE("%s: interactive mode on.\n", __func__); + LOG_INF("%s: interactive mode on.\n", __func__); if (!params.antiprompt.empty()) { for (const auto & antiprompt : params.antiprompt) { - LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str()); + LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str()); if (params.verbose_prompt) { - auto tmp = ::llama_tokenize(ctx, antiprompt, false, true); + auto tmp = common_tokenize(ctx, antiprompt, false, true); for (int i = 0; i < (int) tmp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str()); } } } } if (params.input_prefix_bos) { - LOG_TEE("Input prefix with BOS\n"); + LOG_INF("Input prefix with BOS\n"); } if (!params.input_prefix.empty()) { - LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str()); + LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str()); if (params.verbose_prompt) { - auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true); + auto tmp = common_tokenize(ctx, params.input_prefix, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str()); } } } if (!params.input_suffix.empty()) { - LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str()); + LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str()); if (params.verbose_prompt) { - auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true); + auto tmp = common_tokenize(ctx, params.input_suffix, false, true); for (int i = 0; i < (int) tmp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str()); + LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str()); } } } } - LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str()); - LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str()); - LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); + + smpl = common_sampler_init(model, sparams); + if (!smpl) { + LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__); + return 1; + } + + LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl)); + LOG_INF("sampler params: \n%s\n", sparams.print().c_str()); + LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str()); + + LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); // group-attention state // number of grouped KV tokens so far (used only if params.grp_attn_n > 1) @@ -466,25 +455,29 @@ int main(int argc, char ** argv) { GGML_ASSERT(ga_w % ga_n == 0 && "grp_attn_w must be a multiple of grp_attn_n"); // NOLINT //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of grp_attn_w"); // NOLINT //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT - LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w); + LOG_INF("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w); } - LOG_TEE("\n\n"); + LOG_INF("\n"); if (params.interactive) { - const char *control_message; + const char * control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to the AI, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to the AI.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } - LOG_TEE("== Running in interactive mode. ==\n"); + LOG_INF("== Running in interactive mode. ==\n"); #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) - LOG_TEE( " - Press Ctrl+C to interject at any time.\n"); + LOG_INF( " - Press Ctrl+C to interject at any time.\n"); #endif - LOG_TEE( "%s\n", control_message); + LOG_INF( "%s", control_message); + if (params.conversation_mode && params.enable_chat_template && params.prompt.empty()) { + LOG_INF( " - Using default system message. To change it, set a different value via -p PROMPT or -f FILE argument.\n"); + } + LOG_INF("\n"); is_interacting = params.interactive_first; } @@ -498,20 +491,45 @@ int main(int argc, char ** argv) { int n_remain = params.n_predict; int n_consumed = 0; int n_session_consumed = 0; - int n_past_guidance = 0; std::vector input_tokens; g_input_tokens = &input_tokens; std::vector output_tokens; g_output_tokens = &output_tokens; std::ostringstream output_ss; g_output_ss = &output_ss; + std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode // the first thing we will do is to output the prompt, so set color accordingly console::set_display(console::prompt); display = params.display_prompt; std::vector embd; - std::vector embd_guidance; - struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); + // single-token antiprompts + std::vector antiprompt_token; + + for (const std::string & antiprompt : params.antiprompt) { + auto ids = ::common_tokenize(ctx, antiprompt, false, true); + if (ids.size() == 1) { + antiprompt_token.push_back(ids[0]); + } + } + + if (llama_model_has_encoder(model)) { + int enc_input_size = embd_inp.size(); + llama_token * enc_input_buf = embd_inp.data(); + + if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) { + LOG_ERR("%s : failed to eval\n", __func__); + return 1; + } + + llama_token decoder_start_token_id = llama_model_decoder_start_token(model); + if (decoder_start_token_id == LLAMA_TOKEN_NULL) { + decoder_start_token_id = llama_vocab_bos(vocab); + } + + embd_inp.clear(); + embd_inp.push_back(decoder_start_token_id); + } while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict @@ -526,9 +544,8 @@ int main(int argc, char ** argv) { embd.resize(max_embd_size); console::set_display(console::error); - printf("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); + LOG_WRN("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); console::set_display(console::reset); - fflush(stdout); } if (ga_n == 1) { @@ -536,16 +553,22 @@ int main(int argc, char ** argv) { // if we run out of context: // - take the n_keep first tokens from the original prompt (via n_past) // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches - if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) { + + if (n_past + (int) embd.size() >= n_ctx) { + if (!params.ctx_shift){ + LOG_DBG("\n\n%s: context full and context shift is disabled => stopping\n", __func__); + break; + } + if (params.n_predict == -2) { - LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); + LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); break; } const int n_left = n_past - params.n_keep; const int n_discard = n_left/2; - LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", + LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", n_past, n_left, n_ctx, params.n_keep, n_discard); llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); @@ -553,15 +576,11 @@ int main(int argc, char ** argv) { n_past -= n_discard; - if (ctx_guidance) { - n_past_guidance -= n_discard; - } + LOG_DBG("after swap: n_past = %d\n", n_past); - LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); + LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); - LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); - - LOG("clear session path\n"); + LOG_DBG("clear session path\n"); path_session.clear(); } } else { @@ -571,10 +590,10 @@ int main(int argc, char ** argv) { const int bd = (ga_w/ga_n)*(ga_n - 1); const int dd = (ga_w/ga_n) - ib*bd - ga_w; - LOG("\n"); - LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd); - LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n); - LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd); + LOG_DBG("\n"); + LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd); + LOG_DBG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n); + LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd); llama_kv_cache_seq_add(ctx, 0, ga_i, n_past, ib*bd); llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n); @@ -584,7 +603,7 @@ int main(int argc, char ** argv) { ga_i += ga_w/ga_n; - LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i); + LOG_DBG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i); } } @@ -610,65 +629,25 @@ int main(int argc, char ** argv) { } } - // evaluate tokens in batches - // embd is typically prepared beforehand to fit within a batch, but not always - if (ctx_guidance) { - int input_size = 0; - llama_token * input_buf = NULL; - - if (n_past_guidance < (int) guidance_inp.size()) { - // Guidance context should have the same data with these modifications: - // - // * Replace the initial prompt - // * Shift everything by guidance_offset - embd_guidance = guidance_inp; - if (embd.begin() + original_prompt_len < embd.end()) { - embd_guidance.insert( - embd_guidance.end(), - embd.begin() + original_prompt_len, - embd.end() - ); - } - - input_buf = embd_guidance.data(); - input_size = embd_guidance.size(); - - LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str()); - } else { - input_buf = embd.data(); - input_size = embd.size(); - } - - for (int i = 0; i < input_size; i += params.n_batch) { - int n_eval = std::min(input_size - i, params.n_batch); - if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) { - LOG_TEE("%s : failed to eval\n", __func__); - return 1; - } - - n_past_guidance += n_eval; - } - } - for (int i = 0; i < (int) embd.size(); i += params.n_batch) { int n_eval = (int) embd.size() - i; if (n_eval > params.n_batch) { n_eval = params.n_batch; } - LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); + LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); - if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) { - LOG_TEE("%s : failed to eval\n", __func__); + if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { + LOG_ERR("%s : failed to eval\n", __func__); return 1; } n_past += n_eval; - LOG("n_past = %d\n", n_past); + LOG_DBG("n_past = %d\n", n_past); // Display total tokens alongside total time if (params.n_print > 0 && n_past % params.n_print == 0) { - LOG_TEE("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx); + LOG_DBG("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx); } } @@ -679,22 +658,21 @@ int main(int argc, char ** argv) { } embd.clear(); - embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { // optionally save the session on first sample (for faster prompt loading next time) if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { need_to_save_session = false; - llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); + llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); - LOG("saved session to %s\n", path_session.c_str()); + LOG_DBG("saved session to %s\n", path_session.c_str()); } - const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); + const llama_token id = common_sampler_sample(smpl, ctx, -1); - llama_sampling_accept(ctx_sampling, ctx, id, true); + common_sampler_accept(smpl, id, /* accept_grammar= */ true); - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); + // LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str()); embd.push_back(id); @@ -704,16 +682,16 @@ int main(int argc, char ** argv) { // decrement remaining sampling budget --n_remain; - LOG("n_remain: %d\n", n_remain); + LOG_DBG("n_remain: %d\n", n_remain); } else { // some user input remains from prompt or interaction, forward it to processing - LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); + LOG_DBG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); // push the prompt in the sampling context in order to apply repetition penalties later // for the prompt, we don't apply grammar rules - llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false); + common_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false); ++n_consumed; if ((int) embd.size() >= params.n_batch) { @@ -725,18 +703,24 @@ int main(int argc, char ** argv) { // display text if (input_echo && display) { for (auto id : embd) { - const std::string token_str = llama_token_to_piece(ctx, id); - printf("%s", token_str.c_str()); + const std::string token_str = common_token_to_piece(ctx, id, params.special); + // Console/Stream Output + LOG("%s", token_str.c_str()); + + // Record Displayed Tokens To Log + // Note: Generated tokens are created one by one hence this check if (embd.size() > 1) { + // Incoming Requested Tokens input_tokens.push_back(id); } else { + // Outgoing Generated Tokens output_tokens.push_back(id); output_ss << token_str; } } - fflush(stdout); } + // reset color to default if there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { console::set_display(console::reset); @@ -748,7 +732,7 @@ int main(int argc, char ** argv) { // check for reverse prompt in the last n_prev tokens if (!params.antiprompt.empty()) { const int n_prev = 32; - const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev); + const std::string last_output = common_sampler_prev_str(smpl, ctx, n_prev); is_antiprompt = false; // Check if each of the reverse prompts appears at the end of the output. @@ -769,46 +753,62 @@ int main(int argc, char ** argv) { } } + // check for reverse prompt using special tokens + llama_token last_token = common_sampler_last(smpl); + if (std::find(antiprompt_token.begin(), antiprompt_token.end(), last_token) != antiprompt_token.end()) { + if (params.interactive) { + is_interacting = true; + } + is_antiprompt = true; + } + if (is_antiprompt) { - LOG("found antiprompt: %s\n", last_output.c_str()); + LOG_DBG("found antiprompt: %s\n", last_output.c_str()); } } - // deal with end of text token in interactive mode - if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) { - LOG("found EOS token\n"); + // deal with end of generation tokens in interactive mode + if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) { + LOG_DBG("found an EOG token\n"); if (params.interactive) { if (!params.antiprompt.empty()) { // tokenize and inject first reverse prompt - const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true); + const auto first_antiprompt = common_tokenize(ctx, params.antiprompt.front(), false, true); embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); is_antiprompt = true; } + if (params.enable_chat_template) { + chat_add_and_format("assistant", assistant_ss.str()); + } is_interacting = true; - printf("\n"); - } else if (params.instruct || params.chatml) { - is_interacting = true; + LOG("\n"); } } - if (n_past > 0 && is_interacting) { - LOG("waiting for user input\n"); + // if current token is not EOG, we add it to current assistant message + if (params.conversation_mode) { + const auto id = common_sampler_last(smpl); + assistant_ss << common_token_to_piece(ctx, id, false); + } - if (params.instruct || params.chatml) { - printf("\n> "); + if (n_past > 0 && is_interacting) { + LOG_DBG("waiting for user input\n"); + + if (params.conversation_mode) { + LOG("\n> "); } if (params.input_prefix_bos) { - LOG("adding input prefix BOS token\n"); - embd_inp.push_back(llama_token_bos(model)); + LOG_DBG("adding input prefix BOS token\n"); + embd_inp.push_back(llama_vocab_bos(vocab)); } std::string buffer; - if (!params.input_prefix.empty()) { - LOG("appending input prefix: '%s'\n", params.input_prefix.c_str()); - printf("%s", params.input_prefix.c_str()); + if (!params.input_prefix.empty() && !params.conversation_mode) { + LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str()); + LOG("%s", params.input_prefix.c_str()); } // color user input only @@ -830,61 +830,54 @@ int main(int argc, char ** argv) { // Entering a empty line lets the user pass control back if (buffer.length() > 1) { // append input suffix if any - if (!params.input_suffix.empty()) { - LOG("appending input suffix: '%s'\n", params.input_suffix.c_str()); - printf("%s", params.input_suffix.c_str()); + if (!params.input_suffix.empty() && !params.conversation_mode) { + LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str()); + LOG("%s", params.input_suffix.c_str()); } - LOG("buffer: '%s'\n", buffer.c_str()); + LOG_DBG("buffer: '%s'\n", buffer.c_str()); const size_t original_size = embd_inp.size(); - // instruct mode: insert instruction prefix - if (params.instruct && !is_antiprompt) { - LOG("inserting instruction prefix\n"); - n_consumed = embd_inp.size(); - embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); - } - // chatml mode: insert user chat prefix - if (params.chatml && !is_antiprompt) { - LOG("inserting chatml prefix\n"); - n_consumed = embd_inp.size(); - embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end()); - } if (params.escape) { - process_escapes(buffer); + string_process_escapes(buffer); } - const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); - const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); - const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); - LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); + bool format_chat = params.conversation_mode && params.enable_chat_template; + std::string user_inp = format_chat + ? chat_add_and_format("user", std::move(buffer)) + : std::move(buffer); + // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix) + const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true); + const auto line_inp = common_tokenize(ctx, user_inp, false, format_chat); + const auto line_sfx = common_tokenize(ctx, params.input_suffix, false, true); + + LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str()); + + // if user stop generation mid-way, we must add EOT to finish model's last response + if (need_insert_eot && format_chat) { + llama_token eot = llama_vocab_eot(vocab); + embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_vocab_eos(vocab) : eot); + need_insert_eot = false; + } embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end()); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end()); - // instruct mode: insert response suffix - if (params.instruct) { - LOG("inserting instruction suffix\n"); - embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - } - // chatml mode: insert assistant chat suffix - if (params.chatml) { - LOG("inserting chatml suffix\n"); - embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end()); - } - for (size_t i = original_size; i < embd_inp.size(); ++i) { const llama_token token = embd_inp[i]; output_tokens.push_back(token); - output_ss << llama_token_to_piece(ctx, token); + output_ss << common_token_to_piece(ctx, token); } + // reset assistant message + assistant_ss.str(""); + n_remain -= line_inp.size(); - LOG("n_remain: %d\n", n_remain); + LOG_DBG("n_remain: %d\n", n_remain); } else { - LOG("empty line, passing control back\n"); + LOG_DBG("empty line, passing control back\n"); } input_echo = false; // do not echo this again @@ -892,15 +885,15 @@ int main(int argc, char ** argv) { if (n_past > 0) { if (is_interacting) { - llama_sampling_reset(ctx_sampling); + common_sampler_reset(smpl); } is_interacting = false; } } - // end of text token - if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) { - LOG_TEE(" [end of text]\n"); + // end of generation + if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !(params.interactive)) { + LOG(" [end of text]\n"); break; } @@ -913,23 +906,19 @@ int main(int argc, char ** argv) { } if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) { - LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str()); - llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); + LOG("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str()); + llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); } - llama_print_timings(ctx); - write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); + LOG("\n\n"); + common_perf_print(ctx, smpl); - if (ctx_guidance) { llama_free(ctx_guidance); } - llama_free(ctx); - llama_free_model(model); + common_sampler_free(smpl); - llama_sampling_free(ctx_sampling); llama_backend_free(); -#ifndef LOG_DISABLE_LOGS - LOG_TEE("Log end\n"); -#endif // LOG_DISABLE_LOGS + ggml_threadpool_free_fn(threadpool); + ggml_threadpool_free_fn(threadpool_batch); return 0; } diff --git a/examples/make-ggml.py b/examples/make-ggml.py deleted file mode 100755 index c73485ebf..000000000 --- a/examples/make-ggml.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env python3 -""" -This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them. - -Usage: -python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)] - -Arguments: -- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub. -- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox. -- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used. -- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used. -- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'. -- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created. - -Old quant types (some base model types require these): -- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M -- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L -- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M -- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M - -New quant types (recommended): -- Q2_K: smallest, extreme quality loss - not recommended -- Q3_K: alias for Q3_K_M -- Q3_K_S: very small, very high quality loss -- Q3_K_M: very small, very high quality loss -- Q3_K_L: small, substantial quality loss -- Q4_K: alias for Q4_K_M -- Q4_K_S: small, significant quality loss -- Q4_K_M: medium, balanced quality - recommended -- Q5_K: alias for Q5_K_M -- Q5_K_S: large, low quality loss - recommended -- Q5_K_M: large, very low quality loss - recommended -- Q6_K: very large, extremely low quality loss -- Q8_0: very large, extremely low quality loss - not recommended -- F16: extremely large, virtually no quality loss - not recommended -- F32: absolutely huge, lossless - not recommended -""" -import subprocess -subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True) - -import argparse -import os -from huggingface_hub import snapshot_download - -def main(model, model_type, outname, outdir, quants, keep_fp16): - if not os.path.isdir(model): - print(f"Model not found at {model}. Downloading...") - try: - if outname is None: - outname = model.split('/')[-1] - model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache') - except Exception as e: - raise Exception(f"Could not download the model: {e}") - - if outdir is None: - outdir = f'../models/{outname}' - - if not os.path.isfile(f"{model}/config.json"): - raise Exception(f"Could not find config.json in {model}") - - os.makedirs(outdir, exist_ok=True) - - print("Building llama.cpp") - subprocess.run(f"cd .. && make quantize", shell=True, check=True) - - fp16 = f"{outdir}/{outname}.gguf.fp16.bin" - - print(f"Making unquantised GGUF at {fp16}") - if not os.path.isfile(fp16): - if model_type != "llama": - subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True) - else: - subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True) - else: - print(f"Unquantised GGML already exists at: {fp16}") - - print("Making quants") - for type in quants: - outfile = f"{outdir}/{outname}.gguf.{type}.bin" - print(f"Making {type} : {outfile}") - subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True) - - if not keep_fp16: - os.remove(fp16) - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.') - parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name') - parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.') - parser.add_argument('--outname', default=None, help='Output model(s) name') - parser.add_argument('--outdir', default=None, help='Output directory') - parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types') - parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False) - - args = parser.parse_args() - - main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16) diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt index 319535a6e..847e916de 100644 --- a/examples/parallel/CMakeLists.txt +++ b/examples/parallel/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET parallel) +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 7d11fcd59..7ef43d5e1 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -1,7 +1,10 @@ // A basic application simulating a server with multiple clients. // The clients submit requests to the server and they are processed in parallel. +#include "arg.h" #include "common.h" +#include "sampling.h" +#include "log.h" #include "llama.h" #include @@ -50,8 +53,8 @@ static std::vector k_prompts = { struct client { ~client() { - if (ctx_sampling) { - llama_sampling_free(ctx_sampling); + if (smpl) { + common_sampler_free(smpl); } } @@ -72,7 +75,7 @@ struct client { std::string prompt; std::string response; - struct llama_sampling_context * ctx_sampling = nullptr; + struct common_sampler * smpl = nullptr; }; static void print_date_time() { @@ -81,7 +84,9 @@ static void print_date_time() { char buffer[80]; strftime(buffer, sizeof(buffer), "%Y-%m-%d %H:%M:%S", local_time); - printf("\n\033[35mrun parameters as at %s\033[0m\n", buffer); + LOG_INF("\n"); + LOG_INF("\033[35mrun parameters as of %s\033[0m\n", buffer); + LOG_INF("\n"); } // Define a split string function to ... @@ -98,15 +103,20 @@ static std::vector split_string(const std::string& input, char deli int main(int argc, char ** argv) { srand(1234); - gpt_params params; + common_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) { return 1; } + common_init(); + // number of simultaneous "clients" to simulate const int32_t n_clients = params.n_parallel; + // dedicate one sequence to the system prompt + params.n_parallel += 1; + // requests to simulate const int32_t n_seq = params.n_sequences; @@ -115,42 +125,36 @@ int main(int argc, char ** argv) { const bool dump_kv_cache = params.dump_kv_cache; -#ifndef LOG_DISABLE_LOGS - log_set_target(log_filename_generator("parallel", "log")); - LOG_TEE("Log start\n"); - log_dump_cmdline(argc, argv); -#endif // LOG_DISABLE_LOGS - // init llama.cpp llama_backend_init(); llama_numa_init(params.numa); - llama_model * model = NULL; - llama_context * ctx = NULL; - // load the target model - params.logits_all = true; - std::tie(model, ctx) = llama_init_from_gpt_params(params); + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + const llama_vocab * vocab = llama_model_get_vocab(model); // load the prompts from an external file if there are any if (params.prompt.empty()) { - printf("\n\033[32mNo new questions so proceed with build-in defaults.\033[0m\n"); + LOG_INF("\033[32mNo new questions so proceed with build-in defaults.\033[0m\n"); } else { // Output each line of the input params.prompts vector and copy to k_prompts int index = 0; - printf("\n\033[32mNow printing the external prompt file %s\033[0m\n\n", params.prompt_file.c_str()); + LOG_INF("\033[32mNow printing the external prompt file %s\033[0m\n\n", params.prompt_file.c_str()); std::vector prompts = split_string(params.prompt, '\n'); for (const auto& prompt : prompts) { k_prompts.resize(index + 1); k_prompts[index] = prompt; index++; - printf("%3d prompt: %s\n", index, prompt.c_str()); + LOG_INF("%3d prompt: %s\n", index, prompt.c_str()); } } - fprintf(stderr, "\n\n"); - fflush(stderr); + LOG_INF("\n\n"); const int n_ctx = llama_n_ctx(ctx); @@ -158,11 +162,11 @@ int main(int argc, char ** argv) { for (size_t i = 0; i < clients.size(); ++i) { auto & client = clients[i]; client.id = i; - client.ctx_sampling = llama_sampling_init(params.sparams); + client.smpl = common_sampler_init(model, params.sampling); } std::vector tokens_system; - tokens_system = ::llama_tokenize(ctx, k_system, true); + tokens_system = common_tokenize(ctx, k_system, true); const int32_t n_tokens_system = tokens_system.size(); llama_seq_id g_seq_id = 0; @@ -179,39 +183,39 @@ int main(int argc, char ** argv) { const auto t_main_start = ggml_time_us(); - LOG_TEE("%s: Simulating parallel requests from clients:\n", __func__); - LOG_TEE("%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\n", __func__, n_clients, n_seq, cont_batching, n_tokens_system); - LOG_TEE("\n"); + LOG_INF("%s: Simulating parallel requests from clients:\n", __func__); + LOG_INF("%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\n", __func__, n_clients, n_seq, cont_batching, n_tokens_system); + LOG_INF("\n"); { - LOG_TEE("%s: Evaluating the system prompt ...\n", __func__); + LOG_INF("%s: Evaluating the system prompt ...\n", __func__); for (int32_t i = 0; i < n_tokens_system; ++i) { - llama_batch_add(batch, tokens_system[i], i, { 0 }, false); + common_batch_add(batch, tokens_system[i], i, { 0 }, false); } if (llama_decode(ctx, batch) != 0) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return 1; } // assign the system KV cache to all parallel sequences - for (int32_t i = 1; i < n_clients; ++i) { - llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system); + for (int32_t i = 1; i <= n_clients; ++i) { + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } - LOG_TEE("\n"); + LOG_INF("\n"); } - LOG_TEE("Processing requests ...\n\n"); + LOG_INF("Processing requests ...\n\n"); while (true) { if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + common_kv_cache_dump_view_seqs(kvc_view, 40); } - llama_batch_clear(batch); + common_batch_clear(batch); // decode any currently ongoing sequences for (auto & client : clients) { @@ -221,18 +225,20 @@ int main(int argc, char ** argv) { client.i_batch = batch.n_tokens; - llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true); + common_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true); client.n_decoded += 1; } if (batch.n_tokens == 0) { // all sequences have ended - clear the entire KV cache - for (int i = 0; i < n_clients; ++i) { - llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1); + for (int i = 1; i <= n_clients; ++i) { + llama_kv_cache_seq_rm(ctx, i, -1, -1); + // but keep the system prompt + llama_kv_cache_seq_cp(ctx, 0, i, -1, -1); } - LOG_TEE("%s: clearing the KV cache\n", __func__); + LOG_INF("%s: clearing the KV cache\n", __func__); } // insert new sequences for decoding @@ -248,14 +254,14 @@ int main(int argc, char ** argv) { client.prompt = client.input + "\nAssistant:"; client.response = ""; - llama_sampling_reset(client.ctx_sampling); + common_sampler_reset(client.smpl); // do not prepend BOS because we have a system prompt! std::vector tokens_prompt; - tokens_prompt = ::llama_tokenize(ctx, client.prompt, false); + tokens_prompt = common_tokenize(ctx, client.prompt, false); for (size_t i = 0; i < tokens_prompt.size(); ++i) { - llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false); + common_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false); } // extract the logits only for the last token @@ -267,7 +273,7 @@ int main(int argc, char ** argv) { client.n_decoded = 0; client.i_batch = batch.n_tokens - 1; - LOG_TEE("\033[31mClient %3d, seq %4d, started decoding ...\033[0m\n", client.id, client.seq_id); + LOG_INF("\033[31mClient %3d, seq %4d, started decoding ...\033[0m\n", client.id, client.seq_id); g_seq_id += 1; @@ -304,18 +310,17 @@ int main(int argc, char ** argv) { batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); if (ret != 0) { if (n_batch == 1 || ret < 0) { // if you get here, it means the KV cache is full - try increasing it via the context size - LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); + LOG_ERR("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); return 1; } - LOG("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2); + LOG_ERR("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2); n_cache_miss += 1; @@ -326,7 +331,7 @@ int main(int argc, char ** argv) { continue; } - LOG("%s : decoded batch of %d tokens\n", __func__, n_tokens); + LOG_DBG("%s : decoded batch of %d tokens\n", __func__, n_tokens); for (auto & client : clients) { if (client.i_batch < (int) i || client.i_batch >= (int) (i + n_tokens)) { @@ -336,9 +341,9 @@ int main(int argc, char ** argv) { //printf("client %d, seq %d, token %d, pos %d, batch %d\n", // client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch); - const llama_token id = llama_sampling_sample(client.ctx_sampling, ctx, NULL, client.i_batch - i); + const llama_token id = common_sampler_sample(client.smpl, ctx, client.i_batch - i); - llama_sampling_accept(client.ctx_sampling, ctx, id, true); + common_sampler_accept(client.smpl, id, true); if (client.n_decoded == 1) { // start measuring generation time after the first token to make sure all concurrent clients @@ -346,7 +351,7 @@ int main(int argc, char ** argv) { client.t_start_gen = ggml_time_us(); } - const std::string token_str = llama_token_to_piece(ctx, id); + const std::string token_str = common_token_to_piece(ctx, id); client.response += token_str; client.sampled = id; @@ -355,7 +360,7 @@ int main(int argc, char ** argv) { // client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str()); if (client.n_decoded > 2 && - (id == llama_token_eos(model) || + (llama_vocab_is_eog(vocab, id) || (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) || client.response.find("User:") != std::string::npos || client.response.find('\n') != std::string::npos)) { @@ -366,11 +371,12 @@ int main(int argc, char ** argv) { } // delete only the generated part of the sequence, i.e. keep the system prompt in the cache - llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1); + llama_kv_cache_seq_rm(ctx, client.id + 1, -1, -1); + llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1); const auto t_main_end = ggml_time_us(); - LOG_TEE("\033[31mClient %3d, seq %3d/%3d, prompt %4d t, response %4d t, time %5.2f s, speed %5.2f t/s, cache miss %d \033[0m \nInput: %s\n\033[35mResponse: %s\033[0m\n\n", + LOG_INF("\033[31mClient %3d, seq %3d/%3d, prompt %4d t, response %4d t, time %5.2f s, speed %5.2f t/s, cache miss %d \033[0m \n\nInput: %s\n\033[35mResponse: %s\033[0m\n\n", client.id, client.seq_id, n_seq, client.n_prompt, client.n_decoded, (t_main_end - client.t_start_prompt) / 1e6, (double) (client.n_prompt + client.n_decoded) / (t_main_end - client.t_start_prompt) * 1e6, @@ -393,30 +399,28 @@ int main(int argc, char ** argv) { print_date_time(); - LOG_TEE("\n%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\n", __func__, n_clients, n_seq, cont_batching, n_tokens_system); + LOG_INF("%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\n", __func__, n_clients, n_seq, cont_batching, n_tokens_system); if (params.prompt_file.empty()) { params.prompt_file = "used built-in defaults"; } - LOG_TEE("External prompt file: \033[32m%s\033[0m\n", params.prompt_file.c_str()); - LOG_TEE("Model and path used: \033[32m%s\033[0m\n\n", params.model.c_str()); + LOG_INF("External prompt file: \033[32m%s\033[0m\n", params.prompt_file.c_str()); + LOG_INF("Model and path used: \033[32m%s\033[0m\n\n", params.model.c_str()); - LOG_TEE("Total prompt tokens: %6d, speed: %5.2f t/s\n", n_total_prompt, (double) (n_total_prompt ) / (t_main_end - t_main_start) * 1e6); - LOG_TEE("Total gen tokens: %6d, speed: %5.2f t/s\n", n_total_gen, (double) (n_total_gen ) / (t_main_end - t_main_start) * 1e6); - LOG_TEE("Total speed (AVG): %6s speed: %5.2f t/s\n", "", (double) (n_total_prompt + n_total_gen) / (t_main_end - t_main_start) * 1e6); - LOG_TEE("Cache misses: %6d\n", n_cache_miss); + LOG_INF("Total prompt tokens: %6d, speed: %5.2f t/s\n", n_total_prompt, (double) (n_total_prompt ) / (t_main_end - t_main_start) * 1e6); + LOG_INF("Total gen tokens: %6d, speed: %5.2f t/s\n", n_total_gen, (double) (n_total_gen ) / (t_main_end - t_main_start) * 1e6); + LOG_INF("Total speed (AVG): %6s speed: %5.2f t/s\n", "", (double) (n_total_prompt + n_total_gen) / (t_main_end - t_main_start) * 1e6); + LOG_INF("Cache misses: %6d\n", n_cache_miss); - LOG_TEE("\n"); + LOG_INF("\n"); - llama_print_timings(ctx); + // TODO: print sampling/grammar timings for all clients + llama_perf_context_print(ctx); llama_batch_free(batch); - llama_free(ctx); - llama_free_model(model); - llama_backend_free(); - fprintf(stderr, "\n\n"); + LOG("\n\n"); return 0; } diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt index 3161bf3ef..9bc5110c2 100644 --- a/examples/passkey/CMakeLists.txt +++ b/examples/passkey/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET passkey) +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/passkey/README.md b/examples/passkey/README.md index 4a22bb559..2b8e910f9 100644 --- a/examples/passkey/README.md +++ b/examples/passkey/README.md @@ -1,5 +1,8 @@ # llama.cpp/example/passkey +A passkey retrieval task is an evaluation method used to measure a language +models ability to recall information from long contexts. + See the following PRs for more info: - https://github.com/ggerganov/llama.cpp/pull/3856 @@ -8,5 +11,5 @@ See the following PRs for more info: ### Usage ```bash -make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250 +make -j && ./llama-passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250 ``` diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index 2cbc9e1fa..5953928d4 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -1,4 +1,6 @@ +#include "arg.h" #include "common.h" +#include "log.h" #include "llama.h" #include @@ -6,46 +8,29 @@ #include #include +static void print_usage(int, char ** argv) { + LOG("\nexample usage:\n"); + LOG("\n %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]); + LOG("\n"); +} + int main(int argc, char ** argv) { - gpt_params params; + common_params params; - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]); - return 1 ; + params.n_junk = 250; + params.n_keep = 32; + params.i_pos = -1; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) { + return 1; } - int seed = -1; + common_init(); - int n_junk = 250; // number of times to repeat the junk text - int n_keep = 32; // number of tokens in the prompt prefix - int n_grp = 1; // if more than 1 - perform LongLM SelfExtend - int i_pos = -1; // position of the passkey in the junk text - - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - n_junk = std::stoi(argv[2]); - } - - if (argc >= 4) { - n_grp = std::stoi(argv[3]); - } - - if (argc >= 5) { - i_pos = std::stoi(argv[4]); - } - - if (argc >= 6) { - seed = std::stoi(argv[5]); - } - - if (seed == -1) { - seed = time(NULL); - } - - srand(seed); + int n_junk = params.n_junk; + int n_keep = params.n_keep; + int n_grp = params.grp_attn_n; + int i_pos = params.i_pos; if (i_pos == -1) { i_pos = rand() % n_junk; @@ -76,42 +61,43 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); + llama_model_params model_params = common_model_params_to_llama(params); - model_params.n_gpu_layers = 99; // offload all layers to the GPU - - llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); + llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params); if (model == NULL) { - fprintf(stderr , "%s: error: unable to load model\n" , __func__); + LOG_ERR("%s: unable to load model\n" , __func__); return 1; } + const llama_vocab * vocab = llama_model_get_vocab(model); + // initialize the context - llama_context_params ctx_params = llama_context_default_params(); + llama_context_params ctx_params = common_context_params_to_llama(params); - ctx_params.seed = seed; - ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; - ctx_params.n_batch = 512; - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + ctx_params.n_ctx = llama_model_n_ctx_train(model)*n_grp + n_keep; GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp"); - llama_context * ctx = llama_new_context_with_model(model, ctx_params); - + llama_context * ctx = llama_init_from_model(model, ctx_params); if (ctx == NULL) { - fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + LOG_ERR("%s: failed to create the llama_context\n" , __func__); return 1; } + auto sparams = llama_sampler_chain_default_params(); + + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_greedy()); + // tokenize the prompt std::vector tokens_list; - tokens_list = ::llama_tokenize(ctx, params.prompt, true); + tokens_list = common_tokenize(ctx, params.prompt, true); // tokenize the prefix and use it as a sink - const int n_tokens_prefix = ::llama_tokenize(ctx, prompt_prefix, true).size(); + const int n_tokens_prefix = common_tokenize(ctx, prompt_prefix, true).size(); const int n_tokens_all = tokens_list.size(); @@ -126,16 +112,16 @@ int main(int argc, char ** argv) { const int n_batch = ctx_params.n_batch; const int n_batch_grp = ctx_params.n_batch/n_grp; - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos); + LOG_INF("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos); // print the prompt token-by-token - LOG_TEE("\n"); - LOG_TEE("prefix tokens: %d\n", n_tokens_prefix); - LOG_TEE("prompt tokens: %d\n", n_tokens_all); - //LOG_TEE("prompt: %s\n", params.prompt.c_str()); + LOG_INF("\n"); + LOG_INF("prefix tokens: %d\n", n_tokens_prefix); + LOG_INF("prompt tokens: %d\n", n_tokens_all); + //LOG_INF("prompt: %s\n", params.prompt.c_str()); - llama_batch batch = llama_batch_init(512, 0, 1); + llama_batch batch = llama_batch_init(params.n_batch, 0, 1); int n_past = 0; @@ -153,10 +139,10 @@ int main(int argc, char ** argv) { n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; } - llama_batch_clear(batch); + common_batch_clear(batch); for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) { - llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); + common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); } if (i + n_batch >= n_tokens_all) { @@ -164,11 +150,11 @@ int main(int argc, char ** argv) { } if (llama_decode(ctx, batch) != 0) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_INF("%s: llama_decode() failed\n", __func__); return 1; } - LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all)); + LOG_INF("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all)); if (i + n_batch >= n_tokens_all) { break; @@ -178,7 +164,7 @@ int main(int argc, char ** argv) { for (int i = n_ctx; i < n_tokens_all; i += n_batch) { const int n_discard = n_batch; - LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard); + LOG_INF("%s: shifting KV cache with %d\n", __func__, n_discard); llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); @@ -187,10 +173,10 @@ int main(int argc, char ** argv) { n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; - llama_batch_clear(batch); + common_batch_clear(batch); for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) { - llama_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); + common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false); } if (i + n_batch >= n_tokens_all) { @@ -198,18 +184,18 @@ int main(int argc, char ** argv) { } if (llama_decode(ctx, batch) != 0) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return 1; } - LOG_TEE("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all)); + LOG_INF("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all)); } { const int n_discard = n_past - n_ctx + n_predict; if (n_discard > 0) { - LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard); + LOG_INF("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard); llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); @@ -220,81 +206,69 @@ int main(int argc, char ** argv) { } } - LOG_TEE("\n"); - LOG_TEE("%s: passkey = %d, inserted at position %d / %d (token pos: ~%d)\n", __func__, passkey, i_pos, n_junk, (i_pos * n_tokens_all) / n_junk); - LOG_TEE("\n"); + LOG_INF("\n"); + LOG_INF("%s: passkey = %d, inserted at position %d / %d (token pos: ~%d)\n", __func__, passkey, i_pos, n_junk, (i_pos * n_tokens_all) / n_junk); + LOG_INF("\n"); // main loop int n_cur = n_tokens_all; int n_decode = 0; - LOG_TEE("%s", prompt_suffix.c_str()); - fflush(stdout); + LOG_INF("%s", prompt_suffix.c_str()); const auto t_main_start = ggml_time_us(); while (n_cur <= n_len) { // sample the next token { - auto n_vocab = llama_n_vocab(model); - auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1); + const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1); - std::vector candidates; - candidates.reserve(n_vocab); - - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f }); - } - - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - - // sample the most likely token - const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); - - // is it an end of stream? - if (new_token_id == llama_token_eos(model) || n_cur == n_len) { - LOG_TEE("\n"); + // is it an end of generation? + if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) { + LOG("\n"); break; } - LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str()); - fflush(stdout); + LOG("%s", common_token_to_piece(ctx, new_token_id).c_str()); n_decode += 1; // prepare the next batch - llama_batch_clear(batch); + common_batch_clear(batch); // push this new token for next evaluation - llama_batch_add(batch, new_token_id, n_past++, { 0 }, true); + common_batch_add(batch, new_token_id, n_past++, { 0 }, true); } n_cur += 1; // evaluate the current batch with the transformer model if (llama_decode(ctx, batch)) { - fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); + LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1); return 1; } } - LOG_TEE("\n"); + LOG("\n"); const auto t_main_end = ggml_time_us(); - LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", + LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n", __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); - llama_print_timings(ctx); + LOG("\n"); + llama_perf_context_print(ctx); - fprintf(stderr, "\n"); + LOG("\n"); + + llama_sampler_free(smpl); llama_batch_free(batch); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); llama_backend_free(); diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index 3c76d3221..3e6864093 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET perplexity) +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/README.md b/examples/perplexity/README.md index 50e1af011..33a46d1a2 100644 --- a/examples/perplexity/README.md +++ b/examples/perplexity/README.md @@ -1,21 +1,193 @@ -# perplexity +# Perplexity -TODO +The `perplexity` example can be used to calculate the so-called perplexity value of a language model over a given text corpus. +Perplexity measures how well the model can predict the next token with lower values being better. +Note that perplexity is **not** directly comparable between models, especially if they use different tokenizers. +Also note that finetunes typically result in a higher perplexity value even though the human-rated quality of outputs increases. -## Llama 2 70B Scorechart -Quantization | Model size (GiB) | Perplexity | Delta to fp16 --- | -- | -- | -- -Q4_0 | 36.20 | 3.5550 | 3.61% -Q4_1 | 40.20 | 3.5125 | 2.37% -Q5_0 | 44.20 | 3.4744 | 1.26% -Q2_K | 27.27 | 3.7339 | 8.82% -Q3_K_S | 27.86 | 3.7019 | 7.89% -Q3_K_M | 30.83 | 3.5932 | 4.72% -Q3_K_L | 33.67 | 3.5617 | 3.80% -Q4_K_S | 36.39 | 3.4852 | 1.57% -Q4_K_M | 38.54 | 3.4725 | 1.20% -Q5_K_S | 44.20 | 3.4483 | 0.50% -Q5_K_M | 45.41 | 3.4451 | 0.40% -Q6_K | 52.70 | 3.4367 | 0.16% -fp16 | 128.5 | 3.4313 | - +Within llama.cpp the perplexity of base models is used primarily to judge the quality loss from e.g. quantized models vs. FP16. +The convention among contributors is to use the Wikitext-2 test set for testing unless noted otherwise (can be obtained with `scripts/get-wikitext-2.sh`). +When numbers are listed all command line arguments and compilation options are left at their defaults unless noted otherwise. +llama.cpp numbers are **not** directly comparable to those of other projects because the exact values depend strongly on the implementation details. +By default only the mean perplexity value and the corresponding uncertainty is calculated. +The uncertainty is determined empirically by assuming a Gaussian distribution of the "correct" logits per and then applying error propagation. + +More statistics can be obtained by recording the logits from the FP16 version of a model. +To do this, supply `perplexity` with `--kl-divergence-base path/to/logit/binary/file.kld`. +The program will then record all logits and save them to the provided path in binary format. +**The logit file will be very large, 11 GiB for LLaMA 2 or 37 GiB for LLaMA 3 when using the Wikitext-2 test set.** +Once you have the file, supply `perplexity` with the quantized model, the logits file via `--kl-divergence-base`, +and finally the `--kl-divergence` argument to indicate that the program should calculate the so-called Kullback-Leibler divergence. +This is a measure of how similar the FP16 and the quantized logit distributions are with a value of 0 indicating that the distribution are the same. +The uncertainty on the mean KL divergence is calculated by assuming the KL divergence per token follows a Gaussian distribution. + +In addition to the KL divergence the following statistics are calculated with `--kl-divergence`: + +* Ratio of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same. +* Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. +* Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. +* Pearson correlation coefficient of the "correct" token probabilites between models. +* Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization. +* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 . +* Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution. + +## LLaMA 3 8b Scoreboard + +| Revision | f364eb6f | +|:---------|:-------------------| +| Backend | CUDA | +| CPU | AMD Epyc 7742 | +| GPU | 1x NVIDIA RTX 4090 | + +Results were generated using the CUDA backend and are sorted by Kullback-Leibler divergence relative to FP16. +The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat). +Note: the FP16 logits used for the calculation of all metrics other than perplexity are stored in a binary file between runs. +In order to save space this file does **not** contain the exact same FP32 logits but instead casts them to 16 bit unsigned integers (with some scaling). +So the "f16" results are to be understood as the difference resulting only from this downcast. + +| Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp | +|--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------| +| f16 | None | 14.97 | 6.233160 ± 0.037828 | 0.001524 ± 0.000755 | 0.000551 ± 0.000002 | 0.001 ± 0.002 % | 0.787 ± 0.004 % | +| q8_0 | None | 7.96 | 6.234284 ± 0.037878 | 0.002650 ± 0.001006 | 0.001355 ± 0.000006 | -0.019 ± 0.003 % | 1.198 ± 0.007 % | +| q6_K | None | 6.14 | 6.253382 ± 0.038078 | 0.021748 ± 0.001852 | 0.005452 ± 0.000035 | -0.007 ± 0.006 % | 2.295 ± 0.019 % | +| q5_K_M | None | 5.33 | 6.288607 ± 0.038338 | 0.056974 ± 0.002598 | 0.010762 ± 0.000079 | -0.114 ± 0.008 % | 3.160 ± 0.031 % | +| q5_K_S | None | 5.21 | 6.336598 ± 0.038755 | 0.104964 ± 0.003331 | 0.016595 ± 0.000122 | -0.223 ± 0.010 % | 3.918 ± 0.036 % | +| q5_1 | None | 5.65 | 6.337857 ± 0.038677 | 0.106223 ± 0.003476 | 0.018045 ± 0.000139 | -0.287 ± 0.011 % | 4.123 ± 0.039 % | +| q5_0 | None | 5.21 | 6.363224 ± 0.038861 | 0.131591 ± 0.003894 | 0.022239 ± 0.000166 | -0.416 ± 0.012 % | 4.634 ± 0.043 % | +| q4_K_M | WT 10m | 4.58 | 6.382937 ± 0.039055 | 0.151303 ± 0.004429 | 0.028152 ± 0.000240 | -0.389 ± 0.014 % | 5.251 ± 0.049 % | +| q4_K_M | None | 4.58 | 6.407115 ± 0.039119 | 0.175482 ± 0.004620 | 0.031273 ± 0.000238 | -0.596 ± 0.014 % | 5.519 ± 0.050 % | +| q4_K_S | WT 10m | 4.37 | 6.409697 ± 0.039189 | 0.178064 ± 0.004744 | 0.031951 ± 0.000259 | -0.531 ± 0.015 % | 5.645 ± 0.051 % | +| iq4_NL | WT 10m | 4.35 | 6.455593 ± 0.039630 | 0.223959 ± 0.005201 | 0.035742 ± 0.000288 | -0.590 ± 0.016 % | 5.998 ± 0.054 % | +| iq4_XS | WT 10m | 4.14 | 6.459705 ± 0.039595 | 0.228071 ± 0.005207 | 0.036334 ± 0.000284 | -0.668 ± 0.016 % | 6.044 ± 0.054 % | +| q4_K_S | None | 4.37 | 6.500529 ± 0.039778 | 0.268895 ± 0.005638 | 0.043136 ± 0.000314 | -0.927 ± 0.017 % | 6.562 ± 0.055 % | +| q4_1 | None | 4.78 | 6.682737 ± 0.041285 | 0.451103 ± 0.008030 | 0.071683 ± 0.000505 | -0.927 ± 0.017 % | 8.512 ± 0.063 % | +| q4_0 | None | 4.34 | 6.700147 ± 0.041226 | 0.468514 ± 0.007951 | 0.071940 ± 0.000491 | -1.588 ± 0.022 % | 8.434 ± 0.061 % | +| q3_K_L | WT 10m | 4.03 | 6.671223 ± 0.041427 | 0.439590 ± 0.008154 | 0.073077 ± 0.000529 | -0.940 ± 0.023 % | 8.662 ± 0.064 % | +| q3_K_M | WT 10m | 3.74 | 6.734255 ± 0.041838 | 0.502622 ± 0.008901 | 0.084358 ± 0.000588 | -1.198 ± 0.024 % | 9.292 ± 0.065 % | +| q3_K_L | None | 4.03 | 6.787876 ± 0.042104 | 0.556242 ± 0.009171 | 0.087176 ± 0.000614 | -1.532 ± 0.025 % | 9.432 ± 0.067 % | +| q3_K_M | None | 3.74 | 6.888498 ± 0.042669 | 0.656864 ± 0.010071 | 0.101913 ± 0.000677 | -1.990 ± 0.026 % | 10.203 ± 0.068 % | +| iq3_M | WT 10m | 3.53 | 6.898327 ± 0.041643 | 0.666694 ± 0.009449 | 0.102534 ± 0.000663 | -3.178 ± 0.026 % | 10.513 ± 0.066 % | +| iq3_S | WT 10m | 3.42 | 6.965501 ± 0.042406 | 0.733867 ± 0.010245 | 0.111278 ± 0.000710 | -3.066 ± 0.027 % | 10.845 ± 0.068 % | +| iq3_XS | WT 10m | 3.28 | 7.163043 ± 0.043772 | 0.931409 ± 0.012084 | 0.138693 ± 0.000857 | -3.667 ± 0.031 % | 12.148 ± 0.070 % | +| iq3_XXS | WT 10m | 3.05 | 7.458436 ± 0.046404 | 1.226803 ± 0.015234 | 0.183625 ± 0.001042 | -3.918 ± 0.035 % | 13.836 ± 0.074 % | +| q3_K_S | WT 10m | 3.41 | 7.602878 ± 0.046848 | 1.371244 ± 0.015688 | 0.199821 ± 0.001008 | -5.046 ± 0.037 % | 14.980 ± 0.070 % | +| q3_K_S | None | 3.41 | 7.863786 ± 0.048885 | 1.632152 ± 0.017733 | 0.228217 ± 0.001079 | -5.604 ± 0.038 % | 15.541 ± 0.070 % | +| iq2_M | WT 10m | 2.74 | 8.600799 ± 0.055124 | 2.369166 ± 0.025244 | 0.325989 ± 0.00160 | -6.463 ± 0.046 % | 18.519 ± 0.080 % | +| q2_K | WT 10k | 2.96 | 8.652290 ± 0.055572 | 2.420657 ± 0.025587 | 0.331393 ± 0.001562 | -6.606 ± 0.046 % | 18.790 ± 0.078 % | +| q2_K | WT 100k | 2.96 | 8.641993 ± 0.055406 | 2.410359 ± 0.025495 | 0.331672 ± 0.001569 | -6.628 ± 0.047 % | 18.856 ± 0.078 % | +| q2_K | WT 10m | 2.96 | 8.647825 ± 0.055610 | 2.416191 ± 0.025683 | 0.332223 ± 0.001572 | -6.500 ± 0.047 % | 18.881 ± 0.078 % | +| q2_K | WT 1m | 2.96 | 8.674365 ± 0.055743 | 2.442732 ± 0.025843 | 0.335308 ± 0.001576 | -6.634 ± 0.047 % | 19.009 ± 0.079 % | +| q2_K | WT 1k | 2.96 | 8.682605 ± 0.055916 | 2.450972 ± 0.026069 | 0.337093 ± 0.001596 | -6.596 ± 0.047 % | 18.977 ± 0.079 % | +| q2_K_S | WT 10m | 2.96 | 9.323778 ± 0.061551 | 3.092145 ± 0.031914 | 0.403360 ± 0.001787 | -7.131 ± 0.049 % | 20.050 ± 0.081 % | +| q2_K_S | WT 1m | 2.96 | 9.329321 ± 0.061378 | 3.097688 ± 0.031816 | 0.403590 ± 0.001797 | -7.289 ± 0.049 % | 20.123 ± 0.081 % | +| q2_K_S | WT 100k | 2.96 | 9.362973 ± 0.061740 | 3.131339 ± 0.032169 | 0.408367 ± 0.001802 | -7.198 ± 0.050 % | 20.132 ± 0.081 % | +| q2_K_S | WT 10k | 2.96 | 9.376479 ± 0.062045 | 3.144846 ± 0.032464 | 0.408662 ± 0.001819 | -7.141 ± 0.050 % | 20.120 ± 0.081 % | +| q2_K_S | WT 1k | 2.96 | 9.415200 ± 0.062475 | 3.183567 ± 0.032993 | 0.415865 ± 0.001846 | -7.153 ± 0.050 % | 20.311 ± 0.082 % | +| iq2_S | WT 10m | 2.56 | 9.650781 ± 0.063209 | 3.419148 ± 0.034017 | 0.439197 ± 0.001976 | -8.319 ± 0.052 % | 21.491 ± 0.083 % | +| q2_K | None | 2.96 | 9.751568 ± 0.063312 | 3.519934 ± 0.033863 | 0.445132 ± 0.001835 | -9.123 ± 0.051 % | 21.421 ± 0.079 % | +| iq2_XS | WT 10m | 2.43 | 10.761424 ± 0.071056 | 4.529791 ± 0.042229 | 0.546290 ± 0.002133 | -10.576 ± 0.056 % | 23.872 ± 0.082 % | +| iq2_XXS | WT 10m | 2.24 | 14.091782 ± 0.098396 | 7.860148 ± 0.070752 | 0.812022 ± 0.002741 | -14.363 ± 0.065 % | 28.576 ± 0.084 % | +| iq1_M | WT 10m | 2.01 | 25.493722 ± 0.177903 | 19.262089 ± 0.152396 | 1.393084 ± 0.003529 | -24.672 ± 0.077 % | 38.287 ± 0.084 % | +| iq1_S | WT 1m | 1.88 | 58.097760 ± 0.438604 | 51.866126 ± 0.416604 | 2.211278 ± 0.004688 | -32.471 ± 0.087 % | 46.418 ± 0.085 % | +| iq1_S | WT 1k | 1.88 | 58.267851 ± 0.446208 | 52.036218 ± 0.424373 | 2.214858 ± 0.004778 | -31.880 ± 0.089 % | 46.330 ± 0.086 % | +| iq1_S | WT 100k | 1.88 | 58.581498 ± 0.453145 | 52.349864 ± 0.431360 | 2.220834 ± 0.004818 | -32.261 ± 0.089 % | 46.002 ± 0.086 % | +| iq1_S | WT 10m | 1.88 | 60.694593 ± 0.471290 | 54.462959 ± 0.449644 | 2.254554 ± 0.004868 | -31.973 ± 0.088 % | 46.271 ± 0.086 % | +| iq1_S | WT 10k | 1.88 | 63.221324 ± 0.493077 | 56.989691 ± 0.471423 | 2.293527 ± 0.004885 | -32.261 ± 0.089 % | 46.562 ± 0.086 % | + +There seems to be no consistent improvement from using more Wikitext tokens for the importance matrix. +K-quants score better on mean Δp than the legacy quants than e.g. KL divergence would suggest. + +## LLaMA 2 vs. LLaMA 3 Quantization comparison + +| Revision | f364eb6f | +|:---------|:-------------------| +| Backend | CUDA | +| CPU | AMD Epyc 7742 | +| GPU | 1x NVIDIA RTX 4090 | + +| Metric | L2 7b q2_K | L3 8b q2_K | L2 7b q4_K_M | L3 8b q4_K_M | L2 7b q6_K | L3 8b q6_K | L2 7b q8_0 | L3 8b q8_0 | +|-----------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------| +| Mean PPL | 5.794552 ± 0.032298 | 9.751568 ± 0.063312 | 5.877078 ± 0.032781 | 6.407115 ± 0.039119 | 5.808494 ± 0.032425 | 6.253382 ± 0.038078 | 5.798542 ± 0.032366 | 6.234284 ± 0.037878 | +| Mean PPL ratio | 1.107955 ± 0.001427 | 1.564849 ± 0.004525 | 1.014242 ± 0.000432 | 1.028160 ± 0.000723 | 1.002406 ± 0.000191 | 1.003490 ± 0.000296 | 1.000689 ± 0.000107 | 1.000425 ± 0.000161 | +| Mean ΔPPL | 0.625552 ± 0.008725 | 3.519934 ± 0.033863 | 0.082526 ± 0.002530 | 0.175482 ± 0.004620 | 0.013941 ± 0.001110 | 0.021748 ± 0.001852 | 0.003990 ± 0.000624 | 0.002650 ± 0.001006 | +| PPL correlation | 97.36% | 89.62% | 99.71% | 99.34% | 99.94% | 99.88% | 99.98% | 99.96% | +| Mean KLD | 0.108903 ± 0.000645 | 0.445132 ± 0.001835 | 0.012686 ± 0.000079 | 0.031273 ± 0.000238 | 0.002098 ± 0.000014 | 0.005452 ± 0.000035 | 0.000369 ± 0.000007 | 0.001355 ± 0.000006 | +| Mean Δp | -2.710 ± 0.023 % | -9.123 ± 0.051 % | -0.416 ± 0.008 % | -0.596 ± 0.014 % | -0.035 ± 0.003 % | -0.007 ± 0.006 % | -0.005 ± 0.002 % | -0.019 ± 0.003 % | +| Maximum Δp | 85.136% | 94.268% | 45.209% | 95.054% | 23.593% | 53.601% | 43.925% | 28.734% | +| 99.9% Δp | 37.184% | 50.003% | 17.461% | 27.084% | 7.798% | 13.613% | 3.387% | 6.402% | +| 99.0% Δp | 18.131% | 25.875% | 7.798% | 12.084% | 3.838% | 6.407% | 1.867% | 3.544% | +| Median Δp | -0.391% | -2.476% | -0.026% | -0.024% | -0.001% | 0.000% | -0.000% | -0.000% | +| 1.0% Δp | -39.762% | -87.173% | -11.433% | -19.567% | -4.222% | -6.767% | -1.862% | -3.698% | +| 0.1% Δp | -79.002% | -98.897% | -26.433% | -56.054% | -9.091% | -16.584% | -3.252% | -6.579% | +| Minimum Δp | -99.915% | -99.965% | -83.383% | -98.699% | -43.142% | -68.487% | -9.343% | -24.301% | +| RMS Δp | 9.762 ± 0.053 % | 21.421 ± 0.079 % | 3.252 ± 0.024 % | 5.519 ± 0.050 % | 1.339 ± 0.010 % | 2.295 ± 0.019 % | 0.618 ± 0.011 % | 1.198 ± 0.007 % | +| Same top p | 85.584 ± 0.086 % | 71.138 ± 0.119 % | 94.665 ± 0.055 % | 91.901 ± 0.072 % | 97.520 ± 0.038 % | 96.031 ± 0.051 % | 98.846 ± 0.026 % | 97.674 ± 0.040 % | + +## LLaMA 3 BF16 vs. FP16 comparison + +| Revision | 83330d8c | +|:---------|:--------------| +| Backend | CPU | +| CPU | AMD Epyc 7742 | +| GPU | N/A | + +Results were calculated with LLaMA 3 8b BF16 as `--kl-divergence-base` and LLaMA 3 8b FP16 as the `--model` for comparison. + +| Metric | Value | +|--------------------------------|--------------------------| +| Mean PPL(Q) | 6.227711 ± 0.037833 | +| Mean PPL(base) | 6.225194 ± 0.037771 | +| Cor(ln(PPL(Q)), ln(PPL(base))) | 99.990% | +| Mean ln(PPL(Q)/PPL(base)) | 0.000404 ± 0.000086 | +| Mean PPL(Q)/PPL(base) | 1.000404 ± 0.000086 | +| Mean PPL(Q)-PPL(base) | 0.002517 ± 0.000536 | +| Mean KLD | 0.00002515 ± 0.00000020 | +| Maximum KLD | 0.012206 | +| 99.9% KLD | 0.000799 | +| 99.0% KLD | 0.000222 | +| 99.0% KLD | 0.000222 | +| Median KLD | 0.000013 | +| 10.0% KLD | -0.000002 | +| 5.0% KLD | -0.000008 | +| 1.0% KLD | -0.000023 | +| Minimum KLD | -0.000059 | +| Mean Δp | -0.0000745 ± 0.0003952 % | +| Maximum Δp | 4.186% | +| 99.9% Δp | 1.049% | +| 99.0% Δp | 0.439% | +| 95.0% Δp | 0.207% | +| 90.0% Δp | 0.125% | +| 75.0% Δp | 0.029% | +| Median Δp | 0.000% | +| 25.0% Δp | -0.030% | +| 10.0% Δp | -0.126% | +| 5.0% Δp | -0.207% | +| 1.0% Δp | -0.434% | +| 0.1% Δp | -1.016% | +| Minimum Δp | -4.672% | +| RMS Δp | 0.150 ± 0.001 % | +| Same top p | 99.739 ± 0.013 % | + +## Old Numbers + +
+Llama 2 70B Scoreboard + +| Quantization | Model size (GiB) | Perplexity | Delta to fp16 | +|--------------|------------------|------------|---------------| +| Q4_0 | 36.20 | 3.5550 | 3.61% | +| Q4_1 | 40.20 | 3.5125 | 2.37% | +| Q5_0 | 44.20 | 3.4744 | 1.26% | +| Q2_K | 27.27 | 3.7339 | 8.82% | +| Q3_K_S | 27.86 | 3.7019 | 7.89% | +| Q3_K_M | 30.83 | 3.5932 | 4.72% | +| Q3_K_L | 33.67 | 3.5617 | 3.80% | +| Q4_K_S | 36.39 | 3.4852 | 1.57% | +| Q4_K_M | 38.54 | 3.4725 | 1.20% | +| Q5_K_S | 44.20 | 3.4483 | 0.50% | +| Q5_K_M | 45.41 | 3.4451 | 0.40% | +| Q6_K | 52.70 | 3.4367 | 0.16% | +| fp16 | 128.5 | 3.4313 | - | + +
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 9ec989389..9bf6c5743 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1,18 +1,21 @@ +#include "arg.h" #include "common.h" +#include "log.h" #include "llama.h" +#include +#include +#include #include #include #include #include +#include +#include +#include #include #include -#include -#include #include -#include -#include -#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -31,55 +34,6 @@ struct results_log_softmax { float prob; }; -static void write_logfile( - const llama_context * ctx, const gpt_params & params, const llama_model * model, - const struct results_perplexity & results -) { - if (params.logdir.empty()) { - return; - } - - if (params.hellaswag) { - fprintf(stderr, "%s: warning: logging results is not implemented for HellaSwag. No files will be written.\n", __func__); - return; - } - - const std::string timestamp = get_sortable_timestamp(); - - const bool success = create_directory_with_parents(params.logdir); - if (!success) { - fprintf(stderr, "%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) { - fprintf(stderr, "%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)); - dump_non_result_info_yaml(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"); - - dump_vector_float_yaml(logfile, "logits", results.logits); - fprintf(logfile, "ppl_value: %f\n", results.ppl_value); - dump_vector_float_yaml(logfile, "probs", results.probs); - - llama_dump_timing_info_yaml(logfile, ctx); - fclose(logfile); -} - static std::vector softmax(const std::vector& logits) { std::vector probs(logits.size()); float max_logit = logits[0]; @@ -166,7 +120,7 @@ static void process_logits( break; } lock.unlock(); - const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]); + const results_log_softmax results = log_softmax(n_vocab, logits + size_t(i)*n_vocab, tokens[i+1]); const double v = -results.log_softmax; local_nll += v; local_nll2 += v*v; @@ -200,7 +154,7 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits, break; } lock.unlock(); - const double v = log_softmax(n_vocab, logits + i*n_vocab, log_probs.data() + i*nv, tokens[i+1]); + const double v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, log_probs.data() + i*nv, tokens[i+1]); local_nll += v; local_nll2 += v*v; } @@ -216,17 +170,22 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits, } struct kl_divergence_result { - double sum_nll = 0; - double sum_nll2 = 0; - double sum_kld = 0; - double sum_kld2 = 0; - double sum_nll_diff = 0; - double sum_nll_diff2 = 0; - size_t n_same_top = 0; - size_t count = 0; + double sum_nll = 0.0; + double sum_nll2 = 0.0; + double sum_nll_base = 0.0; + double sum_nll_base2 = 0.0; + double sum_nll_nll_base = 0.0; + double sum_kld = 0.0; + double sum_kld2 = 0.0; + double sum_p_diff = 0.0; + double sum_p_diff2 = 0.0; + double sum_p_diff4 = 0.0; + float max_p_diff = 0.0f; + size_t n_same_top = 0.0; + size_t count = 0.0; }; -static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { +static std::pair log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { float max_logit = logits[0]; int imax = 0; for (int i = 1; i < n_vocab; ++i) { @@ -244,12 +203,17 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba const float scale = d[0]; const float min_log_prob = d[1]; base_log_prob += 4; - float nll = max_logit + log_sum_exp - logits[tok]; + + const float nll = max_logit + log_sum_exp - logits[tok]; kld.sum_nll += nll; kld.sum_nll2 += nll*nll; - nll += (scale*base_log_prob[tok] + min_log_prob); - kld.sum_nll_diff += nll; - kld.sum_nll_diff2 += nll*nll; + + const float nll_base = -(scale*base_log_prob[tok] + min_log_prob); + kld.sum_nll_base += nll_base; + kld.sum_nll_base2 += nll_base*nll_base; + + kld.sum_nll_nll_base += nll*nll_base; + max_logit += log_sum_exp; double sum = 0; int imax_base = -1; @@ -268,35 +232,53 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba kld.sum_kld += sum; kld.sum_kld2 += sum*sum; ++kld.count; - if (imax == imax_base) ++kld.n_same_top; - return sum; + if (imax == imax_base) { + ++kld.n_same_top; + } + + const float p_base = expf(-nll_base); + const float p = expf(-nll); + const float p_diff = p - p_base; + kld.sum_p_diff += p_diff; + const double p_diff2 = p_diff*p_diff; + kld.sum_p_diff2 += p_diff2; + kld.sum_p_diff4 += p_diff2*p_diff2; + kld.max_p_diff = std::max(kld.max_p_diff, std::fabs(p_diff)); + + return std::make_pair(sum, p_diff); } static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, const std::vector & base_log_probs, kl_divergence_result & kld, - float * kld_values) { + float * kld_values, float * p_diff_values) { std::mutex mutex; const int nv = 2*((n_vocab + 1)/2) + 4; int counter = 0; - auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () { + auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values, p_diff_values] () { kl_divergence_result local_kld; while (true) { std::unique_lock lock(mutex); int i = counter++; if (i >= n_token) { - kld.sum_nll += local_kld.sum_nll; - kld.sum_nll2 += local_kld.sum_nll2; - kld.sum_kld += local_kld.sum_kld; - kld.sum_kld2 += local_kld.sum_kld2; - kld.sum_nll_diff += local_kld.sum_nll_diff; - kld.sum_nll_diff2 += local_kld.sum_nll_diff2; - kld.n_same_top += local_kld.n_same_top; - kld.count += local_kld.count; + kld.sum_nll += local_kld.sum_nll; + kld.sum_nll2 += local_kld.sum_nll2; + kld.sum_nll_base += local_kld.sum_nll_base; + kld.sum_nll_base2 += local_kld.sum_nll_base2; + kld.sum_nll_nll_base += local_kld.sum_nll_nll_base; + kld.sum_kld += local_kld.sum_kld; + kld.sum_kld2 += local_kld.sum_kld2; + kld.sum_p_diff += local_kld.sum_p_diff; + kld.sum_p_diff2 += local_kld.sum_p_diff2; + kld.sum_p_diff4 += local_kld.sum_p_diff4; + kld.n_same_top += local_kld.n_same_top; + kld.max_p_diff = std::max(kld.max_p_diff, local_kld.max_p_diff); + kld.count += local_kld.count; break; } lock.unlock(); - double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); - kld_values[i] = (float)v; + std::pair v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); + kld_values[i] = (float)v.first; + p_diff_values[i] = v.second; } }; for (auto & w : workers) { @@ -308,24 +290,28 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens } } -static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) { +static results_perplexity perplexity_v2(llama_context * ctx, const common_params & params) { // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); - fprintf(stderr, "%s: tokenizing the input ..\n", __func__); + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); - std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + LOG_INF("%s: tokenizing the input ..\n", __func__); + + std::vector tokens = common_tokenize(ctx, params.prompt, true); const int n_ctx = llama_n_ctx(ctx); if (int(tokens.size()) < 2*n_ctx) { - fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx, + LOG_ERR("%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx, n_ctx); - fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); return {std::move(tokens), 0., {}, {}}; } @@ -336,16 +322,16 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & prob_history.resize(tokens.size()); if (params.ppl_stride <= 0) { - fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride); + LOG_ERR("%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride); return {tokens, -1, logit_history, prob_history}; } const int calc_chunk = n_ctx; - fprintf(stderr, "%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk); + LOG_INF("%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk); if (int(tokens.size()) <= calc_chunk) { - fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__, + LOG_ERR("%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__, tokens.size(), n_ctx, params.ppl_stride); return {tokens, -1, logit_history, prob_history}; } @@ -353,20 +339,21 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride; const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); const int n_batch = params.n_batch; + const int n_vocab = llama_vocab_n_tokens(vocab); + int count = 0; double nll = 0.0; - fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); + LOG_INF("%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); for (int i = 0; i < n_chunk; ++i) { const int start = i * params.ppl_stride; const int end = start + calc_chunk; const int num_batches = (calc_chunk + n_batch - 1) / n_batch; - //fprintf(stderr, "%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches); + //LOG_DBG("%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches); std::vector logits; @@ -375,13 +362,21 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); - //fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { - //fprintf(stderr, "%s : failed to eval\n", __func__); + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + //LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); + if (llama_decode(ctx, batch)) { + //LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return {tokens, -1, logit_history, prob_history}; } @@ -390,37 +385,38 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + tokens[batch_start] = llama_vocab_bos(vocab); } - const auto batch_logits = llama_get_logits(ctx); - logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); + const auto * batch_logits = llama_get_logits(ctx); + logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab); if (j == 0) { tokens[batch_start] = token_org; } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { const float t_total = std::chrono::duration(t_end - t_start).count(); - fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); + LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); int total_seconds = (int)(t_total * n_chunk); if (total_seconds >= 60*60) { - fprintf(stderr, "%d hours ", total_seconds / (60*60)); + LOG("%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); } - fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); + LOG("%.2f minutes\n", total_seconds / 60.0); } - //fprintf(stderr, "%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start); + //LOG_DBG("%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start); for (int j = n_ctx - params.ppl_stride - 1; j < n_ctx - 1; ++j) { - // Calculate probability of next token, given the previous ones. const std::vector tok_logits( - logits.begin() + (j + 0) * n_vocab, - logits.begin() + (j + 1) * n_vocab); + logits.begin() + size_t(j + 0) * n_vocab, + logits.begin() + size_t(j + 1) * n_vocab); const float prob = softmax(tok_logits)[tokens[start + j + 1]]; logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]]; @@ -431,54 +427,56 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & } // perplexity is e^(average negative log-likelihood) if (params.ppl_output_type == 0) { - printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); + LOG("[%d]%.4lf,", i + 1, std::exp(nll / count)); } else { - printf("%8d %.4lf\n", i*params.ppl_stride, std::exp(nll / count)); + LOG("%8d %.4lf\n", i*params.ppl_stride, std::exp(nll / count)); } - fflush(stdout); } - printf("\n"); + LOG("\n"); return {tokens, std::exp(nll / count), logit_history, prob_history}; } -static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { +static results_perplexity perplexity(llama_context * ctx, const common_params & params, const int32_t n_ctx) { if (params.ppl_stride > 0) { return perplexity_v2(ctx, params); } // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip - // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` + // Run `./llama-perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - const int n_ctx = llama_n_ctx(ctx); + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); std::ofstream logits_stream; if (!params.logits_file.empty()) { logits_stream.open(params.logits_file.c_str(), std::ios::binary); if (!logits_stream.is_open()) { - fprintf(stderr, "%s: failed to open %s for writing\n", __func__, params.logits_file.c_str()); + LOG_ERR("%s: failed to open %s for writing\n", __func__, params.logits_file.c_str()); return {}; } - fprintf(stderr, "%s: saving all logits to %s\n", __func__, params.logits_file.c_str()); + LOG_INF("%s: saving all logits to %s\n", __func__, params.logits_file.c_str()); logits_stream.write("_logits_", 8); logits_stream.write(reinterpret_cast(&n_ctx), sizeof(n_ctx)); } auto tim1 = std::chrono::high_resolution_clock::now(); - fprintf(stderr, "%s: tokenizing the input ..\n", __func__); + LOG_INF("%s: tokenizing the input ..\n", __func__); - std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector tokens = common_tokenize(ctx, params.prompt, true); auto tim2 = std::chrono::high_resolution_clock::now(); - fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); if (int(tokens.size()) < 2*n_ctx) { - fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx, + LOG_ERR("%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx, n_ctx); - fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); return {std::move(tokens), 0., {}, {}}; } @@ -491,21 +489,28 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par const int n_chunk_max = tokens.size() / n_ctx; const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); const int n_batch = params.n_batch; + const int n_vocab = llama_vocab_n_tokens(vocab); + int count = 0; double nll = 0.0; double nll2 = 0.0; const int num_batches = (n_ctx + n_batch - 1) / n_batch; + const int n_seq = std::max(1, n_batch / n_ctx); + + GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0); + GGML_ASSERT(params.n_ctx == n_seq * n_ctx); + + llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1); std::vector logits; if (num_batches > 1) { - logits.reserve((size_t)n_ctx * n_vocab); + logits.reserve(size_t(n_ctx) * n_vocab); } - fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); + LOG_INF("%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq); std::vector workers(std::thread::hardware_concurrency() - 1); @@ -518,10 +523,26 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par log_probs.resize(n_ctx * nv); } - for (int i = 0; i < n_chunk; ++i) { + // We get the logits for all the tokens in the context window (params.n_ctx) + // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, + // calculate the perplexity over the last half of the window (so the model always has + // some context to predict the token). + // + // We rely on the fact that attention in the forward pass only looks at previous + // tokens here, so the logits returned for each token are an accurate representation + // of what the model would have predicted at that point. + // + // Example, we have a context window of 512, we will compute perplexity for each of the + // last 256 tokens. Then, we split the input up into context window size chunks to + // process the entire prompt. + const int first = n_ctx/2; + + for (int i = 0; i < n_chunk; i += n_seq) { const int start = i * n_ctx; const int end = start + n_ctx; + const int n_seq_batch = std::min(n_seq, n_chunk - i); + const auto t_start = std::chrono::high_resolution_clock::now(); // clear the KV cache @@ -531,78 +552,94 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); - // save original token and restore it after eval - const auto token_org = tokens[batch_start]; + int n_outputs = 0; - // add BOS token for the first batch of each chunk - if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + batch.n_tokens = 0; + for (int seq = 0; seq < n_seq_batch; seq++) { + int seq_start = batch_start + seq*n_ctx; + + // save original token and restore it after eval + const auto token_org = tokens[seq_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[seq_start] = llama_vocab_bos(vocab); + } + + for (int k = 0; k < batch_size; ++k) { + const int idx = seq*n_ctx + k; + batch.token [idx] = tokens[seq_start + k]; + batch.pos [idx] = j*n_batch + k; + batch.n_seq_id[idx] = 1; + batch.seq_id [idx][0] = seq; + batch.logits [idx] = batch.pos[idx] >= first ? 1 : 0; + + n_outputs += batch.logits[idx] != 0; + } + batch.n_tokens += batch_size; + + // restore the original token in case it was set to BOS + tokens[seq_start] = token_org; } - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { - fprintf(stderr, "%s : failed to eval\n", __func__); + if (llama_decode(ctx, batch)) { + LOG_INF("%s : failed to eval\n", __func__); return {tokens, -1, logit_history, prob_history}; } - // restore the original token in case it was set to BOS - tokens[batch_start] = token_org; - - if (num_batches > 1) { + if (num_batches > 1 && n_outputs > 0) { const auto * batch_logits = llama_get_logits(ctx); - logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); + logits.insert(logits.end(), batch_logits, batch_logits + size_t(n_outputs) * n_vocab); } } - const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { + llama_synchronize(ctx); + const auto t_end = std::chrono::high_resolution_clock::now(); const float t_total = std::chrono::duration(t_end - t_start).count(); - fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); - int total_seconds = (int)(t_total * n_chunk); + LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); + int total_seconds = (int)(t_total*n_chunk/n_seq); if (total_seconds >= 60*60) { - fprintf(stderr, "%d hours ", total_seconds / (60*60)); + LOG("%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); } - fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); + LOG("%.2f minutes\n", total_seconds / 60.0); } - // We get the logits for all the tokens in the context window (params.n_ctx) - // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, - // calculate the perplexity over the last half of the window (so the model always has - // some context to predict the token). - // - // We rely on the fact that attention in the forward pass only looks at previous - // tokens here, so the logits returned for each token are an accurate representation - // of what the model would have predicted at that point. - // - // Example, we have a context window of 512, we will compute perplexity for each of the - // last 256 tokens. Then, we split the input up into context window size chunks to - // process the entire prompt. - const int first = n_ctx/2; - const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); - if (!params.logits_file.empty()) { - process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, log_probs, nll, nll2); - } else { - process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); - } - count += n_ctx - first - 1; + for (int seq = 0; seq < n_seq_batch; seq++) { + const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first); - // perplexity is e^(average negative log-likelihood) - if (params.ppl_output_type == 0) { - printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); - } else { - double av = nll/count; - double av2 = nll2/count - av*av; - if (av2 > 0) av2 = sqrt(av2/(count-1)); - printf("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2); + llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first; + if (!params.logits_file.empty()) { + process_logits(logits_stream, n_vocab, all_logits, + tokens_data, n_ctx - 1 - first, + workers, log_probs, nll, nll2); + } else { + process_logits(n_vocab, all_logits, + tokens_data, n_ctx - 1 - first, + workers, nll, nll2, + logit_history.data() + start + seq*n_ctx + first, + prob_history.data() + start + seq*n_ctx + first); + } + count += n_ctx - first - 1; + + // perplexity is e^(average negative log-likelihood) + if (params.ppl_output_type == 0) { + LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count)); + } else { + double av = nll/count; + double av2 = nll2/count - av*av; + if (av2 > 0) { + av2 = sqrt(av2/(count-1)); + } + LOG("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2); + } } - fflush(stdout); logits.clear(); } - printf("\n"); + LOG("\n"); nll2 /= count; nll /= count; @@ -610,17 +647,20 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par nll2 -= nll * nll; if (nll2 > 0) { nll2 = sqrt(nll2/(count-1)); - printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); + LOG_INF("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); } else { - printf("Unexpected negative standard deviation of log(prob)\n"); + LOG_ERR("Unexpected negative standard deviation of log(prob)\n"); } + llama_batch_free(batch); + return {tokens, ppl, logit_history, prob_history}; } -static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector & batch_logits, int32_t n_batch, int32_t n_vocab) { - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { - const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); +static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector & batch_logits, int n_batch, int n_vocab) { + int prev_outputs = 0; + for (int i = 0; i < (int) batch.n_tokens; i += n_batch) { + const int n_tokens = std::min(n_batch, batch.n_tokens - i); llama_batch batch_view = { n_tokens, @@ -630,16 +670,22 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector< batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); if (ret != 0) { - LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret); + LOG_ERR("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret); return false; } - memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float)); + int n_outputs = 0; + for (int i = 0; i < n_tokens; ++i) { + n_outputs += batch_view.logits[i] != 0; + } + + memcpy(batch_logits.data() + size_t(prev_outputs)*n_vocab, llama_get_logits(ctx), size_t(n_outputs)*n_vocab*sizeof(float)); + + prev_outputs += n_outputs; } return true; @@ -652,7 +698,9 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto if (eval_results.size() != eval_pairs.size()) { eval_results.resize(eval_pairs.size()); } - if (eval_pairs.empty()) return; + if (eval_pairs.empty()) { + return; + } size_t max_threads = std::min((eval_pairs.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK, workers.size()); @@ -660,11 +708,13 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto auto compute = [&counter, &eval_pairs, &eval_results, batch_logits, n_vocab] () { float local_logprobs[K_TOKEN_CHUNK]; while (true) { - size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed); - if (first >= eval_results.size()) break; - size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size()); + const size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed); + if (first >= eval_results.size()) { + break; + } + const size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size()); for (size_t i = first; i < last; ++i) { - auto logits = batch_logits + eval_pairs[i].first * n_vocab; + const auto * logits = batch_logits + eval_pairs[i].first * n_vocab; float max_logit = logits[0]; for (int j = 1; j < n_vocab; ++j) { max_logit = std::max(max_logit, logits[j]); @@ -687,7 +737,10 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto } } -static void hellaswag_score(llama_context * ctx, const gpt_params & params) { +static void hellaswag_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + // Calculates hellaswag score (acc_norm) from prompt // // Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl @@ -714,18 +767,15 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { } if (prompt_lines.size() % 6 != 0) { - fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__); + LOG_ERR("%s : number of lines in prompt not a multiple of 6.\n", __func__); return; } size_t hs_task_count = prompt_lines.size()/6; - fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count); + LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count); - const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM; - fprintf(stderr, "================================= is_spm = %d\n", is_spm); - - // This is needed as usual for LLaMA models - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + const bool is_spm = llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM; + LOG_INF("================================= is_spm = %d\n", is_spm); // The tasks should be randomized so the score stabilizes quickly. bool randomize_tasks = true; @@ -746,13 +796,13 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { size_t ending_logprob_count[4]; double ending_logprob[4]; - size_t i_batch; // starting index in the llama_batch + size_t i_logits; // starting index of logits in the llama_batch size_t common_prefix; // max number of initial tokens that are the same in all sentences size_t required_tokens; // needed number of tokens to evaluate all 4 endings std::vector seq_tokens[4]; }; - fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") ); + LOG_INF("%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") ); // Select and read data from prompt lines std::vector hs_data(hs_task_count); @@ -771,7 +821,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] ); for (size_t j = 0; j < 4; j++) { hs_cur.ending[j] = prompt_lines[idx*6+2+j]; - hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], add_bos); + hs_cur.seq_tokens[j] = common_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true); } // determine the common prefix of the endings @@ -790,7 +840,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { hs_cur.seq_tokens[2].size() - hs_cur.common_prefix + hs_cur.seq_tokens[3].size() - hs_cur.common_prefix; - //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, add_bos).size()); + //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, true).size()); // Delete the selected random example from the prompt if (randomize_tasks) { @@ -798,23 +848,25 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { } } - fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__); + LOG_INF("%s : calculating hellaswag score over selected tasks.\n", __func__); - printf("\ntask\tacc_norm\n"); + LOG("\ntask\tacc_norm\n"); double acc = 0.0f; - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); const int n_ctx = llama_n_ctx(ctx); const int n_batch = params.n_batch; - const int max_tasks_per_batch = 32; - const int max_seq = 4*max_tasks_per_batch; + const int n_vocab = llama_vocab_n_tokens(vocab); - llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); + const int max_tasks_per_batch = 32; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); + + llama_batch batch = llama_batch_init(n_ctx, 0, 4); std::vector tok_logits(n_vocab); - std::vector batch_logits(n_vocab*n_ctx); + // TODO: this could be made smaller; it's currently the worst-case size + std::vector batch_logits(size_t(n_ctx)*n_vocab); std::vector> eval_pairs; std::vector eval_results; @@ -824,16 +876,17 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { int n_cur = 0; size_t i1 = i0; - size_t i_batch = 0; // this tells us where in `llama_batch` we are currently + size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch - llama_batch_clear(batch); + common_batch_clear(batch); // batch as much tasks as possible into the available context - // each task has 4 unique seuqnce ids - one for each ending + // each task has 4 unique sequence ids - one for each ending // the common prefix is shared among the 4 sequences to save tokens // we extract logits only from the last common token and from all ending tokens of each sequence while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) { auto & hs_cur = hs_data[i1]; + int n_logits = 0; const int s0 = 4*(i1 - i0); if (s0 + 4 > max_seq) { @@ -841,18 +894,23 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { } for (size_t i = 0; i < hs_cur.common_prefix; ++i) { - llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false); + common_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false); } batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix + n_logits += 1; for (int s = 0; s < 4; ++s) { - for (size_t i = hs_cur.common_prefix; i < hs_cur.seq_tokens[s].size(); ++i) { - llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, true); + const size_t seq_tokens_size = hs_cur.seq_tokens[s].size(); + // TODO: don't evaluate the last token of each sequence + for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) { + const bool needs_logits = i < seq_tokens_size - 1; + common_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits); + n_logits += needs_logits; } } - hs_cur.i_batch = i_batch; - i_batch += hs_cur.required_tokens; + hs_cur.i_logits = i_logits; + i_logits += n_logits; n_cur += hs_data[i1].required_tokens; if (++i1 == hs_task_count) { @@ -861,7 +919,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { } if (i0 == i1) { - fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0); + LOG_ERR("%s : task %zu does not fit in the context window\n", __func__, i0); return; } @@ -869,7 +927,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { // decode all tasks [i0, i1) if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) { - fprintf(stderr, "%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return; } @@ -878,12 +936,11 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { eval_pairs.clear(); for (size_t i = i0; i < i1; ++i) { auto & hs_cur = hs_data[i]; - size_t li = hs_cur.common_prefix; + size_t li = 1; // skip the last logit of the common prefix (computed separately below) for (int s = 0; s < 4; ++s) { for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) { - eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]); + eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]); } - ++li; } } // Then we do the actual calculation @@ -895,7 +952,8 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { for (size_t i = i0; i < i1; ++i) { auto & hs_cur = hs_data[i]; - std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(hs_cur.i_batch + hs_cur.common_prefix - 1), n_vocab*sizeof(float)); + // get the logits of the last token of the common prefix + std::memcpy(tok_logits.data(), batch_logits.data() + hs_cur.i_logits*n_vocab, n_vocab*sizeof(float)); const auto first_probs = softmax(tok_logits); @@ -919,7 +977,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { } } - //printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_cur.gold_ending_idx); + //LOG("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_cur.gold_ending_idx); // If the gold ending got the maximum logprobe add one accuracy point if (ending_logprob_max_idx == hs_cur.gold_ending_idx) { @@ -927,8 +985,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { } // Print the accumulated accuracy mean x 100 - printf("%zu\t%.8lf\n", i + 1, acc/double(i + 1)*100.0); - fflush(stdout); + LOG("%zu\t%.8lf\n", i + 1, acc/double(i + 1)*100.0); } i0 = i1 - 1; @@ -936,7 +993,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { llama_batch_free(batch); - printf("\n"); + LOG("\n"); } struct winogrande_entry { @@ -945,7 +1002,7 @@ struct winogrande_entry { std::array choices; int answer; - size_t i_batch; + size_t i_logits; size_t common_prefix; size_t required_tokens; size_t n_base1; // number of tokens for context + choice 1 @@ -953,7 +1010,7 @@ struct winogrande_entry { std::vector seq_tokens[2]; }; -static std::vector load_winogrande_from_csv(const std::string& prompt) { +static std::vector load_winogrande_from_csv(const std::string & prompt) { std::vector result; std::istringstream in(prompt); std::string line; @@ -980,7 +1037,7 @@ static std::vector load_winogrande_from_csv(const std::string& } } if (ipos != 4) { - printf("%s: failed to find comma separators in <%s>\n", __func__, line.c_str()); + LOG_ERR("%s: failed to find comma separators in <%s>\n", __func__, line.c_str()); continue; } auto sentence = line[comma_pos[0]+1] == '"' ? line.substr(comma_pos[0]+2, comma_pos[1] - comma_pos[0] - 3) @@ -994,13 +1051,13 @@ static std::vector load_winogrande_from_csv(const std::string& if (sentence[where] == '_') break; } if (where == int(sentence.size())) { - printf("%s: no _ in <%s>\n", __func__, sentence.c_str()); + LOG_ERR("%s: no _ in <%s>\n", __func__, sentence.c_str()); continue; } std::istringstream stream(answer.c_str()); int i_answer; stream >> i_answer; if (stream.fail() || i_answer < 1 || i_answer > 2) { - printf("%s: failed to parse answer <%s>\n", __func__, answer.c_str()); + LOG_ERR("%s: failed to parse answer <%s>\n", __func__, answer.c_str()); continue; } result.emplace_back(); @@ -1023,20 +1080,22 @@ static std::vector load_winogrande_from_csv(const std::string& * 0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2 * */ -static void winogrande_score(llama_context * ctx, const gpt_params & params) { +static void winogrande_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); constexpr int k_min_trailing_ctx = 3; auto data = load_winogrande_from_csv(params.prompt); if (data.empty()) { - fprintf(stderr, "%s: no tasks\n", __func__); + LOG_ERR("%s: no tasks\n", __func__); return; } - fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, data.size()); + LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, data.size()); if (params.winogrande_tasks > 0 && params.winogrande_tasks < data.size()) { - fprintf(stderr, "%s : selecting %zu random tasks\n", __func__, params.winogrande_tasks); + LOG_INF("%s : selecting %zu random tasks\n", __func__, params.winogrande_tasks); std::mt19937 rng(1); std::vector aux(data.size()); for (int i = 0; i < int(data.size()); ++i) { @@ -1054,14 +1113,11 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { data = std::move(selected); } - fprintf(stderr, "%s : tokenizing selected tasks\n", __func__); - - // This is needed as usual for LLaMA models - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + LOG_INF("%s : tokenizing selected tasks\n", __func__); for (auto & task : data) { - task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, add_bos); - task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, add_bos); + task.seq_tokens[0] = common_tokenize(ctx, task.first + task.choices[0] + task.second, true); + task.seq_tokens[1] = common_tokenize(ctx, task.first + task.choices[1] + task.second, true); task.common_prefix = 0; for (size_t k = 0; k < task.seq_tokens[0].size(); k++) { @@ -1071,27 +1127,30 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { task.common_prefix++; } + // TODO: the last token of each of the sequences don't need to be evaluated task.required_tokens = task.common_prefix + task.seq_tokens[0].size() - task.common_prefix + task.seq_tokens[1].size() - task.common_prefix; - task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos).size(); - task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos).size(); + task.n_base1 = common_tokenize(ctx, task.first + task.choices[0], true).size(); + task.n_base2 = common_tokenize(ctx, task.first + task.choices[1], true).size(); } - fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__); + LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); const int n_ctx = llama_n_ctx(ctx); const int n_batch = params.n_batch; - const int max_tasks_per_batch = 128; - const int max_seq = 2*max_tasks_per_batch; + const int n_vocab = llama_vocab_n_tokens(vocab); - llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); + const int max_tasks_per_batch = 128; + const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); + + llama_batch batch = llama_batch_init(n_ctx, 0, 2); std::vector tok_logits(n_vocab); - std::vector batch_logits(n_vocab*n_ctx); + // TODO: this could be made smaller; it's currently the worst-case size + std::vector batch_logits(size_t(n_ctx)*n_vocab); std::vector> eval_pairs; std::vector eval_results; @@ -1104,29 +1163,33 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { int n_cur = 0; size_t i1 = i0; - size_t i_batch = 0; + size_t i_logits = 0; - llama_batch_clear(batch); + common_batch_clear(batch); while (n_cur + (int) data[i1].required_tokens <= n_ctx) { + int n_logits = 0; const int s0 = 2*(i1 - i0); if (s0 + 2 > max_seq) { break; } for (size_t i = 0; i < data[i1].common_prefix; ++i) { - llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1}, false); + common_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false); } batch.logits[batch.n_tokens - 1] = true; + n_logits += 1; for (int s = 0; s < 2; ++s) { + // TODO: end before the last token, no need to predict past the end of the sequences for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) { - llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true); + common_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true); + n_logits += 1; } } - data[i1].i_batch = i_batch; - i_batch += data[i1].required_tokens; + data[i1].i_logits = i_logits; + i_logits += n_logits; n_cur += data[i1].required_tokens; if (++i1 == data.size()) { @@ -1135,7 +1198,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { } if (i0 == i1) { - fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0); + LOG_ERR("%s : task %zu does not fit in the context window\n", __func__, i0); return; } @@ -1143,7 +1206,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { // decode all tasks [i0, i1) if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) { - fprintf(stderr, "%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return; } @@ -1157,15 +1220,16 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix; const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0; - size_t li = n_base1 - 1; + size_t li = n_base1 - task.common_prefix; for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) { - eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]); + eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]); } const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix; const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0; - li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1; + // FIXME: this uses the wrong first logits when not skipping the choice word + li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix; for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) { - eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]); + eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]); } } compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results); @@ -1202,20 +1266,20 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { ++n_done; // print the accumulated accuracy mean x 100 - printf("%zu\t%.4lf\t%10.6f %10.6f %d %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer); - fflush(stdout); + LOG("%zu\t%.4lf\t%10.6f %10.6f %d %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer); } i0 = i1 - 1; } - printf("\n"); + LOG("\n"); if (n_done < 100) return; const float p = 1.f*n_correct/n_done; const float sigma = 100.f*sqrt(p*(1-p)/(n_done-1)); - printf("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma); + + LOG_INF("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma); } static bool deserialize_string(std::istream & in, std::string & str) { @@ -1254,17 +1318,17 @@ struct multiple_choice_task { } // For evaluation - size_t i_batch; // starting index in the llama_batch + size_t i_logits; // starting index of logits in the llama_batch size_t common_prefix; // max number of initial tokens that are the same in all sentences size_t required_tokens; // needed number of tokens to evaluate all answers std::vector> seq_tokens; std::vector log_probs; }; -static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, multiple_choice_task& task, bool log_error) { +static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choice_task& task, bool log_error) { if (task.question.empty() || task.mc1.answers.empty()) { if (log_error) { - printf("%s: found bad task with empty question and/or answers\n", __func__); + LOG_ERR("%s: found bad task with empty question and/or answers\n", __func__); } return false; } @@ -1272,11 +1336,11 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, for (auto& answer : task.mc1.answers) { if (answer.empty()) { if (log_error) { - printf("%s: found empty answer\n", __func__); + LOG_ERR("%s: found empty answer\n", __func__); } return false; } - task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos)); + task.seq_tokens.emplace_back(::common_tokenize(ctx, task.question + " " + answer, true)); } auto min_len = task.seq_tokens.front().size(); for (auto& seq : task.seq_tokens) { @@ -1320,20 +1384,22 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, // git@hf.co:datasets/Stevross/mmlu // https://huggingface.co/datasets/truthful_qa // -static void multiple_choice_score(llama_context * ctx, const gpt_params & params) { +static void multiple_choice_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); std::istringstream strstream(params.prompt); uint32_t n_task; strstream.read((char *)&n_task, sizeof(n_task)); if (strstream.fail() || n_task == 0) { - printf("%s: no tasks\n", __func__); + LOG_ERR("%s: no tasks\n", __func__); return; } - printf("%s: there are %u tasks in prompt\n", __func__, n_task); + LOG_INF("%s: there are %u tasks in prompt\n", __func__, n_task); std::vector task_pos(n_task); strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t)); if (strstream.fail()) { - printf("%s: failed to raad task positions from prompt\n", __func__); + LOG_ERR("%s: failed to read task positions from prompt\n", __func__); return; } @@ -1341,21 +1407,21 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params if (params.multiple_choice_tasks == 0 || params.multiple_choice_tasks >= (size_t)n_task) { // Use all tasks tasks.resize(n_task); - printf("%s: reading tasks", __func__); - int n_dot = n_task/100; + LOG_INF("%s: reading tasks", __func__); + int n_dot = std::max((int) n_task/100, 1); int i = 0; for (auto& task : tasks) { ++i; if (!task.deserialize(strstream)) { - printf("%s: failed to read task %d of %u\n", __func__, i, n_task); + LOG_ERR("%s: failed to read task %d of %u\n", __func__, i, n_task); return; } - if (i%n_dot == 0) printf("."); + if (i%n_dot == 0) LOG("."); } - printf("done\n"); + LOG("done\n"); } else { - printf("%s: selecting %zu random tasks from %u tasks available\n", __func__, params.multiple_choice_tasks, n_task); + LOG_INF("%s: selecting %zu random tasks from %u tasks available\n", __func__, params.multiple_choice_tasks, n_task); std::mt19937 rng(1); std::vector aux(n_task); for (uint32_t i = 0; i < n_task; ++i) aux[i] = i; @@ -1368,24 +1434,19 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params aux.pop_back(); strstream.seekg(task_pos[idx], std::ios::beg); if (!task.deserialize(strstream)) { - printf("%s: failed to read task %d at position %u\n", __func__, idx, task_pos[idx]); + LOG_ERR("%s: failed to read task %d at position %u\n", __func__, idx, task_pos[idx]); return; } } n_task = params.multiple_choice_tasks; } - // This is needed as usual for LLaMA models - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - - printf("%s: preparing task data", __func__); - fflush(stdout); + LOG_INF("%s: preparing task data", __func__); if (n_task > 500) { - printf("..."); - fflush(stdout); + LOG("..."); std::atomic counter(0); std::atomic n_bad(0); - auto prepare = [&counter, &n_bad, &tasks, ctx, add_bos] () { + auto prepare = [&counter, &n_bad, &tasks, ctx] () { int num_tasks = tasks.size(); int n_bad_local = 0; while (true) { @@ -1396,7 +1457,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params } int last = std::min(first + K_TOKEN_CHUNK, num_tasks); for (int i = first; i < last; ++i) { - if (!multiple_choice_prepare_one_task(ctx, add_bos, tasks[i], false)) ++n_bad_local; + if (!multiple_choice_prepare_one_task(ctx, tasks[i], false)) ++n_bad_local; } } }; @@ -1406,44 +1467,43 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params for (auto& w : workers) w = std::thread(prepare); prepare(); for (auto& w : workers) w.join(); - printf("done\n"); - fflush(stdout); + LOG("done\n"); int nbad = n_bad; if (nbad > 0) { - printf("%s: found %d malformed tasks\n", __func__, nbad); + LOG_ERR("%s: found %d malformed tasks\n", __func__, nbad); return; } } else { - int n_dot = n_task/100; + int n_dot = std::max((int) n_task/100, 1); int i_task = 0; for (auto& task : tasks) { ++i_task; - if (!multiple_choice_prepare_one_task(ctx, add_bos, task, true)) { + if (!multiple_choice_prepare_one_task(ctx, task, true)) { return; } if (i_task%n_dot == 0) { - printf("."); - fflush(stdout); + LOG("."); } } - printf("done\n"); + LOG("done\n"); } - printf("%s : calculating TruthfulQA score over %zu tasks.\n", __func__, tasks.size()); + LOG_INF("%s : calculating TruthfulQA score over %zu tasks.\n", __func__, tasks.size()); - printf("\ntask\tacc_norm\n"); + LOG("\ntask\tacc_norm\n"); - const int n_vocab = llama_n_vocab(llama_get_model(ctx)); const int n_ctx = llama_n_ctx(ctx); const int n_batch = params.n_batch; + const int n_vocab = llama_vocab_n_tokens(vocab); + const int max_tasks_per_batch = 32; - const int max_seq = 4*max_tasks_per_batch; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); std::vector tok_logits(n_vocab); - std::vector batch_logits(n_vocab*n_ctx); + std::vector batch_logits(size_t(n_ctx)*n_vocab); std::vector> eval_pairs; std::vector eval_results; @@ -1458,17 +1518,18 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params int n_cur = 0; size_t i1 = i0; - size_t i_batch = 0; // this tells us where in `llama_batch` we are currently + size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch - llama_batch_clear(batch); + common_batch_clear(batch); // batch as much tasks as possible into the available context - // each task has 4 unique seuqnce ids - one for each ending + // each task has 4 unique sequence ids - one for each ending // the common prefix is shared among the 4 sequences to save tokens // we extract logits only from the last common token and from all ending tokens of each sequence int s0 = 0; while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) { auto& cur_task = tasks[i1]; + int n_logits = 0; int num_answers = cur_task.seq_tokens.size(); if (s0 + num_answers > max_seq) { @@ -1482,20 +1543,25 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params for (size_t i = 0; i < cur_task.common_prefix; ++i) { //llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false); - llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false); + common_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false); } batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix + n_logits += 1; for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { - for (size_t i = cur_task.common_prefix; i < cur_task.seq_tokens[s].size(); ++i) { - llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, true); + const size_t seq_tokens_size = cur_task.seq_tokens[s].size(); + // TODO: don't evaluate the last token of each sequence + for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) { + const bool needs_logits = i < seq_tokens_size - 1; + common_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits); + n_logits += needs_logits; } } s0 += num_answers; - cur_task.i_batch = i_batch; - i_batch += cur_task.required_tokens; + cur_task.i_logits = i_logits; + i_logits += n_logits; n_cur += cur_task.required_tokens; if (++i1 == tasks.size()) { @@ -1504,7 +1570,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params } if (i0 == i1) { - fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0); + LOG_ERR("%s : task %zu does not fit in the context window\n", __func__, i0); return; } @@ -1512,7 +1578,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params // decode all tasks [i0, i1) if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) { - fprintf(stderr, "%s: llama_decode() failed\n", __func__); + LOG_ERR("%s: llama_decode() failed\n", __func__); return; } @@ -1521,12 +1587,11 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params eval_pairs.clear(); for (size_t i = i0; i < i1; ++i) { auto& cur_task = tasks[i]; - size_t li = cur_task.common_prefix; + size_t li = 1; // skip the last logit of the common prefix (computed separately below) for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) { - eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]); + eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]); } - ++li; } } // Then we do the actual calculation @@ -1537,15 +1602,16 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params // compute the logprobs for each ending of the decoded tasks for (size_t i = i0; i < i1; ++i) { auto & cur_task = tasks[i]; - //printf("==== Evaluating <%s> with correct answer ", cur_task.question.c_str()); + //LOG("==== Evaluating <%s> with correct answer ", cur_task.question.c_str()); //for (int j = 0; j < int(cur_task.mc1.labels.size()); ++j) { // if (cur_task.mc1.labels[j] == 1) { - // printf("%d", j+1); + // LOG("%d", j+1); // } //} - //printf("\n common_prefix: %zu\n", cur_task.common_prefix); + //LOG("\n common_prefix: %zu\n", cur_task.common_prefix); - std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(cur_task.i_batch + cur_task.common_prefix - 1), n_vocab*sizeof(float)); + // get the logits of the last token of the common prefix + std::memcpy(tok_logits.data(), batch_logits.data() + cur_task.i_logits*n_vocab, n_vocab*sizeof(float)); const auto first_probs = softmax(tok_logits); @@ -1554,13 +1620,13 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params size_t count = 1; float log_prob = std::log(first_probs[cur_task.seq_tokens[s][cur_task.common_prefix]]); for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) { - //printf(" %zu %g\n", ir, eval_results[ir]); + //LOG(" %zu %g\n", ir, eval_results[ir]); ++count; log_prob += eval_results[ir++]; } cur_task.log_probs[s] = log_prob / count; - //printf(" Final: %g\n", log_prob / count); - //printf(" <%s> : %g\n", cur_task.mc1.answers[s].c_str(), log_prob/count); + //LOG(" Final: %g\n", log_prob / count); + //LOG(" <%s> : %g\n", cur_task.mc1.answers[s].c_str(), log_prob/count); } // Find the ending with maximum logprob @@ -1580,8 +1646,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params ++n_done; // Print the accumulated accuracy mean x 100 - printf("%d\t%.8lf\n", n_done, 100.*n_correct/n_done); - fflush(stdout); + LOG("%d\t%.8lf\n", n_done, 100.*n_correct/n_done); } i0 = i1 - 1; @@ -1589,33 +1654,37 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params llama_batch_free(batch); - if (n_done < 100) return; + if (n_done < 100 && (params.multiple_choice_tasks != 0 && params.multiple_choice_tasks < (size_t)n_task)) return; float p = 1.f*n_correct/n_done; float sigma = sqrt(p*(1-p)/(n_done-1)); - printf("\n Final result: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma); + LOG("\n"); + LOG_INF("Final result: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma); p = 1.f*n_done/n_tot_answers; sigma = sqrt(p*(1-p)/(n_done-1)); - printf("Random chance: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma); + LOG_INF("Random chance: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma); - printf("\n"); + LOG_INF("\n"); } -static void kl_divergence(llama_context * ctx, const gpt_params & params) { +static void kl_divergence(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + if (params.logits_file.empty()) { - fprintf(stderr, "%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__); + LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__); return; } std::ifstream in(params.logits_file.c_str(), std::ios::binary); if (!in) { - fprintf(stderr, "%s: failed to open %s\n", __func__, params.logits_file.c_str()); + LOG_ERR("%s: failed to open %s\n", __func__, params.logits_file.c_str()); return; } { char check[9]; check[8] = 0; in.read(check, 8); if (in.fail() || strncmp("_logits_", check, 8) != 0) { - fprintf(stderr, "%s: %s does not look like a file containing log-probabilities\n", __func__, params.logits_file.c_str()); + LOG_ERR("%s: %s does not look like a file containing log-probabilities\n", __func__, params.logits_file.c_str()); return; } } @@ -1623,37 +1692,40 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { uint32_t n_ctx; in.read((char *)&n_ctx, sizeof(n_ctx)); if (n_ctx > llama_n_ctx(ctx)) { - fprintf(stderr, "%s: %s has been computed with %u, while the current context is %d. Increase it with -c and retry\n", + LOG_ERR("%s: %s has been computed with %u, while the current context is %d. Increase it with -c and retry\n", __func__, params.logits_file.c_str(), n_ctx, params.n_ctx); } - int n_vocab, n_chunk; + int n_vocab; + int n_chunk; in.read((char *)&n_vocab, sizeof(n_vocab)); in.read((char *)&n_chunk, sizeof(n_chunk)); if (in.fail()) { - fprintf(stderr, "%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str()); + LOG_ERR("%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str()); return; } - if (n_vocab != llama_n_vocab(llama_get_model(ctx))) { - fprintf(stderr, "%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx))); + if (n_vocab != llama_vocab_n_tokens(vocab)) { + LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_vocab_n_tokens(vocab)); } - std::vector tokens(n_ctx * n_chunk); + std::vector tokens(size_t(n_ctx) * n_chunk); if (in.read((char *)tokens.data(), tokens.size()*sizeof(tokens[0])).fail()) { - fprintf(stderr, "%s: failed reading evaluation tokens from %s\n", __func__, params.logits_file.c_str()); + LOG_ERR("%s: failed reading evaluation tokens from %s\n", __func__, params.logits_file.c_str()); return; } const int n_batch = params.n_batch; const int num_batches = (n_ctx + n_batch - 1)/n_batch; const int nv = 2*((n_vocab + 1)/2) + 4; - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); std::vector log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); - std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); + std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); + std::vector p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); std::vector logits; if (num_batches > 1) { - logits.reserve(n_ctx * n_vocab); + logits.reserve(size_t(n_ctx) * n_vocab); } std::vector workers(std::thread::hardware_concurrency() - 1); @@ -1667,9 +1739,18 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.; return std::make_pair(f, df); }; + auto covariance = [] (double suma, double sumb, double sumab, size_t count) { + if (count < 10) { + return 0.0; + } + double var = sumab/count - (suma/count)*(sumb/count); + var /= count - 1; + return var; + }; kl_divergence_result kld; - auto kld_ptr = kld_values.data(); + auto kld_ptr = kld_values.data(); + auto p_diff_ptr = p_diff_values.data(); for (int i = 0; i < n_chunk; ++i) { const int start = i * n_ctx; @@ -1678,13 +1759,15 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { const auto t_start = std::chrono::high_resolution_clock::now(); if (in.read((char *)log_probs_uint16.data(), log_probs_uint16.size()*sizeof(uint16_t)).fail()) { - fprintf(stderr, "%s: failed reading log-probs for chunk %d\n", __func__, i); + LOG_ERR("%s: failed reading log-probs for chunk %d\n", __func__, i); return; } // clear the KV cache llama_kv_cache_clear(ctx); + llama_batch batch = llama_batch_init(n_batch, 0, 1); + for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); @@ -1694,11 +1777,17 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { // add BOS token for the first batch of each chunk if (add_bos && j == 0) { - tokens[batch_start] = llama_token_bos(llama_get_model(ctx)); + tokens[batch_start] = llama_vocab_bos(vocab); } - if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) { - fprintf(stderr, "%s : failed to eval\n", __func__); + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { + LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); return; } @@ -1707,131 +1796,232 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { if (num_batches > 1) { const auto * batch_logits = llama_get_logits(ctx); - logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); + logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab); } } + llama_batch_free(batch); + const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { const float t_total = std::chrono::duration(t_end - t_start).count(); - fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); + LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); int total_seconds = (int)(t_total * n_chunk); if (total_seconds >= 60*60) { - fprintf(stderr, "%d hours ", total_seconds / (60*60)); + LOG("%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); } - fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); - - printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL-Divergence Same top\n"); + LOG("%.2f minutes\n", total_seconds / 60.0); } + LOG("\n"); + LOG("chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p\n"); const int first = n_ctx/2; const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); - process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, log_probs_uint16, kld, kld_ptr); - kld_ptr += n_ctx - 1 - first; + process_logits(n_vocab, all_logits + size_t(first)*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, + workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr); + p_diff_ptr += n_ctx - 1 - first; + kld_ptr += n_ctx - 1 - first; - auto ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); - auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count); - auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); - auto p_top = 1.*kld.n_same_top/kld.count; - auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1)); + LOG("%4d", i+1); - printf("%4d %10.4lf %10.5lf ± %10.5f %10.5f ± %10.5lf %.5f ± %.5f\n", i+1, exp(ppl.first), - log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second, - p_top, d_p_top); + auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); + const double ppl_val = exp(log_ppl.first); + const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 ) + LOG(" %9.4lf ± %9.4lf", ppl_val, ppl_unc); - fflush(stdout); + auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count); + const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count); + const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first; + const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov); + LOG(" %10.5lf ± %10.5lf", log_ppl_ratio_val, log_ppl_ratio_unc); + + auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); + LOG(" %10.5lf ± %10.5lf", kl_div.first, kl_div.second); + + auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count); + const double p_diff_rms_val = sqrt(p_diff_mse.first); + const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second; + LOG(" %6.3lf ± %6.3lf %%", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc); + + double p_top_val = 1.*kld.n_same_top/kld.count; + double p_top_unc = sqrt(p_top_val*(1 - p_top_val)/(kld.count - 1)); + LOG(" %6.3lf ± %6.3lf %%", 100.0*p_top_val, 100.0*p_top_unc); + + LOG("\n"); logits.clear(); } - printf("\n"); + LOG("\n"); if (kld.count < 100) return; // we do not wish to do statistics on so few values std::sort(kld_values.begin(), kld_values.end()); + std::sort(p_diff_values.begin(), p_diff_values.end()); - printf("===== KL-divergence statistics\n"); + LOG("====== Perplexity statistics ======\n"); + + auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); + const double ppl_val = exp(log_ppl.first); + const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 ) + LOG("Mean PPL(Q) : %10.6lf ± %10.6lf\n", ppl_val, ppl_unc); + + auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count); + const double ppl_base_val = exp(log_ppl_base.first); + const double ppl_base_unc = ppl_base_val * log_ppl_base.second; // ppl_base_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_base.second ** 2 ) + LOG("Mean PPL(base) : %10.6lf ± %10.6lf\n", ppl_base_val, ppl_base_unc); + + const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count); + // LOG("Cov(ln(PPL(Q)), ln(PPL(base))): %10.6lf\n", log_ppl_cov); + const double log_ppl_cor = log_ppl_cov / (log_ppl.second*log_ppl_base.second); + LOG("Cor(ln(PPL(Q)), ln(PPL(base))): %6.2lf%%\n", 100.0*log_ppl_cor); + + const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first; + const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov); + LOG("Mean ln(PPL(Q)/PPL(base)) : %10.6lf ± %10.6lf\n", log_ppl_ratio_val, log_ppl_ratio_unc); + + const double ppl_ratio_val = exp(log_ppl_ratio_val); + const double ppl_ratio_unc = ppl_ratio_val * log_ppl_ratio_unc; // ppl_ratio_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_ratio.second ** 2 ) + LOG("Mean PPL(Q)/PPL(base) : %10.6lf ± %10.6lf\n", ppl_ratio_val, ppl_ratio_unc); + + const double ppl_cov = ppl_val * ppl_base_val * log_ppl_cov; + const double ppl_diff_val = ppl_val - ppl_base_val; + const double ppl_diff_unc = sqrt(ppl_unc*ppl_unc + ppl_base_unc*ppl_base_unc - 2.0*ppl_cov); + LOG("Mean PPL(Q)-PPL(base) : %10.6lf ± %10.6lf\n", ppl_diff_val, ppl_diff_unc); + + LOG("\n"); + + LOG("====== KL divergence statistics ======\n"); auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); - printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second); + LOG("Mean KLD: %10.6lf ± %10.6lf\n", kl_div.first, kl_div.second); auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1]) : kld_values[kld_values.size()/2]; - printf("Median : %10.6f\n", kld_median); - auto percentile = [&kld_values] (float fraction) { - if (fraction <= 0) return kld_values.front(); - if (fraction >= 1) return kld_values.back(); - float p = fraction*(kld_values.size() - 1); + auto percentile = [] (std::vector values, float fraction) { + if (fraction <= 0) return values.front(); + if (fraction >= 1) return values.back(); + float p = fraction*(values.size() - 1); size_t ip = size_t(p); p -= ip; - return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)]; + return (1 - p)*values[ip] + p*values[std::min(ip+1, values.size()-1)]; }; - printf("Maximum: %10.6f\n", kld_values.back()); - printf("KLD_99 : %10.6f\n", percentile(0.99f)); - printf("KLD_95 : %10.6f\n", percentile(0.95f)); - printf("KLD_90 : %10.6f\n", percentile(0.90f)); + LOG("Maximum KLD: %10.6f\n", kld_values.back()); + LOG("99.9%% KLD: %10.6f\n", percentile(kld_values, 0.999f)); + LOG("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f)); + LOG("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f)); + LOG("Median KLD: %10.6f\n", kld_median); + LOG("10.0%% KLD: %10.6f\n", percentile(kld_values, 0.100f)); + LOG(" 5.0%% KLD: %10.6f\n", percentile(kld_values, 0.050f)); + LOG(" 1.0%% KLD: %10.6f\n", percentile(kld_values, 0.010f)); + LOG("Minimum KLD: %10.6f\n", kld_values.front()); - printf("Minimum: %10.6f\n", kld_values.front()); - printf("KLD_01 : %10.6f\n", percentile(0.01f)); - printf("KLD_05 : %10.6f\n", percentile(0.05f)); - printf("KLD_10 : %10.6f\n", percentile(0.10f)); + LOG("\n"); + LOG("====== Token probability statistics ======\n"); + + auto p_diff = mean_and_uncertainty(kld.sum_p_diff, kld.sum_p_diff2, kld.count); + LOG("Mean Δp: %6.3lf ± %5.3lf %%\n", 100.0*p_diff.first, 100.0*p_diff.second); + + auto p_diff_median = p_diff_values.size()%2 == 0 ? 0.5f*(p_diff_values[p_diff_values.size()/2] + p_diff_values[p_diff_values.size()/2-1]) + : p_diff_values[p_diff_values.size()/2]; + + LOG("Maximum Δp: %6.3lf%%\n", 100.0*p_diff_values.back()); + LOG("99.9%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.999f)); + LOG("99.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.990f)); + LOG("95.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.950f)); + LOG("90.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.900f)); + LOG("75.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.750f)); + LOG("Median Δp: %6.3lf%%\n", 100.0*p_diff_median); + LOG("25.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.250f)); + LOG("10.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.100f)); + LOG(" 5.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.050f)); + LOG(" 1.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.010f)); + LOG(" 0.1%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.001f)); + LOG("Minimum Δp: %6.3lf%%\n", 100.0*p_diff_values.front()); + + auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count); + // LOG("MSE Δp : %10.6lf ± %10.6lf\n", p_diff_mse.first, p_diff_mse.second); + + const double p_diff_rms_val = sqrt(p_diff_mse.first); + const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second; + LOG("RMS Δp : %6.3lf ± %5.3lf %%\n", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc); + + const double same_top_p = 1.0*kld.n_same_top/kld.count; + LOG("Same top p: %6.3lf ± %5.3lf %%\n", 100.0*same_top_p, 100.0*sqrt(same_top_p*(1.0 - same_top_p)/(kld.count - 1))); } int main(int argc, char ** argv) { - gpt_params params; + common_params params; - params.n_batch = 512; - if (!gpt_params_parse(argc, argv, params)) { + params.n_ctx = 512; + params.logits_all = true; + params.escape = false; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) { return 1; } - params.logits_all = true; - params.n_batch = std::min(params.n_batch, params.n_ctx); + common_init(); + + const int32_t n_ctx = params.n_ctx; + + if (n_ctx <= 0) { + LOG_ERR("%s: perplexity tool requires '--ctx-size' > 0\n", __func__); + return 1; + } + + const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence; + + if (ppl) { + const int32_t n_seq = std::max(1, params.n_batch / n_ctx); + const int32_t n_kv = n_seq * n_ctx; + + params.n_parallel = n_seq; + params.n_ctx = n_kv; + + params.n_batch = std::min(params.n_batch, n_kv); + } else { + params.n_batch = std::min(params.n_batch, params.n_ctx); + if (params.kl_divergence) { + params.n_parallel = 1; + } else { + // ensure there's at least enough seq_ids for HellaSwag + params.n_parallel = std::max(4, params.n_parallel); + } + } if (params.ppl_stride > 0) { - fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n", + LOG_INF("Will perform strided perplexity calculation -> adjusting context size from %d to %d\n", params.n_ctx, params.n_ctx + params.ppl_stride/2); params.n_ctx += params.ppl_stride/2; } - print_build_info(); - - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); - } - - fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } - llama_backend_init(); llama_numa_init(params.numa); - llama_model * model; - llama_context * ctx; - // load the model and apply lora adapter, if any - std::tie(model, ctx) = llama_init_from_gpt_params(params); + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + if (model == NULL) { - fprintf(stderr, "%s: error: unable to load model\n", __func__); + LOG_ERR("%s: unable to load model\n", __func__); return 1; } - const int n_ctx_train = llama_n_ctx_train(model); + const int n_ctx_train = llama_model_n_ctx_train(model); + if (params.n_ctx > n_ctx_train) { - fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", + LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, params.n_ctx); } // print system information { - fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } struct results_perplexity results; @@ -1844,14 +2034,11 @@ int main(int argc, char ** argv) { } else if (params.kl_divergence) { kl_divergence(ctx, params); } else { - results = perplexity(ctx, params); + results = perplexity(ctx, params, n_ctx); } - llama_print_timings(ctx); - write_logfile(ctx, params, model, results); - - llama_free(ctx); - llama_free_model(model); + LOG("\n"); + llama_perf_context_print(ctx); llama_backend_free(); diff --git a/examples/pydantic-models-to-grammar-examples.py b/examples/pydantic-models-to-grammar-examples.py deleted file mode 100644 index 160966649..000000000 --- a/examples/pydantic-models-to-grammar-examples.py +++ /dev/null @@ -1,224 +0,0 @@ -# Function calling example using pydantic models. -import datetime -import importlib -import json -from enum import Enum -from typing import Optional, Union - -import requests -from pydantic import BaseModel, Field -from pydantic_models_to_grammar import (add_run_method_to_dynamic_model, convert_dictionary_to_pydantic_model, - create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation) - - -# Function to get completion on the llama.cpp server with grammar. -def create_completion(prompt, grammar): - headers = {"Content-Type": "application/json"} - data = {"prompt": prompt, "grammar": grammar} - - response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data) - data = response.json() - - print(data["content"]) - return data["content"] - - -# A function for the agent to send a message to the user. -class SendMessageToUser(BaseModel): - """ - Send a message to the User. - """ - chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.") - message: str = Field(..., description="Message you want to send to the user.") - - def run(self): - print(self.message) - - -# Enum for the calculator tool. -class MathOperation(Enum): - ADD = "add" - SUBTRACT = "subtract" - MULTIPLY = "multiply" - DIVIDE = "divide" - - -# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt. -class Calculator(BaseModel): - """ - Perform a math operation on two numbers. - """ - number_one: Union[int, float] = Field(..., description="First number.") - operation: MathOperation = Field(..., description="Math operation to perform.") - number_two: Union[int, float] = Field(..., description="Second number.") - - def run(self): - if self.operation == MathOperation.ADD: - return self.number_one + self.number_two - elif self.operation == MathOperation.SUBTRACT: - return self.number_one - self.number_two - elif self.operation == MathOperation.MULTIPLY: - return self.number_one * self.number_two - elif self.operation == MathOperation.DIVIDE: - return self.number_one / self.number_two - else: - raise ValueError("Unknown operation.") - - -# Here the grammar gets generated by passing the available function models to generate_gbnf_grammar_and_documentation function. This also generates a documentation usable by the LLM. -# pydantic_model_list is the list of pydanitc models -# outer_object_name is an optional name for an outer object around the actual model object. Like a "function" object with "function_parameters" which contains the actual model object. If None, no outer object will be generated -# outer_object_content is the name of outer object content. -# model_prefix is the optional prefix for models in the documentation. (Default="Output Model") -# fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields") -gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation( - pydantic_model_list=[SendMessageToUser, Calculator], outer_object_name="function", - outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters") - -print(gbnf_grammar) -print(documentation) - -system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation - -user_message = "What is 42 * 42?" -prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" - -text = create_completion(prompt=prompt, grammar=gbnf_grammar) -# This should output something like this: -# { -# "function": "calculator", -# "function_parameters": { -# "number_one": 42, -# "operation": "multiply", -# "number_two": 42 -# } -# } -function_dictionary = json.loads(text) -if function_dictionary["function"] == "calculator": - function_parameters = {**function_dictionary["function_parameters"]} - - print(Calculator(**function_parameters).run()) - # This should output: 1764 - - -# A example structured output based on pydantic models. The LLM will create an entry for a Book database out of an unstructured text. -class Category(Enum): - """ - The category of the book. - """ - Fiction = "Fiction" - NonFiction = "Non-Fiction" - - -class Book(BaseModel): - """ - Represents an entry about a book. - """ - title: str = Field(..., description="Title of the book.") - author: str = Field(..., description="Author of the book.") - published_year: Optional[int] = Field(..., description="Publishing year of the book.") - keywords: list[str] = Field(..., description="A list of keywords.") - category: Category = Field(..., description="Category of the book.") - summary: str = Field(..., description="Summary of the book.") - - -# We need no additional parameters other than our list of pydantic models. -gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation([Book]) - -system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation - -text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands.""" -prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant" - -text = create_completion(prompt=prompt, grammar=gbnf_grammar) - -json_data = json.loads(text) - -print(Book(**json_data)) -# An example for parallel function calling with a Python function, a pydantic function model and an OpenAI like function definition. - -def get_current_datetime(output_format: Optional[str] = None): - """ - Get the current date and time in the given format. - Args: - output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S' - """ - if output_format is None: - output_format = '%Y-%m-%d %H:%M:%S' - return datetime.datetime.now().strftime(output_format) - - -# Example function to get the weather -def get_current_weather(location, unit): - """Get the current weather in a given location""" - if "London" in location: - return json.dumps({"location": "London", "temperature": "42", "unit": unit.value}) - elif "New York" in location: - return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value}) - elif "North Pole" in location: - return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value}) - else: - return json.dumps({"location": location, "temperature": "unknown"}) - - -# Here is a function definition in OpenAI style -current_weather_tool = { - "type": "function", - "function": { - "name": "get_current_weather", - "description": "Get the current weather in a given location", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, - }, - "required": ["location"], - }, - }, -} - -# Convert OpenAI function definition into pydantic model -current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool) -# Add the actual function to a pydantic model -current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather) - -# Convert normal Python function to a pydantic model -current_datetime_model = create_dynamic_model_from_function(get_current_datetime) - -tool_list = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model] - - -gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation( - pydantic_model_list=tool_list, outer_object_name="function", - outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True) - -system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation - - -text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42""" -prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant" - -text = create_completion(prompt=prompt, grammar=gbnf_grammar) - -json_data = json.loads(text) - -print(json_data) -# Should output something like this: -# [{'function': 'get_current_datetime', 'params': {'output_format': '%Y-%m-%d %H:%M:%S'}}, {'function': 'get_current_weather', 'params': {'location': 'London', 'unit': 'celsius'}}, {'function': 'Calculator', 'params': {'number_one': 42, 'operation': 'multiply', 'number_two': 42}}] - - -for call in json_data: - if call["function"] == "Calculator": - print(Calculator(**call["params"]).run()) - elif call["function"] == "get_current_datetime": - print(current_datetime_model(**call["params"]).run()) - elif call["function"] == "get_current_weather": - print(current_weather_tool_model(**call["params"]).run()) -# Should output something like this: -# 2024-01-14 13:36:06 -# {"location": "London", "temperature": "42", "unit": "celsius"} -# 1764 diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py index 9acc7cc6d..93e5dcb6c 100644 --- a/examples/pydantic_models_to_grammar.py +++ b/examples/pydantic_models_to_grammar.py @@ -9,7 +9,7 @@ from inspect import getdoc, isclass from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin, get_type_hints from docstring_parser import parse -from pydantic import BaseModel, Field, create_model +from pydantic import BaseModel, create_model if TYPE_CHECKING: from types import GenericAlias @@ -17,6 +17,9 @@ else: # python 3.8 compat from typing import _GenericAlias as GenericAlias +# TODO: fix this +# pyright: reportAttributeAccessIssue=information + class PydanticDataType(Enum): """ @@ -50,35 +53,38 @@ class PydanticDataType(Enum): def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str: - if isclass(pydantic_type) and issubclass(pydantic_type, str): + origin_type = get_origin(pydantic_type) + origin_type = pydantic_type if origin_type is None else origin_type + + if isclass(origin_type) and issubclass(origin_type, str): return PydanticDataType.STRING.value - elif isclass(pydantic_type) and issubclass(pydantic_type, bool): + elif isclass(origin_type) and issubclass(origin_type, bool): return PydanticDataType.BOOLEAN.value - elif isclass(pydantic_type) and issubclass(pydantic_type, int): + elif isclass(origin_type) and issubclass(origin_type, int): return PydanticDataType.INTEGER.value - elif isclass(pydantic_type) and issubclass(pydantic_type, float): + elif isclass(origin_type) and issubclass(origin_type, float): return PydanticDataType.FLOAT.value - elif isclass(pydantic_type) and issubclass(pydantic_type, Enum): + elif isclass(origin_type) and issubclass(origin_type, Enum): return PydanticDataType.ENUM.value - elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel): - return format_model_and_field_name(pydantic_type.__name__) - elif get_origin(pydantic_type) is list: + elif isclass(origin_type) and issubclass(origin_type, BaseModel): + return format_model_and_field_name(origin_type.__name__) + elif origin_type is list: element_type = get_args(pydantic_type)[0] return f"{map_pydantic_type_to_gbnf(element_type)}-list" - elif get_origin(pydantic_type) is set: + elif origin_type is set: element_type = get_args(pydantic_type)[0] return f"{map_pydantic_type_to_gbnf(element_type)}-set" - elif get_origin(pydantic_type) is Union: + elif origin_type is Union: union_types = get_args(pydantic_type) union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types] return f"union-{'-or-'.join(union_rules)}" - elif get_origin(pydantic_type) is Optional: + elif origin_type is Optional: element_type = get_args(pydantic_type)[0] return f"optional-{map_pydantic_type_to_gbnf(element_type)}" - elif isclass(pydantic_type): - return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}" - elif get_origin(pydantic_type) is dict: + elif isclass(origin_type): + return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(origin_type.__name__)}" + elif origin_type is dict: key_type, value_type = get_args(pydantic_type) return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}" else: @@ -115,7 +121,7 @@ def get_members_structure(cls, rule_name): # Modify this comprehension members = [ f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}' - for name, param_type in cls.__annotations__.items() + for name, param_type in get_type_hints(cls).items() if name != "self" ] @@ -234,8 +240,9 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None # Define the integer part rule integer_part_rule = ( - "integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + ( - f"-min{min_digit}" if min_digit is not None else "") + "integer-part" + + (f"-max{max_digit}" if max_digit is not None else "") + + (f"-min{min_digit}" if min_digit is not None else "") ) # Define the fractional part rule based on precision constraints @@ -293,17 +300,20 @@ def generate_gbnf_rule_for_type( field_name = format_model_and_field_name(field_name) gbnf_type = map_pydantic_type_to_gbnf(field_type) - if isclass(field_type) and issubclass(field_type, BaseModel): + origin_type = get_origin(field_type) + origin_type = field_type if origin_type is None else origin_type + + if isclass(origin_type) and issubclass(origin_type, BaseModel): nested_model_name = format_model_and_field_name(field_type.__name__) nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules) rules.extend(nested_model_rules) gbnf_type, rules = nested_model_name, rules - elif isclass(field_type) and issubclass(field_type, Enum): + elif isclass(origin_type) and issubclass(origin_type, Enum): enum_values = [f'"\\"{e.value}\\""' for e in field_type] # Adding escaped quotes enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}" rules.append(enum_rule) gbnf_type, rules = model_name + "-" + field_name, rules - elif get_origin(field_type) == list: # Array + elif origin_type is list: # Array element_type = get_args(field_type)[0] element_rule_name, additional_rules = generate_gbnf_rule_for_type( model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules @@ -313,7 +323,7 @@ def generate_gbnf_rule_for_type( rules.append(array_rule) gbnf_type, rules = model_name + "-" + field_name, rules - elif get_origin(field_type) == set or field_type == set: # Array + elif origin_type is set: # Array element_type = get_args(field_type)[0] element_rule_name, additional_rules = generate_gbnf_rule_for_type( model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules @@ -367,7 +377,7 @@ def generate_gbnf_rule_for_type( gbnf_type = f"{model_name}-{field_name}-optional" else: gbnf_type = f"{model_name}-{field_name}-union" - elif isclass(field_type) and issubclass(field_type, str): + elif isclass(origin_type) and issubclass(origin_type, str): if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None: triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False) markdown_string = field_info.json_schema_extra.get("markdown_code_block", False) @@ -383,8 +393,8 @@ def generate_gbnf_rule_for_type( gbnf_type = PydanticDataType.STRING.value elif ( - isclass(field_type) - and issubclass(field_type, float) + isclass(origin_type) + and issubclass(origin_type, float) and field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None @@ -409,8 +419,8 @@ def generate_gbnf_rule_for_type( ) elif ( - isclass(field_type) - and issubclass(field_type, int) + isclass(origin_type) + and issubclass(origin_type, int) and field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None @@ -458,7 +468,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas if not issubclass(model, BaseModel): # For non-Pydantic classes, generate model_fields from __annotations__ or __init__ if hasattr(model, "__annotations__") and model.__annotations__: - model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} + model_fields = {name: (typ, ...) for name, typ in get_type_hints(model).items()} else: init_signature = inspect.signature(model.__init__) parameters = init_signature.parameters @@ -466,7 +476,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas name != "self"} else: # For Pydantic models, use model_fields and check for ellipsis (required fields) - model_fields = model.__annotations__ + model_fields = get_type_hints(model) model_rule_parts = [] nested_rules = [] @@ -624,7 +634,7 @@ string ::= "\"" ( "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) )* "\"" ws ws ::= ([ \t\n] ws)? -float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws +float ::= ("-"? ([0] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws integer ::= [0-9]+""" @@ -680,7 +690,7 @@ def generate_markdown_documentation( str: Generated text documentation. """ documentation = "" - pyd_models = [(model, True) for model in pydantic_models] + pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models] for model, add_prefix in pyd_models: if add_prefix: documentation += f"{model_prefix}: {model.__name__}\n" @@ -700,9 +710,9 @@ def generate_markdown_documentation( # Indenting the fields section documentation += f" {fields_prefix}:\n" else: - documentation += f" Fields:\n" + documentation += f" Fields:\n" # noqa: F541 if isclass(model) and issubclass(model, BaseModel): - for name, field_type in model.__annotations__.items(): + for name, field_type in get_type_hints(model).items(): # if name == "markdown_code_block": # continue if get_origin(field_type) == list: @@ -750,14 +760,17 @@ def generate_field_markdown( field_info = model.model_fields.get(field_name) field_description = field_info.description if field_info and field_info.description else "" - if get_origin(field_type) == list: + origin_type = get_origin(field_type) + origin_type = field_type if origin_type is None else origin_type + + if origin_type == list: element_type = get_args(field_type)[0] field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})" if field_description != "": field_text += ":\n" else: field_text += "\n" - elif get_origin(field_type) == Union: + elif origin_type == Union: element_types = get_args(field_type) types = [] for element_type in element_types: @@ -778,7 +791,7 @@ def generate_field_markdown( return field_text if field_description != "": - field_text += f" Description: " + field_description + "\n" + field_text += f" Description: {field_description}\n" # Check for and include field-specific examples if available if hasattr(model, "Config") and hasattr(model.Config, @@ -788,9 +801,9 @@ def generate_field_markdown( example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example field_text += f"{indent} Example: {example_text}\n" - if isclass(field_type) and issubclass(field_type, BaseModel): + if isclass(origin_type) and issubclass(origin_type, BaseModel): field_text += f"{indent} Details:\n" - for name, type_ in field_type.__annotations__.items(): + for name, type_ in get_type_hints(field_type).items(): field_text += generate_field_markdown(name, type_, field_type, depth + 2) return field_text @@ -833,7 +846,7 @@ def generate_text_documentation( str: Generated text documentation. """ documentation = "" - pyd_models = [(model, True) for model in pydantic_models] + pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models] for model, add_prefix in pyd_models: if add_prefix: documentation += f"{model_prefix}: {model.__name__}\n" @@ -851,7 +864,7 @@ def generate_text_documentation( if isclass(model) and issubclass(model, BaseModel): documentation_fields = "" - for name, field_type in model.__annotations__.items(): + for name, field_type in get_type_hints(model).items(): # if name == "markdown_code_block": # continue if get_origin(field_type) == list: @@ -944,7 +957,7 @@ def generate_field_text( if isclass(field_type) and issubclass(field_type, BaseModel): field_text += f"{indent} Details:\n" - for name, type_ in field_type.__annotations__.items(): + for name, type_ in get_type_hints(field_type).items(): field_text += generate_field_text(name, type_, field_type, depth + 2) return field_text @@ -1164,7 +1177,7 @@ def create_dynamic_model_from_function(func: Callable[..., Any]): dynamic_fields[param.name] = ( param.annotation if param.annotation != inspect.Parameter.empty else str, default_value) # Creating the dynamic model - dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) # type: ignore[call-overload] + dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) for name, param_doc in param_docs: dynamic_model.model_fields[name].description = param_doc.description @@ -1228,9 +1241,6 @@ def map_grammar_names_to_pydantic_model_class(pydantic_model_list): return output -from enum import Enum - - def json_schema_to_python_types(schema): type_map = { "any": Any, @@ -1275,7 +1285,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name: if items != {}: array = {"properties": items} array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items") - fields[field_name] = (List[array_type], ...) # type: ignore[valid-type] + fields[field_name] = (List[array_type], ...) else: fields[field_name] = (list, ...) elif field_type == "object": @@ -1285,7 +1295,8 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name: required = field_data.get("enum", []) for key, field in fields.items(): if key not in required: - fields[key] = (Optional[fields[key][0]], ...) + optional_type = fields[key][0] + fields[key] = (Optional[optional_type], ...) else: field_type = json_schema_to_python_types(field_type) fields[field_name] = (field_type, ...) @@ -1305,6 +1316,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name: required = dictionary.get("required", []) for key, field in fields.items(): if key not in required: - fields[key] = (Optional[fields[key][0]], ...) + optional_type = fields[key][0] + fields[key] = (Optional[optional_type], ...) custom_model = create_model(model_name, **fields) return custom_model diff --git a/examples/pydantic_models_to_grammar_examples.py b/examples/pydantic_models_to_grammar_examples.py new file mode 100755 index 000000000..eb000d5cc --- /dev/null +++ b/examples/pydantic_models_to_grammar_examples.py @@ -0,0 +1,312 @@ +#!/usr/bin/env python3 + +"""Function calling example using pydantic models.""" + +from __future__ import annotations + +import argparse +import datetime +import json +import logging +import textwrap +import sys +from enum import Enum +from typing import Optional, Union + +import requests +from pydantic import BaseModel, Field +from pydantic_models_to_grammar import (add_run_method_to_dynamic_model, convert_dictionary_to_pydantic_model, + create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation) + + +def create_completion(host, prompt, gbnf_grammar): + """Calls the /completion API on llama-server. + + See + https://github.com/ggerganov/llama.cpp/tree/HEAD/examples/server#api-endpoints + """ + print(f" Request:\n Grammar:\n{textwrap.indent(gbnf_grammar, ' ')}\n Prompt:\n{textwrap.indent(prompt.rstrip(), ' ')}") + headers = {"Content-Type": "application/json"} + data = {"prompt": prompt, "grammar": gbnf_grammar} + result = requests.post(f"http://{host}/completion", headers=headers, json=data).json() + assert data.get("error") is None, data + logging.info("Result: %s", result) + content = result["content"] + print(f" Model: {result['model']}") + print(f" Result:\n{textwrap.indent(json.dumps(json.loads(content), indent=2), ' ')}") + return content + + +# A function for the agent to send a message to the user. +class SendMessageToUser(BaseModel): + """Send a message to the User.""" + chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.") + message: str = Field(..., description="Message you want to send to the user.") + + def run(self): + print(f"SendMessageToUser: {self.message}") + + +def example_rce(host): + """Minimal test case where the LLM call an arbitrary python function.""" + print("- example_rce") + tools = [SendMessageToUser] + gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation( + pydantic_model_list=tools, outer_object_name="function", + outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters") + system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation + user_message = "What is 42 * 42?" + prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" + text = create_completion(host, prompt, gbnf_grammar) + json_data = json.loads(text) + tools_map = {tool.__name__:tool for tool in tools} + # This finds "SendMessageToUser": + tool = tools_map.get(json_data["function"]) + if not tool: + print(f"Error: unknown tool {json_data['function']}") + return 1 + tool(**json_data["function_parameters"]).run() + return 0 + + +# Enum for the calculator tool. +class MathOperation(Enum): + ADD = "add" + SUBTRACT = "subtract" + MULTIPLY = "multiply" + DIVIDE = "divide" + + +# Simple pydantic calculator tool for the agent that can add, subtract, +# multiply, and divide. Docstring and description of fields will be used in +# system prompt. +class Calculator(BaseModel): + """Perform a math operation on two numbers.""" + number_one: Union[int, float] = Field(..., description="First number.") + operation: MathOperation = Field(..., description="Math operation to perform.") + number_two: Union[int, float] = Field(..., description="Second number.") + + def run(self): + if self.operation == MathOperation.ADD: + return self.number_one + self.number_two + elif self.operation == MathOperation.SUBTRACT: + return self.number_one - self.number_two + elif self.operation == MathOperation.MULTIPLY: + return self.number_one * self.number_two + elif self.operation == MathOperation.DIVIDE: + return self.number_one / self.number_two + else: + raise ValueError("Unknown operation.") + + +def example_calculator(host): + """Have the LLM ask to get a calculation done. + + Here the grammar gets generated by passing the available function models to + generate_gbnf_grammar_and_documentation function. This also generates a + documentation usable by the LLM. + + pydantic_model_list is the list of pydantic models outer_object_name is an + optional name for an outer object around the actual model object. Like a + "function" object with "function_parameters" which contains the actual model + object. If None, no outer object will be generated outer_object_content is + the name of outer object content. + + model_prefix is the optional prefix for models in the documentation. (Default="Output Model") + fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields") + """ + print("- example_calculator") + tools = [SendMessageToUser, Calculator] + gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation( + pydantic_model_list=tools, outer_object_name="function", + outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters") + system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation + user_message1 = "What is 42 * 42?" + prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message1}<|im_end|>\n<|im_start|>assistant" + text = create_completion(host, prompt, gbnf_grammar) + json_data = json.loads(text) + expected = { + "function": "Calculator", + "function_parameters": { + "number_one": 42, + "operation": "multiply", + "number_two": 42 + } + } + if json_data != expected: + print(" Result is not as expected!") + tools_map = {tool.__name__:tool for tool in tools} + # This finds "Calculator": + tool = tools_map.get(json_data["function"]) + if not tool: + print(f"Error: unknown tool {json_data['function']}") + return 1 + result = tool(**json_data["function_parameters"]).run() + print(f" Call {json_data['function']} gave result {result}") + return 0 + + +class Category(Enum): + """The category of the book.""" + Fiction = "Fiction" + NonFiction = "Non-Fiction" + + +class Book(BaseModel): + """Represents an entry about a book.""" + title: str = Field(..., description="Title of the book.") + author: str = Field(..., description="Author of the book.") + published_year: Optional[int] = Field(..., description="Publishing year of the book.") + keywords: list[str] = Field(..., description="A list of keywords.") + category: Category = Field(..., description="Category of the book.") + summary: str = Field(..., description="Summary of the book.") + + +def example_struct(host): + """A example structured output based on pydantic models. + + The LLM will create an entry for a Book database out of an unstructured + text. We need no additional parameters other than our list of pydantic + models. + """ + print("- example_struct") + tools = [Book] + gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(pydantic_model_list=tools) + system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation + text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands.""" + prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant" + text = create_completion(host, prompt, gbnf_grammar) + json_data = json.loads(text) + # In this case, there's no function nor function_parameters. + # Here the result will vary based on the LLM used. + keys = sorted(["title", "author", "published_year", "keywords", "category", "summary"]) + if keys != sorted(json_data.keys()): + print(f"Unexpected result: {sorted(json_data.keys())}") + return 1 + book = Book(**json_data) + print(f" As a Book object: %s" % book) + return 0 + + +def get_current_datetime(output_format: Optional[str] = None): + """Get the current date and time in the given format. + + Args: + output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S' + """ + return datetime.datetime.now().strftime(output_format or "%Y-%m-%d %H:%M:%S") + + +# Example function to get the weather. +def get_current_weather(location, unit): + """Get the current weather in a given location""" + if "London" in location: + return json.dumps({"location": "London", "temperature": "42", "unit": unit.value}) + elif "New York" in location: + return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value}) + elif "North Pole" in location: + return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value}) + return json.dumps({"location": location, "temperature": "unknown"}) + + +def example_concurrent(host): + """An example for parallel function calling with a Python function, a pydantic + function model and an OpenAI like function definition. + """ + print("- example_concurrent") + # Function definition in OpenAI style. + current_weather_tool = { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + # Convert OpenAI function definition into pydantic model. + current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool) + # Add the actual function to a pydantic model. + current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather) + + # Convert normal Python function to a pydantic model. + current_datetime_model = create_dynamic_model_from_function(get_current_datetime) + + tools = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model] + gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation( + pydantic_model_list=tools, outer_object_name="function", + outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True) + system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation + text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42""" + prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant" + text = create_completion(host, prompt, gbnf_grammar) + json_data = json.loads(text) + expected = [ + { + "function": "get_current_datetime", + "params": { + "output_format": "%Y-%m-%d %H:%M:%S" + } + }, + { + "function": "get_current_weather", + "params": { + "location": "London", + "unit": "celsius" + } + }, + { + "function": "Calculator", + "params": { + "number_one": 42, + "operation": "multiply", + "number_two": 42 + } + } + ] + res = 0 + if json_data != expected: + print(" Result is not as expected!") + print(" This can happen on highly quantized models") + res = 1 + tools_map = {tool.__name__:tool for tool in tools} + for call in json_data: + tool = tools_map.get(call["function"]) + if not tool: + print(f"Error: unknown tool {call['function']}") + return 1 + result = tool(**call["params"]).run() + print(f" Call {call['function']} returned {result}") + # Should output something like this: + # Call get_current_datetime returned 2024-07-15 09:50:38 + # Call get_current_weather returned {"location": "London", "temperature": "42", "unit": "celsius"} + # Call Calculator returned 1764 + return res + + +def main(): + parser = argparse.ArgumentParser(description=sys.modules[__name__].__doc__) + parser.add_argument("--host", default="localhost:8080", help="llama.cpp server") + parser.add_argument("-v", "--verbose", action="store_true", help="enables logging") + args = parser.parse_args() + logging.basicConfig(level=logging.INFO if args.verbose else logging.ERROR) + ret = 0 + # Comment out below to only run the example you want. + ret = ret or example_rce(args.host) + ret = ret or example_calculator(args.host) + ret = ret or example_struct(args.host) + ret = ret or example_concurrent(args.host) + return ret + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index e31cf5e38..9a3a0d3cd 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -1,6 +1,6 @@ -set(TARGET quantize-stats) +set(TARGET llama-quantize-stats) 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 1d05f1391..bd2f73467 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -1,7 +1,7 @@ -#define LLAMA_API_INTERNAL -#include "common.h" #include "ggml.h" #include "llama.h" +#include "llama-context.h" +#include "common.h" #include #include @@ -9,11 +9,9 @@ #include #include #include -#include #include #include #include -#include #include #include #include @@ -23,7 +21,7 @@ #endif struct quantize_stats_params { - std::string model = "models/7B/ggml-model-f16.gguf"; + std::string model = DEFAULT_MODEL_PATH; bool verbose = false; bool per_layer_stats = false; bool print_histogram = false; @@ -142,7 +140,7 @@ 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_t & 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) { @@ -154,9 +152,9 @@ static void test_roundtrip_on_chunk( } if (use_reference) { - qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size); + 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 +164,7 @@ 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_t & 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 +185,13 @@ 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 +203,7 @@ 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); } }; @@ -311,7 +309,7 @@ int main(int argc, char ** argv) { auto mparams = llama_model_default_params(); mparams.use_mlock = false; - model = llama_load_model_from_file(params.model.c_str(), mparams); + model = llama_model_load_from_file(params.model.c_str(), mparams); if (model == NULL) { fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); @@ -319,25 +317,24 @@ int main(int argc, char ** argv) { } auto cparams = llama_context_default_params(); - cparams.n_ctx = 256; - cparams.seed = 1; + cparams.n_ctx = 256; - ctx = llama_new_context_with_model(model, cparams); + ctx = llama_init_from_model(model, cparams); if (ctx == NULL) { fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); - llama_free_model(model); + llama_model_free(model); return 1; } } - const auto &tensors = llama_internal_get_tensor_map(ctx); + const auto & tensors = llama_internal_get_tensor_map(ctx); // check layer tensors int included_layers = 0; int64_t max_nelements = 0; bool is_f16 = false; - for (const auto& kv_tensor : tensors) { + for (const auto & kv_tensor : tensors) { if (!layer_included(params, kv_tensor.first)) { continue; } @@ -350,7 +347,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: error: Quantization should be tested with a float model, " "this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type); llama_free(ctx); - llama_free_model(model); + llama_model_free(model); return 1; } included_layers++; @@ -372,8 +369,9 @@ 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; } - ggml_type_traits_t qfns = ggml_internal_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)); } @@ -382,7 +380,7 @@ int main(int argc, char ** argv) { error_stats global_stats {}; - for (const auto& kv_tensor : tensors) { + for (const auto & kv_tensor : tensors) { if (!layer_included(params, kv_tensor.first)) { continue; } @@ -394,7 +392,7 @@ 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, @@ -411,7 +409,7 @@ int main(int argc, char ** argv) { llama_free(ctx); - llama_free_model(model); + llama_model_free(model); // report timing { const int64_t t_main_end_us = ggml_time_us(); diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 6f374a2bd..47e5cbe30 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -1,6 +1,6 @@ -set(TARGET quantize) +set(TARGET llama-quantize) add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${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/quantize/README.md b/examples/quantize/README.md index c8b9a27a0..f9cce7b21 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -1,22 +1,107 @@ # quantize -TODO +You can also use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to build your own quants without any setup. -## Llama 2 7B +Note: It is synced from llama.cpp `main` every 6 hours. -Quantization | Bits per Weight (BPW) --- | -- -Q2_K | 3.35 -Q3_K_S | 3.50 -Q3_K_M | 3.91 -Q3_K_L | 4.27 -Q4_K_S | 4.58 -Q4_K_M | 4.84 -Q5_K_S | 5.52 -Q5_K_M | 5.68 -Q6_K | 6.56 +Example usage: + +```bash +# obtain the official LLaMA model weights and place them in ./models +ls ./models +llama-2-7b tokenizer_checklist.chk tokenizer.model +# [Optional] for models using BPE tokenizers +ls ./models + vocab.json +# [Optional] for PyTorch .bin models like Mistral-7B +ls ./models + + +# install Python dependencies +python3 -m pip install -r requirements.txt + +# convert the model to ggml FP16 format +python3 convert_hf_to_gguf.py models/mymodel/ + +# quantize the model to 4-bits (using Q4_K_M method) +./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M + +# update the gguf filetype to current version if older version is now unsupported +./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY +``` + +Run the quantized model: + +```bash +# start inference on a gguf model +./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -cnv -p "You are a helpful assistant" +``` + +When running the larger models, make sure you have enough disk space to store all the intermediate files. + +## Memory/Disk Requirements + +As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. + +| Model | Original size | Quantized size (Q4_0) | +|------:|--------------:|----------------------:| +| 7B | 13 GB | 3.9 GB | +| 13B | 24 GB | 7.8 GB | +| 30B | 60 GB | 19.5 GB | +| 65B | 120 GB | 38.5 GB | + +## Quantization + +Several quantization methods are supported. They differ in the resulting model disk size and inference speed. + +*(outdated)* + +| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | +|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:| +| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 | +| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G | +| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 | +| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 | +| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | +| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 | +| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G | +| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 | +| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 | +| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | + +- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) +- recent k-quants improvements and new i-quants + - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) + - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) + - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) + - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) + - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) + - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) + - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) + - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) + - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) + - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) + - [#4996 - k-quants tuning](https://github.com/ggerganov/llama.cpp/pull/4996) + - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) + - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) + - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) + +**Llama 2 7B** + +| Quantization | Bits per Weight (BPW) | +|--------------|-----------------------| +| Q2_K | 3.35 | +| Q3_K_S | 3.50 | +| Q3_K_M | 3.91 | +| Q3_K_L | 4.27 | +| Q4_K_S | 4.58 | +| Q4_K_M | 4.84 | +| Q5_K_S | 5.52 | +| Q5_K_M | 5.68 | +| Q6_K | 6.56 | + +**Llama 2 13B** -## Llama 2 13B Quantization | Bits per Weight (BPW) -- | -- Q2_K | 3.34 @@ -29,7 +114,7 @@ Q5_K_S | 5.51 Q5_K_M | 5.67 Q6_K | 6.56 -# Llama 2 70B +**Llama 2 70B** Quantization | Bits per Weight (BPW) -- | -- diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 7662ec80c..8d47b17b6 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -8,7 +8,6 @@ #include #include #include -#include struct quant_option { std::string name; @@ -17,41 +16,59 @@ struct quant_option { }; static const std::vector QUANT_OPTIONS = { - { "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0, " 3.56G, +0.2166 ppl @ LLaMA-v1-7B", }, - { "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", }, - { "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", }, - { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", }, - { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization", }, - { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, - { "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", }, - { "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", }, - { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, - { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, - { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, - { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", }, - { "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", }, - { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, - { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, - { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization" , }, - { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, - { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", }, - { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", }, - { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", }, - { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", }, - { "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", }, - { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", }, - { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", }, - { "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", }, - { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", }, - { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", }, - { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", }, - { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", }, - { "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", }, - { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, + { "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0, " 4.34G, +0.4685 ppl @ Llama-3-8B", }, + { "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 4.78G, +0.4511 ppl @ Llama-3-8B", }, + { "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 5.21G, +0.1316 ppl @ Llama-3-8B", }, + { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 5.65G, +0.1062 ppl @ Llama-3-8B", }, + { "IQ2_XXS", LLAMA_FTYPE_MOSTLY_IQ2_XXS, " 2.06 bpw quantization", }, + { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, + { "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", }, + { "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", }, + { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, + { "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", }, + { "TQ1_0", LLAMA_FTYPE_MOSTLY_TQ1_0, " 1.69 bpw ternarization", }, + { "TQ2_0", LLAMA_FTYPE_MOSTLY_TQ2_0, " 2.06 bpw ternarization", }, + { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.96G, +3.5199 ppl @ Llama-3-8B", }, + { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.96G, +3.1836 ppl @ Llama-3-8B", }, + { "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", }, + { "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", }, + { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, + { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, + { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization", }, + { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 3.41G, +1.6321 ppl @ Llama-3-8B", }, + { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.74G, +0.6569 ppl @ Llama-3-8B", }, + { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 4.03G, +0.5562 ppl @ Llama-3-8B", }, + { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", }, + { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", }, + { "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", }, + { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 4.37G, +0.2689 ppl @ Llama-3-8B", }, + { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 4.58G, +0.1754 ppl @ Llama-3-8B", }, + { "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", }, + { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 5.21G, +0.1049 ppl @ Llama-3-8B", }, + { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B", }, + { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 6.14G, +0.0217 ppl @ Llama-3-8B", }, + { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 7.96G, +0.0026 ppl @ Llama-3-8B", }, + { "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, +0.0020 ppl @ Mistral-7B", }, + { "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", }, + { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching. - { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, + { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, }; +static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count"; + +static bool striequals(const char * a, const char * b) { + while (*a && *b) { + if (std::tolower(*a) != std::tolower(*b)) { + return false; + } + a++; b++; + } + return *a == *b; +} static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) { std::string ftype_str; @@ -60,7 +77,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp ftype_str.push_back(std::toupper(ch)); } for (auto & it : QUANT_OPTIONS) { - if (it.name == ftype_str) { + if (striequals(it.name.c_str(), ftype_str.c_str())) { ftype = it.ftype; ftype_str_out = it.name; return true; @@ -83,17 +100,22 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp } // usage: -// ./quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] +// ./llama-quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] // [[noreturn]] static void usage(const char * executable) { - printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n"); printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n"); printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n"); printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n"); + printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n"); + printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n"); + printf(" --keep-split: will generate quantized model in the same shards as input\n"); + printf(" --override-kv KEY=TYPE:VALUE\n"); + printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n"); printf("Note: --include-weights and --exclude-weights cannot be used together\n"); printf("\nAllowed quantization types:\n"); for (auto & it : QUANT_OPTIONS) { @@ -107,61 +129,80 @@ static void usage(const char * executable) { exit(1); } -static void load_imatrix(const std::string& imatrix_file, std::unordered_map>& imatrix_data) { +static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map> & imatrix_data) { std::ifstream in(imatrix_file.c_str(), std::ios::binary); if (!in) { - printf("%s: failed to open %s\n",__func__,imatrix_file.c_str()); - return; + printf("%s: failed to open %s\n",__func__, imatrix_file.c_str()); + exit(1); } int n_entries; - in.read((char*)&n_entries, sizeof(n_entries)); + in.read((char *)&n_entries, sizeof(n_entries)); if (in.fail() || n_entries < 1) { printf("%s: no data in file %s\n", __func__, imatrix_file.c_str()); - return; + exit(1); } for (int i = 0; i < n_entries; ++i) { int len; in.read((char *)&len, sizeof(len)); std::vector name_as_vec(len+1); in.read((char *)name_as_vec.data(), len); if (in.fail()) { - printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file.c_str()); - return; + printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str()); + exit(1); } name_as_vec[len] = 0; std::string name{name_as_vec.data()}; - auto& e = imatrix_data[std::move(name)]; + auto & e = imatrix_data[name]; int ncall; - in.read((char*)&ncall, sizeof(ncall)); + in.read((char *)&ncall, sizeof(ncall)); int nval; in.read((char *)&nval, sizeof(nval)); if (in.fail() || nval < 1) { - printf("%s: failed reading number of values for entry %d\n",__func__,i); + printf("%s: failed reading number of values for entry %d\n", __func__, i); imatrix_data = {}; - return; + exit(1); } e.resize(nval); - in.read((char*)e.data(), nval*sizeof(float)); + in.read((char *)e.data(), nval*sizeof(float)); if (in.fail()) { - printf("%s: failed reading data for entry %d\n",__func__,i); + printf("%s: failed reading data for entry %d\n", __func__, i); imatrix_data = {}; - return; + exit(1); } if (ncall > 0) { for (auto& v : e) v /= ncall; } + + if (getenv("LLAMA_TRACE")) { + printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str()); + } } - printf("%s: loaded %d importance matrix entries from %s\n",__func__,int(imatrix_data.size()),imatrix_file.c_str()); + + // latest imatrix version contains the dataset filename at the end of the file + int m_last_call = 0; + if (in.peek() != EOF) { + in.read((char *)&m_last_call, sizeof(m_last_call)); + int dataset_len; + in.read((char *)&dataset_len, sizeof(dataset_len)); + std::vector dataset_as_vec(dataset_len); + in.read(dataset_as_vec.data(), dataset_len); + imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end()); + printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str()); + } + printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call); + return m_last_call; } -static void prepare_imatrix(const std::string& imatrix_file, - const std::vector& included_weights, - const std::vector& excluded_weights, - std::unordered_map>& imatrix_data) { +static int prepare_imatrix(const std::string & imatrix_file, + std::string & imatrix_dataset, + const std::vector & included_weights, + const std::vector & excluded_weights, + std::unordered_map> & imatrix_data) { + int m_last_call = -1; if (!imatrix_file.empty()) { - load_imatrix(imatrix_file, imatrix_data); + m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data); } if (imatrix_data.empty()) { - return; + return m_last_call; } if (!excluded_weights.empty()) { for (auto& name : excluded_weights) { @@ -187,6 +228,19 @@ static void prepare_imatrix(const std::string& imatrix_file, if (!imatrix_data.empty()) { printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size())); } + return m_last_call; +} + +static ggml_type parse_ggml_type(const char * arg) { + for (int i = 0; i < GGML_TYPE_COUNT; ++i) { + auto type = (ggml_type)i; + const auto * name = ggml_type_name(type); + if (name && striequals(name, arg)) { + return type; + } + } + fprintf(stderr, "%s: invalid ggml_type '%s'\n", __func__, arg); + return GGML_TYPE_COUNT; } int main(int argc, char ** argv) { @@ -199,10 +253,33 @@ int main(int argc, char ** argv) { int arg_idx = 1; std::string imatrix_file; std::vector included_weights, excluded_weights; + std::vector kv_overrides; for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) { if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) { params.quantize_output_tensor = false; + } else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) { + if (arg_idx < argc-1) { + params.output_tensor_type = parse_ggml_type(argv[++arg_idx]); + if (params.output_tensor_type == GGML_TYPE_COUNT) { + usage(argv[0]); + } + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) { + if (arg_idx < argc-1) { + params.token_embedding_type = parse_ggml_type(argv[++arg_idx]); + if (params.token_embedding_type == GGML_TYPE_COUNT) { + usage(argv[0]); + } + } else { + usage(argv[0]); + } + } else if (strcmp(argv[arg_idx], "--override-kv") == 0) { + if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) { + usage(argv[0]); + } } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) { params.allow_requantize = true; } else if (strcmp(argv[arg_idx], "--pure") == 0) { @@ -225,6 +302,8 @@ int main(int argc, char ** argv) { } else { usage(argv[0]); } + } else if (strcmp(argv[arg_idx], "--keep-split") == 0) { + params.keep_split = true; } else { usage(argv[0]); } @@ -238,10 +317,48 @@ int main(int argc, char ** argv) { usage(argv[0]); } + std::string imatrix_dataset; std::unordered_map> imatrix_data; - prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data); + int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data); if (!imatrix_data.empty()) { params.imatrix = &imatrix_data; + { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + strncpy(kvo.val_str, imatrix_file.c_str(), 127); + kvo.val_str[127] = '\0'; + kv_overrides.emplace_back(std::move(kvo)); + } + if (!imatrix_dataset.empty()) { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + strncpy(kvo.val_str, imatrix_dataset.c_str(), 127); + kvo.val_str[127] = '\0'; + kv_overrides.emplace_back(std::move(kvo)); + } + + { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = imatrix_data.size(); + kv_overrides.emplace_back(std::move(kvo)); + } + + if (m_last_call > 0) { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = m_last_call; + kv_overrides.emplace_back(std::move(kvo)); + } + } + if (!kv_overrides.empty()) { + kv_overrides.emplace_back(); + kv_overrides.back().key[0] = 0; + params.kv_overrides = &kv_overrides; } llama_backend_init(); @@ -252,21 +369,28 @@ int main(int argc, char ** argv) { std::string fname_out; std::string ftype_str; + std::string suffix = ".gguf"; if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { std::string fpath; const size_t pos = fname_inp.find_last_of("/\\"); if (pos != std::string::npos) { fpath = fname_inp.substr(0, pos + 1); } - // export as [inp path]/ggml-model-[ftype].gguf - fname_out = fpath + "ggml-model-" + ftype_str + ".gguf"; + + // export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting + fname_out = fpath + "ggml-model-" + ftype_str; + if (!params.keep_split) { + fname_out += suffix; + } arg_idx++; if (ftype_str == "COPY") { params.only_copy = true; } - } - else { + } else { fname_out = argv[arg_idx]; + if (params.keep_split && fname_out.find(suffix) != std::string::npos) { + fname_out = fname_out.substr(0, fname_out.length() - suffix.length()); + } arg_idx++; if (argc <= arg_idx) { @@ -296,10 +420,12 @@ int main(int argc, char ** argv) { if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || - params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) { - fprintf(stderr, "\n===============================================================================================\n"); - fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); - fprintf(stderr, "===============================================================================================\n\n\n"); + params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || + params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || + params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) { + fprintf(stderr, "\n==========================================================================================================\n"); + fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); + fprintf(stderr, "==========================================================================================================\n\n\n"); return 1; } diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh new file mode 100644 index 000000000..70f7610f9 --- /dev/null +++ b/examples/quantize/tests.sh @@ -0,0 +1,65 @@ +#!/bin/bash + +set -eu + +if [ $# -lt 1 ] +then + echo "usage: $0 path_to_build_binary [path_to_temp_folder]" + echo "example: $0 ../../build/bin ../../tmp" + exit 1 +fi + +if [ $# -gt 1 ] +then + TMP_DIR=$2 +else + TMP_DIR=/tmp +fi + +set -x + +SPLIT=$1/llama-gguf-split +QUANTIZE=$1/llama-quantize +MAIN=$1/llama-cli +WORK_PATH=$TMP_DIR/quantize +ROOT_DIR=$(realpath $(dirname $0)/../../) + +mkdir -p "$WORK_PATH" + +# Clean up in case of previously failed test +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf + +# 1. Get a model +( +cd $WORK_PATH +"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf +) +echo PASS + +# 2. Split model +$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split +echo PASS +echo + +# 3. Requant model with '--keep-split' +$QUANTIZE --allow-requantize --keep-split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K +echo PASS +echo + +# 3a. Test the requanted model is loading properly +$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32 +echo PASS +echo + +# 4. Requant mode without '--keep-split' +$QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K +echo PASS +echo + +# 4b. Test the requanted model is loading properly +$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32 +echo PASS +echo + +# Clean up +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf diff --git a/examples/reason-act.sh b/examples/reason-act.sh index 046c48db5..06d592799 100755 --- a/examples/reason-act.sh +++ b/examples/reason-act.sh @@ -8,7 +8,7 @@ if [ "$1" == "-m" ]; then MODEL="-m $2 " fi -./main $MODEL --color \ +./llama-cli $MODEL --color \ -f ./prompts/reason-act.txt \ -i --interactive-first \ --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7 -c 2048 \ diff --git a/examples/regex_to_grammar.py b/examples/regex_to_grammar.py new file mode 100644 index 000000000..5cd9210a4 --- /dev/null +++ b/examples/regex_to_grammar.py @@ -0,0 +1,20 @@ +import json, subprocess, sys, os + +assert len(sys.argv) >= 2 +[_, pattern, *rest] = sys.argv + +print(subprocess.check_output( + [ + "python", + os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "json_schema_to_grammar.py"), + *rest, + "-", + "--raw-pattern", + ], + text=True, + input=json.dumps({ + "type": "string", + "pattern": pattern, + }, indent=2))) diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt new file mode 100644 index 000000000..512a602ec --- /dev/null +++ b/examples/retrieval/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-retrieval) +add_executable(${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_17) diff --git a/examples/retrieval/README.md b/examples/retrieval/README.md new file mode 100644 index 000000000..bc5f22e2f --- /dev/null +++ b/examples/retrieval/README.md @@ -0,0 +1,69 @@ +# llama.cpp/examples/retrieval + +Demonstration of simple retrieval technique based on cosine similarity + +More info: +https://github.com/ggerganov/llama.cpp/pull/6193 + +### How to use + +`retieval.cpp` has parameters of its own: +- `--context-file`: file to be embedded - state this option multiple times to embed multiple files +- `--chunk-size`: minimum size of each text chunk to be embedded +- `--chunk-separator`: STRING to divide chunks by. newline by default + +`retrieval` example can be tested as follows: + +```bash +make -j && ./llama-retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator . +``` + +This chunks and embeds all given files and starts a loop requesting query inputs: + +``` +Enter query: +``` + +On each query input, top k chunks are shown along with file name, chunk position within file and original text: + +``` +Enter query: describe the mit license +batch_decode: n_tokens = 6, n_seq = 1 +Top 3 similar chunks: +filename: README.md +filepos: 119 +similarity: 0.762334 +textdata: +png) + +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) + +[Roadmap](https://github. +-------------------- +filename: License +filepos: 0 +similarity: 0.725146 +textdata: +MIT License + +Copyright (c) 2023 Georgi Gerganov + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +-------------------- +filename: README.md +filepos: 9178 +similarity: 0.621722 +textdata: +com/cztomsik/ava) (MIT) +- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) +- [pythops/tenere](https://github. +-------------------- +``` diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp new file mode 100644 index 000000000..2439022a2 --- /dev/null +++ b/examples/retrieval/retrieval.cpp @@ -0,0 +1,304 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" + +#include +#include +#include // TODO: remove me + +static void print_usage(int, char ** argv) { + LOG("\nexample usage:\n"); + LOG("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]); + LOG("\n"); +} + +struct chunk { + // filename + std::string filename; + // original file position + size_t filepos; + // original text data + std::string textdata; + // tokenized text data + std::vector tokens; + // embedding + std::vector embedding; +}; + +// chunk file data to chunks of size >= chunk_size +// chunk_separator is the separator between chunks +static std::vector chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) { + std::vector chunks; + std::ifstream f(filename.c_str()); + + if (!f.is_open()) { + LOG_ERR("could not open file %s\n", filename.c_str()); + return chunks; + } + + chunk current_chunk; + char buffer[1024]; + int64_t filepos = 0; + std::string current; + while (f.read(buffer, 1024)) { + current += std::string(buffer, f.gcount()); + size_t pos; + while ((pos = current.find(chunk_separator)) != std::string::npos) { + current_chunk.textdata += current.substr(0, pos + chunk_separator.size()); + if ((int) current_chunk.textdata.size() > chunk_size) { + // save chunk + current_chunk.filepos = filepos; + current_chunk.filename = filename; + chunks.push_back(current_chunk); + // update filepos + filepos += (int) current_chunk.textdata.size(); + // reset current_chunk + current_chunk = chunk(); + } + current = current.substr(pos + chunk_separator.size()); + } + + } + // add leftover data to last chunk + if (current_chunk.textdata.size() > 0) { + if (chunks.empty()) { + current_chunk.filepos = filepos; + current_chunk.filename = filename; + chunks.push_back(current_chunk); + } else { + chunks.back().textdata += current_chunk.textdata; + } + } + f.close(); + return chunks; +} + +static void batch_add_seq(llama_batch & batch, const std::vector & tokens, llama_seq_id seq_id) { + size_t n_tokens = tokens.size(); + for (size_t i = 0; i < n_tokens; i++) { + common_batch_add(batch, tokens[i], i, { seq_id }, true); + } +} + +static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) { + // clear previous kv_cache values (irrelevant for embeddings) + llama_kv_cache_clear(ctx); + + // run model + LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq); + if (llama_decode(ctx, batch) < 0) { + LOG_ERR("%s : failed to decode\n", __func__); + } + + for (int i = 0; i < batch.n_tokens; i++) { + if (!batch.logits[i]) { + continue; + } + + // try to get sequence embeddings - supported only when pooling_type is not NONE + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + if (embd == NULL) { + LOG_ERR("%s: failed to get embeddings for token %d\n", __func__, i); + continue; + } + } + + float * out = output + batch.seq_id[i][0] * n_embd; + common_embd_normalize(embd, out, n_embd, 2); + } +} + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) { + return 1; + } + + common_init(); + + // For BERT models, batch size must be equal to ubatch size + params.n_ubatch = params.n_batch; + params.embedding = true; + + if (params.chunk_size <= 0) { + LOG_ERR("chunk_size must be positive\n"); + return 1; + } + if (params.context_files.empty()) { + LOG_ERR("context_files must be specified\n"); + return 1; + } + + LOG_INF("processing files:\n"); + for (auto & context_file : params.context_files) { + LOG_INF("%s\n", context_file.c_str()); + } + + std::vector chunks; + for (auto & context_file : params.context_files) { + std::vector file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator); + chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end()); + } + LOG_INF("Number of chunks: %zu\n", chunks.size()); + + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); + + if (model == NULL) { + LOG_ERR("%s: unable to load model\n", __func__); + return 1; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_ctx_train = llama_model_n_ctx_train(model); + const int n_ctx = llama_n_ctx(ctx); + + const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + LOG_ERR("%s: pooling type NONE not supported\n", __func__); + return 1; + } + + if (n_ctx > n_ctx_train) { + LOG_WRN("%s: warning: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, n_ctx); + } + + // print system information + { + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + } + + // max batch size + const uint64_t n_batch = params.n_batch; + GGML_ASSERT(params.n_batch >= params.n_ctx); + + // tokenize the prompts and trim + for (auto & chunk : chunks) { + auto inp = common_tokenize(ctx, chunk.textdata, true, false); + if (inp.size() > n_batch) { + LOG_ERR("%s: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n", + __func__, (long long int) inp.size(), (long long int) n_batch); + return 1; + } + // add eos if not present + if (llama_vocab_eos(vocab) >= 0 && (inp.empty() || inp.back() != llama_vocab_eos(vocab))) { + inp.push_back(llama_vocab_eos(vocab)); + } + chunk.tokens = inp; + } + + // tokenization stats + if (params.verbose_prompt) { + for (int i = 0; i < (int) chunks.size(); i++) { + LOG_INF("%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str()); + LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size()); + for (int j = 0; j < (int) chunks[i].tokens.size(); j++) { + LOG_INF("%6d -> '%s'\n", chunks[i].tokens[j], common_token_to_piece(ctx, chunks[i].tokens[j]).c_str()); + } + LOG_INF("\n\n"); + } + } + + // initialize batch + const int n_chunks = chunks.size(); + struct llama_batch batch = llama_batch_init(n_batch, 0, 1); + + // allocate output + const int n_embd = llama_model_n_embd(model); + std::vector embeddings(n_chunks * n_embd, 0); + float * emb = embeddings.data(); + + // break into batches + int p = 0; // number of prompts processed already + int s = 0; // number of prompts in current batch + for (int k = 0; k < n_chunks; k++) { + // clamp to n_batch tokens + auto & inp = chunks[k].tokens; + + const uint64_t n_toks = inp.size(); + + // encode if at capacity + if (batch.n_tokens + n_toks > n_batch) { + float * out = emb + p * n_embd; + batch_decode(ctx, batch, out, s, n_embd); + common_batch_clear(batch); + p += s; + s = 0; + } + + // add to batch + batch_add_seq(batch, inp, s); + s += 1; + } + + // final batch + float * out = emb + p * n_embd; + batch_decode(ctx, batch, out, s, n_embd); + + // save embeddings to chunks + for (int i = 0; i < n_chunks; i++) { + chunks[i].embedding = std::vector(emb + i * n_embd, emb + (i + 1) * n_embd); + // clear tokens as they are no longer needed + chunks[i].tokens.clear(); + } + + struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1); + + // start loop, receive query and return top k similar chunks based on cosine similarity + std::string query; + while (true) { + LOG("Enter query: "); + std::getline(std::cin, query); + std::vector query_tokens = common_tokenize(ctx, query, true); + + batch_add_seq(query_batch, query_tokens, 0); + + std::vector query_emb(n_embd, 0); + batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd); + + common_batch_clear(query_batch); + + // compute cosine similarities + { + std::vector> similarities; + for (int i = 0; i < n_chunks; i++) { + float sim = common_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd); + similarities.push_back(std::make_pair(i, sim)); + } + + // sort similarities + std::sort(similarities.begin(), similarities.end(), [](const std::pair & a, const std::pair & b) { + return a.second > b.second; + }); + + 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); + LOG("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str()); + LOG("--------------------\n"); + } + } + } + + LOG("\n"); + llama_perf_context_print(ctx); + + // clean up + llama_batch_free(query_batch); + llama_backend_free(); +} diff --git a/examples/rpc/CMakeLists.txt b/examples/rpc/CMakeLists.txt new file mode 100644 index 000000000..ae48fb98d --- /dev/null +++ b/examples/rpc/CMakeLists.txt @@ -0,0 +1,2 @@ +add_executable(rpc-server rpc-server.cpp) +target_link_libraries(rpc-server PRIVATE ggml llama) diff --git a/examples/rpc/README.md b/examples/rpc/README.md new file mode 100644 index 000000000..312bb634d --- /dev/null +++ b/examples/rpc/README.md @@ -0,0 +1,74 @@ +## Overview + +> [!IMPORTANT] +> This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and +> insecure. **Never run the RPC server on an open network or in a sensitive environment!** + +The `rpc-server` allows running `ggml` backend on a remote host. +The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them. +This can be used for distributed LLM inference with `llama.cpp` in the following way: + +```mermaid +flowchart TD + rpcb<-->|TCP|srva + rpcb<-->|TCP|srvb + rpcb<-.->|TCP|srvn + subgraph hostn[Host N] + srvn[rpc-server]<-.->backend3["Backend (CUDA,Metal,etc.)"] + end + subgraph hostb[Host B] + srvb[rpc-server]<-->backend2["Backend (CUDA,Metal,etc.)"] + end + subgraph hosta[Host A] + srva[rpc-server]<-->backend["Backend (CUDA,Metal,etc.)"] + end + subgraph host[Main Host] + local["Backend (CUDA,Metal,etc.)"]<-->ggml[llama-cli] + ggml[llama-cli]<-->rpcb[RPC backend] + end + style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5 +``` + +Each host can run a different backend, e.g. one with CUDA and another with Metal. +You can also run multiple `rpc-server` instances on the same host, each with a different backend. + +## Usage + +On each host, build the corresponding backend with `cmake` and add `-DGGML_RPC=ON` to the build options. +For example, to build the CUDA backend with RPC support: + +```bash +mkdir build-rpc-cuda +cd build-rpc-cuda +cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON +cmake --build . --config Release +``` + +Then, start the `rpc-server` with the backend: + +```bash +$ bin/rpc-server -p 50052 +create_backend: using CUDA backend +ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no +ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes +ggml_cuda_init: found 1 CUDA devices: + Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes +Starting RPC server on 0.0.0.0:50052 +``` + +When using the CUDA backend, you can specify the device with the `CUDA_VISIBLE_DEVICES` environment variable, e.g.: +```bash +$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052 +``` +This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device. + + +On the main host build `llama.cpp` for the local backend and add `-DGGML_RPC=ON` to the build options. +Finally, when running `llama-cli`, use the `--rpc` option to specify the host and port of each `rpc-server`: + +```bash +$ bin/llama-cli -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99 +``` + +This way you can offload model layers to both local and remote devices. + diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp new file mode 100644 index 000000000..8b1b23eda --- /dev/null +++ b/examples/rpc/rpc-server.cpp @@ -0,0 +1,171 @@ +#include "ggml-cpu.h" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#ifdef GGML_USE_VULKAN +#include "ggml-vulkan.h" +#endif + +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + +#include "ggml-rpc.h" +#ifdef _WIN32 +# include +#else +# include +#endif +#include +#include + +struct rpc_server_params { + std::string host = "127.0.0.1"; + int port = 50052; + size_t backend_mem = 0; +}; + +static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) { + fprintf(stderr, "Usage: %s [options]\n\n", argv[0]); + fprintf(stderr, "options:\n"); + fprintf(stderr, " -h, --help show this help message and exit\n"); + fprintf(stderr, " -H HOST, --host HOST host to bind to (default: %s)\n", params.host.c_str()); + fprintf(stderr, " -p PORT, --port PORT port to bind to (default: %d)\n", params.port); + fprintf(stderr, " -m MEM, --mem MEM backend memory size (in MB)\n"); + fprintf(stderr, "\n"); +} + +static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & params) { + std::string arg; + for (int i = 1; i < argc; i++) { + arg = argv[i]; + if (arg == "-H" || arg == "--host") { + if (++i >= argc) { + return false; + } + params.host = argv[i]; + } else if (arg == "-p" || arg == "--port") { + if (++i >= argc) { + return false; + } + params.port = std::stoi(argv[i]); + if (params.port <= 0 || params.port > 65535) { + return false; + } + } else if (arg == "-m" || arg == "--mem") { + if (++i >= argc) { + return false; + } + params.backend_mem = std::stoul(argv[i]) * 1024 * 1024; + } else if (arg == "-h" || arg == "--help") { + print_usage(argc, argv, params); + exit(0); + } else { + fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); + print_usage(argc, argv, params); + exit(0); + } + } + return true; +} + +static ggml_backend_t create_backend() { + ggml_backend_t backend = NULL; +#ifdef GGML_USE_CUDA + fprintf(stderr, "%s: using CUDA backend\n", __func__); + backend = ggml_backend_cuda_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); + } +#elif GGML_USE_METAL + fprintf(stderr, "%s: using Metal backend\n", __func__); + backend = ggml_backend_metal_init(); + if (!backend) { + fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); + } +#elif GGML_USE_VULKAN + fprintf(stderr, "%s: using Vulkan backend\n", __func__); + backend = ggml_backend_vk_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__); + } +#elif GGML_USE_SYCL + fprintf(stderr, "%s: using SYCL backend\n", __func__); + backend = ggml_backend_sycl_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__); + } +#endif + + // if there aren't GPU Backends fallback to CPU backend + if (!backend) { + fprintf(stderr, "%s: using CPU backend\n", __func__); + backend = ggml_backend_cpu_init(); + } + return backend; +} + +static void get_backend_memory(size_t * free_mem, size_t * total_mem) { +#ifdef GGML_USE_CUDA + ggml_backend_cuda_get_device_memory(0, free_mem, total_mem); +#elif GGML_USE_VULKAN + ggml_backend_vk_get_device_memory(0, free_mem, total_mem); +#elif GGML_USE_SYCL + ggml_backend_sycl_get_device_memory(0, free_mem, total_mem); +#else + #ifdef _WIN32 + MEMORYSTATUSEX status; + status.dwLength = sizeof(status); + GlobalMemoryStatusEx(&status); + *total_mem = status.ullTotalPhys; + *free_mem = status.ullAvailPhys; + #else + long pages = sysconf(_SC_PHYS_PAGES); + long page_size = sysconf(_SC_PAGE_SIZE); + *total_mem = pages * page_size; + *free_mem = *total_mem; + #endif +#endif +} + +int main(int argc, char * argv[]) { + rpc_server_params params; + if (!rpc_server_params_parse(argc, argv, params)) { + fprintf(stderr, "Invalid parameters\n"); + return 1; + } + + if (params.host != "127.0.0.1") { + fprintf(stderr, "\n"); + fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"); + fprintf(stderr, "WARNING: Host ('%s') is != '127.0.0.1'\n", params.host.c_str()); + fprintf(stderr, " Never expose the RPC server to an open network!\n"); + fprintf(stderr, " This is an experimental feature and is not secure!\n"); + fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"); + fprintf(stderr, "\n"); + } + + ggml_backend_t backend = create_backend(); + if (!backend) { + fprintf(stderr, "Failed to create backend\n"); + return 1; + } + std::string endpoint = params.host + ":" + std::to_string(params.port); + size_t free_mem, total_mem; + if (params.backend_mem > 0) { + free_mem = params.backend_mem; + total_mem = params.backend_mem; + } else { + get_backend_memory(&free_mem, &total_mem); + } + printf("Starting RPC server on %s, backend memory: %zu MB\n", endpoint.c_str(), free_mem / (1024 * 1024)); + ggml_backend_rpc_start_server(backend, endpoint.c_str(), free_mem, total_mem); + ggml_backend_free(backend); + return 0; +} diff --git a/examples/run/CMakeLists.txt b/examples/run/CMakeLists.txt new file mode 100644 index 000000000..cd6b0520e --- /dev/null +++ b/examples/run/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-run) +add_executable(${TARGET} run.cpp linenoise.cpp/linenoise.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/run/README.md b/examples/run/README.md new file mode 100644 index 000000000..89a552079 --- /dev/null +++ b/examples/run/README.md @@ -0,0 +1,50 @@ +# llama.cpp/example/run + +The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models. + +```bash +llama-run granite3-moe +``` + +```bash +Description: + Runs a llm + +Usage: + llama-run [options] model [prompt] + +Options: + -c, --context-size + Context size (default: 2048) + -n, -ngl, --ngl + Number of GPU layers (default: 0) + --temp + Temperature (default: 0.8) + -v, --verbose, --log-verbose + Set verbosity level to infinity (i.e. log all messages, useful for debugging) + -h, --help + Show help message + +Commands: + model + Model is a string with an optional prefix of + huggingface:// (hf://), ollama://, https:// or file://. + If no protocol is specified and a file exists in the specified + path, file:// is assumed, otherwise if a file does not exist in + the specified path, ollama:// is assumed. Models that are being + pulled are downloaded with .partial extension while being + downloaded and then renamed as the file without the .partial + extension when complete. + +Examples: + llama-run llama3 + llama-run ollama://granite-code + llama-run ollama://smollm:135m + llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf + llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf + llama-run https://example.com/some-file1.gguf + llama-run some-file2.gguf + llama-run file://some-file3.gguf + llama-run --ngl 999 some-file4.gguf + llama-run --ngl 999 some-file5.gguf Hello World +``` diff --git a/examples/run/linenoise.cpp/LICENSE b/examples/run/linenoise.cpp/LICENSE new file mode 100644 index 000000000..b006b3b24 --- /dev/null +++ b/examples/run/linenoise.cpp/LICENSE @@ -0,0 +1,26 @@ +Copyright (c) 2010-2014, Salvatore Sanfilippo +Copyright (c) 2010-2013, Pieter Noordhuis +Copyright (c) 2025, Eric Curtin + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/examples/run/linenoise.cpp/linenoise.cpp b/examples/run/linenoise.cpp/linenoise.cpp new file mode 100644 index 000000000..a68f12a1a --- /dev/null +++ b/examples/run/linenoise.cpp/linenoise.cpp @@ -0,0 +1,1350 @@ +#ifndef _WIN32 +/* + * You can find the latest source code at: + * + * http://github.com/ericcurtin/linenoise.cpp + * + * Does a number of crazy assumptions that happen to be true in 99.9999% of + * the 2010 UNIX computers around. + * + * ------------------------------------------------------------------------ + * + * Copyright (c) 2010-2023, Salvatore Sanfilippo + * Copyright (c) 2010-2013, Pieter Noordhuis + * Copyright (c) 2025, Eric Curtin + * + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * ------------------------------------------------------------------------ + * + * References: + * - http://invisible-island.net/xterm/ctlseqs/ctlseqs.html + * - http://www.3waylabs.com/nw/WWW/products/wizcon/vt220.html + * + * Todo list: + * - Filter bogus Ctrl+ combinations. + * - Win32 support + * + * Bloat: + * - History search like Ctrl+r in readline? + * + * List of escape sequences used by this program, we do everything just + * with three sequences. In order to be so cheap we may have some + * flickering effect with some slow terminal, but the lesser sequences + * the more compatible. + * + * EL (Erase Line) + * Sequence: ESC [ n K + * Effect: if n is 0 or missing, clear from cursor to end of line + * Effect: if n is 1, clear from beginning of line to cursor + * Effect: if n is 2, clear entire line + * + * CUF (CUrsor Forward) + * Sequence: ESC [ n C + * Effect: moves cursor forward n chars + * + * CUB (CUrsor Backward) + * Sequence: ESC [ n D + * Effect: moves cursor backward n chars + * + * The following is used to get the terminal width if getting + * the width with the TIOCGWINSZ ioctl fails + * + * DSR (Device Status Report) + * Sequence: ESC [ 6 n + * Effect: reports the current cusor position as ESC [ n ; m R + * where n is the row and m is the column + * + * When multi line mode is enabled, we also use an additional escape + * sequence. However multi line editing is disabled by default. + * + * CUU (Cursor Up) + * Sequence: ESC [ n A + * Effect: moves cursor up of n chars. + * + * CUD (Cursor Down) + * Sequence: ESC [ n B + * Effect: moves cursor down of n chars. + * + * When linenoiseClearScreen() is called, two additional escape sequences + * are used in order to clear the screen and position the cursor at home + * position. + * + * CUP (Cursor position) + * Sequence: ESC [ H + * Effect: moves the cursor to upper left corner + * + * ED (Erase display) + * Sequence: ESC [ 2 J + * Effect: clear the whole screen + * + */ + +# include "linenoise.h" + +# include +# include +# include +# include +# include +# include +# include +# include +# include +# include + +# include +# include +# include + +# define LINENOISE_DEFAULT_HISTORY_MAX_LEN 100 +# define LINENOISE_MAX_LINE 4096 +static std::vector unsupported_term = { "dumb", "cons25", "emacs" }; +static linenoiseCompletionCallback *completionCallback = NULL; +static linenoiseHintsCallback *hintsCallback = NULL; +static linenoiseFreeHintsCallback *freeHintsCallback = NULL; +static char *linenoiseNoTTY(void); +static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseCompletions *lc, int flags); +static void refreshLineWithFlags(struct linenoiseState *l, int flags); + +static struct termios orig_termios; /* In order to restore at exit.*/ +static int maskmode = 0; /* Show "***" instead of input. For passwords. */ +static int rawmode = 0; /* For atexit() function to check if restore is needed*/ +static int mlmode = 0; /* Multi line mode. Default is single line. */ +static int atexit_registered = 0; /* Register atexit just 1 time. */ +static int history_max_len = LINENOISE_DEFAULT_HISTORY_MAX_LEN; +static int history_len = 0; +static char **history = NULL; + +enum KEY_ACTION{ + KEY_NULL = 0, /* NULL */ + CTRL_A = 1, /* Ctrl+a */ + CTRL_B = 2, /* Ctrl-b */ + CTRL_C = 3, /* Ctrl-c */ + CTRL_D = 4, /* Ctrl-d */ + CTRL_E = 5, /* Ctrl-e */ + CTRL_F = 6, /* Ctrl-f */ + CTRL_H = 8, /* Ctrl-h */ + TAB = 9, /* Tab */ + CTRL_K = 11, /* Ctrl+k */ + CTRL_L = 12, /* Ctrl+l */ + ENTER = 13, /* Enter */ + CTRL_N = 14, /* Ctrl-n */ + CTRL_P = 16, /* Ctrl-p */ + CTRL_T = 20, /* Ctrl-t */ + CTRL_U = 21, /* Ctrl+u */ + CTRL_W = 23, /* Ctrl+w */ + ESC = 27, /* Escape */ + BACKSPACE = 127 /* Backspace */ +}; + +static void linenoiseAtExit(void); +int linenoiseHistoryAdd(const char *line); +#define REFRESH_CLEAN (1<<0) // Clean the old prompt from the screen +#define REFRESH_WRITE (1<<1) // Rewrite the prompt on the screen. +#define REFRESH_ALL (REFRESH_CLEAN|REFRESH_WRITE) // Do both. +static void refreshLine(struct linenoiseState *l); + +class File { + public: + FILE * file = nullptr; + + FILE * open(const std::string & filename, const char * mode) { + file = fopen(filename.c_str(), mode); + + return file; + } + + int lock() { + if (file) { + fd = fileno(file); + if (flock(fd, LOCK_EX | LOCK_NB) != 0) { + fd = -1; + + return 1; + } + } + + return 0; + } + + ~File() { + if (fd >= 0) { + flock(fd, LOCK_UN); + } + + if (file) { + fclose(file); + } + } + + private: + int fd = -1; +}; + +__attribute__((format(printf, 1, 2))) +/* Debugging function. */ +#if 0 +static void lndebug(const char *fmt, ...) { + static File file; + if (file.file == nullptr) { + file.open("/tmp/lndebug.txt", "a"); + } + + if (file.file != nullptr) { + va_list args; + va_start(args, fmt); + vfprintf(file.file, fmt, args); + va_end(args); + fflush(file.file); + } +} +#else +static void lndebug(const char *, ...) { +} +#endif + +/* ======================= Low level terminal handling ====================== */ + +/* Enable "mask mode". When it is enabled, instead of the input that + * the user is typing, the terminal will just display a corresponding + * number of asterisks, like "****". This is useful for passwords and other + * secrets that should not be displayed. */ +void linenoiseMaskModeEnable(void) { + maskmode = 1; +} + +/* Disable mask mode. */ +void linenoiseMaskModeDisable(void) { + maskmode = 0; +} + +/* Set if to use or not the multi line mode. */ +void linenoiseSetMultiLine(int ml) { + mlmode = ml; +} + +/* Return true if the terminal name is in the list of terminals we know are + * not able to understand basic escape sequences. */ +static int isUnsupportedTerm(void) { + char *term = getenv("TERM"); + if (term == NULL) return 0; + for (size_t j = 0; j < unsupported_term.size(); ++j) { + if (!strcasecmp(term, unsupported_term[j])) { + return 1; + } + } + return 0; +} + +/* Raw mode: 1960 magic shit. */ +static int enableRawMode(int fd) { + struct termios raw; + + if (!isatty(STDIN_FILENO)) goto fatal; + if (!atexit_registered) { + atexit(linenoiseAtExit); + atexit_registered = 1; + } + if (tcgetattr(fd,&orig_termios) == -1) goto fatal; + + raw = orig_termios; /* modify the original mode */ + /* input modes: no break, no CR to NL, no parity check, no strip char, + * no start/stop output control. */ + raw.c_iflag &= ~(BRKINT | ICRNL | INPCK | ISTRIP | IXON); + /* output modes - disable post processing */ + raw.c_oflag &= ~(OPOST); + /* control modes - set 8 bit chars */ + raw.c_cflag |= (CS8); + /* local modes - choing off, canonical off, no extended functions, + * no signal chars (^Z,^C) */ + raw.c_lflag &= ~(ECHO | ICANON | IEXTEN | ISIG); + /* control chars - set return condition: min number of bytes and timer. + * We want read to return every single byte, without timeout. */ + raw.c_cc[VMIN] = 1; raw.c_cc[VTIME] = 0; /* 1 byte, no timer */ + + /* put terminal in raw mode after flushing */ + if (tcsetattr(fd,TCSAFLUSH,&raw) < 0) goto fatal; + rawmode = 1; + return 0; + +fatal: + errno = ENOTTY; + return -1; +} + +static void disableRawMode(int fd) { + /* Don't even check the return value as it's too late. */ + if (rawmode && tcsetattr(fd,TCSAFLUSH,&orig_termios) != -1) + rawmode = 0; +} + +/* Use the ESC [6n escape sequence to query the horizontal cursor position + * and return it. On error -1 is returned, on success the position of the + * cursor. */ +static int getCursorPosition(int ifd, int ofd) { + char buf[32]; + int cols, rows; + unsigned int i = 0; + + /* Report cursor location */ + if (write(ofd, "\x1b[6n", 4) != 4) return -1; + + /* Read the response: ESC [ rows ; cols R */ + while (i < sizeof(buf)-1) { + if (read(ifd,buf+i,1) != 1) break; + if (buf[i] == 'R') break; + i++; + } + buf[i] = '\0'; + + /* Parse it. */ + if (buf[0] != ESC || buf[1] != '[') return -1; + if (sscanf(buf+2,"%d;%d",&rows,&cols) != 2) return -1; + return cols; +} + +/* Try to get the number of columns in the current terminal, or assume 80 + * if it fails. */ +static int getColumns(int ifd, int ofd) { + struct winsize ws; + + if (ioctl(1, TIOCGWINSZ, &ws) == -1 || ws.ws_col == 0) { + /* ioctl() failed. Try to query the terminal itself. */ + int start, cols; + + /* Get the initial position so we can restore it later. */ + start = getCursorPosition(ifd,ofd); + if (start == -1) goto failed; + + /* Go to right margin and get position. */ + if (write(ofd,"\x1b[999C",6) != 6) goto failed; + cols = getCursorPosition(ifd,ofd); + if (cols == -1) goto failed; + + /* Restore position. */ + if (cols > start) { + char seq[32]; + snprintf(seq,32,"\x1b[%dD",cols-start); + if (write(ofd,seq,strlen(seq)) == -1) { + /* Can't recover... */ + } + } + return cols; + } else { + return ws.ws_col; + } + +failed: + return 80; +} + +/* Clear the screen. Used to handle ctrl+l */ +void linenoiseClearScreen(void) { + if (write(STDOUT_FILENO,"\x1b[H\x1b[2J",7) <= 0) { + /* nothing to do, just to avoid warning. */ + } +} + +/* Beep, used for completion when there is nothing to complete or when all + * the choices were already shown. */ +static void linenoiseBeep(void) { + fprintf(stderr, "\x7"); + fflush(stderr); +} + +/* Called by completeLine() and linenoiseShow() to render the current + * edited line with the proposed completion. If the current completion table + * is already available, it is passed as second argument, otherwise the + * function will use the callback to obtain it. + * + * Flags are the same as refreshLine*(), that is REFRESH_* macros. */ +static void refreshLineWithCompletion(struct linenoiseState *ls, linenoiseCompletions *lc, int flags) { + /* Obtain the table of completions if the caller didn't provide one. */ + linenoiseCompletions ctable; + if (lc == NULL) { + completionCallback(ls->buf, &ctable); + lc = &ctable; + } + + /* Show the edited line with completion if possible, or just refresh. */ + if (ls->completion_idx < lc->len) { + struct linenoiseState saved = *ls; + ls->len = ls->pos = strlen(lc->cvec[ls->completion_idx]); + ls->buf = lc->cvec[ls->completion_idx]; + refreshLineWithFlags(ls, flags); + ls->len = saved.len; + ls->pos = saved.pos; + ls->buf = saved.buf; + } else { + refreshLineWithFlags(ls, flags); + } + + if (lc == &ctable) { + ctable.to_free = false; + } +} + +/* This is an helper function for linenoiseEdit*() and is called when the + * user types the key in order to complete the string currently in the + * input. + * + * The state of the editing is encapsulated into the pointed linenoiseState + * structure as described in the structure definition. + * + * If the function returns non-zero, the caller should handle the + * returned value as a byte read from the standard input, and process + * it as usually: this basically means that the function may return a byte + * read from the termianl but not processed. Otherwise, if zero is returned, + * the input was consumed by the completeLine() function to navigate the + * possible completions, and the caller should read for the next characters + * from stdin. */ +static int completeLine(struct linenoiseState *ls, int keypressed) { + linenoiseCompletions lc; + int nwritten; + char c = keypressed; + + completionCallback(ls->buf, &lc); + if (lc.len == 0) { + linenoiseBeep(); + ls->in_completion = 0; + } else { + switch(c) { + case 9: /* tab */ + if (ls->in_completion == 0) { + ls->in_completion = 1; + ls->completion_idx = 0; + } else { + ls->completion_idx = (ls->completion_idx + 1) % (lc.len + 1); + if (ls->completion_idx == lc.len) linenoiseBeep(); + } + c = 0; + break; + case 27: /* escape */ + /* Re-show original buffer */ + if (ls->completion_idx < lc.len) refreshLine(ls); + ls->in_completion = 0; + c = 0; + break; + default: + /* Update buffer and return */ + if (ls->completion_idx < lc.len) { + nwritten = snprintf(ls->buf, ls->buflen, "%s", lc.cvec[ls->completion_idx]); + ls->len = ls->pos = nwritten; + } + ls->in_completion = 0; + break; + } + + /* Show completion or original buffer */ + if (ls->in_completion && ls->completion_idx < lc.len) { + refreshLineWithCompletion(ls, &lc, REFRESH_ALL); + } else { + refreshLine(ls); + } + } + + return c; /* Return last read character */ +} + +/* Register a callback function to be called for tab-completion. */ +void linenoiseSetCompletionCallback(linenoiseCompletionCallback *fn) { + completionCallback = fn; +} + +/* Register a hits function to be called to show hits to the user at the + * right of the prompt. */ +void linenoiseSetHintsCallback(linenoiseHintsCallback *fn) { + hintsCallback = fn; +} + +/* Register a function to free the hints returned by the hints callback + * registered with linenoiseSetHintsCallback(). */ +void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *fn) { + freeHintsCallback = fn; +} + +/* This function is used by the callback function registered by the user + * in order to add completion options given the input string when the + * user typed . See the example.c source code for a very easy to + * understand example. */ +void linenoiseAddCompletion(linenoiseCompletions *lc, const char *str) { + const size_t len = strlen(str); + auto copy = std::make_unique(len + 1); + if (!copy) { + return; + } + + memcpy(copy.get(), str, len + 1); + char ** cvec = static_cast(std::realloc(lc->cvec, sizeof(char *) * (lc->len + 1))); + if (cvec == nullptr) { + return; + } + + lc->cvec = cvec; + lc->cvec[lc->len++] = copy.release(); +} + +/* Helper of refreshSingleLine() and refreshMultiLine() to show hints + * to the right of the prompt. */ +static void refreshShowHints(std::string & ab, struct linenoiseState * l, int plen) { + char seq[64]; + if (hintsCallback && plen+l->len < l->cols) { + int color = -1, bold = 0; + const char *hint = hintsCallback(l->buf,&color,&bold); + if (hint) { + int hintlen = strlen(hint); + int hintmaxlen = l->cols-(plen+l->len); + if (hintlen > hintmaxlen) hintlen = hintmaxlen; + if (bold == 1 && color == -1) color = 37; + if (color != -1 || bold != 0) + snprintf(seq,64,"\033[%d;%d;49m",bold,color); + else + seq[0] = '\0'; + ab.append(seq); + ab.append(hint, hintlen); + if (color != -1 || bold != 0) + ab.append("\033[0m"); + + /* Call the function to free the hint returned. */ + if (freeHintsCallback) freeHintsCallback(hint); + } + } +} + +/* Single line low level line refresh. + * + * Rewrite the currently edited line accordingly to the buffer content, + * cursor position, and number of columns of the terminal. + * + * Flags is REFRESH_* macros. The function can just remove the old + * prompt, just write it, or both. */ +static void refreshSingleLine(struct linenoiseState *l, int flags) { + char seq[64]; + size_t plen = strlen(l->prompt); + int fd = l->ofd; + char *buf = l->buf; + size_t len = l->len; + size_t pos = l->pos; + std::string ab; + while((plen+pos) >= l->cols) { + buf++; + len--; + pos--; + } + while (plen+len > l->cols) { + len--; + } + + /* Cursor to left edge */ + snprintf(seq,sizeof(seq),"\r"); + ab.append(seq); + + if (flags & REFRESH_WRITE) { + /* Write the prompt and the current buffer content */ + ab.append(l->prompt); + if (maskmode == 1) { + while (len--) { + ab.append("*"); + } + } else { + ab.append(buf, len); + } + /* Show hits if any. */ + refreshShowHints(ab, l, plen); + } + + /* Erase to right */ + snprintf(seq,sizeof(seq),"\x1b[0K"); + ab.append(seq); + if (flags & REFRESH_WRITE) { + /* Move cursor to original position. */ + snprintf(seq,sizeof(seq),"\r\x1b[%dC", (int)(pos+plen)); + ab.append(seq); + } + + (void) !write(fd, ab.c_str(), ab.size()); /* Can't recover from write error. */ +} + +/* Multi line low level line refresh. + * + * Rewrite the currently edited line accordingly to the buffer content, + * cursor position, and number of columns of the terminal. + * + * Flags is REFRESH_* macros. The function can just remove the old + * prompt, just write it, or both. */ +static void refreshMultiLine(struct linenoiseState *l, int flags) { + char seq[64]; + int plen = strlen(l->prompt); + int rows = (plen+l->len+l->cols-1)/l->cols; /* rows used by current buf. */ + int rpos = (plen+l->oldpos+l->cols)/l->cols; /* cursor relative row. */ + int rpos2; /* rpos after refresh. */ + int col; /* colum position, zero-based. */ + int old_rows = l->oldrows; + int fd = l->ofd, j; + std::string ab; + l->oldrows = rows; + + /* First step: clear all the lines used before. To do so start by + * going to the last row. */ + if (flags & REFRESH_CLEAN) { + if (old_rows-rpos > 0) { + lndebug("go down %d", old_rows-rpos); + snprintf(seq,64,"\x1b[%dB", old_rows-rpos); + ab.append(seq); + } + + /* Now for every row clear it, go up. */ + for (j = 0; j < old_rows-1; j++) { + lndebug("clear+up"); + snprintf(seq,64,"\r\x1b[0K\x1b[1A"); + ab.append(seq); + } + } + + if (flags & REFRESH_ALL) { + /* Clean the top line. */ + lndebug("clear"); + snprintf(seq,64,"\r\x1b[0K"); + ab.append(seq); + } + + if (flags & REFRESH_WRITE) { + /* Write the prompt and the current buffer content */ + ab.append(l->prompt); + if (maskmode == 1) { + for (unsigned int i = 0; i < l->len; ++i) { + ab.append("*"); + } + } else { + ab.append(l->buf, l->len); + } + + /* Show hits if any. */ + refreshShowHints(ab, l, plen); + + /* If we are at the very end of the screen with our prompt, we need to + * emit a newline and move the prompt to the first column. */ + if (l->pos && + l->pos == l->len && + (l->pos+plen) % l->cols == 0) + { + lndebug(""); + ab.append("\n"); + snprintf(seq,64,"\r"); + ab.append(seq); + rows++; + if (rows > (int)l->oldrows) l->oldrows = rows; + } + + /* Move cursor to right position. */ + rpos2 = (plen+l->pos+l->cols)/l->cols; /* Current cursor relative row */ + lndebug("rpos2 %d", rpos2); + + /* Go up till we reach the expected positon. */ + if (rows-rpos2 > 0) { + lndebug("go-up %d", rows-rpos2); + snprintf(seq,64,"\x1b[%dA", rows-rpos2); + ab.append(seq); + } + + /* Set column. */ + col = (plen+(int)l->pos) % (int)l->cols; + lndebug("set col %d", 1+col); + if (col) + snprintf(seq,64,"\r\x1b[%dC", col); + else + snprintf(seq,64,"\r"); + ab.append(seq); + } + + lndebug("\n"); + l->oldpos = l->pos; + (void) !write(fd, ab.c_str(), ab.size()); /* Can't recover from write error. */ +} + +/* Calls the two low level functions refreshSingleLine() or + * refreshMultiLine() according to the selected mode. */ +static void refreshLineWithFlags(struct linenoiseState *l, int flags) { + if (mlmode) + refreshMultiLine(l,flags); + else + refreshSingleLine(l,flags); +} + +/* Utility function to avoid specifying REFRESH_ALL all the times. */ +static void refreshLine(struct linenoiseState *l) { + refreshLineWithFlags(l,REFRESH_ALL); +} + +/* Hide the current line, when using the multiplexing API. */ +void linenoiseHide(struct linenoiseState *l) { + if (mlmode) + refreshMultiLine(l,REFRESH_CLEAN); + else + refreshSingleLine(l,REFRESH_CLEAN); +} + +/* Show the current line, when using the multiplexing API. */ +void linenoiseShow(struct linenoiseState *l) { + if (l->in_completion) { + refreshLineWithCompletion(l,NULL,REFRESH_WRITE); + } else { + refreshLineWithFlags(l,REFRESH_WRITE); + } +} + +/* Insert the character 'c' at cursor current position. + * + * On error writing to the terminal -1 is returned, otherwise 0. */ +static int linenoiseEditInsert(struct linenoiseState * l, char c) { + if (l->len < l->buflen) { + if (l->len == l->pos) { + l->buf[l->pos] = c; + l->pos++; + l->len++; + l->buf[l->len] = '\0'; + if ((!mlmode && l->plen+l->len < l->cols && !hintsCallback)) { + /* Avoid a full update of the line in the + * trivial case. */ + char d = (maskmode==1) ? '*' : c; + if (write(l->ofd,&d,1) == -1) return -1; + } else { + refreshLine(l); + } + } else { + memmove(l->buf+l->pos+1,l->buf+l->pos,l->len-l->pos); + l->buf[l->pos] = c; + l->len++; + l->pos++; + l->buf[l->len] = '\0'; + refreshLine(l); + } + } + return 0; +} + +/* Move cursor on the left. */ +static void linenoiseEditMoveLeft(struct linenoiseState * l) { + if (l->pos > 0) { + l->pos--; + refreshLine(l); + } +} + +/* Move cursor on the right. */ +static void linenoiseEditMoveRight(struct linenoiseState * l) { + if (l->pos != l->len) { + l->pos++; + refreshLine(l); + } +} + +/* Move cursor to the start of the line. */ +static void linenoiseEditMoveHome(struct linenoiseState * l) { + if (l->pos != 0) { + l->pos = 0; + refreshLine(l); + } +} + +/* Move cursor to the end of the line. */ +static void linenoiseEditMoveEnd(struct linenoiseState * l) { + if (l->pos != l->len) { + l->pos = l->len; + refreshLine(l); + } +} + +/* Substitute the currently edited line with the next or previous history + * entry as specified by 'dir'. */ +#define LINENOISE_HISTORY_NEXT 0 +#define LINENOISE_HISTORY_PREV 1 + +static void linenoiseEditHistoryNext(struct linenoiseState * l, int dir) { + if (history_len > 1) { + /* Update the current history entry before to + * overwrite it with the next one. */ + free(history[history_len - 1 - l->history_index]); + history[history_len - 1 - l->history_index] = strdup(l->buf); + /* Show the new entry */ + l->history_index += (dir == LINENOISE_HISTORY_PREV) ? 1 : -1; + if (l->history_index < 0) { + l->history_index = 0; + return; + } else if (l->history_index >= history_len) { + l->history_index = history_len-1; + return; + } + strncpy(l->buf,history[history_len - 1 - l->history_index],l->buflen); + l->buf[l->buflen-1] = '\0'; + l->len = l->pos = strlen(l->buf); + refreshLine(l); + } +} + +/* Delete the character at the right of the cursor without altering the cursor + * position. Basically this is what happens with the "Delete" keyboard key. */ +static void linenoiseEditDelete(struct linenoiseState * l) { + if (l->len > 0 && l->pos < l->len) { + memmove(l->buf+l->pos,l->buf+l->pos+1,l->len-l->pos-1); + l->len--; + l->buf[l->len] = '\0'; + refreshLine(l); + } +} + +/* Backspace implementation. */ +static void linenoiseEditBackspace(struct linenoiseState * l) { + if (l->pos > 0 && l->len > 0) { + memmove(l->buf+l->pos-1,l->buf+l->pos,l->len-l->pos); + l->pos--; + l->len--; + l->buf[l->len] = '\0'; + refreshLine(l); + } +} + +/* Delete the previosu word, maintaining the cursor at the start of the + * current word. */ +static void linenoiseEditDeletePrevWord(struct linenoiseState * l) { + size_t old_pos = l->pos; + size_t diff; + + while (l->pos > 0 && l->buf[l->pos-1] == ' ') + l->pos--; + while (l->pos > 0 && l->buf[l->pos-1] != ' ') + l->pos--; + diff = old_pos - l->pos; + memmove(l->buf+l->pos,l->buf+old_pos,l->len-old_pos+1); + l->len -= diff; + refreshLine(l); +} + +/* This function is part of the multiplexed API of Linenoise, that is used + * in order to implement the blocking variant of the API but can also be + * called by the user directly in an event driven program. It will: + * + * 1. Initialize the linenoise state passed by the user. + * 2. Put the terminal in RAW mode. + * 3. Show the prompt. + * 4. Return control to the user, that will have to call linenoiseEditFeed() + * each time there is some data arriving in the standard input. + * + * The user can also call linenoiseEditHide() and linenoiseEditShow() if it + * is required to show some input arriving asyncronously, without mixing + * it with the currently edited line. + * + * When linenoiseEditFeed() returns non-NULL, the user finished with the + * line editing session (pressed enter CTRL-D/C): in this case the caller + * needs to call linenoiseEditStop() to put back the terminal in normal + * mode. This will not destroy the buffer, as long as the linenoiseState + * is still valid in the context of the caller. + * + * The function returns 0 on success, or -1 if writing to standard output + * fails. If stdin_fd or stdout_fd are set to -1, the default is to use + * STDIN_FILENO and STDOUT_FILENO. + */ +int linenoiseEditStart(struct linenoiseState *l, int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt) { + /* Populate the linenoise state that we pass to functions implementing + * specific editing functionalities. */ + l->in_completion = 0; + l->ifd = stdin_fd != -1 ? stdin_fd : STDIN_FILENO; + l->ofd = stdout_fd != -1 ? stdout_fd : STDOUT_FILENO; + l->buf = buf; + l->buflen = buflen; + l->prompt = prompt; + l->plen = strlen(prompt); + l->oldpos = l->pos = 0; + l->len = 0; + + /* Enter raw mode. */ + if (enableRawMode(l->ifd) == -1) return -1; + + l->cols = getColumns(stdin_fd, stdout_fd); + l->oldrows = 0; + l->history_index = 0; + + /* Buffer starts empty. */ + l->buf[0] = '\0'; + l->buflen--; /* Make sure there is always space for the nulterm */ + + /* If stdin is not a tty, stop here with the initialization. We + * will actually just read a line from standard input in blocking + * mode later, in linenoiseEditFeed(). */ + if (!isatty(l->ifd)) return 0; + + /* The latest history entry is always our current buffer, that + * initially is just an empty string. */ + linenoiseHistoryAdd(""); + + if (write(l->ofd,prompt,l->plen) == -1) return -1; + return 0; +} + +const char* linenoiseEditMore = "If you see this, you are misusing the API: when linenoiseEditFeed() is called, if it returns linenoiseEditMore the user is yet editing the line. See the README file for more information."; + +/* This function is part of the multiplexed API of linenoise, see the top + * comment on linenoiseEditStart() for more information. Call this function + * each time there is some data to read from the standard input file + * descriptor. In the case of blocking operations, this function can just be + * called in a loop, and block. + * + * The function returns linenoiseEditMore to signal that line editing is still + * in progress, that is, the user didn't yet pressed enter / CTRL-D. Otherwise + * the function returns the pointer to the heap-allocated buffer with the + * edited line, that the user should free with linenoiseFree(). + * + * On special conditions, NULL is returned and errno is populated: + * + * EAGAIN if the user pressed Ctrl-C + * ENOENT if the user pressed Ctrl-D + * + * Some other errno: I/O error. + */ +const char *linenoiseEditFeed(struct linenoiseState *l) { + /* Not a TTY, pass control to line reading without character + * count limits. */ + if (!isatty(l->ifd)) return linenoiseNoTTY(); + + char c; + int nread; + char seq[3]; + + nread = read(l->ifd,&c,1); + if (nread <= 0) return NULL; + + /* Only autocomplete when the callback is set. It returns < 0 when + * there was an error reading from fd. Otherwise it will return the + * character that should be handled next. */ + if ((l->in_completion || c == 9) && completionCallback != NULL) { + c = completeLine(l,c); + /* Read next character when 0 */ + if (c == 0) return linenoiseEditMore; + } + + switch(c) { + case ENTER: /* enter */ + history_len--; + free(history[history_len]); + if (mlmode) linenoiseEditMoveEnd(l); + if (hintsCallback) { + /* Force a refresh without hints to leave the previous + * line as the user typed it after a newline. */ + linenoiseHintsCallback *hc = hintsCallback; + hintsCallback = NULL; + refreshLine(l); + hintsCallback = hc; + } + return strdup(l->buf); + case CTRL_C: /* ctrl-c */ + errno = EAGAIN; + return NULL; + case BACKSPACE: /* backspace */ + case 8: /* ctrl-h */ + linenoiseEditBackspace(l); + break; + case CTRL_D: /* ctrl-d, remove char at right of cursor, or if the + line is empty, act as end-of-file. */ + if (l->len > 0) { + linenoiseEditDelete(l); + } else { + history_len--; + free(history[history_len]); + errno = ENOENT; + return NULL; + } + break; + case CTRL_T: /* ctrl-t, swaps current character with previous. */ + if (l->pos > 0 && l->pos < l->len) { + int aux = l->buf[l->pos-1]; + l->buf[l->pos-1] = l->buf[l->pos]; + l->buf[l->pos] = aux; + if (l->pos != l->len-1) l->pos++; + refreshLine(l); + } + break; + case CTRL_B: /* ctrl-b */ + linenoiseEditMoveLeft(l); + break; + case CTRL_F: /* ctrl-f */ + linenoiseEditMoveRight(l); + break; + case CTRL_P: /* ctrl-p */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_PREV); + break; + case CTRL_N: /* ctrl-n */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_NEXT); + break; + case ESC: /* escape sequence */ + /* Read the next two bytes representing the escape sequence. + * Use two calls to handle slow terminals returning the two + * chars at different times. */ + if (read(l->ifd,seq,1) == -1) break; + if (read(l->ifd,seq+1,1) == -1) break; + + /* ESC [ sequences. */ + if (seq[0] == '[') { + if (seq[1] >= '0' && seq[1] <= '9') { + /* Extended escape, read additional byte. */ + if (read(l->ifd,seq+2,1) == -1) break; + if (seq[2] == '~') { + switch(seq[1]) { + case '3': /* Delete key. */ + linenoiseEditDelete(l); + break; + } + } + } else { + switch(seq[1]) { + case 'A': /* Up */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_PREV); + break; + case 'B': /* Down */ + linenoiseEditHistoryNext(l, LINENOISE_HISTORY_NEXT); + break; + case 'C': /* Right */ + linenoiseEditMoveRight(l); + break; + case 'D': /* Left */ + linenoiseEditMoveLeft(l); + break; + case 'H': /* Home */ + linenoiseEditMoveHome(l); + break; + case 'F': /* End*/ + linenoiseEditMoveEnd(l); + break; + } + } + } + + /* ESC O sequences. */ + else if (seq[0] == 'O') { + switch(seq[1]) { + case 'H': /* Home */ + linenoiseEditMoveHome(l); + break; + case 'F': /* End*/ + linenoiseEditMoveEnd(l); + break; + } + } + break; + default: + if (linenoiseEditInsert(l,c)) return NULL; + break; + case CTRL_U: /* Ctrl+u, delete the whole line. */ + l->buf[0] = '\0'; + l->pos = l->len = 0; + refreshLine(l); + break; + case CTRL_K: /* Ctrl+k, delete from current to end of line. */ + l->buf[l->pos] = '\0'; + l->len = l->pos; + refreshLine(l); + break; + case CTRL_A: /* Ctrl+a, go to the start of the line */ + linenoiseEditMoveHome(l); + break; + case CTRL_E: /* ctrl+e, go to the end of the line */ + linenoiseEditMoveEnd(l); + break; + case CTRL_L: /* ctrl+l, clear screen */ + linenoiseClearScreen(); + refreshLine(l); + break; + case CTRL_W: /* ctrl+w, delete previous word */ + linenoiseEditDeletePrevWord(l); + break; + } + return linenoiseEditMore; +} + +/* This is part of the multiplexed linenoise API. See linenoiseEditStart() + * for more information. This function is called when linenoiseEditFeed() + * returns something different than NULL. At this point the user input + * is in the buffer, and we can restore the terminal in normal mode. */ +void linenoiseEditStop(struct linenoiseState *l) { + if (!isatty(l->ifd)) return; + disableRawMode(l->ifd); + printf("\n"); +} + +/* This just implements a blocking loop for the multiplexed API. + * In many applications that are not event-drivern, we can just call + * the blocking linenoise API, wait for the user to complete the editing + * and return the buffer. */ +static const char *linenoiseBlockingEdit(int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt) +{ + struct linenoiseState l; + + /* Editing without a buffer is invalid. */ + if (buflen == 0) { + errno = EINVAL; + return NULL; + } + + linenoiseEditStart(&l,stdin_fd,stdout_fd,buf,buflen,prompt); + const char *res; + while((res = linenoiseEditFeed(&l)) == linenoiseEditMore); + linenoiseEditStop(&l); + return res; +} + +/* This special mode is used by linenoise in order to print scan codes + * on screen for debugging / development purposes. It is implemented + * by the linenoise_example program using the --keycodes option. */ +void linenoisePrintKeyCodes(void) { + char quit[4]; + + printf("Linenoise key codes debugging mode.\n" + "Press keys to see scan codes. Type 'quit' at any time to exit.\n"); + if (enableRawMode(STDIN_FILENO) == -1) return; + memset(quit,' ',4); + while(1) { + char c; + int nread; + + nread = read(STDIN_FILENO,&c,1); + if (nread <= 0) continue; + memmove(quit,quit+1,sizeof(quit)-1); /* shift string to left. */ + quit[sizeof(quit)-1] = c; /* Insert current char on the right. */ + if (memcmp(quit,"quit",sizeof(quit)) == 0) break; + + printf("'%c' %02x (%d) (type quit to exit)\n", + isprint(c) ? c : '?', (int)c, (int)c); + printf("\r"); /* Go left edge manually, we are in raw mode. */ + fflush(stdout); + } + disableRawMode(STDIN_FILENO); +} + +/* This function is called when linenoise() is called with the standard + * input file descriptor not attached to a TTY. So for example when the + * program using linenoise is called in pipe or with a file redirected + * to its standard input. In this case, we want to be able to return the + * line regardless of its length (by default we are limited to 4k). */ +static char *linenoiseNoTTY(void) { + char *line = NULL; + size_t len = 0, maxlen = 0; + + while(1) { + if (len == maxlen) { + if (maxlen == 0) maxlen = 16; + maxlen *= 2; + char *oldval = line; + line = (char*) realloc(line,maxlen); + if (line == NULL) { + if (oldval) free(oldval); + return NULL; + } + } + int c = fgetc(stdin); + if (c == EOF || c == '\n') { + if (c == EOF && len == 0) { + free(line); + return NULL; + } else { + line[len] = '\0'; + return line; + } + } else { + line[len] = c; + len++; + } + } +} + +/* The high level function that is the main API of the linenoise library. + * This function checks if the terminal has basic capabilities, just checking + * for a blacklist of stupid terminals, and later either calls the line + * editing function or uses dummy fgets() so that you will be able to type + * something even in the most desperate of the conditions. */ +const char *linenoise(const char *prompt) { + char buf[LINENOISE_MAX_LINE]; + + if (!isatty(STDIN_FILENO)) { + /* Not a tty: read from file / pipe. In this mode we don't want any + * limit to the line size, so we call a function to handle that. */ + return linenoiseNoTTY(); + } else if (isUnsupportedTerm()) { + size_t len; + + printf("%s",prompt); + fflush(stdout); + if (fgets(buf,LINENOISE_MAX_LINE,stdin) == NULL) return NULL; + len = strlen(buf); + while(len && (buf[len-1] == '\n' || buf[len-1] == '\r')) { + len--; + buf[len] = '\0'; + } + return strdup(buf); + } else { + const char *retval = linenoiseBlockingEdit(STDIN_FILENO,STDOUT_FILENO,buf,LINENOISE_MAX_LINE,prompt); + return retval; + } +} + +/* This is just a wrapper the user may want to call in order to make sure + * the linenoise returned buffer is freed with the same allocator it was + * created with. Useful when the main program is using an alternative + * allocator. */ +void linenoiseFree(void *ptr) { + if (ptr == linenoiseEditMore) return; // Protect from API misuse. + free(ptr); +} + +/* ================================ History ================================= */ + +/* Free the history, but does not reset it. Only used when we have to + * exit() to avoid memory leaks are reported by valgrind & co. */ +static void freeHistory(void) { + if (history) { + int j; + + for (j = 0; j < history_len; j++) + free(history[j]); + free(history); + } +} + +/* At exit we'll try to fix the terminal to the initial conditions. */ +static void linenoiseAtExit(void) { + disableRawMode(STDIN_FILENO); + freeHistory(); +} + +/* This is the API call to add a new entry in the linenoise history. + * It uses a fixed array of char pointers that are shifted (memmoved) + * when the history max length is reached in order to remove the older + * entry and make room for the new one, so it is not exactly suitable for huge + * histories, but will work well for a few hundred of entries. + * + * Using a circular buffer is smarter, but a bit more complex to handle. */ +int linenoiseHistoryAdd(const char *line) { + char *linecopy; + + if (history_max_len == 0) return 0; + + /* Initialization on first call. */ + if (history == NULL) { + history = (char**) malloc(sizeof(char*)*history_max_len); + if (history == NULL) return 0; + memset(history,0,(sizeof(char*)*history_max_len)); + } + + /* Don't add duplicated lines. */ + if (history_len && !strcmp(history[history_len-1], line)) return 0; + + /* Add an heap allocated copy of the line in the history. + * If we reached the max length, remove the older line. */ + linecopy = strdup(line); + if (!linecopy) return 0; + if (history_len == history_max_len) { + free(history[0]); + memmove(history,history+1,sizeof(char*)*(history_max_len-1)); + history_len--; + } + history[history_len] = linecopy; + history_len++; + return 1; +} + +/* Set the maximum length for the history. This function can be called even + * if there is already some history, the function will make sure to retain + * just the latest 'len' elements if the new history length value is smaller + * than the amount of items already inside the history. */ +int linenoiseHistorySetMaxLen(int len) { + char **new_ptr; + + if (len < 1) return 0; + if (history) { + int tocopy = history_len; + + new_ptr = (char**) malloc(sizeof(char*)*len); + if (new_ptr == NULL) return 0; + + /* If we can't copy everything, free the elements we'll not use. */ + if (len < tocopy) { + int j; + + for (j = 0; j < tocopy-len; j++) free(history[j]); + tocopy = len; + } + memset(new_ptr,0,sizeof(char*)*len); + memcpy(new_ptr,history+(history_len-tocopy), sizeof(char*)*tocopy); + free(history); + history = new_ptr; + } + history_max_len = len; + if (history_len > history_max_len) + history_len = history_max_len; + return 1; +} + +/* Save the history in the specified file. On success 0 is returned + * otherwise -1 is returned. */ +int linenoiseHistorySave(const char *filename) { + mode_t old_umask = umask(S_IXUSR|S_IRWXG|S_IRWXO); + File file; + file.open(filename, "w"); + umask(old_umask); + if (file.file == NULL) { + return -1; + } + chmod(filename,S_IRUSR|S_IWUSR); + for (int j = 0; j < history_len; ++j) { + fprintf(file.file, "%s\n", history[j]); + } + + return 0; +} + +/* Load the history from the specified file. If the file does not exist + * zero is returned and no operation is performed. + * + * If the file exists and the operation succeeded 0 is returned, otherwise + * on error -1 is returned. */ +int linenoiseHistoryLoad(const char *filename) { + File file; + file.open(filename, "r"); + char buf[LINENOISE_MAX_LINE]; + if (file.file == NULL) { + return -1; + } + + while (fgets(buf, LINENOISE_MAX_LINE, file.file) != NULL) { + char *p; + + p = strchr(buf,'\r'); + if (!p) p = strchr(buf,'\n'); + if (p) *p = '\0'; + linenoiseHistoryAdd(buf); + } + return 0; +} +#endif diff --git a/examples/run/linenoise.cpp/linenoise.h b/examples/run/linenoise.cpp/linenoise.h new file mode 100644 index 000000000..a14ec6c74 --- /dev/null +++ b/examples/run/linenoise.cpp/linenoise.h @@ -0,0 +1,128 @@ +/* linenoise.h -- VERSION 1.0 + * + * Guerrilla line editing library against the idea that a line editing lib + * needs to be 20,000 lines of C++ code. + * + * See linenoise.cpp for more information. + * + * ------------------------------------------------------------------------ + * + * Copyright (c) 2010-2023, Salvatore Sanfilippo + * Copyright (c) 2010-2013, Pieter Noordhuis + * Copyright (c) 2025, Eric Curtin + * + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are + * met: + * + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + * HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +#ifndef __LINENOISE_H +#define __LINENOISE_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include /* For size_t. */ +#include + +extern const char *linenoiseEditMore; + +/* The linenoiseState structure represents the state during line editing. + * We pass this state to functions implementing specific editing + * functionalities. */ +struct linenoiseState { + int in_completion; /* The user pressed TAB and we are now in completion + * mode, so input is handled by completeLine(). */ + size_t completion_idx; /* Index of next completion to propose. */ + int ifd; /* Terminal stdin file descriptor. */ + int ofd; /* Terminal stdout file descriptor. */ + char *buf; /* Edited line buffer. */ + size_t buflen; /* Edited line buffer size. */ + const char *prompt; /* Prompt to display. */ + size_t plen; /* Prompt length. */ + size_t pos; /* Current cursor position. */ + size_t oldpos; /* Previous refresh cursor position. */ + size_t len; /* Current edited line length. */ + size_t cols; /* Number of columns in terminal. */ + size_t oldrows; /* Rows used by last refrehsed line (multiline mode) */ + int history_index; /* The history index we are currently editing. */ +}; + +struct linenoiseCompletions { + size_t len = 0; + char ** cvec = nullptr; + bool to_free = true; + + ~linenoiseCompletions() { + if (!to_free) { + return; + } + + for (size_t i = 0; i < len; ++i) { + free(cvec[i]); + } + + free(cvec); + } +}; + +/* Non blocking API. */ +int linenoiseEditStart(struct linenoiseState *l, int stdin_fd, int stdout_fd, char *buf, size_t buflen, const char *prompt); +const char *linenoiseEditFeed(struct linenoiseState *l); +void linenoiseEditStop(struct linenoiseState *l); +void linenoiseHide(struct linenoiseState *l); +void linenoiseShow(struct linenoiseState *l); + +/* Blocking API. */ +const char *linenoise(const char *prompt); +void linenoiseFree(void *ptr); + +/* Completion API. */ +typedef void(linenoiseCompletionCallback)(const char *, linenoiseCompletions *); +typedef const char*(linenoiseHintsCallback)(const char *, int *color, int *bold); +typedef void(linenoiseFreeHintsCallback)(const char *); +void linenoiseSetCompletionCallback(linenoiseCompletionCallback *); +void linenoiseSetHintsCallback(linenoiseHintsCallback *); +void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *); +void linenoiseAddCompletion(linenoiseCompletions *, const char *); + +/* History API. */ +int linenoiseHistoryAdd(const char *line); +int linenoiseHistorySetMaxLen(int len); +int linenoiseHistorySave(const char *filename); +int linenoiseHistoryLoad(const char *filename); + +/* Other utilities. */ +void linenoiseClearScreen(void); +void linenoiseSetMultiLine(int ml); +void linenoisePrintKeyCodes(void); +void linenoiseMaskModeEnable(void); +void linenoiseMaskModeDisable(void); + +#ifdef __cplusplus +} +#endif + +#endif /* __LINENOISE_H */ diff --git a/examples/run/run.cpp b/examples/run/run.cpp new file mode 100644 index 000000000..9362da220 --- /dev/null +++ b/examples/run/run.cpp @@ -0,0 +1,1173 @@ +#if defined(_WIN32) +# include +# include +#else +# include +# include +# include +#endif + +#if defined(LLAMA_USE_CURL) +# include +#endif + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "chat-template.hpp" +#include "common.h" +#include "json.hpp" +#include "linenoise.cpp/linenoise.h" +#include "llama-cpp.h" +#include "log.h" + +#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32) +[[noreturn]] static void sigint_handler(int) { + printf("\n" LOG_COL_DEFAULT); + exit(0); // not ideal, but it's the only way to guarantee exit in all cases +} +#endif + +GGML_ATTRIBUTE_FORMAT(1, 2) +static std::string fmt(const char * fmt, ...) { + va_list ap; + va_list ap2; + va_start(ap, fmt); + va_copy(ap2, ap); + const int size = vsnprintf(NULL, 0, fmt, ap); + GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT + std::string buf; + buf.resize(size); + const int size2 = vsnprintf(const_cast(buf.data()), buf.size() + 1, fmt, ap2); + GGML_ASSERT(size2 == size); + va_end(ap2); + va_end(ap); + + return buf; +} + +GGML_ATTRIBUTE_FORMAT(1, 2) +static int printe(const char * fmt, ...) { + va_list args; + va_start(args, fmt); + const int ret = vfprintf(stderr, fmt, args); + va_end(args); + + return ret; +} + +static std::string strftime_fmt(const char * fmt, const std::tm & tm) { + std::ostringstream oss; + oss << std::put_time(&tm, fmt); + + return oss.str(); +} + +class Opt { + public: + int init(int argc, const char ** argv) { + ctx_params = llama_context_default_params(); + model_params = llama_model_default_params(); + context_size_default = ctx_params.n_batch; + ngl_default = model_params.n_gpu_layers; + common_params_sampling sampling; + temperature_default = sampling.temp; + + if (argc < 2) { + printe("Error: No arguments provided.\n"); + print_help(); + return 1; + } + + // Parse arguments + if (parse(argc, argv)) { + printe("Error: Failed to parse arguments.\n"); + print_help(); + return 1; + } + + // If help is requested, show help and exit + if (help) { + print_help(); + return 2; + } + + ctx_params.n_batch = context_size >= 0 ? context_size : context_size_default; + ctx_params.n_ctx = ctx_params.n_batch; + model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default; + temperature = temperature >= 0 ? temperature : temperature_default; + + return 0; // Success + } + + llama_context_params ctx_params; + llama_model_params model_params; + std::string model_; + std::string user; + bool use_jinja = false; + int context_size = -1, ngl = -1; + float temperature = -1; + bool verbose = false; + + private: + int context_size_default = -1, ngl_default = -1; + float temperature_default = -1; + bool help = false; + + bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) { + return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0; + } + + int handle_option_with_value(int argc, const char ** argv, int & i, int & option_value) { + if (i + 1 >= argc) { + return 1; + } + + option_value = std::atoi(argv[++i]); + + return 0; + } + + int handle_option_with_value(int argc, const char ** argv, int & i, float & option_value) { + if (i + 1 >= argc) { + return 1; + } + + option_value = std::atof(argv[++i]); + + return 0; + } + + int parse(int argc, const char ** argv) { + bool options_parsing = true; + for (int i = 1, positional_args_i = 0; i < argc; ++i) { + if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) { + if (handle_option_with_value(argc, argv, i, context_size) == 1) { + return 1; + } + } else if (options_parsing && + (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) { + if (handle_option_with_value(argc, argv, i, ngl) == 1) { + return 1; + } + } else if (options_parsing && strcmp(argv[i], "--temp") == 0) { + if (handle_option_with_value(argc, argv, i, temperature) == 1) { + return 1; + } + } else if (options_parsing && + (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) { + verbose = true; + } else if (options_parsing && strcmp(argv[i], "--jinja") == 0) { + use_jinja = true; + } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) { + help = true; + return 0; + } else if (options_parsing && strcmp(argv[i], "--") == 0) { + options_parsing = false; + } else if (positional_args_i == 0) { + if (!argv[i][0] || argv[i][0] == '-') { + return 1; + } + + ++positional_args_i; + model_ = argv[i]; + } else if (positional_args_i == 1) { + ++positional_args_i; + user = argv[i]; + } else { + user += " " + std::string(argv[i]); + } + } + + if (model_.empty()){ + return 1; + } + + return 0; + } + + void print_help() const { + printf( + "Description:\n" + " Runs a llm\n" + "\n" + "Usage:\n" + " llama-run [options] model [prompt]\n" + "\n" + "Options:\n" + " -c, --context-size \n" + " Context size (default: %d)\n" + " -n, -ngl, --ngl \n" + " Number of GPU layers (default: %d)\n" + " --temp \n" + " Temperature (default: %.1f)\n" + " -v, --verbose, --log-verbose\n" + " Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n" + " -h, --help\n" + " Show help message\n" + "\n" + "Commands:\n" + " model\n" + " Model is a string with an optional prefix of \n" + " huggingface:// (hf://), ollama://, https:// or file://.\n" + " If no protocol is specified and a file exists in the specified\n" + " path, file:// is assumed, otherwise if a file does not exist in\n" + " the specified path, ollama:// is assumed. Models that are being\n" + " pulled are downloaded with .partial extension while being\n" + " downloaded and then renamed as the file without the .partial\n" + " extension when complete.\n" + "\n" + "Examples:\n" + " llama-run llama3\n" + " llama-run ollama://granite-code\n" + " llama-run ollama://smollm:135m\n" + " llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n" + " llama-run " + "huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n" + " llama-run https://example.com/some-file1.gguf\n" + " llama-run some-file2.gguf\n" + " llama-run file://some-file3.gguf\n" + " llama-run --ngl 999 some-file4.gguf\n" + " llama-run --ngl 999 some-file5.gguf Hello World\n", + context_size_default, ngl_default, temperature_default); + } +}; + +struct progress_data { + size_t file_size = 0; + std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now(); + bool printed = false; +}; + +static int get_terminal_width() { +#if defined(_WIN32) + CONSOLE_SCREEN_BUFFER_INFO csbi; + GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi); + return csbi.srWindow.Right - csbi.srWindow.Left + 1; +#else + struct winsize w; + ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); + return w.ws_col; +#endif +} + +#ifdef LLAMA_USE_CURL +class File { + public: + FILE * file = nullptr; + + FILE * open(const std::string & filename, const char * mode) { + file = fopen(filename.c_str(), mode); + + return file; + } + + int lock() { + if (file) { +# ifdef _WIN32 + fd = _fileno(file); + hFile = (HANDLE) _get_osfhandle(fd); + if (hFile == INVALID_HANDLE_VALUE) { + fd = -1; + + return 1; + } + + OVERLAPPED overlapped = {}; + if (!LockFileEx(hFile, LOCKFILE_EXCLUSIVE_LOCK | LOCKFILE_FAIL_IMMEDIATELY, 0, MAXDWORD, MAXDWORD, + &overlapped)) { + fd = -1; + + return 1; + } +# else + fd = fileno(file); + if (flock(fd, LOCK_EX | LOCK_NB) != 0) { + fd = -1; + + return 1; + } +# endif + } + + return 0; + } + + ~File() { + if (fd >= 0) { +# ifdef _WIN32 + if (hFile != INVALID_HANDLE_VALUE) { + OVERLAPPED overlapped = {}; + UnlockFileEx(hFile, 0, MAXDWORD, MAXDWORD, &overlapped); + } +# else + flock(fd, LOCK_UN); +# endif + } + + if (file) { + fclose(file); + } + } + + private: + int fd = -1; +# ifdef _WIN32 + HANDLE hFile = nullptr; +# endif +}; + +class HttpClient { + public: + int init(const std::string & url, const std::vector & headers, const std::string & output_file, + const bool progress, std::string * response_str = nullptr) { + if (std::filesystem::exists(output_file)) { + return 0; + } + + std::string output_file_partial; + curl = curl_easy_init(); + if (!curl) { + return 1; + } + + progress_data data; + File out; + if (!output_file.empty()) { + output_file_partial = output_file + ".partial"; + if (!out.open(output_file_partial, "ab")) { + printe("Failed to open file for writing\n"); + + return 1; + } + + if (out.lock()) { + printe("Failed to exclusively lock file\n"); + + return 1; + } + } + + set_write_options(response_str, out); + data.file_size = set_resume_point(output_file_partial); + set_progress_options(progress, data); + set_headers(headers); + CURLcode res = perform(url); + if (res != CURLE_OK){ + printe("Fetching resource '%s' failed: %s\n", url.c_str(), curl_easy_strerror(res)); + return 1; + } + if (!output_file.empty()) { + std::filesystem::rename(output_file_partial, output_file); + } + + return 0; + } + + ~HttpClient() { + if (chunk) { + curl_slist_free_all(chunk); + } + + if (curl) { + curl_easy_cleanup(curl); + } + } + + private: + CURL * curl = nullptr; + struct curl_slist * chunk = nullptr; + + void set_write_options(std::string * response_str, const File & out) { + if (response_str) { + curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data); + curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str); + } else { + curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data); + curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.file); + } + } + + size_t set_resume_point(const std::string & output_file) { + size_t file_size = 0; + if (std::filesystem::exists(output_file)) { + file_size = std::filesystem::file_size(output_file); + curl_easy_setopt(curl, CURLOPT_RESUME_FROM_LARGE, static_cast(file_size)); + } + + return file_size; + } + + void set_progress_options(bool progress, progress_data & data) { + if (progress) { + curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L); + curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data); + curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, update_progress); + } + } + + void set_headers(const std::vector & headers) { + if (!headers.empty()) { + if (chunk) { + curl_slist_free_all(chunk); + chunk = 0; + } + + for (const auto & header : headers) { + chunk = curl_slist_append(chunk, header.c_str()); + } + + curl_easy_setopt(curl, CURLOPT_HTTPHEADER, chunk); + } + } + + CURLcode perform(const std::string & url) { + curl_easy_setopt(curl, CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L); + curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https"); + curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L); + return curl_easy_perform(curl); + } + + static std::string human_readable_time(double seconds) { + int hrs = static_cast(seconds) / 3600; + int mins = (static_cast(seconds) % 3600) / 60; + int secs = static_cast(seconds) % 60; + + if (hrs > 0) { + return fmt("%dh %02dm %02ds", hrs, mins, secs); + } else if (mins > 0) { + return fmt("%dm %02ds", mins, secs); + } else { + return fmt("%ds", secs); + } + } + + static std::string human_readable_size(curl_off_t size) { + static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" }; + char length = sizeof(suffix) / sizeof(suffix[0]); + int i = 0; + double dbl_size = size; + if (size > 1024) { + for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) { + dbl_size = size / 1024.0; + } + } + + return fmt("%.2f %s", dbl_size, suffix[i]); + } + + static int update_progress(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t, + curl_off_t) { + progress_data * data = static_cast(ptr); + if (total_to_download <= 0) { + return 0; + } + + total_to_download += data->file_size; + const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size; + const curl_off_t percentage = calculate_percentage(now_downloaded_plus_file_size, total_to_download); + std::string progress_prefix = generate_progress_prefix(percentage); + + const double speed = calculate_speed(now_downloaded, data->start_time); + const double tim = (total_to_download - now_downloaded) / speed; + std::string progress_suffix = + generate_progress_suffix(now_downloaded_plus_file_size, total_to_download, speed, tim); + + int progress_bar_width = calculate_progress_bar_width(progress_prefix, progress_suffix); + std::string progress_bar; + generate_progress_bar(progress_bar_width, percentage, progress_bar); + + print_progress(progress_prefix, progress_bar, progress_suffix); + data->printed = true; + + return 0; + } + + static curl_off_t calculate_percentage(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download) { + return (now_downloaded_plus_file_size * 100) / total_to_download; + } + + static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", static_cast(percentage)); } + + static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) { + const auto now = std::chrono::steady_clock::now(); + const std::chrono::duration elapsed_seconds = now - start_time; + return now_downloaded / elapsed_seconds.count(); + } + + static std::string generate_progress_suffix(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download, + double speed, double estimated_time) { + const int width = 10; + return fmt("%*s/%*s%*s/s%*s", width, human_readable_size(now_downloaded_plus_file_size).c_str(), width, + human_readable_size(total_to_download).c_str(), width, human_readable_size(speed).c_str(), width, + human_readable_time(estimated_time).c_str()); + } + + static int calculate_progress_bar_width(const std::string & progress_prefix, const std::string & progress_suffix) { + int progress_bar_width = get_terminal_width() - progress_prefix.size() - progress_suffix.size() - 3; + if (progress_bar_width < 1) { + progress_bar_width = 1; + } + + return progress_bar_width; + } + + static std::string generate_progress_bar(int progress_bar_width, curl_off_t percentage, + std::string & progress_bar) { + const curl_off_t pos = (percentage * progress_bar_width) / 100; + for (int i = 0; i < progress_bar_width; ++i) { + progress_bar.append((i < pos) ? "█" : " "); + } + + return progress_bar; + } + + static void print_progress(const std::string & progress_prefix, const std::string & progress_bar, + const std::string & progress_suffix) { + printe("\r" LOG_CLR_TO_EOL "%s%s| %s", progress_prefix.c_str(), progress_bar.c_str(), progress_suffix.c_str()); + } + // Function to write data to a file + static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) { + FILE * out = static_cast(stream); + return fwrite(ptr, size, nmemb, out); + } + + // Function to capture data into a string + static size_t capture_data(void * ptr, size_t size, size_t nmemb, void * stream) { + std::string * str = static_cast(stream); + str->append(static_cast(ptr), size * nmemb); + return size * nmemb; + } +}; +#endif + +class LlamaData { + public: + llama_model_ptr model; + llama_sampler_ptr sampler; + llama_context_ptr context; + std::vector messages; + std::list msg_strs; + std::vector fmtted; + + int init(Opt & opt) { + model = initialize_model(opt); + if (!model) { + return 1; + } + + context = initialize_context(model, opt); + if (!context) { + return 1; + } + + sampler = initialize_sampler(opt); + + return 0; + } + + private: +#ifdef LLAMA_USE_CURL + int download(const std::string & url, const std::string & output_file, const bool progress, + const std::vector & headers = {}, std::string * response_str = nullptr) { + HttpClient http; + if (http.init(url, headers, output_file, progress, response_str)) { + return 1; + } + + return 0; + } +#else + int download(const std::string &, const std::string &, const bool, const std::vector & = {}, + std::string * = nullptr) { + printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); + + return 1; + } +#endif + + // Helper function to handle model tag extraction and URL construction + std::pair extract_model_and_tag(std::string & model, const std::string & base_url) { + std::string model_tag = "latest"; + const size_t colon_pos = model.find(':'); + if (colon_pos != std::string::npos) { + model_tag = model.substr(colon_pos + 1); + model = model.substr(0, colon_pos); + } + + std::string url = base_url + model + "/manifests/" + model_tag; + + return { model, url }; + } + + // Helper function to download and parse the manifest + int download_and_parse_manifest(const std::string & url, const std::vector & headers, + nlohmann::json & manifest) { + std::string manifest_str; + int ret = download(url, "", false, headers, &manifest_str); + if (ret) { + return ret; + } + + manifest = nlohmann::json::parse(manifest_str); + + return 0; + } + + int huggingface_dl(std::string & model, const std::string & bn) { + // Find the second occurrence of '/' after protocol string + size_t pos = model.find('/'); + pos = model.find('/', pos + 1); + std::string hfr, hff; + std::vector headers = { "User-Agent: llama-cpp", "Accept: application/json" }; + std::string url; + + if (pos == std::string::npos) { + auto [model_name, manifest_url] = extract_model_and_tag(model, "https://huggingface.co/v2/"); + hfr = model_name; + + nlohmann::json manifest; + int ret = download_and_parse_manifest(manifest_url, headers, manifest); + if (ret) { + return ret; + } + + hff = manifest["ggufFile"]["rfilename"]; + } else { + hfr = model.substr(0, pos); + hff = model.substr(pos + 1); + } + + url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff; + + return download(url, bn, true, headers); + } + + int ollama_dl(std::string & model, const std::string & bn) { + const std::vector headers = { "Accept: application/vnd.docker.distribution.manifest.v2+json" }; + if (model.find('/') == std::string::npos) { + model = "library/" + model; + } + + auto [model_name, manifest_url] = extract_model_and_tag(model, "https://registry.ollama.ai/v2/"); + nlohmann::json manifest; + int ret = download_and_parse_manifest(manifest_url, {}, manifest); + if (ret) { + return ret; + } + + std::string layer; + for (const auto & l : manifest["layers"]) { + if (l["mediaType"] == "application/vnd.ollama.image.model") { + layer = l["digest"]; + break; + } + } + + std::string blob_url = "https://registry.ollama.ai/v2/" + model_name + "/blobs/" + layer; + + return download(blob_url, bn, true, headers); + } + + int github_dl(const std::string & model, const std::string & bn) { + std::string repository = model; + std::string branch = "main"; + const size_t at_pos = model.find('@'); + if (at_pos != std::string::npos) { + repository = model.substr(0, at_pos); + branch = model.substr(at_pos + 1); + } + + const std::vector repo_parts = string_split(repository, "/"); + if (repo_parts.size() < 3) { + printe("Invalid GitHub repository format\n"); + return 1; + } + + const std::string & org = repo_parts[0]; + const std::string & project = repo_parts[1]; + std::string url = "https://raw.githubusercontent.com/" + org + "/" + project + "/" + branch; + for (size_t i = 2; i < repo_parts.size(); ++i) { + url += "/" + repo_parts[i]; + } + + return download(url, bn, true); + } + + int s3_dl(const std::string & model, const std::string & bn) { + const size_t slash_pos = model.find('/'); + if (slash_pos == std::string::npos) { + return 1; + } + + const std::string bucket = model.substr(0, slash_pos); + const std::string key = model.substr(slash_pos + 1); + const char * access_key = std::getenv("AWS_ACCESS_KEY_ID"); + const char * secret_key = std::getenv("AWS_SECRET_ACCESS_KEY"); + if (!access_key || !secret_key) { + printe("AWS credentials not found in environment\n"); + return 1; + } + + // Generate AWS Signature Version 4 headers + // (Implementation requires HMAC-SHA256 and date handling) + // Get current timestamp + const time_t now = time(nullptr); + const tm tm = *gmtime(&now); + const std::string date = strftime_fmt("%Y%m%d", tm); + const std::string datetime = strftime_fmt("%Y%m%dT%H%M%SZ", tm); + const std::vector headers = { + "Authorization: AWS4-HMAC-SHA256 Credential=" + std::string(access_key) + "/" + date + + "/us-east-1/s3/aws4_request", + "x-amz-content-sha256: UNSIGNED-PAYLOAD", "x-amz-date: " + datetime + }; + + const std::string url = "https://" + bucket + ".s3.amazonaws.com/" + key; + + return download(url, bn, true, headers); + } + + std::string basename(const std::string & path) { + const size_t pos = path.find_last_of("/\\"); + if (pos == std::string::npos) { + return path; + } + + return path.substr(pos + 1); + } + + int rm_until_substring(std::string & model_, const std::string & substring) { + const std::string::size_type pos = model_.find(substring); + if (pos == std::string::npos) { + return 1; + } + + model_ = model_.substr(pos + substring.size()); // Skip past the substring + return 0; + } + + int resolve_model(std::string & model_) { + int ret = 0; + if (string_starts_with(model_, "file://") || std::filesystem::exists(model_)) { + rm_until_substring(model_, "://"); + + return ret; + } + + const std::string bn = basename(model_); + if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://") || + string_starts_with(model_, "hf.co/")) { + rm_until_substring(model_, "hf.co/"); + rm_until_substring(model_, "://"); + ret = huggingface_dl(model_, bn); + } else if ((string_starts_with(model_, "https://") || string_starts_with(model_, "http://")) && + !string_starts_with(model_, "https://ollama.com/library/")) { + ret = download(model_, bn, true); + } else if (string_starts_with(model_, "github:") || string_starts_with(model_, "github://")) { + rm_until_substring(model_, "github:"); + rm_until_substring(model_, "://"); + ret = github_dl(model_, bn); + } else if (string_starts_with(model_, "s3://")) { + rm_until_substring(model_, "://"); + ret = s3_dl(model_, bn); + } else { // ollama:// or nothing + rm_until_substring(model_, "ollama.com/library/"); + rm_until_substring(model_, "://"); + ret = ollama_dl(model_, bn); + } + + model_ = bn; + + return ret; + } + + // Initializes the model and returns a unique pointer to it + llama_model_ptr initialize_model(Opt & opt) { + ggml_backend_load_all(); + resolve_model(opt.model_); + printe("\r" LOG_CLR_TO_EOL "Loading model"); + llama_model_ptr model(llama_model_load_from_file(opt.model_.c_str(), opt.model_params)); + if (!model) { + printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str()); + } + + printe("\r" LOG_CLR_TO_EOL); + return model; + } + + // Initializes the context with the specified parameters + llama_context_ptr initialize_context(const llama_model_ptr & model, const Opt & opt) { + llama_context_ptr context(llama_init_from_model(model.get(), opt.ctx_params)); + if (!context) { + printe("%s: error: failed to create the llama_context\n", __func__); + } + + return context; + } + + // Initializes and configures the sampler + llama_sampler_ptr initialize_sampler(const Opt & opt) { + 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(opt.temperature)); + 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 `msg_strs` +static void add_message(const char * role, const std::string & text, LlamaData & llama_data) { + llama_data.msg_strs.push_back(std::move(text)); + llama_data.messages.push_back({ role, llama_data.msg_strs.back().c_str() }); +} + +// Function to apply the chat template and resize `formatted` if needed +static int apply_chat_template(const common_chat_template & tmpl, LlamaData & llama_data, const bool append, bool use_jinja) { + if (use_jinja) { + json messages = json::array(); + for (const auto & msg : llama_data.messages) { + messages.push_back({ + {"role", msg.role}, + {"content", msg.content}, + }); + } + try { + minja::chat_template_inputs tmpl_inputs; + tmpl_inputs.messages = messages; + tmpl_inputs.add_generation_prompt = append; + + minja::chat_template_options tmpl_opts; + tmpl_opts.use_bos_token = false; + tmpl_opts.use_eos_token = false; + + auto result = tmpl.apply(tmpl_inputs, tmpl_opts); + llama_data.fmtted.resize(result.size() + 1); + memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1); + return result.size(); + } catch (const std::exception & e) { + printe("failed to render the chat template: %s\n", e.what()); + return -1; + } + } + int result = llama_chat_apply_template( + tmpl.source().c_str(), llama_data.messages.data(), llama_data.messages.size(), append, + append ? llama_data.fmtted.data() : nullptr, append ? llama_data.fmtted.size() : 0); + if (append && result > static_cast(llama_data.fmtted.size())) { + llama_data.fmtted.resize(result); + result = llama_chat_apply_template(tmpl.source().c_str(), llama_data.messages.data(), + llama_data.messages.size(), append, llama_data.fmtted.data(), + llama_data.fmtted.size()); + } + + return result; +} + +// Function to tokenize the prompt +static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt, + std::vector & prompt_tokens, const LlamaData & llama_data) { + const bool is_first = llama_get_kv_cache_used_cells(llama_data.context.get()) == 0; + + const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true); + prompt_tokens.resize(n_prompt_tokens); + if (llama_tokenize(vocab, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), is_first, + true) < 0) { + printe("failed to tokenize the prompt\n"); + return -1; + } + + 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(LOG_COL_DEFAULT "\n"); + printe("context size exceeded\n"); + return 1; + } + + return 0; +} + +// convert the token to a string +static int convert_token_to_string(const llama_vocab * vocab, const llama_token token_id, std::string & piece) { + char buf[256]; + int n = llama_token_to_piece(vocab, token_id, buf, sizeof(buf), 0, true); + if (n < 0) { + printe("failed to convert token to piece\n"); + return 1; + } + + 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) { + const llama_vocab * vocab = llama_model_get_vocab(llama_data.model.get()); + + std::vector tokens; + if (tokenize_prompt(vocab, prompt, tokens, llama_data) < 0) { + return 1; + } + + // prepare a batch for the prompt + llama_batch batch = llama_batch_get_one(tokens.data(), tokens.size()); + llama_token new_token_id; + while (true) { + check_context_size(llama_data.context, batch); + if (llama_decode(llama_data.context.get(), batch)) { + printe("failed to decode\n"); + return 1; + } + + // 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_vocab_is_eog(vocab, new_token_id)) { + break; + } + + std::string piece; + if (convert_token_to_string(vocab, 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); + } + + printf(LOG_COL_DEFAULT); + return 0; +} + +static int read_user_input(std::string & user_input) { + static const char * prompt_prefix = "> "; +#ifdef WIN32 + printf("\r" LOG_CLR_TO_EOL LOG_COL_DEFAULT "%s", prompt_prefix); + + std::getline(std::cin, user_input); + if (std::cin.eof()) { + printf("\n"); + return 1; + } +#else + std::unique_ptr line(const_cast(linenoise(prompt_prefix)), free); + if (!line) { + return 1; + } + + user_input = line.get(); +#endif + + if (user_input == "/bye") { + return 1; + } + + if (user_input.empty()) { + return 2; + } + +#ifndef WIN32 + linenoiseHistoryAdd(line.get()); +#endif + + return 0; // Should have data in happy path +} + +// Function to generate a response based on the prompt +static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response, + const bool stdout_a_terminal) { + // Set response color + if (stdout_a_terminal) { + printf(LOG_COL_YELLOW); + } + + if (generate(llama_data, prompt, response)) { + printe("failed to generate response\n"); + return 1; + } + + // End response with color reset and newline + printf("\n%s", stdout_a_terminal ? LOG_COL_DEFAULT : ""); + return 0; +} + +// Helper function to apply the chat template and handle errors +static int apply_chat_template_with_error_handling(const common_chat_template & tmpl, LlamaData & llama_data, const bool append, int & output_length, bool use_jinja) { + const int new_len = apply_chat_template(tmpl, llama_data, append, use_jinja); + if (new_len < 0) { + printe("failed to apply the chat template\n"); + return -1; + } + + output_length = new_len; + return 0; +} + +// Helper function to handle user input +static int handle_user_input(std::string & user_input, const std::string & user) { + if (!user.empty()) { + user_input = user; + return 0; // No need for interactive input + } + + return read_user_input(user_input); // Returns true if input ends the loop +} + +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 bool is_stdout_a_terminal() { +#if defined(_WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + DWORD mode; + return GetConsoleMode(hStdout, &mode); +#else + return isatty(STDOUT_FILENO); +#endif +} + +// Function to handle user input +static int get_user_input(std::string & user_input, const std::string & user) { + while (true) { + const int ret = handle_user_input(user_input, user); + if (ret == 1) { + return 1; + } + + if (ret == 2) { + continue; + } + + break; + } + + return 0; +} + +// Main chat loop function +static int chat_loop(LlamaData & llama_data, const std::string & user, bool use_jinja) { + int prev_len = 0; + llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get())); + auto chat_templates = common_chat_templates_from_model(llama_data.model.get(), ""); + GGML_ASSERT(chat_templates.template_default); + static const bool stdout_a_terminal = is_stdout_a_terminal(); + while (true) { + // Get user input + std::string user_input; + if (get_user_input(user_input, user) == 1) { + return 0; + } + + add_message("user", user.empty() ? user_input : user, llama_data); + int new_len; + if (apply_chat_template_with_error_handling(*chat_templates.template_default, llama_data, true, new_len, use_jinja) < 0) { + return 1; + } + + std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len); + std::string response; + if (generate_response(llama_data, prompt, response, stdout_a_terminal)) { + return 1; + } + + if (!user.empty()) { + break; + } + + add_message("assistant", response, llama_data); + if (apply_chat_template_with_error_handling(*chat_templates.template_default, llama_data, false, prev_len, use_jinja) < 0) { + return 1; + } + } + + return 0; +} + +static void log_callback(const enum ggml_log_level level, const char * text, void * p) { + const Opt * opt = static_cast(p); + if (opt->verbose || level == GGML_LOG_LEVEL_ERROR) { + printe("%s", text); + } +} + +static std::string read_pipe_data() { + std::ostringstream result; + result << std::cin.rdbuf(); // Read all data from std::cin + return result.str(); +} + +static void ctrl_c_handling() { +#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) + struct sigaction sigint_action; + sigint_action.sa_handler = sigint_handler; + sigemptyset(&sigint_action.sa_mask); + sigint_action.sa_flags = 0; + sigaction(SIGINT, &sigint_action, NULL); +#elif defined(_WIN32) + auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { + return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false; + }; + SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); +#endif +} + +int main(int argc, const char ** argv) { + ctrl_c_handling(); + Opt opt; + const int ret = opt.init(argc, argv); + if (ret == 2) { + return 0; + } else if (ret) { + return 1; + } + + if (!is_stdin_a_terminal()) { + if (!opt.user.empty()) { + opt.user += "\n\n"; + } + + opt.user += read_pipe_data(); + } + + llama_log_set(log_callback, &opt); + LlamaData llama_data; + if (llama_data.init(opt)) { + return 1; + } + + if (chat_loop(llama_data, opt.user, opt.use_jinja)) { + return 1; + } + + return 0; +} diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index cc6ed8554..0f50e50de 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET save-load-state) +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 ef952e2bd..cf7cbd815 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -1,16 +1,17 @@ +#include "arg.h" #include "common.h" #include "llama.h" #include #include -#include int main(int argc, char ** argv) { - gpt_params params; + common_params params; params.prompt = "The quick brown fox"; + params.sampling.seed = 1234; - if (!gpt_params_parse(argc, argv, params)) { + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { return 1; } @@ -24,28 +25,43 @@ int main(int argc, char ** argv) { std::string result0; std::string result1; + std::string result2; // init - llama_model * model; - llama_context * ctx; + common_init_result llama_init = common_init_from_params(params); + + llama_model * model = llama_init.model.get(); + llama_context * ctx = llama_init.context.get(); - std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr || ctx == nullptr) { fprintf(stderr, "%s : failed to init\n", __func__); return 1; } + auto sparams = llama_sampler_chain_default_params(); + + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed)); + // tokenize prompt - auto tokens = llama_tokenize(ctx, params.prompt, true); + auto tokens = common_tokenize(ctx, params.prompt, true); + + // prepare the batch + llama_batch batch = llama_batch_init(tokens.size(), 0, 1); + for (size_t i = 0; i < tokens.size(); i++) { + common_batch_add(batch, tokens[i], i, {0}, false); + } + batch.logits[batch.n_tokens - 1] = true; // generate next token // evaluate prompt - llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0)); - n_past += tokens.size(); + llama_decode(ctx, batch); + n_past += batch.n_tokens; // save state (rng, logits, embedding and kv_cache) to file { - std::vector state_mem(llama_get_state_size(ctx)); - const size_t written = llama_copy_state_data(ctx, state_mem.data()); + std::vector state_mem(llama_state_get_size(ctx)); + const size_t written = llama_state_get_data(ctx, state_mem.data(), state_mem.size()); FILE *fp_write = fopen("dump_state.bin", "wb"); fwrite(state_mem.data(), 1, written, fp_write); @@ -61,25 +77,18 @@ int main(int argc, char ** argv) { printf("\nfirst run: %s", params.prompt.c_str()); for (auto i = 0; i < params.n_predict; i++) { - auto * logits = llama_get_logits(ctx); - auto n_vocab = llama_n_vocab(model); - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - auto next_token = llama_sample_token(ctx, &candidates_p); - auto next_token_str = llama_token_to_piece(ctx, next_token); + auto next_token = llama_sampler_sample(smpl, ctx, -1); + auto next_token_str = common_token_to_piece(ctx, next_token); printf("%s", next_token_str.c_str()); result0 += next_token_str; - if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); - llama_free(ctx); - llama_free_model(model); + llama_batch_free(batch); return 1; } n_past += 1; @@ -87,26 +96,28 @@ int main(int argc, char ** argv) { printf("\n\n"); - // free old context - llama_free(ctx); - // make new context - auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params)); + llama_context * ctx2 = llama_init_from_model(model, common_context_params_to_llama(params)); + + llama_sampler * smpl2 = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sampling.seed)); printf("\nsecond run: %s", params.prompt.c_str()); // load state (rng, logits, embedding and kv_cache) from file { - std::vector state_mem(llama_get_state_size(ctx2)); + std::vector state_mem; FILE * fp_read = fopen("dump_state.bin", "rb"); + fseek(fp_read, 0, SEEK_END); + state_mem.resize(ftell(fp_read)); + fseek(fp_read, 0, SEEK_SET); const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read); fclose(fp_read); - if (read != llama_set_state_data(ctx2, state_mem.data())) { + if (read != llama_state_set_data(ctx2, state_mem.data(), state_mem.size())) { fprintf(stderr, "\n%s : failed to read state\n", __func__); - llama_free(ctx2); - llama_free_model(model); return 1; } @@ -118,24 +129,99 @@ int main(int argc, char ** argv) { // second run for (auto i = 0; i < params.n_predict; i++) { - auto * logits = llama_get_logits(ctx2); - auto n_vocab = llama_n_vocab(model); - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; - auto next_token = llama_sample_token(ctx2, &candidates_p); - auto next_token_str = llama_token_to_piece(ctx2, next_token); + auto next_token = llama_sampler_sample(smpl2, ctx2, -1); + auto next_token_str = common_token_to_piece(ctx2, next_token); printf("%s", next_token_str.c_str()); result1 += next_token_str; - if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) { + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {0}, true); + + if (llama_decode(ctx2, batch)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); - llama_free(ctx2); - llama_free_model(model); + llama_batch_free(batch); + return 1; + } + n_past += 1; + } + + printf("\n\n"); + + if (result0 != result1) { + fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__); + return 1; + } + + // make new context + llama_context * ctx3 = llama_init_from_model(model, common_context_params_to_llama(params)); + + llama_sampler * smpl3 = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sampling.seed)); + + printf("\nsingle seq run: %s", params.prompt.c_str()); + + // load state (rng, logits, embedding and kv_cache) from file + { + std::vector state_mem; + + FILE * fp_read = fopen("dump_state.bin", "rb"); + fseek(fp_read, 0, SEEK_END); + state_mem.resize(ftell(fp_read)); + fseek(fp_read, 0, SEEK_SET); + const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read); + fclose(fp_read); + + if (read != llama_state_set_data(ctx3, state_mem.data(), state_mem.size())) { + fprintf(stderr, "\n%s : failed to read state\n", __func__); + return 1; + } + + fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size()); + } + + // restore state (last tokens) + n_past = n_past_saved; + + // save seq 0 and load into seq 1 + { + // save kv of seq 0 + std::vector seq_store(llama_state_seq_get_size(ctx3, 0)); + const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), seq_store.size(), 0); + if (ncopy != seq_store.size()) { + fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size()); + return 1; + } + fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy); + + // erase whole kv + llama_kv_cache_clear(ctx3); + fprintf(stderr, "%s : kv cache cleared\n", __func__); + + // restore kv into seq 1 + const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), seq_store.size(), 1); + if (nset != seq_store.size()) { + fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size()); + return 1; + } + fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset); + } + + // third run with seq 1 instead of 0 + for (auto i = 0; i < params.n_predict; i++) { + auto next_token = llama_sampler_sample(smpl3, ctx3, -1); + auto next_token_str = common_token_to_piece(ctx3, next_token); + + printf("%s", next_token_str.c_str()); + result2 += next_token_str; + + common_batch_clear(batch); + common_batch_add(batch, next_token, n_past, {1}, true); + + if (llama_decode(ctx3, batch)) { + fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_batch_free(batch); return 1; } n_past += 1; @@ -143,11 +229,14 @@ int main(int argc, char ** argv) { printf("\n"); - llama_free(ctx2); - llama_free_model(model); + llama_sampler_free(smpl); + llama_sampler_free(smpl2); + llama_sampler_free(smpl3); - if (result0 != result1) { - fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__); + llama_batch_free(batch); + + if (result0 != result2) { + fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__); return 1; } diff --git a/examples/server-llama2-13B.sh b/examples/server-llama2-13B.sh index 17fedc2b1..4ce79b7fa 100755 --- a/examples/server-llama2-13B.sh +++ b/examples/server-llama2-13B.sh @@ -16,7 +16,7 @@ GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}" # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS -./server $GEN_OPTIONS \ +./llama-server $GEN_OPTIONS \ --model "$MODEL" \ --threads "$N_THREAD" \ --rope-freq-scale 1.0 \ diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index cc13b2d63..1b7cc8c13 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,13 +1,50 @@ -set(TARGET server) -option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) -include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h) -install(TARGETS ${TARGET} RUNTIME) -target_compile_definitions(${TARGET} PRIVATE - SERVER_VERBOSE=$ +set(TARGET llama-server) + +option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF) + +include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR}) + +if (MINGW) + # fix: https://github.com/ggerganov/llama.cpp/actions/runs/9651004652/job/26617901362?pr=8006 + add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER}) +endif() + +set(TARGET_SRCS + server.cpp + utils.hpp + httplib.h ) -target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +set(PUBLIC_ASSETS + index.html.gz + loading.html +) + +foreach(asset ${PUBLIC_ASSETS}) + set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}") + set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp") + list(APPEND TARGET_SRCS ${output}) + add_custom_command( + DEPENDS "${input}" + 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 ${TARGET} RUNTIME) + +target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}) +target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) + +if (LLAMA_SERVER_SSL) + find_package(OpenSSL REQUIRED) + target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto) + target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT) +endif() + if (WIN32) TARGET_LINK_LIBRARIES(${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 397ee8252..d0b262f0e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -5,76 +5,251 @@ Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/ Set of LLM REST APIs and a simple web front end to interact with llama.cpp. **Features:** - * LLM inference of F16 and quantum models on GPU and CPU + * LLM inference of F16 and quantized models on GPU and CPU * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes + * Reranking endoint (WIP: https://github.com/ggerganov/llama.cpp/pull/9510) * Parallel decoding with multi-user support * Continuous batching * Multimodal (wip) * Monitoring endpoints + * Schema-constrained JSON response format The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216). -**Command line options:** +## Usage -- `--threads N`, `-t N`: Set the number of threads to use during generation. -- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. -- `--threads-http N`: number of threads in the http server pool to process requests (default: `std::thread::hardware_concurrency()`) -- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). -- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. -- `-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. The size may differ in other models, for example, baichuan models were build with a context of 4096. -- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. -- `-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. Requires cuBLAS. -- `-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. Requires cuBLAS. -- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`. -- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended. -- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. -- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. -- `--numa STRATEGY`: Attempt one of the below optimization strategies that help on some NUMA systems -- `--numa distribute`: Spread execution evenly over all nodes -- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on -- `--numa 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 + -- `--numa`: Attempt optimizations that help on some NUMA systems. -- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. -- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. -- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. -- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. -- `--port`: Set the port to listen. Default: `8080`. -- `--path`: path from which to serve static files (default examples/server/public) -- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys. -- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s. -- `--embedding`: Enable embedding extraction, Default: disabled. -- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) -- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) -- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) -- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. -- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w` -- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` -- `-n N, --n-predict N`: Set the maximum tokens to predict (default: -1) -- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included. -- `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled) -- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) -- `--log-disable`: Output logs to stdout only, default: enabled. -- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json) +**Common params** + +| Argument | Explanation | +| -------- | ----------- | +| `-h, --help, --usage` | print usage and exit | +| `--version` | show version and build info | +| `--verbose-prompt` | print a verbose prompt before generation (default: false) | +| `-t, --threads N` | number of threads to use during generation (default: -1)
(env: LLAMA_ARG_THREADS) | +| `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) | +| `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") | +| `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask | +| `--cpu-strict <0\|1>` | use strict CPU placement (default: 0)
| +| `--prio N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)
| +| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)
| +| `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) | +| `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch | +| `--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: 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) | +| `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) | +| `-fa, --flash-attn` | enable Flash Attention (default: disabled)
(env: LLAMA_ARG_FLASH_ATTN) | +| `--no-perf` | disable internal libllama performance timings (default: false)
(env: LLAMA_ARG_NO_PERF) | +| `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | +| `--no-escape` | do not process escape sequences | +| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model
(env: LLAMA_ARG_ROPE_SCALING_TYPE) | +| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N
(env: LLAMA_ARG_ROPE_SCALE) | +| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | +| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N
(env: LLAMA_ARG_ROPE_FREQ_SCALE) | +| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)
(env: LLAMA_ARG_YARN_ORIG_CTX) | +| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | +| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | +| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | +| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | +| `-dkvc, --dump-kv-cache` | verbose print of the KV cache | +| `-nkvo, --no-kv-offload` | disable KV offload
(env: LLAMA_ARG_NO_KV_OFFLOAD) | +| `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | +| `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | +| `-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) | +| `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)
(env: LLAMA_ARG_MAIN_GPU) | +| `--check-tensors` | check model tensor data for invalid values (default: false) | +| `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false | +| `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) | +| `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) | +| `--control-vector FNAME` | add a control vector
note: this argument can be repeated to add multiple control vectors | +| `--control-vector-scaled FNAME SCALE` | add a control vector with user defined scaling SCALE
note: this argument can be repeated to add multiple scaled control vectors | +| `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive | +| `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)
(env: LLAMA_ARG_MODEL) | +| `-mu, --model-url MODEL_URL` | model download url (default: unused)
(env: LLAMA_ARG_MODEL_URL) | +| `-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) | +| `--log-disable` | Log disable | +| `--log-file FNAME` | Log to file | +| `--log-colors` | Enable colored logging
(env: LLAMA_LOG_COLORS) | +| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) | +| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored.
(env: LLAMA_LOG_VERBOSITY) | +| `--log-prefix` | Enable prefx in log messages
(env: LLAMA_LOG_PREFIX) | +| `--log-timestamps` | Enable timestamps in log messages
(env: LLAMA_LOG_TIMESTAMPS) | + + +**Sampling params** + +| Argument | Explanation | +| -------- | ----------- | +| `--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: dkypmxt) | +| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | +| `--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` | 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) | +| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | +| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | +| `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | +| `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | +| `--grammar-file FNAME` | file to read grammar from | +| `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | +| `--jinja` | Enable experimental Jinja templating engine (required for tool use) | + +**Example-specific params** + +| Argument | Explanation | +| -------- | ----------- | +| `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)
(env: LLAMA_ARG_NO_CONTEXT_SHIFT) | +| `-sp, --special` | special tokens output enabled (default: false) | +| `--no-warmup` | skip warming up the model with an empty run | +| `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | +| `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING) | +| `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | +| `-nocb, --no-cont-batching` | disable continuous batching
(env: LLAMA_ARG_NO_CONT_BATCHING) | +| `-a, --alias STRING` | set alias for model name (to be used by REST API)
(env: LLAMA_ARG_ALIAS) | +| `--host HOST` | ip address to listen (default: 127.0.0.1)
(env: LLAMA_ARG_HOST) | +| `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | +| `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | +| `--no-webui` | Disable the Web UI (default: enabled)
(env: LLAMA_ARG_NO_WEBUI) | +| `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS) | +| `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING) | +| `--api-key KEY` | API key to use for authentication (default: none)
(env: LLAMA_API_KEY) | +| `--api-key-file FNAME` | path to file containing API keys (default: none) | +| `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key
(env: LLAMA_ARG_SSL_KEY_FILE) | +| `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate
(env: LLAMA_ARG_SSL_CERT_FILE) | +| `-to, --timeout N` | server read/write timeout in seconds (default: 600)
(env: LLAMA_ARG_TIMEOUT) | +| `--threads-http N` | number of threads used to process HTTP requests (default: -1)
(env: LLAMA_ARG_THREADS_HTTP) | +| `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
(env: LLAMA_ARG_CACHE_REUSE) | +| `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | +| `--slots` | enable slots monitoring endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | +| `--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
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)
(env: LLAMA_ARG_DRAFT_MAX) | +| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5)
(env: LLAMA_ARG_DRAFT_MIN) | +| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9)
(env: LLAMA_ARG_DRAFT_P_MIN) | +| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE_DRAFT) | +| `-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
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | +| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_MODEL_DRAFT) | + + +Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. + +Example usage of docker compose with environment variables: + +```yml +services: + llamacpp-server: + image: ghcr.io/ggerganov/llama.cpp:server + ports: + - 8080:8080 + volumes: + - ./models:/models + environment: + # alternatively, you can use "LLAMA_ARG_MODEL_URL" to download the model + LLAMA_ARG_MODEL: /models/my_model.gguf + LLAMA_ARG_CTX_SIZE: 4096 + LLAMA_ARG_N_PARALLEL: 2 + LLAMA_ARG_ENDPOINT_METRICS: 1 + LLAMA_ARG_PORT: 8080 +``` ## Build -server is build alongside everything else from the root of the project - -- Using `make`: - - ```bash - make - ``` +`llama-server` is built alongside everything else from the root of the project - Using `CMake`: ```bash - cmake --build . --config Release + cmake -B build + cmake --build build --config Release -t llama-server ``` + Binary is at `./build/bin/llama-server` + +## Build with SSL + +`llama-server` can also be built with SSL support using OpenSSL 3 + +- Using `CMake`: + + ```bash + cmake -B build -DLLAMA_SERVER_SSL=ON + 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: +- `react` 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.gz +npm run build +``` +After `public/index.html.gz` has been generated we need to generate the c++ +headers (like build/examples/server/index.html.gz.hpp) that will be included +by server.cpp. This is done by building `llama-server` as described in the +[build](#build) section above. + +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: @@ -82,13 +257,13 @@ To get started right away, run the following command, making sure to use the cor ### Unix-based systems (Linux, macOS, etc.) ```bash -./server -m models/7B/ggml-model.gguf -c 2048 +./llama-server -m models/7B/ggml-model.gguf -c 2048 ``` ### Windows ```powershell -server.exe -m models\7B\ggml-model.gguf -c 2048 +llama-server.exe -m models\7B\ggml-model.gguf -c 2048 ``` The above command will start a server that by default listens on `127.0.0.1:8080`. @@ -97,15 +272,15 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can ### Docker ```bash -docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 +docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 # or, with CUDA: -docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 +docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 ``` ## Testing with CURL -Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS. +Using [curl](https://curl.se/). On Windows, `curl.exe` should be available in the base OS. ```sh curl --request POST \ @@ -129,23 +304,23 @@ mkdir llama-client cd llama-client ``` -Create a index.js file and put inside this: +Create an index.js file and put this inside: ```javascript -const prompt = `Building a website can be done in 10 simple steps:`; +const prompt = "Building a website can be done in 10 simple steps:" -async function Test() { +async function test() { let response = await fetch("http://127.0.0.1:8080/completion", { - method: 'POST', + method: "POST", body: JSON.stringify({ prompt, - n_predict: 512, + n_predict: 64, }) }) console.log((await response.json()).content) } -Test() +test() ``` And run it: @@ -156,358 +331,1007 @@ node index.js ## API Endpoints -- **GET** `/health`: Returns the current state of the server: - - 503 -> `{"status": "loading model"}` if the model is still being loaded. - - 500 -> `{"status": "error"}` if the model failed to load. - - 200 -> `{"status": "ok", "slots_idle": 1, "slots_processing": 2 }` if the model is successfully loaded and the server is ready for further requests mentioned below. - - 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available. - - 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slot are currently available. +### GET `/health`: Returns heath check result - If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set. +**Response format** -- **POST** `/completion`: Given a `prompt`, it returns the predicted completion. +- HTTP status code 503 + - Body: `{"error": {"code": 503, "message": "Loading model", "type": "unavailable_error"}}` + - Explanation: the model is still being loaded. +- HTTP status code 200 + - Body: `{"status": "ok" }` + - Explanation: the model is successfully loaded and the server is ready. - *Options:* +### POST `/completion`: Given a `prompt`, it returns the predicted completion. - `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does. +> [!IMPORTANT] +> +> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/completions` instead. - `temperature`: Adjust the randomness of the generated text (default: 0.8). +*Options:* - `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` (default: 0.0, 0.0 = disabled). +`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: - `dynatemp_exponent`: Dynamic temperature exponent (default: 1.0). + - 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` - `top_k`: Limit the next token selection to the K most probable tokens (default: 40). +These input shapes and data type are allowed for `prompt`: - `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). + - Single string: `"string"` + - Single sequence of tokens: `[12, 34, 56]` + - Mixed tokens and strings: `[12, 34, "string", 56, 78]` - `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05). +Multiple prompts are also supported. In this case, the completion result will be an array. - `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, -1 = infinity). + - Only strings: `["string1", "string2"]` + - Strings and sequences of tokens: `["string1", [12, 34, 56]]` + - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]` - `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. - By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt. +`temperature`: Adjust the randomness of the generated text. Default: `0.8` - `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. +`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. - `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: []). +`dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0` - `tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled). +`top_k`: Limit the next token selection to the K most probable tokens. Default: `40` - `typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled). +`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95` - `repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1). +`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05` - `repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size). +`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. - `penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true). +`n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0` - `presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled). +`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. - `frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled); +`stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`. - `penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`). +`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: `[]` - `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0). +`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled. - `mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0). +`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1` - `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1). +`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size. - `grammar`: Set grammar for grammar-based sampling (default: no grammar) +`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled. - `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed). +`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled. - `ignore_eos`: Ignore end of stream token and continue generating (default: false). +`dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled. - `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: []). +`dry_base`: Set the DRY repetition penalty base value. Default: `1.75` - `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0) +`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` - `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum (default: 0) +`dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size. - `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. +`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']` - `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1) +`xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled. - `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. (default: false) +`xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC) - `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) +`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0. - `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", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values) +`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0` -### Result JSON +`mirostat_eta`: Set the Mirostat learning rate, parameter eta. Default: `0.1` -- Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. +`grammar`: Set grammar for grammar-based sampling. Default: no grammar -- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure: +`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. -```json -{ - "content": "", - "probs": [ - { - "prob": float, - "tok_str": "" - }, - { - "prob": float, - "tok_str": "" - }, +`seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed. + +`ignore_eos`: Ignore end of stream token and continue generating. Default: `false` + +`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: `[]` + +`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` + +`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0` + +`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. + +`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. + +`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` + +`return_tokens`: Return the raw generated token ids in the `tokens` field. Otherwise `tokens` remains empty. Default: `false` + +`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` + +`post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain. + +`response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. Note that fields with a slash will be unnested; for example, `generation_settings/n_predict` will move the field `n_predict` from the `generation_settings` object to the root of the response and give it a new name. + +`lora`: A list of LoRA adapters to be applied to this specific request. Each object in the list must contain `id` and `scale` fields. For example: `[{"id": 0, "scale": 0.5}, {"id": 1, "scale": 1.1}]`. If a LoRA adapter is not specified in the list, its scale will default to `0.0`. Please note that requests with different LoRA configurations will not be batched together, which may result in performance degradation. + +**Response format** + +- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support. + +- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements: + ``` + { + "content": "", + "tokens": [ generated token ids if requested ], ... - ] -}, -``` - -Notice that each `probs` is an array of length `n_probs`. + "probs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + ... + ] + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + ... + ] + }, + ... + ] + }, + ``` + Please note that if `post_sampling_probs` is set to `true`: + - `logprob` will be replaced with `prob`, with the value between 0.0 and 1.0 + - `top_logprobs` will be replaced with `top_probs`. Each element contains: + - `id`: token ID + - `token`: token in string + - `bytes`: token in bytes + - `prob`: token probability, with the value between 0.0 and 1.0 + - Number of elements in `top_probs` may be less than `n_probs` - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. +- `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request. - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.). -- `model`: The path to the model loaded with `-m` -- `prompt`: The provided `prompt` -- `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token -- `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered -- `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided +- `model`: The model alias (for model path, please use `/props` endpoint) +- `prompt`: The processed `prompt` (special tokens may be added) +- `stop_type`: Indicating whether the completion has stopped. Possible values are: + - `none`: Generating (not stopped) + - `eos`: Stopped because it encountered the EOS token + - `limit`: Stopped because `n_predict` tokens were generated before stop words or EOS was encountered + - `word`: Stopped due to encountering a stopping word from `stop` JSON array provided - `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word) - `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second` - `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`) - `tokens_evaluated`: Number of tokens evaluated in total from the prompt - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) -- **POST** `/tokenize`: Tokenize a given text. - *Options:* +### POST `/tokenize`: Tokenize a given text - `content`: Set the text to tokenize. +*Options:* - Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`. +`content`: (Required) The text to tokenize. -- **POST** `/detokenize`: Convert tokens to text. +`add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false` - *Options:* +`with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs. Default: `false` - `tokens`: Set the tokens to detokenize. +**Response:** -- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does. +Returns a JSON object with a `tokens` field containing the tokenization result. The `tokens` array contains either just token IDs or objects with `id` and `piece` fields, depending on the `with_pieces` parameter. The piece field is a string if the piece is valid unicode or a list of bytes otherwise. - *Options:* - - `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. - -- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. - - *Options:* - - `input_prefix`: Set the prefix of the code to infill. - - `input_suffix`: Set the suffix of the code to infill. - - It also accepts all the options of `/completion` except `stream` and `prompt`. - -- **GET** `/props`: Return current server settings. - -### Result JSON +If `with_pieces` is `false`: ```json { - "assistant_name": "", - "user_name": "", - "default_generation_settings": { ... }, - "total_slots": 1 + "tokens": [123, 456, 789] } ``` -- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots. -- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. -- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. -- `total_slots` - the total number of slots for process requests (defined by `--parallel` option) +If `with_pieces` is `true`: +```json +{ + "tokens": [ + {"id": 123, "piece": "Hello"}, + {"id": 456, "piece": " world"}, + {"id": 789, "piece": "!"} + ] +} +``` -- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. 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 ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. +With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k +``` +{ + "tokens": [ + {"id": 198, "piece": [195]}, // hex C3 + {"id": 164, "piece": [161]} // hex A1 + ] +} +``` - *Options:* +### POST `/detokenize`: Convert tokens to text - 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 are `mirostat` are supported. +*Options:* - *Examples:* +`tokens`: Set the tokens to detokenize. - You can use either Python `openai` library with appropriate checkpoints: +### POST `/apply-template`: Apply chat template to a conversation - ```python - import openai +Uses the server's prompt template formatting functionality to convert chat messages to a single string expected by a chat model as input, but does not perform inference. Instead, the prompt string is returned in the `prompt` field of the JSON response. The prompt can then be modified as desired (for example, to insert "Sure!" at the beginning of the model's response) before sending to `/completion` to generate the chat response. - client = openai.OpenAI( - base_url="http://localhost:8080/v1", # "http://:port" - api_key = "sk-no-key-required" - ) +*Options:* - 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"} - ] - ) +`messages`: (Required) Chat turns in the same format as `/v1/chat/completions`. - print(completion.choices[0].message) - ``` +**Response format** - ... or raw HTTP requests: +Returns a JSON object with a field `prompt` containing a string of the input messages formatted according to the model's chat template format. - ```shell - curl http://localhost:8080/v1/chat/completions \ +### POST `/embedding`: Generate embedding of a given text + +> [!IMPORTANT] +> +> This endpoint is **not** OAI-compatible. For OAI-compatible client, use `/v1/embeddings` instead. + +The same as [the embedding example](../embedding) does. + +*Options:* + +`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. + +### 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:* + +`query`: The query against which the documents will be ranked. + +`documents`: An array strings representing the documents to be ranked. + +*Aliases:* + - `/rerank` + - `/v1/rerank` + - `/v1/reranking` + +*Examples:* + +```shell +curl http://127.0.0.1:8012/v1/rerank \ -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" - } - ] - }' - ``` + "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** `/v1/embeddings`: OpenAI-compatible embeddings API. +### POST `/infill`: For code infilling. - *Options:* +Takes a prefix and a suffix and returns the predicted completion as stream. - See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). +*Options:* - *Examples:* +- `input_prefix`: Set the prefix of the code to infill. +- `input_suffix`: Set the suffix of the code to infill. +- `input_extra`: Additional context inserted before the FIM prefix. +- `prompt`: Added after the `FIM_MID` token - - input as string +`input_extra` is array of `{"filename": string, "text": string}` objects. - ```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" - }' - ``` +The endpoint also accepts all the options of `/completion`. - - `input` as string array +If the model has `FIM_REPO` and `FIM_FILE_SEP` tokens, the [repo-level pattern](https://arxiv.org/pdf/2409.12186) is used: - ```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" - }' - ``` +```txt +myproject +{chunk 0 filename} +{chunk 0 text} +{chunk 1 filename} +{chunk 1 text} +... +filename +[input_prefix][input_suffix][prompt] +``` -- **GET** `/slots`: Returns the current slots processing state. Can be disabled with `--slots-endpoint-disable`. +If the tokens are missing, then the extra context is simply prefixed at the start: -### Result JSON +```txt +[input_extra][input_prefix][input_suffix][prompt] +``` + +### **GET** `/props`: Get server global properties. + +This endpoint is public (no API key check). By default, it is read-only. To make POST request to change global properties, you need to start server with `--props` + +**Response format** ```json -[ - { - "dynatemp_exponent": 1.0, - "dynatemp_range": 0.0, - "frequency_penalty": 0.0, - "grammar": "", - "id": 0, - "ignore_eos": false, - "logit_bias": [], - "min_p": 0.05000000074505806, - "mirostat": 0, - "mirostat_eta": 0.10000000149011612, - "mirostat_tau": 5.0, - "model": "llama-2-7b-32k-instruct.Q2_K.gguf", - "n_ctx": 2048, - "n_keep": 0, - "n_predict": 100000, - "n_probs": 0, - "next_token": { - "has_next_token": true, - "n_remain": -1, - "num_tokens_predicted": 0, - "stopped_eos": false, - "stopped_limit": false, - "stopped_word": false, - "stopping_word": "" - }, - "penalize_nl": true, - "penalty_prompt_tokens": [], - "presence_penalty": 0.0, - "prompt": "Say hello to llama.cpp", - "repeat_last_n": 64, - "repeat_penalty": 1.100000023841858, - "samplers": [ - "top_k", - "tfs_z", - "typical_p", - "top_p", - "min_p", - "temperature" - ], - "seed": 42, - "state": 1, - "stop": [ - "\n" - ], - "stream": false, - "task_id": 0, - "temperature": 0.0, - "tfs_z": 1.0, - "top_k": 40, - "top_p": 0.949999988079071, - "typical_p": 1.0, - "use_penalty_prompt_tokens": false +{ + "default_generation_settings": { + "id": 0, + "id_task": -1, + "n_ctx": 1024, + "speculative": false, + "is_processing": false, + "params": { + "n_predict": -1, + "seed": 4294967295, + "temperature": 0.800000011920929, + "dynatemp_range": 0.0, + "dynatemp_exponent": 1.0, + "top_k": 40, + "top_p": 0.949999988079071, + "min_p": 0.05000000074505806, + "xtc_probability": 0.0, + "xtc_threshold": 0.10000000149011612, + "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, + "dry_sequence_breakers": [ + "\n", + ":", + "\"", + "*" + ], + "mirostat": 0, + "mirostat_tau": 5.0, + "mirostat_eta": 0.10000000149011612, + "stop": [], + "max_tokens": -1, + "n_keep": 0, + "n_discard": 0, + "ignore_eos": false, + "stream": true, + "n_probs": 0, + "min_keep": 0, + "grammar": "", + "samplers": [ + "dry", + "top_k", + "typ_p", + "top_p", + "min_p", + "xtc", + "temperature" + ], + "speculative.n_max": 16, + "speculative.n_min": 5, + "speculative.p_min": 0.8999999761581421, + "timings_per_token": false + }, + "prompt": "", + "next_token": { + "has_next_token": true, + "has_new_line": false, + "n_remain": -1, + "n_decoded": 0, + "stopping_word": "" } + }, + "total_slots": 1, + "model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", + "chat_template": "...", + "build_info": "b(build number)-(build commit hash)" +} +``` + +- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint. +- `total_slots` - the total number of slots for process requests (defined by `--parallel` option) +- `model_path` - the path to model file (same with `-m` argument) +- `chat_template` - the model's original Jinja2 prompt template + +### POST `/props`: Change server global properties. + +To use this endpoint with POST method, you need to start server with `--props` + +*Options:* + +- None yet + +### POST `/embeddings`: non-OpenAI-compatible embeddings API + +This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm. + +Note that the response format of this endpoint is different from `/v1/embeddings`. + +*Options:* + +Same as the `/v1/embeddings` endpoint. + +*Examples:* + +Same as the `/v1/embeddings` endpoint. + +**Response format** + +``` +[ + { + "index": 0, + "embedding": [ + [ ... embeddings for token 0 ... ], + [ ... embeddings for token 1 ... ], + [ ... ] + [ ... embeddings for token N-1 ... ], + ] + }, + ... + { + "index": P, + "embedding": [ + [ ... embeddings for token 0 ... ], + [ ... embeddings for token 1 ... ], + [ ... ] + [ ... embeddings for token N-1 ... ], + ] + } ] ``` -- **GET** `/metrics`: [Prometheus](https://prometheus.io/) compatible metrics exporter endpoint if `--metrics` is enabled: +### GET `/slots`: Returns the current slots processing state + +> [!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. + +**Response format** + +Example: + +```json +[ + { + "id": 0, + "id_task": -1, + "n_ctx": 1024, + "speculative": false, + "is_processing": false, + "params": { + "n_predict": -1, + "seed": 4294967295, + "temperature": 0.800000011920929, + "dynatemp_range": 0.0, + "dynatemp_exponent": 1.0, + "top_k": 40, + "top_p": 0.949999988079071, + "min_p": 0.05000000074505806, + "xtc_probability": 0.0, + "xtc_threshold": 0.10000000149011612, + "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, + "dry_sequence_breakers": [ + "\n", + ":", + "\"", + "*" + ], + "mirostat": 0, + "mirostat_tau": 5.0, + "mirostat_eta": 0.10000000149011612, + "stop": [], + "max_tokens": -1, + "n_keep": 0, + "n_discard": 0, + "ignore_eos": false, + "stream": true, + "n_probs": 0, + "min_keep": 0, + "grammar": "", + "samplers": [ + "dry", + "top_k", + "typ_p", + "top_p", + "min_p", + "xtc", + "temperature" + ], + "speculative.n_max": 16, + "speculative.n_min": 5, + "speculative.p_min": 0.8999999761581421, + "timings_per_token": false + }, + "prompt": "", + "next_token": { + "has_next_token": true, + "has_new_line": false, + "n_remain": -1, + "n_decoded": 0, + "stopping_word": "" + } + } +] +``` + +### GET `/metrics`: Prometheus compatible metrics exporter + +This endpoint is only accessible if `--metrics` is set. Available metrics: - `llamacpp:prompt_tokens_total`: Number of prompt tokens processed. - `llamacpp:tokens_predicted_total`: Number of generation tokens processed. - `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s. - `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s. -- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. 1 means 100 percent usage. +- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. `1` means 100 percent usage. - `llamacpp:kv_cache_tokens`: KV-cache tokens. -- `llamacpp:requests_processing`: Number of request processing. -- `llamacpp:requests_deferred`: Number of request deferred. +- `llamacpp:requests_processing`: Number of requests processing. +- `llamacpp:requests_deferred`: Number of requests deferred. -## More examples +### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file. -### Change system prompt on runtime +*Options:* -To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it. +`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. -`prompt`: Specify a context that you want all connecting clients to respect. - -`anti_prompt`: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the `/props` endpoint. - -`assistant_name`: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the `/props` endpoint. +**Response format** ```json { - "system_prompt": { - "prompt": "Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:", - "anti_prompt": "User:", - "assistant_name": "Assistant:" + "id_slot": 0, + "filename": "slot_save_file.bin", + "n_saved": 1745, + "n_written": 14309796, + "timings": { + "save_ms": 49.865 } } ``` -**NOTE**: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option `-spf FNAME` or `--system-prompt-file FNAME`. +### POST `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file. + +*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. + +**Response format** + +```json +{ + "id_slot": 0, + "filename": "slot_save_file.bin", + "n_restored": 1745, + "n_read": 14309796, + "timings": { + "restore_ms": 42.937 + } +} +``` + +### POST `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot. + +**Response format** + +```json +{ + "id_slot": 0, + "n_erased": 1745 +} +``` + +### GET `/lora-adapters`: Get list of all LoRA adapters + +This endpoint returns the loaded LoRA adapters. You can add adapters using `--lora` when starting the server, for example: `--lora my_adapter_1.gguf --lora my_adapter_2.gguf ...` + +By default, all adapters will be loaded with scale set to 1. To initialize all adapters scale to 0, add `--lora-init-without-apply` + +Please note that this value will be overwritten by the `lora` field for each request. + +If an adapter is disabled, the scale will be set to 0. + +**Response format** + +```json +[ + { + "id": 0, + "path": "my_adapter_1.gguf", + "scale": 0.0 + }, + { + "id": 1, + "path": "my_adapter_2.gguf", + "scale": 0.0 + } +] +``` + +### POST `/lora-adapters`: Set list of LoRA adapters + +This sets the global scale for LoRA adapters. Please note that this value will be overwritten by the `lora` field for each request. + +To disable an adapter, either remove it from the list below, or set scale to 0. + +**Request format** + +To know the `id` of the adapter, use GET `/lora-adapters` + +```json +[ + {"id": 0, "scale": 0.2}, + {"id": 1, "scale": 0.8} +] +``` + +## OpenAI-compatible API Endpoints + +### GET `/v1/models`: OpenAI-compatible Model Info API + +Returns information about the loaded model. See [OpenAI Models API documentation](https://platform.openai.com/docs/api-reference/models). + +The returned list always has one single element. + +By default, model `id` field is the path to model file, specified via `-m`. You can set a custom value for model `id` field via `--alias` argument. For example, `--alias gpt-4o-mini`. + +Example: + +```json +{ + "object": "list", + "data": [ + { + "id": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", + "object": "model", + "created": 1735142223, + "owned_by": "llamacpp", + "meta": { + "vocab_type": 2, + "n_vocab": 128256, + "n_ctx_train": 131072, + "n_embd": 4096, + "n_params": 8030261312, + "size": 4912898304 + } + } + ] +} +``` + +### POST `/v1/completions`: OpenAI-compatible Completions API + +Given an input `prompt`, it returns the predicted completion. Streaming mode is also supported. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. + +*Options:* + +See [OpenAI Completions API documentation](https://platform.openai.com/docs/api-reference/completions). + +llama.cpp `/completion`-specific features such as `mirostat` are supported. + +*Examples:* + +Example usage with `openai` python library: + +```python +import openai + +client = openai.OpenAI( + base_url="http://localhost:8080/v1", # "http://:port" + api_key = "sk-no-key-required" +) + +completion = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8 +) + +print(completion.choices[0].text) +``` + +### POST `/v1/chat/completions`: OpenAI-compatible Chat Completions API + +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:* + +See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). llama.cpp `/completion`-specific features such as `mirostat` are also 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. + +*Examples:* + +You can use either Python `openai` library with appropriate checkpoints: + +```python +import openai + +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"} + ] +) + +print(completion.choices[0].message) +``` + +... 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" +} +] +}' +``` + +*Tool call support* + +[Function calling](https://platform.openai.com/docs/guides/function-calling) is supported for all models (see https://github.com/ggerganov/llama.cpp/pull/9639): + +- Requires `--jinja` flag +- Native tool call formats supported: + - Llama 3.1 / 3.3 (including builtin tools support - tool names for `wolfram_alpha`, `web_search` / `brave_search`, `code_interpreter`), Llama 3.2 + - Functionary v3.1 / v3.2 + - Hermes 2/3, Qwen 2.5 + - Mistral Nemo + - Firefunction v2 + - Command R7B + - DeepSeek R1 (WIP / seems reluctant to call any tools?) + +
+ Show some common templates and which format handler they use + + | Template | Format | + |----------|--------| + | CohereForAI-c4ai-command-r-plus-default.jinja | generic tool calls | + | CohereForAI-c4ai-command-r-plus-rag.jinja | generic tool calls | + | CohereForAI-c4ai-command-r-plus-tool_use.jinja | generic tool calls | + | MiniMaxAI-MiniMax-Text-01.jinja | generic tool calls | + | NexaAIDev-Octopus-v2.jinja | generic tool calls | + | NousResearch-Hermes-2-Pro-Llama-3-8B-default.jinja | generic tool calls | + | NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja | hermes 2 pro tool calls | + | NousResearch-Hermes-2-Pro-Mistral-7B-default.jinja | generic tool calls | + | NousResearch-Hermes-2-Pro-Mistral-7B-tool_use.jinja | hermes 2 pro tool calls | + | NousResearch-Hermes-3-Llama-3.1-70B-default.jinja | generic tool calls | + | NousResearch-Hermes-3-Llama-3.1-70B-tool_use.jinja | hermes 2 pro tool calls | + | OrionStarAI-Orion-14B-Chat.jinja | generic tool calls | + | Qwen-QwQ-32B-Preview.jinja | hermes 2 pro tool calls | + | Qwen-Qwen2-7B-Instruct.jinja | generic tool calls | + | Qwen-Qwen2-VL-7B-Instruct.jinja | generic tool calls | + | Qwen-Qwen2.5-7B-Instruct.jinja | hermes 2 pro tool calls | + | Qwen-Qwen2.5-Math-7B-Instruct.jinja | hermes 2 pro tool calls | + | TheBloke-FusionNet_34Bx2_MoE-AWQ.jinja | generic tool calls | + | abacusai-Fewshot-Metamath-OrcaVicuna-Mistral.jinja | generic tool calls | + | bofenghuang-vigogne-2-70b-chat.jinja | generic tool calls | + | databricks-dbrx-instruct.jinja | generic tool calls | + | deepseek-ai-DeepSeek-Coder-V2-Instruct.jinja | generic tool calls | + | deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja | deepseek r1 tool calls | + | deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja | deepseek r1 tool calls | + | deepseek-ai-DeepSeek-R1-Distill-Qwen-7B.jinja | deepseek r1 tool calls | + | deepseek-ai-DeepSeek-V2.5.jinja | deepseek r1 tool calls | + | deepseek-ai-deepseek-coder-33b-instruct.jinja | generic tool calls | + | google-gemma-2-2b-it.jinja | generic tool calls | + | google-gemma-7b-it.jinja | generic tool calls | + | indischepartij-MiniCPM-3B-OpenHermes-2.5-v2.jinja | generic tool calls | + | mattshumer-Reflection-Llama-3.1-70B.jinja | generic tool calls | + | meetkai-functionary-medium-v3.2.jinja | functionary v3.2 tool calls | + | meta-llama-Llama-3.1-8B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) | + | meta-llama-Llama-3.2-3B-Instruct.jinja | llama 3.x tool calls | + | meta-llama-Llama-3.3-70B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) | + | meta-llama-Meta-Llama-3.1-8B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) | + | microsoft-Phi-3-medium-4k-instruct.jinja | generic tool calls | + | microsoft-Phi-3-mini-4k-instruct.jinja | generic tool calls | + | microsoft-Phi-3-small-8k-instruct.jinja | generic tool calls | + | microsoft-Phi-3.5-mini-instruct.jinja | generic tool calls | + | microsoft-Phi-3.5-vision-instruct.jinja | generic tool calls | + | mistralai-Mistral-7B-Instruct-v0.2.jinja | generic tool calls | + | mistralai-Mistral-Large-Instruct-2407.jinja | mistral nemo tool calls | + | mistralai-Mistral-Large-Instruct-2411.jinja | generic tool calls | + | mistralai-Mistral-Nemo-Instruct-2407.jinja | mistral nemo tool calls | + | mistralai-Mixtral-8x7B-Instruct-v0.1.jinja | generic tool calls | + | mlabonne-AlphaMonarch-7B.jinja | generic tool calls | + | nvidia-Llama-3.1-Nemotron-70B-Instruct-HF.jinja | llama 3.x tool calls (w/ builtin tools) | + | openchat-openchat-3.5-0106.jinja | generic tool calls | + | teknium-OpenHermes-2.5-Mistral-7B.jinja | generic tool calls | + + This table can be generated with: + + ```bash + ./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null + +
+ +- Generic tool call is supported when the template isn't recognized by native format handlers (you'll see `Chat format: Generic` in the logs). + - Use `--chat-template-file` to override the template when appropriate (see examples below) + - Generic support may consume more tokens and be less efficient than a model's native format. + +- Run with: + + ```shell + # Native support: + llama-server --jinja -fa -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M + llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q6_K_L + llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M + llama-server --jinja -fa -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M + + # Native support requires the right template for these GGUFs: + + llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \ + --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B tool_use ) + + llama-server --jinja -fa -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M \ + --chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use ) + + llama-server --jinja -fa -hf bartowski/firefunction-v2-GGUF -hff firefunction-v2-IQ1_M.gguf \ + --chat-template-file <( python scripts/get_chat_template.py fireworks-ai/llama-3-firefunction-v2 tool_use ) + + llama-server --jinja -fa -hf bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L \ + --chat-template-file <( python scripts/get_chat_template.py CohereForAI/c4ai-command-r7b-12-2024 tool_use ) + + # Generic format support + llama-server --jinja -fa -hf bartowski/phi-4-GGUF:Q4_0 + llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q8_0 + llama-server --jinja -fa -hf bartowski/c4ai-command-r-v01-GGUF:Q2_K + ``` + +- Test in CLI: + + ```bash + curl http://localhost:8080/v1/chat/completions -d '{ + "model": "gpt-3.5-turbo", + "tools": [ + { + "type":"function", + "function":{ + "name":"get_current_weather", + "description":"Get the current weather in a given location", + "parameters":{ + "type":"object", + "properties":{ + "location":{ + "type":"string", + "description":"The city and state, e.g. San Francisco, CA" + } + }, + "required":["location"] + } + } + } + ], + "messages": [ + { + "role": "user", + "content": "What is the weather like in Istanbul?." + } + ] + }' + ``` + +
+ Show output + + ```json + { + "choices": [ + { + "finish_reason": "tool", + "index": 0, + "message": { + "content": null, + "tool_calls": [ + { + "name": "python", + "arguments": "{\"code\":\" \\nprint(\\\"Hello, World!\\\")\"}" + } + ], + "role": "assistant" + } + } + ], + "created": 1727287211, + "model": "gpt-3.5-turbo", + "object": "chat.completion", + "usage": { + "completion_tokens": 16, + "prompt_tokens": 44, + "total_tokens": 60 + }, + "id": "chatcmpl-Htbgh9feMmGM0LEH2hmQvwsCxq3c6Ni8" + } + ``` + +
+ +### POST `/v1/embeddings`: OpenAI-compatible embeddings API + +This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm. + +*Options:* + +See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). + +*Examples:* + +- 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" + }' + ``` + +- `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" + }' + ``` + +## More examples ### Interactive mode @@ -526,13 +1350,65 @@ Run with bash: bash chat.sh ``` -### API like OAI +### OAI-like API -The HTTP server supports OAI-like API +The HTTP `llama-server` supports an OAI-like API: https://github.com/openai/openai-openapi + +### API errors + +`llama-server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi + +Example of an error: + +```json +{ + "error": { + "code": 401, + "message": "Invalid API Key", + "type": "authentication_error" + } +} +``` + +Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp: + +**When /metrics or /slots endpoint is disabled** + +```json +{ + "error": { + "code": 501, + "message": "This server does not support metrics endpoint.", + "type": "not_supported_error" + } +} +``` + +**When the server receives invalid grammar via */completions endpoint** + +```json +{ + "error": { + "code": 400, + "message": "Failed to parse grammar", + "type": "invalid_request_error" + } +} +``` + +### 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 -The default location for the static files is `examples/server/public`. 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. +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. Read the documentation in `/completion.js` to see convenient ways to access llama. diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md new file mode 100644 index 000000000..9549795ec --- /dev/null +++ b/examples/server/bench/README.md @@ -0,0 +1,119 @@ +### Server benchmark tools + +Benchmark is using [k6](https://k6.io/). + +##### Install k6 and sse extension + +SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension. + +Example (assuming golang >= 1.21 is installed): +```shell +go install go.k6.io/xk6/cmd/xk6@latest +$GOPATH/bin/xk6 build master \ +--with github.com/phymbert/xk6-sse +``` + +#### Download a dataset + +This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md). + +```shell +wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json +``` + +#### Download a model +Example for PHI-2 + +```shell +../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf +``` + +#### Start the server +The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`. + +Example: +```shell +llama-server --host localhost --port 8080 \ + --model ggml-model-q4_0.gguf \ + --cont-batching \ + --metrics \ + --parallel 8 \ + --batch-size 512 \ + --ctx-size 4096 \ + -ngl 33 +``` + +#### Run the benchmark + +For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run: +```shell +./k6 run script.js --duration 10m --iterations 500 --vus 8 +``` + +The benchmark values can be overridden with: +- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1` +- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480` +- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model` +- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512` +- `SERVER_BENCH_DATASET` path to the benchmark dataset file +- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024` +- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048` + +Note: the local tokenizer is just a string space split, real number of tokens will differ. + +Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/): + +```shell +SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8 +``` + +To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`. + +#### Metrics + +Following metrics are available computed from the OAI chat completions response `usage`: +- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration` +- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens` +- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens` +- `llamacpp_completion_tokens` Trend of `usage.completion_tokens` +- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens` +- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'` +- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'` + +The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`. + +K6 metrics might be compared against [server metrics](../README.md), with: + +```shell +curl http://localhost:8080/metrics +``` + +### Using the CI python script +The `bench.py` script does several steps: +- start the server +- define good variable for k6 +- run k6 script +- extract metrics from prometheus + +It aims to be used in the CI, but you can run it manually: + +```shell +LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/llama-server python bench.py \ + --runner-label local \ + --name local \ + --branch `git rev-parse --abbrev-ref HEAD` \ + --commit `git rev-parse HEAD` \ + --scenario script.js \ + --duration 5m \ + --hf-repo ggml-org/models \ + --hf-file phi-2/ggml-model-q4_0.gguf \ + --model-path-prefix models \ + --parallel 4 \ + -ngl 33 \ + --batch-size 2048 \ + --ubatch-size 256 \ + --ctx-size 4096 \ + --n-prompts 200 \ + --max-prompt-tokens 256 \ + --max-tokens 256 +``` diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py new file mode 100644 index 000000000..5cc6f92ab --- /dev/null +++ b/examples/server/bench/bench.py @@ -0,0 +1,323 @@ +from __future__ import annotations + +import argparse +import json +import os +import re +import signal +import socket +import subprocess +import sys +import threading +import time +import traceback +from contextlib import closing +from datetime import datetime + +import matplotlib +import matplotlib.dates +import matplotlib.pyplot as plt +import requests +from statistics import mean + + +def main(args_in: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description="Start server benchmark scenario") + parser.add_argument("--name", type=str, help="Bench name", required=True) + parser.add_argument("--runner-label", type=str, help="Runner label", required=True) + parser.add_argument("--branch", type=str, help="Branch name", default="detached") + parser.add_argument("--commit", type=str, help="Commit name", default="dirty") + parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0") + parser.add_argument("--port", type=int, help="Server listen host", default="8080") + parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models") + parser.add_argument("--n-prompts", type=int, + help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True) + parser.add_argument("--max-prompt-tokens", type=int, + help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset", + required=True) + parser.add_argument("--max-tokens", type=int, + help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens", + required=True) + parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True) + parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True) + parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True) + parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True) + parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True) + parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True) + parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True) + parser.add_argument("--scenario", type=str, help="Scenario to run", required=True) + parser.add_argument("--duration", type=str, help="Bench scenario", required=True) + + args = parser.parse_args(args_in) + + start_time = time.time() + + # Start the server and performance scenario + try: + server_process = start_server(args) + except Exception: + print("bench: server start error :") + traceback.print_exc(file=sys.stdout) + sys.exit(1) + + # start the benchmark + iterations = 0 + data = {} + try: + start_benchmark(args) + + with open("results.github.env", 'w') as github_env: + # parse output + with open('k6-results.json', 'r') as bench_results: + # Load JSON data from file + data = json.load(bench_results) + for metric_name in data['metrics']: + for metric_metric in data['metrics'][metric_name]: + value = data['metrics'][metric_name][metric_metric] + if isinstance(value, float) or isinstance(value, int): + value = round(value, 2) + data['metrics'][metric_name][metric_metric]=value + github_env.write( + f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n") + iterations = data['root_group']['checks']['success completion']['passes'] + + except Exception: + print("bench: error :") + traceback.print_exc(file=sys.stdout) + + # Stop the server + if server_process: + try: + print(f"bench: shutting down server pid={server_process.pid} ...") + if os.name == 'nt': + interrupt = signal.CTRL_C_EVENT + else: + interrupt = signal.SIGINT + server_process.send_signal(interrupt) + server_process.wait(0.5) + + except subprocess.TimeoutExpired: + print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...") + server_process.kill() # SIGKILL + server_process.wait() + + while is_server_listening(args.host, args.port): + time.sleep(0.1) + + title = (f"llama.cpp {args.name} on {args.runner_label}\n " + f"duration={args.duration} {iterations} iterations") + xlabel = (f"{args.hf_repo}/{args.hf_file}\n" + f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n" + f"branch={args.branch} commit={args.commit}") + + # Prometheus + end_time = time.time() + prometheus_metrics = {} + if is_server_listening("0.0.0.0", 9090): + metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds', + 'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred'] + + for metric in metrics: + resp = requests.get(f"http://localhost:9090/api/v1/query_range", + params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2}) + + with open(f"{metric}.json", 'w') as metric_json: + metric_json.write(resp.text) + + if resp.status_code != 200: + print(f"bench: unable to extract prometheus metric {metric}: {resp.text}") + else: + metric_data = resp.json() + values = metric_data['data']['result'][0]['values'] + timestamps, metric_values = zip(*values) + metric_values = [float(value) for value in metric_values] + prometheus_metrics[metric] = metric_values + timestamps_dt = [str(datetime.fromtimestamp(int(ts))) for ts in timestamps] + plt.figure(figsize=(16, 10), dpi=80) + plt.plot(timestamps_dt, metric_values, label=metric) + plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7) + plt.yticks(fontsize=12, alpha=.7) + + ylabel = f"llamacpp:{metric}" + plt.title(title, + fontsize=14, wrap=True) + plt.grid(axis='both', alpha=.3) + plt.ylabel(ylabel, fontsize=22) + plt.xlabel(xlabel, fontsize=14, wrap=True) + plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator()) + plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S")) + plt.gcf().autofmt_xdate() + + # Remove borders + plt.gca().spines["top"].set_alpha(0.0) + plt.gca().spines["bottom"].set_alpha(0.3) + plt.gca().spines["right"].set_alpha(0.0) + plt.gca().spines["left"].set_alpha(0.3) + + # Save the plot as a jpg image + plt.savefig(f'{metric}.jpg', dpi=60) + plt.close() + + # Mermaid format in case images upload failed + with open(f"{metric}.mermaid", 'w') as mermaid_f: + mermaid = ( + f"""--- +config: + xyChart: + titleFontSize: 12 + width: 900 + height: 600 + themeVariables: + xyChart: + titleColor: "#000000" +--- +xychart-beta + title "{title}" + y-axis "llamacpp:{metric}" + x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))} + line [{', '.join([str(round(float(value), 2)) for value in metric_values])}] + """) + mermaid_f.write(mermaid) + + # 140 chars max for commit status description + bench_results = { + "i": iterations, + "req": { + "p95": round(data['metrics']["http_req_duration"]["p(95)"], 2), + "avg": round(data['metrics']["http_req_duration"]["avg"], 2), + }, + "pp": { + "p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2), + "avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2), + "0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2) if 'prompt_tokens_seconds' in prometheus_metrics else 0, + }, + "tg": { + "p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2), + "avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2), + "0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2) if 'predicted_tokens_seconds' in prometheus_metrics else 0, + }, + } + with open("results.github.env", 'a') as github_env: + github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n") + github_env.write(f"BENCH_ITERATIONS={iterations}\n") + + title = title.replace('\n', ' ') + xlabel = xlabel.replace('\n', ' ') + github_env.write(f"BENCH_GRAPH_TITLE={title}\n") + github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n") + + +def start_benchmark(args): + k6_path = './k6' + if 'BENCH_K6_BIN_PATH' in os.environ: + k6_path = os.environ['BENCH_K6_BIN_PATH'] + k6_args = [ + 'run', args.scenario, + '--no-color', + '--no-connection-reuse', + '--no-vu-connection-reuse', + ] + k6_args.extend(['--duration', args.duration]) + k6_args.extend(['--iterations', args.n_prompts]) + k6_args.extend(['--vus', args.parallel]) + k6_args.extend(['--summary-export', 'k6-results.json']) + k6_args.extend(['--out', 'csv=k6-results.csv']) + args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} " + args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]]) + print(f"bench: starting k6 with: {args}") + k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr) + if k6_completed.returncode != 0: + raise Exception("bench: unable to run k6") + + +def start_server(args): + server_process = start_server_background(args) + + attempts = 0 + max_attempts = 600 + if 'GITHUB_ACTIONS' in os.environ: + max_attempts *= 2 + + while not is_server_listening(args.host, args.port): + attempts += 1 + if attempts > max_attempts: + assert False, "server not started" + print(f"bench: waiting for server to start ...") + time.sleep(0.5) + + attempts = 0 + while not is_server_ready(args.host, args.port): + attempts += 1 + if attempts > max_attempts: + assert False, "server not ready" + print(f"bench: waiting for server to be ready ...") + time.sleep(0.5) + + print("bench: server started and ready.") + return server_process + + +def start_server_background(args): + # Start the server + server_path = '../../../build/bin/llama-server' + if 'LLAMA_SERVER_BIN_PATH' in os.environ: + server_path = os.environ['LLAMA_SERVER_BIN_PATH'] + server_args = [ + '--host', args.host, + '--port', args.port, + ] + server_args.extend(['--hf-repo', args.hf_repo]) + server_args.extend(['--hf-file', args.hf_file]) + server_args.extend(['--n-gpu-layers', args.n_gpu_layers]) + server_args.extend(['--ctx-size', args.ctx_size]) + server_args.extend(['--parallel', args.parallel]) + server_args.extend(['--batch-size', args.batch_size]) + server_args.extend(['--ubatch-size', args.ubatch_size]) + server_args.extend(['--n-predict', args.max_tokens * 2]) + server_args.extend(['--defrag-thold', "0.1"]) + server_args.append('--cont-batching') + server_args.append('--metrics') + server_args.append('--flash-attn') + args = [str(arg) for arg in [server_path, *server_args]] + print(f"bench: starting server with: {' '.join(args)}") + pkwargs = { + 'stdout': subprocess.PIPE, + 'stderr': subprocess.PIPE + } + server_process = subprocess.Popen( + args, + **pkwargs) # pyright: ignore[reportArgumentType, reportCallIssue] + + def server_log(in_stream, out_stream): + for line in iter(in_stream.readline, b''): + print(line.decode('utf-8'), end='', file=out_stream) + + thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout)) + thread_stdout.start() + thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr)) + thread_stderr.start() + + return server_process + + +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 + + +def is_server_ready(server_fqdn, server_port): + url = f"http://{server_fqdn}:{server_port}/health" + response = requests.get(url) + return response.status_code == 200 + + +def escape_metric_name(metric_name): + return re.sub('[^A-Z0-9]', '_', metric_name.upper()) + + +if __name__ == '__main__': + main() diff --git a/examples/server/bench/prometheus.yml b/examples/server/bench/prometheus.yml new file mode 100644 index 000000000..b15ee5244 --- /dev/null +++ b/examples/server/bench/prometheus.yml @@ -0,0 +1,9 @@ +global: + scrape_interval: 10s + external_labels: + llamacpp: 'server' + +scrape_configs: + - job_name: 'llama.cpp server' + static_configs: + - targets: ['localhost:8080'] diff --git a/examples/server/bench/requirements.txt b/examples/server/bench/requirements.txt new file mode 100644 index 000000000..66ed226ed --- /dev/null +++ b/examples/server/bench/requirements.txt @@ -0,0 +1,2 @@ +matplotlib +requests diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js new file mode 100644 index 000000000..2772bee5e --- /dev/null +++ b/examples/server/bench/script.js @@ -0,0 +1,162 @@ +import sse from 'k6/x/sse' +import {check, sleep} from 'k6' +import {SharedArray} from 'k6/data' +import {Counter, Rate, Trend} from 'k6/metrics' +import exec from 'k6/execution'; + +// Server chat completions prefix +const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1' + +// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users +const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8 + +// Model name to request +const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model' + +// Dataset path +const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json' + +// Max tokens to predict +const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512 + +// Max prompt tokens +const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024 + +// Max slot context +const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048 + +export function setup() { + console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`) +} + +const data = new SharedArray('conversations', function () { + const tokenizer = (message) => message.split(/[\s,'".?]/) + + return JSON.parse(open(dataset_path)) + // Filter out the conversations with less than 2 turns. + .filter(data => data["conversations"].length >= 2) + .filter(data => data["conversations"][0]["from"] === "human") + .map(data => { + return { + prompt: data["conversations"][0]["value"], + n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length, + n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length, + } + }) + // Filter out too short sequences + .filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4) + // Filter out too long sequences. + .filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot) + // Keep only first n prompts + .slice(0, n_prompt) +}) + +const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens') +const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens') + +const llamacpp_tokens_second = new Trend('llamacpp_tokens_second') +const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second') +const llamacpp_emit_first_token_second = new Trend('llamacpp_emit_first_token_second') + +const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter') +const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter') + +const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate') +const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate') + +export const options = { + thresholds: { + llamacpp_completions_truncated_rate: [ + // more than 80% of truncated input will abort the test + {threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'}, + ], + }, + duration: '10m', + vus: 8, +} + +export default function () { + const conversation = data[exec.scenario.iterationInInstance % data.length] + const payload = { + "messages": [ + { + "role": "system", + "content": "You are ChatGPT, an AI assistant.", + }, + { + "role": "user", + "content": conversation.prompt, + } + ], + "model": model, + "stream": true, + "stream_options": { + "include_usage": true, // False to be supported in llama.cpp server + }, + "seed": 42, + "max_tokens": max_tokens, + "stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS + } + + const params = {method: 'POST', body: JSON.stringify(payload)}; + + const startTime = new Date() + let promptEvalEndTime = null + let prompt_tokens = 0 + let completions_tokens = 0 + let finish_reason = null + const res = sse.open(`${server_url}/chat/completions`, params, function (client) { + client.on('event', function (event) { + if (promptEvalEndTime == null) { + promptEvalEndTime = new Date() + llamacpp_emit_first_token_second.add((promptEvalEndTime - startTime) / 1.e3) + } + + if (event.data === '[DONE]' || event.data === '') { + return + } + + let chunk = JSON.parse(event.data) + + if (chunk.choices && chunk.choices.length > 0) { + let choice = chunk.choices[0] + if (choice.finish_reason) { + finish_reason = choice.finish_reason + } + } + + if (chunk.usage) { + prompt_tokens = chunk.usage.prompt_tokens + llamacpp_prompt_tokens.add(prompt_tokens) + llamacpp_prompt_tokens_total_counter.add(prompt_tokens) + + completions_tokens = chunk.usage.completion_tokens + llamacpp_completion_tokens.add(completions_tokens) + llamacpp_completion_tokens_total_counter.add(completions_tokens) + } + }) + + client.on('error', function (e) { + console.log('An unexpected error occurred: ', e.error()); + throw e; + }) + }) + + check(res, {'success completion': (r) => r.status === 200}) + + const endTime = new Date() + + const promptEvalTime = promptEvalEndTime - startTime + if (promptEvalTime > 0) { + llamacpp_prompt_processing_second.add(prompt_tokens / (promptEvalEndTime - startTime) * 1.e3) + } + + const completion_time = endTime - promptEvalEndTime + if (completions_tokens > 0 && completion_time > 0) { + llamacpp_tokens_second.add(completions_tokens / completion_time * 1.e3) + } + llamacpp_completions_truncated_rate.add(finish_reason === 'length') + llamacpp_completions_stop_rate.add(finish_reason === 'stop') + + sleep(0.3) +} diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index 219ebb51a..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( @@ -26,8 +26,9 @@ const propOrder = grammarJsonSchemaPropOrder let grammar = null if (grammarJsonSchemaFile) { - const schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8')) - const converter = new SchemaConverter(propOrder) + let schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8')) + const converter = new SchemaConverter({prop_order: propOrder, allow_fetch: true}) + schema = await converter.resolveRefs(schema, grammarJsonSchemaFile) converter.visit(schema, '') grammar = converter.formatGrammar() } diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp deleted file mode 100644 index f5e696e17..000000000 --- a/examples/server/completion.js.hpp +++ /dev/null @@ -1,485 +0,0 @@ -unsigned char completion_js[] = { - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, - 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72, - 0x75, 0x65, 0x2c, 0x0a, 0x20, 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-echo >> $PUBLIC/index.js # add newline - -FILES=$(ls $PUBLIC) - -cd $PUBLIC -for FILE in $FILES; do - echo "generate $FILE.hpp" - - # use simple flag for old version of xxd - xxd -i $FILE > $DIR/$FILE.hpp -done diff --git a/examples/server/httplib.h b/examples/server/httplib.h index 28746000c..c2f12dd2a 100644 --- a/examples/server/httplib.h +++ b/examples/server/httplib.h @@ -1,14 +1,14 @@ // // httplib.h // -// Copyright (c) 2023 Yuji Hirose. All rights reserved. +// Copyright (c) 2024 Yuji Hirose. All rights reserved. // MIT License // #ifndef CPPHTTPLIB_HTTPLIB_H #define CPPHTTPLIB_HTTPLIB_H -#define CPPHTTPLIB_VERSION "0.12.2" +#define CPPHTTPLIB_VERSION "0.18.5" /* * Configuration @@ -18,8 +18,12 @@ #define CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND 5 #endif +#ifndef CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND +#define CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND 10000 +#endif + #ifndef CPPHTTPLIB_KEEPALIVE_MAX_COUNT -#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 5 +#define CPPHTTPLIB_KEEPALIVE_MAX_COUNT 100 #endif #ifndef CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND @@ -30,20 +34,36 @@ #define CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND 0 #endif -#ifndef CPPHTTPLIB_READ_TIMEOUT_SECOND -#define CPPHTTPLIB_READ_TIMEOUT_SECOND 5 +#ifndef CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND +#define CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND 5 #endif -#ifndef CPPHTTPLIB_READ_TIMEOUT_USECOND -#define CPPHTTPLIB_READ_TIMEOUT_USECOND 0 +#ifndef CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND +#define CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND 0 #endif -#ifndef CPPHTTPLIB_WRITE_TIMEOUT_SECOND -#define CPPHTTPLIB_WRITE_TIMEOUT_SECOND 5 +#ifndef CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND +#define CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND 5 #endif -#ifndef CPPHTTPLIB_WRITE_TIMEOUT_USECOND -#define CPPHTTPLIB_WRITE_TIMEOUT_USECOND 0 +#ifndef CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND +#define CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND 0 +#endif + +#ifndef CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND +#define CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND 300 +#endif + +#ifndef CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND +#define CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND 0 +#endif + +#ifndef CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND +#define CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND 5 +#endif + +#ifndef CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND +#define CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND 0 #endif #ifndef CPPHTTPLIB_IDLE_INTERVAL_SECOND @@ -82,12 +102,20 @@ #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192 #endif +#ifndef CPPHTTPLIB_RANGE_MAX_COUNT +#define CPPHTTPLIB_RANGE_MAX_COUNT 1024 +#endif + #ifndef CPPHTTPLIB_TCP_NODELAY #define CPPHTTPLIB_TCP_NODELAY false #endif +#ifndef CPPHTTPLIB_IPV6_V6ONLY +#define CPPHTTPLIB_IPV6_V6ONLY false +#endif + #ifndef CPPHTTPLIB_RECV_BUFSIZ -#define CPPHTTPLIB_RECV_BUFSIZ size_t(4096u) +#define CPPHTTPLIB_RECV_BUFSIZ size_t(16384u) #endif #ifndef CPPHTTPLIB_COMPRESSION_BUFSIZ @@ -141,11 +169,11 @@ using ssize_t = long; #endif // _MSC_VER #ifndef S_ISREG -#define S_ISREG(m) (((m)&S_IFREG) == S_IFREG) +#define S_ISREG(m) (((m) & S_IFREG) == S_IFREG) #endif // S_ISREG #ifndef S_ISDIR -#define S_ISDIR(m) (((m)&S_IFDIR) == S_IFDIR) +#define S_ISDIR(m) (((m) & S_IFDIR) == S_IFDIR) #endif // S_ISDIR #ifndef NOMINMAX @@ -160,10 +188,6 @@ using ssize_t = long; #define WSA_FLAG_NO_HANDLE_INHERIT 0x80 #endif -#ifndef strcasecmp -#define strcasecmp _stricmp -#endif // strcasecmp - using socket_t = SOCKET; #ifdef CPPHTTPLIB_USE_POLL #define poll(fds, nfds, timeout) WSAPoll(fds, nfds, timeout) @@ -172,9 +196,15 @@ using socket_t = SOCKET; #else // not _WIN32 #include -#ifndef _AIX +#if !defined(_AIX) && !defined(__MVS__) #include #endif +#ifdef __MVS__ +#include +#ifndef NI_MAXHOST +#define NI_MAXHOST 1025 +#endif +#endif #include #include #include @@ -187,6 +217,7 @@ using socket_t = SOCKET; #endif #include #include +#include #include #include #include @@ -207,6 +238,7 @@ using socket_t = int; #include #include #include +#include #include #include #include @@ -223,6 +255,9 @@ using socket_t = int; #include #include #include +#include +#include +#include #ifdef CPPHTTPLIB_OPENSSL_SUPPORT #ifdef _WIN32 @@ -237,7 +272,6 @@ using socket_t = int; #ifdef _MSC_VER #pragma comment(lib, "crypt32.lib") -#pragma comment(lib, "cryptui.lib") #endif #elif defined(CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN) && defined(__APPLE__) #include @@ -259,10 +293,13 @@ using socket_t = int; #include #include -#if OPENSSL_VERSION_NUMBER < 0x1010100fL -#error Sorry, OpenSSL versions prior to 1.1.1 are not supported -#elif OPENSSL_VERSION_NUMBER < 0x30000000L +#if defined(OPENSSL_IS_BORINGSSL) || defined(LIBRESSL_VERSION_NUMBER) +#if OPENSSL_VERSION_NUMBER < 0x1010107f +#error Please use OpenSSL or a current version of BoringSSL +#endif #define SSL_get1_peer_certificate SSL_get_peer_certificate +#elif OPENSSL_VERSION_NUMBER < 0x30000000L +#error Sorry, OpenSSL versions prior to 3.0.0 are not supported #endif #endif @@ -304,16 +341,63 @@ make_unique(std::size_t n) { return std::unique_ptr(new RT[n]); } -struct ci { - bool operator()(const std::string &s1, const std::string &s2) const { - return std::lexicographical_compare(s1.begin(), s1.end(), s2.begin(), - s2.end(), - [](unsigned char c1, unsigned char c2) { - return ::tolower(c1) < ::tolower(c2); - }); +namespace case_ignore { + +inline unsigned char to_lower(int c) { + const static unsigned char table[256] = { + 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, + 60, 61, 62, 63, 64, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, + 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, + 122, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, + 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, + 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, + 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, + 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, + 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 224, 225, 226, + 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, + 242, 243, 244, 245, 246, 215, 248, 249, 250, 251, 252, 253, 254, 223, 224, + 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, + 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, + 255, + }; + return table[(unsigned char)(char)c]; +} + +inline bool equal(const std::string &a, const std::string &b) { + return a.size() == b.size() && + std::equal(a.begin(), a.end(), b.begin(), [](char ca, char cb) { + return to_lower(ca) == to_lower(cb); + }); +} + +struct equal_to { + bool operator()(const std::string &a, const std::string &b) const { + return equal(a, b); } }; +struct hash { + size_t operator()(const std::string &key) const { + return hash_core(key.data(), key.size(), 0); + } + + size_t hash_core(const char *s, size_t l, size_t h) const { + return (l == 0) ? h + : hash_core(s + 1, l - 1, + // Unsets the 6 high bits of h, therefore no + // overflow happens + (((std::numeric_limits::max)() >> 6) & + h * 33) ^ + static_cast(to_lower(*s))); + } +}; + +} // namespace case_ignore + // This is based on // "http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2014/n4189". @@ -321,7 +405,7 @@ struct scope_exit { explicit scope_exit(std::function &&f) : exit_function(std::move(f)), execute_on_destruction{true} {} - scope_exit(scope_exit &&rhs) + scope_exit(scope_exit &&rhs) noexcept : exit_function(std::move(rhs.exit_function)), execute_on_destruction{rhs.execute_on_destruction} { rhs.release(); @@ -344,7 +428,84 @@ private: } // namespace detail -using Headers = std::multimap; +enum StatusCode { + // Information responses + Continue_100 = 100, + SwitchingProtocol_101 = 101, + Processing_102 = 102, + EarlyHints_103 = 103, + + // Successful responses + OK_200 = 200, + Created_201 = 201, + Accepted_202 = 202, + NonAuthoritativeInformation_203 = 203, + NoContent_204 = 204, + ResetContent_205 = 205, + PartialContent_206 = 206, + MultiStatus_207 = 207, + AlreadyReported_208 = 208, + IMUsed_226 = 226, + + // Redirection messages + MultipleChoices_300 = 300, + MovedPermanently_301 = 301, + Found_302 = 302, + SeeOther_303 = 303, + NotModified_304 = 304, + UseProxy_305 = 305, + unused_306 = 306, + TemporaryRedirect_307 = 307, + PermanentRedirect_308 = 308, + + // Client error responses + BadRequest_400 = 400, + Unauthorized_401 = 401, + PaymentRequired_402 = 402, + Forbidden_403 = 403, + NotFound_404 = 404, + MethodNotAllowed_405 = 405, + NotAcceptable_406 = 406, + ProxyAuthenticationRequired_407 = 407, + RequestTimeout_408 = 408, + Conflict_409 = 409, + Gone_410 = 410, + LengthRequired_411 = 411, + PreconditionFailed_412 = 412, + PayloadTooLarge_413 = 413, + UriTooLong_414 = 414, + UnsupportedMediaType_415 = 415, + RangeNotSatisfiable_416 = 416, + ExpectationFailed_417 = 417, + ImATeapot_418 = 418, + MisdirectedRequest_421 = 421, + UnprocessableContent_422 = 422, + Locked_423 = 423, + FailedDependency_424 = 424, + TooEarly_425 = 425, + UpgradeRequired_426 = 426, + PreconditionRequired_428 = 428, + TooManyRequests_429 = 429, + RequestHeaderFieldsTooLarge_431 = 431, + UnavailableForLegalReasons_451 = 451, + + // Server error responses + InternalServerError_500 = 500, + NotImplemented_501 = 501, + BadGateway_502 = 502, + ServiceUnavailable_503 = 503, + GatewayTimeout_504 = 504, + HttpVersionNotSupported_505 = 505, + VariantAlsoNegotiates_506 = 506, + InsufficientStorage_507 = 507, + LoopDetected_508 = 508, + NotExtended_510 = 510, + NetworkAuthenticationRequired_511 = 511, +}; + +using Headers = + std::unordered_multimap; using Params = std::multimap; using Match = std::smatch; @@ -373,17 +534,18 @@ public: DataSink &operator=(DataSink &&) = delete; std::function write; + std::function is_writable; std::function done; std::function done_with_trailer; std::ostream os; private: - class data_sink_streambuf : public std::streambuf { + class data_sink_streambuf final : public std::streambuf { public: explicit data_sink_streambuf(DataSink &sink) : sink_(sink) {} protected: - std::streamsize xsputn(const char *s, std::streamsize n) { + std::streamsize xsputn(const char *s, std::streamsize n) override { sink_.write(s, static_cast(n)); return n; } @@ -450,6 +612,7 @@ using Ranges = std::vector; struct Request { std::string method; std::string path; + Params params; Headers headers; std::string body; @@ -461,10 +624,11 @@ struct Request { // for server std::string version; std::string target; - Params params; MultipartFormDataMap files; Ranges ranges; Match matches; + std::unordered_map path_params; + std::function is_connection_closed = []() { return true; }; // for client ResponseHandler response_handler; @@ -475,9 +639,10 @@ struct Request { #endif bool has_header(const std::string &key) const; - std::string get_header_value(const std::string &key, size_t id = 0) const; - template - T get_header_value(const std::string &key, size_t id = 0) const; + std::string get_header_value(const std::string &key, const char *def = "", + size_t id = 0) const; + uint64_t get_header_value_u64(const std::string &key, uint64_t def = 0, + size_t id = 0) const; size_t get_header_value_count(const std::string &key) const; void set_header(const std::string &key, const std::string &val); @@ -508,15 +673,17 @@ struct Response { std::string location; // Redirect location bool has_header(const std::string &key) const; - std::string get_header_value(const std::string &key, size_t id = 0) const; - template - T get_header_value(const std::string &key, size_t id = 0) const; + std::string get_header_value(const std::string &key, const char *def = "", + size_t id = 0) const; + uint64_t get_header_value_u64(const std::string &key, uint64_t def = 0, + size_t id = 0) const; size_t get_header_value_count(const std::string &key) const; void set_header(const std::string &key, const std::string &val); - void set_redirect(const std::string &url, int status = 302); + void set_redirect(const std::string &url, int status = StatusCode::Found_302); void set_content(const char *s, size_t n, const std::string &content_type); void set_content(const std::string &s, const std::string &content_type); + void set_content(std::string &&s, const std::string &content_type); void set_content_provider( size_t length, const std::string &content_type, ContentProvider provider, @@ -530,6 +697,10 @@ struct Response { const std::string &content_type, ContentProviderWithoutLength provider, ContentProviderResourceReleaser resource_releaser = nullptr); + void set_file_content(const std::string &path, + const std::string &content_type); + void set_file_content(const std::string &path); + Response() = default; Response(const Response &) = default; Response &operator=(const Response &) = default; @@ -547,6 +718,8 @@ struct Response { ContentProviderResourceReleaser content_provider_resource_releaser_; bool is_chunked_content_provider_ = false; bool content_provider_success_ = false; + std::string file_content_path_; + std::string file_content_content_type_; }; class Stream { @@ -562,8 +735,6 @@ public: virtual void get_local_ip_and_port(std::string &ip, int &port) const = 0; virtual socket_t socket() const = 0; - template - ssize_t write_format(const char *fmt, const Args &...args); ssize_t write(const char *ptr); ssize_t write(const std::string &s); }; @@ -573,15 +744,16 @@ public: TaskQueue() = default; virtual ~TaskQueue() = default; - virtual void enqueue(std::function fn) = 0; + virtual bool enqueue(std::function fn) = 0; virtual void shutdown() = 0; virtual void on_idle() {} }; -class ThreadPool : public TaskQueue { +class ThreadPool final : public TaskQueue { public: - explicit ThreadPool(size_t n) : shutdown_(false) { + explicit ThreadPool(size_t n, size_t mqr = 0) + : shutdown_(false), max_queued_requests_(mqr) { while (n) { threads_.emplace_back(worker(*this)); n--; @@ -591,13 +763,17 @@ public: ThreadPool(const ThreadPool &) = delete; ~ThreadPool() override = default; - void enqueue(std::function fn) override { + bool enqueue(std::function fn) override { { std::unique_lock lock(mutex_); + if (max_queued_requests_ > 0 && jobs_.size() >= max_queued_requests_) { + return false; + } jobs_.push_back(std::move(fn)); } cond_.notify_one(); + return true; } void shutdown() override { @@ -630,13 +806,18 @@ private: if (pool_.shutdown_ && pool_.jobs_.empty()) { break; } - fn = std::move(pool_.jobs_.front()); + fn = pool_.jobs_.front(); pool_.jobs_.pop_front(); } assert(true == static_cast(fn)); fn(); } + +#if defined(CPPHTTPLIB_OPENSSL_SUPPORT) && !defined(OPENSSL_IS_BORINGSSL) && \ + !defined(LIBRESSL_VERSION_NUMBER) + OPENSSL_thread_stop(); +#endif } ThreadPool &pool_; @@ -647,6 +828,7 @@ private: std::list> jobs_; bool shutdown_; + size_t max_queued_requests_ = 0; std::condition_variable cond_; std::mutex mutex_; @@ -658,6 +840,81 @@ using SocketOptions = std::function; void default_socket_options(socket_t sock); +const char *status_message(int status); + +std::string get_bearer_token_auth(const Request &req); + +namespace detail { + +class MatcherBase { +public: + virtual ~MatcherBase() = default; + + // Match request path and populate its matches and + virtual bool match(Request &request) const = 0; +}; + +/** + * Captures parameters in request path and stores them in Request::path_params + * + * Capture name is a substring of a pattern from : to /. + * The rest of the pattern is matched agains the request path directly + * Parameters are captured starting from the next character after + * the end of the last matched static pattern fragment until the next /. + * + * Example pattern: + * "/path/fragments/:capture/more/fragments/:second_capture" + * Static fragments: + * "/path/fragments/", "more/fragments/" + * + * Given the following request path: + * "/path/fragments/:1/more/fragments/:2" + * the resulting capture will be + * {{"capture", "1"}, {"second_capture", "2"}} + */ +class PathParamsMatcher final : public MatcherBase { +public: + PathParamsMatcher(const std::string &pattern); + + bool match(Request &request) const override; + +private: + // Treat segment separators as the end of path parameter capture + // Does not need to handle query parameters as they are parsed before path + // matching + static constexpr char separator = '/'; + + // Contains static path fragments to match against, excluding the '/' after + // path params + // Fragments are separated by path params + std::vector static_fragments_; + // Stores the names of the path parameters to be used as keys in the + // Request::path_params map + std::vector param_names_; +}; + +/** + * Performs std::regex_match on request path + * and stores the result in Request::matches + * + * Note that regex match is performed directly on the whole request. + * This means that wildcard patterns may match multiple path segments with /: + * "/begin/(.*)/end" will match both "/begin/middle/end" and "/begin/1/2/end". + */ +class RegexMatcher final : public MatcherBase { +public: + RegexMatcher(const std::string &pattern) : regex_(pattern) {} + + bool match(Request &request) const override; + +private: + std::regex regex_; +}; + +ssize_t write_headers(Stream &strm, const Headers &headers); + +} // namespace detail + class Server { public: using Handler = std::function; @@ -702,10 +959,16 @@ public: bool remove_mount_point(const std::string &mount_point); Server &set_file_extension_and_mimetype_mapping(const std::string &ext, const std::string &mime); + Server &set_default_file_mimetype(const std::string &mime); Server &set_file_request_handler(Handler handler); - Server &set_error_handler(HandlerWithResponse handler); - Server &set_error_handler(Handler handler); + template + Server &set_error_handler(ErrorHandlerFunc &&handler) { + return set_error_handler_core( + std::forward(handler), + std::is_convertible{}); + } + Server &set_exception_handler(ExceptionHandler handler); Server &set_pre_routing_handler(HandlerWithResponse handler); Server &set_post_routing_handler(Handler handler); @@ -715,9 +978,12 @@ public: Server &set_address_family(int family); Server &set_tcp_nodelay(bool on); + Server &set_ipv6_v6only(bool on); Server &set_socket_options(SocketOptions socket_options); Server &set_default_headers(Headers headers); + Server & + set_header_writer(std::function const &writer); Server &set_keep_alive_max_count(size_t count); Server &set_keep_alive_timeout(time_t sec); @@ -745,29 +1011,40 @@ public: bool is_running() const; void wait_until_ready() const; void stop(); + void decommission(); std::function new_task_queue; protected: - bool process_request(Stream &strm, bool close_connection, + bool process_request(Stream &strm, const std::string &remote_addr, + int remote_port, const std::string &local_addr, + int local_port, bool close_connection, bool &connection_closed, const std::function &setup_request); std::atomic svr_sock_{INVALID_SOCKET}; size_t keep_alive_max_count_ = CPPHTTPLIB_KEEPALIVE_MAX_COUNT; time_t keep_alive_timeout_sec_ = CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND; - time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND; - time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND; - time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND; - time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND; + time_t read_timeout_sec_ = CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND; + time_t read_timeout_usec_ = CPPHTTPLIB_SERVER_READ_TIMEOUT_USECOND; + time_t write_timeout_sec_ = CPPHTTPLIB_SERVER_WRITE_TIMEOUT_SECOND; + time_t write_timeout_usec_ = CPPHTTPLIB_SERVER_WRITE_TIMEOUT_USECOND; time_t idle_interval_sec_ = CPPHTTPLIB_IDLE_INTERVAL_SECOND; time_t idle_interval_usec_ = CPPHTTPLIB_IDLE_INTERVAL_USECOND; size_t payload_max_length_ = CPPHTTPLIB_PAYLOAD_MAX_LENGTH; private: - using Handlers = std::vector>; + using Handlers = + std::vector, Handler>>; using HandlersForContentReader = - std::vector>; + std::vector, + HandlerWithContentReader>>; + + static std::unique_ptr + make_matcher(const std::string &pattern); + + Server &set_error_handler_core(HandlerWithResponse handler, std::true_type); + Server &set_error_handler_core(Handler handler, std::false_type); socket_t create_server_socket(const std::string &host, int port, int socket_flags, @@ -778,16 +1055,16 @@ private: bool routing(Request &req, Response &res, Stream &strm); bool handle_file_request(const Request &req, Response &res, bool head = false); - bool dispatch_request(Request &req, Response &res, const Handlers &handlers); - bool - dispatch_request_for_content_reader(Request &req, Response &res, - ContentReader content_reader, - const HandlersForContentReader &handlers); + bool dispatch_request(Request &req, Response &res, + const Handlers &handlers) const; + bool dispatch_request_for_content_reader( + Request &req, Response &res, ContentReader content_reader, + const HandlersForContentReader &handlers) const; - bool parse_request_line(const char *s, Request &req); + bool parse_request_line(const char *s, Request &req) const; void apply_ranges(const Request &req, Response &res, - std::string &content_type, std::string &boundary); - bool write_response(Stream &strm, bool close_connection, const Request &req, + std::string &content_type, std::string &boundary) const; + bool write_response(Stream &strm, bool close_connection, Request &req, Response &res); bool write_response_with_content(Stream &strm, bool close_connection, const Request &req, Response &res); @@ -806,21 +1083,23 @@ private: bool read_content_core(Stream &strm, Request &req, Response &res, ContentReceiver receiver, MultipartContentHeader multipart_header, - ContentReceiver multipart_receiver); + ContentReceiver multipart_receiver) const; virtual bool process_and_close_socket(socket_t sock); + std::atomic is_running_{false}; + std::atomic is_decommisioned{false}; + struct MountPointEntry { std::string mount_point; std::string base_dir; Headers headers; }; std::vector base_dirs_; - - std::atomic is_running_{false}; - std::atomic done_{false}; std::map file_extension_and_mimetype_map_; + std::string default_file_mimetype_ = "application/octet-stream"; Handler file_request_handler_; + Handlers get_handlers_; Handlers post_handlers_; HandlersForContentReader post_handlers_for_content_reader_; @@ -831,18 +1110,23 @@ private: Handlers delete_handlers_; HandlersForContentReader delete_handlers_for_content_reader_; Handlers options_handlers_; + HandlerWithResponse error_handler_; ExceptionHandler exception_handler_; HandlerWithResponse pre_routing_handler_; Handler post_routing_handler_; - Logger logger_; Expect100ContinueHandler expect_100_continue_handler_; + Logger logger_; + int address_family_ = AF_UNSPEC; bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY; + bool ipv6_v6only_ = CPPHTTPLIB_IPV6_V6ONLY; SocketOptions socket_options_ = default_socket_options; Headers default_headers_; + std::function header_writer_ = + detail::write_headers; }; enum class Error { @@ -857,20 +1141,23 @@ enum class Error { SSLConnection, SSLLoadingCerts, SSLServerVerification, + SSLServerHostnameVerification, UnsupportedMultipartBoundaryChars, Compression, ConnectionTimeout, + ProxyConnection, // For internal use only SSLPeerCouldBeClosed_, }; -std::string to_string(const Error error); +std::string to_string(Error error); std::ostream &operator<<(std::ostream &os, const Error &obj); class Result { public: + Result() = default; Result(std::unique_ptr &&res, Error err, Headers &&request_headers = Headers{}) : res_(std::move(res)), err_(err), @@ -892,14 +1179,15 @@ public: // Request Headers bool has_request_header(const std::string &key) const; std::string get_request_header_value(const std::string &key, + const char *def = "", size_t id = 0) const; - template - T get_request_header_value(const std::string &key, size_t id = 0) const; + uint64_t get_request_header_value_u64(const std::string &key, + uint64_t def = 0, size_t id = 0) const; size_t get_request_header_value_count(const std::string &key) const; private: std::unique_ptr res_; - Error err_; + Error err_ = Error::Unknown; Headers request_headers_; }; @@ -958,10 +1246,18 @@ public: const std::string &content_type); Result Post(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Post(const std::string &path, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Post(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -977,6 +1273,8 @@ public: Result Post(const std::string &path, const Params ¶ms); Result Post(const std::string &path, const Headers &headers, const Params ¶ms); + Result Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Post(const std::string &path, const MultipartFormDataItems &items); Result Post(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -991,10 +1289,18 @@ public: const std::string &content_type); Result Put(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Put(const std::string &path, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Put(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); Result Put(const std::string &path, @@ -1009,6 +1315,8 @@ public: Result Put(const std::string &path, const Params ¶ms); Result Put(const std::string &path, const Headers &headers, const Params ¶ms); + Result Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Put(const std::string &path, const MultipartFormDataItems &items); Result Put(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -1021,13 +1329,23 @@ public: Result Patch(const std::string &path); Result Patch(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Patch(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -1045,13 +1363,24 @@ public: Result Delete(const std::string &path, const Headers &headers); Result Delete(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Options(const std::string &path); Result Options(const std::string &path, const Headers &headers); @@ -1059,18 +1388,24 @@ public: bool send(Request &req, Response &res, Error &error); Result send(const Request &req); - size_t is_socket_open() const; - - socket_t socket() const; - void stop(); + std::string host() const; + int port() const; + + size_t is_socket_open() const; + socket_t socket() const; + void set_hostname_addr_map(std::map addr_map); void set_default_headers(Headers headers); + void + set_header_writer(std::function const &writer); + void set_address_family(int family); void set_tcp_nodelay(bool on); + void set_ipv6_v6only(bool on); void set_socket_options(SocketOptions socket_options); void set_connection_timeout(time_t sec, time_t usec = 0); @@ -1117,10 +1452,13 @@ public: void set_ca_cert_path(const std::string &ca_cert_file_path, const std::string &ca_cert_dir_path = std::string()); void set_ca_cert_store(X509_STORE *ca_cert_store); + X509_STORE *create_ca_cert_store(const char *ca_cert, std::size_t size) const; #endif #ifdef CPPHTTPLIB_OPENSSL_SUPPORT void enable_server_certificate_verification(bool enabled); + void enable_server_hostname_verification(bool enabled); + void set_server_certificate_verifier(std::function verifier); #endif void set_logger(Logger logger); @@ -1145,14 +1483,14 @@ protected: // Also, shutdown_ssl and close_socket should also NOT be called concurrently // with a DIFFERENT thread sending requests using that socket. virtual void shutdown_ssl(Socket &socket, bool shutdown_gracefully); - void shutdown_socket(Socket &socket); + void shutdown_socket(Socket &socket) const; void close_socket(Socket &socket); bool process_request(Stream &strm, Request &req, Response &res, bool close_connection, Error &error); bool write_content_with_provider(Stream &strm, const Request &req, - Error &error); + Error &error) const; void copy_settings(const ClientImpl &rhs); @@ -1177,16 +1515,20 @@ protected: // Default headers Headers default_headers_; + // Header writer + std::function header_writer_ = + detail::write_headers; + // Settings std::string client_cert_path_; std::string client_key_path_; time_t connection_timeout_sec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_SECOND; time_t connection_timeout_usec_ = CPPHTTPLIB_CONNECTION_TIMEOUT_USECOND; - time_t read_timeout_sec_ = CPPHTTPLIB_READ_TIMEOUT_SECOND; - time_t read_timeout_usec_ = CPPHTTPLIB_READ_TIMEOUT_USECOND; - time_t write_timeout_sec_ = CPPHTTPLIB_WRITE_TIMEOUT_SECOND; - time_t write_timeout_usec_ = CPPHTTPLIB_WRITE_TIMEOUT_USECOND; + time_t read_timeout_sec_ = CPPHTTPLIB_CLIENT_READ_TIMEOUT_SECOND; + time_t read_timeout_usec_ = CPPHTTPLIB_CLIENT_READ_TIMEOUT_USECOND; + time_t write_timeout_sec_ = CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_SECOND; + time_t write_timeout_usec_ = CPPHTTPLIB_CLIENT_WRITE_TIMEOUT_USECOND; std::string basic_auth_username_; std::string basic_auth_password_; @@ -1203,6 +1545,7 @@ protected: int address_family_ = AF_UNSPEC; bool tcp_nodelay_ = CPPHTTPLIB_TCP_NODELAY; + bool ipv6_v6only_ = CPPHTTPLIB_IPV6_V6ONLY; SocketOptions socket_options_ = nullptr; bool compress_ = false; @@ -1230,6 +1573,8 @@ protected: #ifdef CPPHTTPLIB_OPENSSL_SUPPORT bool server_certificate_verification_ = true; + bool server_hostname_verification_ = true; + std::function server_certificate_verifier_; #endif Logger logger_; @@ -1238,8 +1583,12 @@ private: bool send_(Request &req, Response &res, Error &error); Result send_(Request &&req); +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + bool is_ssl_peer_could_be_closed(SSL *ssl) const; +#endif socket_t create_client_socket(Error &error) const; - bool read_response_line(Stream &strm, const Request &req, Response &res); + bool read_response_line(Stream &strm, const Request &req, + Response &res) const; bool write_request(Stream &strm, Request &req, bool close_connection, Error &error); bool redirect(Request &req, Response &res, Error &error); @@ -1255,10 +1604,10 @@ private: const Headers &headers, const char *body, size_t content_length, ContentProvider content_provider, ContentProviderWithoutLength content_provider_without_length, - const std::string &content_type); + const std::string &content_type, Progress progress); ContentProviderWithoutLength get_multipart_content_provider( const std::string &boundary, const MultipartFormDataItems &items, - const MultipartFormDataProviderItems &provider_items); + const MultipartFormDataProviderItems &provider_items) const; std::string adjust_host_string(const std::string &host) const; @@ -1284,6 +1633,7 @@ public: const std::string &client_key_path); Client(Client &&) = default; + Client &operator=(Client &&) = default; ~Client(); @@ -1330,10 +1680,18 @@ public: const std::string &content_type); Result Post(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Post(const std::string &path, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Post(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Post(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -1349,6 +1707,8 @@ public: Result Post(const std::string &path, const Params ¶ms); Result Post(const std::string &path, const Headers &headers, const Params ¶ms); + Result Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Post(const std::string &path, const MultipartFormDataItems &items); Result Post(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -1363,10 +1723,18 @@ public: const std::string &content_type); Result Put(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Put(const std::string &path, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Put(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Put(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); Result Put(const std::string &path, @@ -1381,6 +1749,8 @@ public: Result Put(const std::string &path, const Params ¶ms); Result Put(const std::string &path, const Headers &headers, const Params ¶ms); + Result Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress); Result Put(const std::string &path, const MultipartFormDataItems &items); Result Put(const std::string &path, const Headers &headers, const MultipartFormDataItems &items); @@ -1393,13 +1763,23 @@ public: Result Patch(const std::string &path); Result Patch(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Patch(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Patch(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type); @@ -1417,13 +1797,24 @@ public: Result Delete(const std::string &path, const Headers &headers); Result Delete(const std::string &path, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const char *body, + size_t content_length, const std::string &content_type, + Progress progress); Result Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress); Result Delete(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type); + Result Delete(const std::string &path, const Headers &headers, + const std::string &body, const std::string &content_type, + Progress progress); Result Options(const std::string &path); Result Options(const std::string &path, const Headers &headers); @@ -1431,16 +1822,21 @@ public: bool send(Request &req, Response &res, Error &error); Result send(const Request &req); - size_t is_socket_open() const; - - socket_t socket() const; - void stop(); + std::string host() const; + int port() const; + + size_t is_socket_open() const; + socket_t socket() const; + void set_hostname_addr_map(std::map addr_map); void set_default_headers(Headers headers); + void + set_header_writer(std::function const &writer); + void set_address_family(int family); void set_tcp_nodelay(bool on); void set_socket_options(SocketOptions socket_options); @@ -1487,6 +1883,8 @@ public: #ifdef CPPHTTPLIB_OPENSSL_SUPPORT void enable_server_certificate_verification(bool enabled); + void enable_server_hostname_verification(bool enabled); + void set_server_certificate_verifier(std::function verifier); #endif void set_logger(Logger logger); @@ -1497,6 +1895,7 @@ public: const std::string &ca_cert_dir_path = std::string()); void set_ca_cert_store(X509_STORE *ca_cert_store); + void load_ca_cert_store(const char *ca_cert, std::size_t size); long get_openssl_verify_result() const; @@ -1531,6 +1930,9 @@ public: SSL_CTX *ssl_context() const; + void update_certs(X509 *cert, EVP_PKEY *private_key, + X509_STORE *client_ca_cert_store = nullptr); + private: bool process_and_close_socket(socket_t sock) override; @@ -1538,7 +1940,7 @@ private: std::mutex ctx_mutex_; }; -class SSLClient : public ClientImpl { +class SSLClient final : public ClientImpl { public: explicit SSLClient(const std::string &host); @@ -1546,16 +1948,19 @@ public: explicit SSLClient(const std::string &host, int port, const std::string &client_cert_path, - const std::string &client_key_path); + const std::string &client_key_path, + const std::string &private_key_password = std::string()); explicit SSLClient(const std::string &host, int port, X509 *client_cert, - EVP_PKEY *client_key); + EVP_PKEY *client_key, + const std::string &private_key_password = std::string()); ~SSLClient() override; bool is_valid() const override; void set_ca_cert_store(X509_STORE *ca_cert_store); + void load_ca_cert_store(const char *ca_cert, std::size_t size); long get_openssl_verify_result() const; @@ -1564,7 +1969,7 @@ public: private: bool create_and_connect_socket(Socket &socket, Error &error) override; void shutdown_ssl(Socket &socket, bool shutdown_gracefully) override; - void shutdown_ssl_impl(Socket &socket, bool shutdown_socket); + void shutdown_ssl_impl(Socket &socket, bool shutdown_gracefully); bool process_socket(const Socket &socket, std::function callback) override; @@ -1608,78 +2013,147 @@ inline void duration_to_sec_and_usec(const T &duration, U callback) { callback(static_cast(sec), static_cast(usec)); } -template -inline T get_header_value(const Headers & /*headers*/, - const std::string & /*key*/, size_t /*id*/ = 0, - uint64_t /*def*/ = 0) {} +inline bool is_numeric(const std::string &str) { + return !str.empty() && std::all_of(str.begin(), str.end(), ::isdigit); +} -template <> -inline uint64_t get_header_value(const Headers &headers, - const std::string &key, size_t id, - uint64_t def) { +inline uint64_t get_header_value_u64(const Headers &headers, + const std::string &key, uint64_t def, + size_t id, bool &is_invalid_value) { + is_invalid_value = false; auto rng = headers.equal_range(key); auto it = rng.first; std::advance(it, static_cast(id)); if (it != rng.second) { - return std::strtoull(it->second.data(), nullptr, 10); + if (is_numeric(it->second)) { + return std::strtoull(it->second.data(), nullptr, 10); + } else { + is_invalid_value = true; + } } return def; } +inline uint64_t get_header_value_u64(const Headers &headers, + const std::string &key, uint64_t def, + size_t id) { + bool dummy = false; + return get_header_value_u64(headers, key, def, id, dummy); +} + } // namespace detail -template -inline T Request::get_header_value(const std::string &key, size_t id) const { - return detail::get_header_value(headers, key, id, 0); +inline uint64_t Request::get_header_value_u64(const std::string &key, + uint64_t def, size_t id) const { + return detail::get_header_value_u64(headers, key, def, id); } -template -inline T Response::get_header_value(const std::string &key, size_t id) const { - return detail::get_header_value(headers, key, id, 0); -} - -template -inline ssize_t Stream::write_format(const char *fmt, const Args &...args) { - const auto bufsiz = 2048; - std::array buf{}; - - auto sn = snprintf(buf.data(), buf.size() - 1, fmt, args...); - if (sn <= 0) { return sn; } - - auto n = static_cast(sn); - - if (n >= buf.size() - 1) { - std::vector glowable_buf(buf.size()); - - while (n >= glowable_buf.size() - 1) { - glowable_buf.resize(glowable_buf.size() * 2); - n = static_cast( - snprintf(&glowable_buf[0], glowable_buf.size() - 1, fmt, args...)); - } - return write(&glowable_buf[0], n); - } else { - return write(buf.data(), n); - } +inline uint64_t Response::get_header_value_u64(const std::string &key, + uint64_t def, size_t id) const { + return detail::get_header_value_u64(headers, key, def, id); } inline void default_socket_options(socket_t sock) { - int yes = 1; + int opt = 1; #ifdef _WIN32 - setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&yes), - sizeof(yes)); - setsockopt(sock, SOL_SOCKET, SO_EXCLUSIVEADDRUSE, - reinterpret_cast(&yes), sizeof(yes)); + setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, + reinterpret_cast(&opt), sizeof(opt)); #else #ifdef SO_REUSEPORT - setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, reinterpret_cast(&yes), - sizeof(yes)); + setsockopt(sock, SOL_SOCKET, SO_REUSEPORT, + reinterpret_cast(&opt), sizeof(opt)); #else - setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, reinterpret_cast(&yes), - sizeof(yes)); + setsockopt(sock, SOL_SOCKET, SO_REUSEADDR, + reinterpret_cast(&opt), sizeof(opt)); #endif #endif } +inline const char *status_message(int status) { + switch (status) { + case StatusCode::Continue_100: return "Continue"; + case StatusCode::SwitchingProtocol_101: return "Switching Protocol"; + case StatusCode::Processing_102: return "Processing"; + case StatusCode::EarlyHints_103: return "Early Hints"; + case StatusCode::OK_200: return "OK"; + case StatusCode::Created_201: return "Created"; + case StatusCode::Accepted_202: return "Accepted"; + case StatusCode::NonAuthoritativeInformation_203: + return "Non-Authoritative Information"; + case StatusCode::NoContent_204: return "No Content"; + case StatusCode::ResetContent_205: return "Reset Content"; + case StatusCode::PartialContent_206: return "Partial Content"; + case StatusCode::MultiStatus_207: return "Multi-Status"; + case StatusCode::AlreadyReported_208: return "Already Reported"; + case StatusCode::IMUsed_226: return "IM Used"; + case StatusCode::MultipleChoices_300: return "Multiple Choices"; + case StatusCode::MovedPermanently_301: return "Moved Permanently"; + case StatusCode::Found_302: return "Found"; + case StatusCode::SeeOther_303: return "See Other"; + case StatusCode::NotModified_304: return "Not Modified"; + case StatusCode::UseProxy_305: return "Use Proxy"; + case StatusCode::unused_306: return "unused"; + case StatusCode::TemporaryRedirect_307: return "Temporary Redirect"; + case StatusCode::PermanentRedirect_308: return "Permanent Redirect"; + case StatusCode::BadRequest_400: return "Bad Request"; + case StatusCode::Unauthorized_401: return "Unauthorized"; + case StatusCode::PaymentRequired_402: return "Payment Required"; + case StatusCode::Forbidden_403: return "Forbidden"; + case StatusCode::NotFound_404: return "Not Found"; + case StatusCode::MethodNotAllowed_405: return "Method Not Allowed"; + case StatusCode::NotAcceptable_406: return "Not Acceptable"; + case StatusCode::ProxyAuthenticationRequired_407: + return "Proxy Authentication Required"; + case StatusCode::RequestTimeout_408: return "Request Timeout"; + case StatusCode::Conflict_409: return "Conflict"; + case StatusCode::Gone_410: return "Gone"; + case StatusCode::LengthRequired_411: return "Length Required"; + case StatusCode::PreconditionFailed_412: return "Precondition Failed"; + case StatusCode::PayloadTooLarge_413: return "Payload Too Large"; + case StatusCode::UriTooLong_414: return "URI Too Long"; + case StatusCode::UnsupportedMediaType_415: return "Unsupported Media Type"; + case StatusCode::RangeNotSatisfiable_416: return "Range Not Satisfiable"; + case StatusCode::ExpectationFailed_417: return "Expectation Failed"; + case StatusCode::ImATeapot_418: return "I'm a teapot"; + case StatusCode::MisdirectedRequest_421: return "Misdirected Request"; + case StatusCode::UnprocessableContent_422: return "Unprocessable Content"; + case StatusCode::Locked_423: return "Locked"; + case StatusCode::FailedDependency_424: return "Failed Dependency"; + case StatusCode::TooEarly_425: return "Too Early"; + case StatusCode::UpgradeRequired_426: return "Upgrade Required"; + case StatusCode::PreconditionRequired_428: return "Precondition Required"; + case StatusCode::TooManyRequests_429: return "Too Many Requests"; + case StatusCode::RequestHeaderFieldsTooLarge_431: + return "Request Header Fields Too Large"; + case StatusCode::UnavailableForLegalReasons_451: + return "Unavailable For Legal Reasons"; + case StatusCode::NotImplemented_501: return "Not Implemented"; + case StatusCode::BadGateway_502: return "Bad Gateway"; + case StatusCode::ServiceUnavailable_503: return "Service Unavailable"; + case StatusCode::GatewayTimeout_504: return "Gateway Timeout"; + case StatusCode::HttpVersionNotSupported_505: + return "HTTP Version Not Supported"; + case StatusCode::VariantAlsoNegotiates_506: return "Variant Also Negotiates"; + case StatusCode::InsufficientStorage_507: return "Insufficient Storage"; + case StatusCode::LoopDetected_508: return "Loop Detected"; + case StatusCode::NotExtended_510: return "Not Extended"; + case StatusCode::NetworkAuthenticationRequired_511: + return "Network Authentication Required"; + + default: + case StatusCode::InternalServerError_500: return "Internal Server Error"; + } +} + +inline std::string get_bearer_token_auth(const Request &req) { + if (req.has_header("Authorization")) { + static std::string BearerHeaderPrefix = "Bearer "; + return req.get_header_value("Authorization") + .substr(BearerHeaderPrefix.length()); + } + return ""; +} + template inline Server & Server::set_read_timeout(const std::chrono::duration &duration) { @@ -1716,10 +2190,13 @@ inline std::string to_string(const Error error) { case Error::SSLConnection: return "SSL connection failed"; case Error::SSLLoadingCerts: return "SSL certificate loading failed"; case Error::SSLServerVerification: return "SSL server verification failed"; + case Error::SSLServerHostnameVerification: + return "SSL server hostname verification failed"; case Error::UnsupportedMultipartBoundaryChars: return "Unsupported HTTP multipart boundary characters"; case Error::Compression: return "Compression failed"; case Error::ConnectionTimeout: return "Connection timed out"; + case Error::ProxyConnection: return "Proxy connection failed"; case Error::Unknown: return "Unknown"; default: break; } @@ -1733,10 +2210,10 @@ inline std::ostream &operator<<(std::ostream &os, const Error &obj) { return os; } -template -inline T Result::get_request_header_value(const std::string &key, - size_t id) const { - return detail::get_header_value(request_headers_, key, id, 0); +inline uint64_t Result::get_request_header_value_u64(const std::string &key, + uint64_t def, + size_t id) const { + return detail::get_header_value_u64(request_headers_, key, def, id); } template @@ -1790,7 +2267,7 @@ void hosted_at(const std::string &hostname, std::vector &addrs); std::string append_query_params(const std::string &path, const Params ¶ms); -std::pair make_range_header(Ranges ranges); +std::pair make_range_header(const Ranges &ranges); std::pair make_basic_authentication_header(const std::string &username, @@ -1799,6 +2276,36 @@ make_basic_authentication_header(const std::string &username, namespace detail { +#if defined(_WIN32) +inline std::wstring u8string_to_wstring(const char *s) { + std::wstring ws; + auto len = static_cast(strlen(s)); + auto wlen = ::MultiByteToWideChar(CP_UTF8, 0, s, len, nullptr, 0); + if (wlen > 0) { + ws.resize(wlen); + wlen = ::MultiByteToWideChar( + CP_UTF8, 0, s, len, + const_cast(reinterpret_cast(ws.data())), wlen); + if (wlen != static_cast(ws.size())) { ws.clear(); } + } + return ws; +} +#endif + +struct FileStat { + FileStat(const std::string &path); + bool is_file() const; + bool is_dir() const; + +private: +#if defined(_WIN32) + struct _stat st_; +#else + struct stat st_; +#endif + int ret_ = -1; +}; + std::string encode_query_param(const std::string &value); std::string decode_url(const std::string &s, bool convert_plus_to_space); @@ -1807,26 +2314,44 @@ void read_file(const std::string &path, std::string &out); std::string trim_copy(const std::string &s); +void divide( + const char *data, std::size_t size, char d, + std::function + fn); + +void divide( + const std::string &str, char d, + std::function + fn); + void split(const char *b, const char *e, char d, std::function fn); +void split(const char *b, const char *e, char d, size_t m, + std::function fn); + bool process_client_socket(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, time_t write_timeout_usec, std::function callback); -socket_t create_client_socket( - const std::string &host, const std::string &ip, int port, - int address_family, bool tcp_nodelay, SocketOptions socket_options, - time_t connection_timeout_sec, time_t connection_timeout_usec, - time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, - time_t write_timeout_usec, const std::string &intf, Error &error); +socket_t create_client_socket(const std::string &host, const std::string &ip, + int port, int address_family, bool tcp_nodelay, + bool ipv6_v6only, SocketOptions socket_options, + time_t connection_timeout_sec, + time_t connection_timeout_usec, + time_t read_timeout_sec, time_t read_timeout_usec, + time_t write_timeout_sec, + time_t write_timeout_usec, + const std::string &intf, Error &error); const char *get_header_value(const Headers &headers, const std::string &key, - size_t id = 0, const char *def = nullptr); + const char *def, size_t id); std::string params_to_query_str(const Params ¶ms); +void parse_query_text(const char *data, std::size_t size, Params ¶ms); + void parse_query_text(const std::string &s, Params ¶ms); bool parse_multipart_boundary(const std::string &content_type, @@ -1844,7 +2369,7 @@ enum class EncodingType { None = 0, Gzip, Brotli }; EncodingType encoding_type(const Request &req, const Response &res); -class BufferStream : public Stream { +class BufferStream final : public Stream { public: BufferStream() = default; ~BufferStream() override = default; @@ -1884,19 +2409,19 @@ public: Callback callback) = 0; }; -class nocompressor : public compressor { +class nocompressor final : public compressor { public: - virtual ~nocompressor() = default; + ~nocompressor() override = default; bool compress(const char *data, size_t data_length, bool /*last*/, Callback callback) override; }; #ifdef CPPHTTPLIB_ZLIB_SUPPORT -class gzip_compressor : public compressor { +class gzip_compressor final : public compressor { public: gzip_compressor(); - ~gzip_compressor(); + ~gzip_compressor() override; bool compress(const char *data, size_t data_length, bool last, Callback callback) override; @@ -1906,10 +2431,10 @@ private: z_stream strm_; }; -class gzip_decompressor : public decompressor { +class gzip_decompressor final : public decompressor { public: gzip_decompressor(); - ~gzip_decompressor(); + ~gzip_decompressor() override; bool is_valid() const override; @@ -1923,7 +2448,7 @@ private: #endif #ifdef CPPHTTPLIB_BROTLI_SUPPORT -class brotli_compressor : public compressor { +class brotli_compressor final : public compressor { public: brotli_compressor(); ~brotli_compressor(); @@ -1935,7 +2460,7 @@ private: BrotliEncoderState *state_ = nullptr; }; -class brotli_decompressor : public decompressor { +class brotli_decompressor final : public decompressor { public: brotli_decompressor(); ~brotli_decompressor(); @@ -1972,6 +2497,84 @@ private: std::string glowable_buffer_; }; +class mmap { +public: + mmap(const char *path); + ~mmap(); + + bool open(const char *path); + void close(); + + bool is_open() const; + size_t size() const; + const char *data() const; + +private: +#if defined(_WIN32) + HANDLE hFile_ = NULL; + HANDLE hMapping_ = NULL; +#else + int fd_ = -1; +#endif + size_t size_ = 0; + void *addr_ = nullptr; + bool is_open_empty_file = false; +}; + +// NOTE: https://www.rfc-editor.org/rfc/rfc9110#section-5 +namespace fields { + +inline bool is_token_char(char c) { + return std::isalnum(c) || c == '!' || c == '#' || c == '$' || c == '%' || + c == '&' || c == '\'' || c == '*' || c == '+' || c == '-' || + c == '.' || c == '^' || c == '_' || c == '`' || c == '|' || c == '~'; +} + +inline bool is_token(const std::string &s) { + if (s.empty()) { return false; } + for (auto c : s) { + if (!is_token_char(c)) { return false; } + } + return true; +} + +inline bool is_field_name(const std::string &s) { return is_token(s); } + +inline bool is_vchar(char c) { return c >= 33 && c <= 126; } + +inline bool is_obs_text(char c) { return 128 <= static_cast(c); } + +inline bool is_field_vchar(char c) { return is_vchar(c) || is_obs_text(c); } + +inline bool is_field_content(const std::string &s) { + if (s.empty()) { return false; } + + if (s.size() == 1) { + return is_field_vchar(s[0]); + } else if (s.size() == 2) { + return is_field_vchar(s[0]) && is_field_vchar(s[1]); + } else { + size_t i = 0; + + if (!is_field_vchar(s[i])) { return false; } + i++; + + while (i < s.size() - 1) { + auto c = s[i++]; + if (c == ' ' || c == '\t' || is_field_vchar(c)) { + } else { + return false; + } + } + + return is_field_vchar(s[i]); + } +} + +inline bool is_field_value(const std::string &s) { return is_field_content(s); } + +} // namespace fields + } // namespace detail // ---------------------------------------------------------------------------- @@ -2003,7 +2606,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt, val = 0; for (; cnt; i++, cnt--) { if (!s[i]) { return false; } - int v = 0; + auto v = 0; if (is_hex(s[i], v)) { val = val * 16 + v; } else { @@ -2014,7 +2617,7 @@ inline bool from_hex_to_i(const std::string &s, size_t i, size_t cnt, } inline std::string from_i_to_hex(size_t n) { - const char *charset = "0123456789abcdef"; + static const auto charset = "0123456789abcdef"; std::string ret; do { ret = charset[n & 15] + ret; @@ -2025,7 +2628,7 @@ inline std::string from_i_to_hex(size_t n) { inline size_t to_utf8(int code, char *buff) { if (code < 0x0080) { - buff[0] = (code & 0x7F); + buff[0] = static_cast(code & 0x7F); return 1; } else if (code < 0x0800) { buff[0] = static_cast(0xC0 | ((code >> 6) & 0x1F)); @@ -2064,8 +2667,8 @@ inline std::string base64_encode(const std::string &in) { std::string out; out.reserve(in.size()); - int val = 0; - int valb = -6; + auto val = 0; + auto valb = -6; for (auto c : in) { val = (val << 8) + static_cast(c); @@ -2085,20 +2688,6 @@ inline std::string base64_encode(const std::string &in) { return out; } -inline bool is_file(const std::string &path) { -#ifdef _WIN32 - return _access_s(path.c_str(), 0) == 0; -#else - struct stat st; - return stat(path.c_str(), &st) >= 0 && S_ISREG(st.st_mode); -#endif -} - -inline bool is_dir(const std::string &path) { - struct stat st; - return stat(path.c_str(), &st) >= 0 && S_ISDIR(st.st_mode); -} - inline bool is_valid_path(const std::string &path) { size_t level = 0; size_t i = 0; @@ -2112,6 +2701,11 @@ inline bool is_valid_path(const std::string &path) { // Read component auto beg = i; while (i < path.size() && path[i] != '/') { + if (path[i] == '\0') { + return false; + } else if (path[i] == '\\') { + return false; + } i++; } @@ -2136,6 +2730,21 @@ inline bool is_valid_path(const std::string &path) { return true; } +inline FileStat::FileStat(const std::string &path) { +#if defined(_WIN32) + auto wpath = u8string_to_wstring(path.c_str()); + ret_ = _wstat(wpath.c_str(), &st_); +#else + ret_ = stat(path.c_str(), &st_); +#endif +} +inline bool FileStat::is_file() const { + return ret_ >= 0 && S_ISREG(st_.st_mode); +} +inline bool FileStat::is_dir() const { + return ret_ >= 0 && S_ISDIR(st_.st_mode); +} + inline std::string encode_query_param(const std::string &value) { std::ostringstream escaped; escaped.fill('0'); @@ -2196,7 +2805,7 @@ inline std::string decode_url(const std::string &s, for (size_t i = 0; i < s.size(); i++) { if (s[i] == '%' && i + 1 < s.size()) { if (s[i + 1] == 'u') { - int val = 0; + auto val = 0; if (from_hex_to_i(s, i + 2, 4, val)) { // 4 digits Unicode codes char buff[4]; @@ -2207,7 +2816,7 @@ inline std::string decode_url(const std::string &s, result += s[i]; } } else { - int val = 0; + auto val = 0; if (from_hex_to_i(s, i + 1, 2, val)) { // 2 digits hex codes result += static_cast(val); @@ -2260,16 +2869,51 @@ inline std::string trim_copy(const std::string &s) { return s.substr(r.first, r.second - r.first); } +inline std::string trim_double_quotes_copy(const std::string &s) { + if (s.length() >= 2 && s.front() == '"' && s.back() == '"') { + return s.substr(1, s.size() - 2); + } + return s; +} + +inline void +divide(const char *data, std::size_t size, char d, + std::function + fn) { + const auto it = std::find(data, data + size, d); + const auto found = static_cast(it != data + size); + const auto lhs_data = data; + const auto lhs_size = static_cast(it - data); + const auto rhs_data = it + found; + const auto rhs_size = size - lhs_size - found; + + fn(lhs_data, lhs_size, rhs_data, rhs_size); +} + +inline void +divide(const std::string &str, char d, + std::function + fn) { + divide(str.data(), str.size(), d, std::move(fn)); +} + inline void split(const char *b, const char *e, char d, std::function fn) { + return split(b, e, d, (std::numeric_limits::max)(), std::move(fn)); +} + +inline void split(const char *b, const char *e, char d, size_t m, + std::function fn) { size_t i = 0; size_t beg = 0; + size_t count = 1; while (e ? (b + i < e) : (b[i] != '\0')) { - if (b[i] == d) { + if (b[i] == d && count < m) { auto r = trim(b, e, beg, i); if (r.first < r.second) { fn(&b[r.first], &b[r.second]); } beg = i + 1; + count++; } i++; } @@ -2310,6 +2954,10 @@ inline bool stream_line_reader::getline() { fixed_buffer_used_size_ = 0; glowable_buffer_.clear(); +#ifndef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR + char prev_byte = 0; +#endif + for (size_t i = 0;; i++) { char byte; auto n = strm_.read(&byte, 1); @@ -2326,7 +2974,12 @@ inline bool stream_line_reader::getline() { append(byte); +#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR if (byte == '\n') { break; } +#else + if (prev_byte == '\r' && byte == '\n') { break; } + prev_byte = byte; +#endif } return true; @@ -2345,6 +2998,133 @@ inline void stream_line_reader::append(char c) { } } +inline mmap::mmap(const char *path) { open(path); } + +inline mmap::~mmap() { close(); } + +inline bool mmap::open(const char *path) { + close(); + +#if defined(_WIN32) + auto wpath = u8string_to_wstring(path); + if (wpath.empty()) { return false; } + +#if _WIN32_WINNT >= _WIN32_WINNT_WIN8 + hFile_ = ::CreateFile2(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ, + OPEN_EXISTING, NULL); +#else + hFile_ = ::CreateFileW(wpath.c_str(), GENERIC_READ, FILE_SHARE_READ, NULL, + OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); +#endif + + if (hFile_ == INVALID_HANDLE_VALUE) { return false; } + + LARGE_INTEGER size{}; + if (!::GetFileSizeEx(hFile_, &size)) { return false; } + // If the following line doesn't compile due to QuadPart, update Windows SDK. + // See: + // https://github.com/yhirose/cpp-httplib/issues/1903#issuecomment-2316520721 + if (static_cast(size.QuadPart) > + (std::numeric_limits::max)()) { + // `size_t` might be 32-bits, on 32-bits Windows. + return false; + } + size_ = static_cast(size.QuadPart); + +#if _WIN32_WINNT >= _WIN32_WINNT_WIN8 + hMapping_ = + ::CreateFileMappingFromApp(hFile_, NULL, PAGE_READONLY, size_, NULL); +#else + hMapping_ = ::CreateFileMappingW(hFile_, NULL, PAGE_READONLY, 0, 0, NULL); +#endif + + // Special treatment for an empty file... + if (hMapping_ == NULL && size_ == 0) { + close(); + is_open_empty_file = true; + return true; + } + + if (hMapping_ == NULL) { + close(); + return false; + } + +#if _WIN32_WINNT >= _WIN32_WINNT_WIN8 + addr_ = ::MapViewOfFileFromApp(hMapping_, FILE_MAP_READ, 0, 0); +#else + addr_ = ::MapViewOfFile(hMapping_, FILE_MAP_READ, 0, 0, 0); +#endif + + if (addr_ == nullptr) { + close(); + return false; + } +#else + fd_ = ::open(path, O_RDONLY); + if (fd_ == -1) { return false; } + + struct stat sb; + if (fstat(fd_, &sb) == -1) { + close(); + return false; + } + size_ = static_cast(sb.st_size); + + addr_ = ::mmap(NULL, size_, PROT_READ, MAP_PRIVATE, fd_, 0); + + // Special treatment for an empty file... + if (addr_ == MAP_FAILED && size_ == 0) { + close(); + is_open_empty_file = true; + return false; + } +#endif + + return true; +} + +inline bool mmap::is_open() const { + return is_open_empty_file ? true : addr_ != nullptr; +} + +inline size_t mmap::size() const { return size_; } + +inline const char *mmap::data() const { + return is_open_empty_file ? "" : static_cast(addr_); +} + +inline void mmap::close() { +#if defined(_WIN32) + if (addr_) { + ::UnmapViewOfFile(addr_); + addr_ = nullptr; + } + + if (hMapping_) { + ::CloseHandle(hMapping_); + hMapping_ = NULL; + } + + if (hFile_ != INVALID_HANDLE_VALUE) { + ::CloseHandle(hFile_); + hFile_ = INVALID_HANDLE_VALUE; + } + + is_open_empty_file = false; +#else + if (addr_ != nullptr) { + munmap(addr_, size_); + addr_ = nullptr; + } + + if (fd_ != -1) { + ::close(fd_); + fd_ = -1; + } +#endif + size_ = 0; +} inline int close_socket(socket_t sock) { #ifdef _WIN32 return closesocket(sock); @@ -2354,10 +3134,13 @@ inline int close_socket(socket_t sock) { } template inline ssize_t handle_EINTR(T fn) { - ssize_t res = false; + ssize_t res = 0; while (true) { res = fn(); - if (res < 0 && errno == EINTR) { continue; } + if (res < 0 && errno == EINTR) { + std::this_thread::sleep_for(std::chrono::microseconds{1}); + continue; + } break; } return res; @@ -2399,7 +3182,7 @@ inline ssize_t select_read(socket_t sock, time_t sec, time_t usec) { return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); }); #else #ifndef _WIN32 - if (sock >= FD_SETSIZE) { return 1; } + if (sock >= FD_SETSIZE) { return -1; } #endif fd_set fds; @@ -2427,7 +3210,7 @@ inline ssize_t select_write(socket_t sock, time_t sec, time_t usec) { return handle_EINTR([&]() { return poll(&pfd_read, 1, timeout); }); #else #ifndef _WIN32 - if (sock >= FD_SETSIZE) { return 1; } + if (sock >= FD_SETSIZE) { return -1; } #endif fd_set fds; @@ -2458,7 +3241,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec, if (poll_res == 0) { return Error::ConnectionTimeout; } if (poll_res > 0 && pfd_read.revents & (POLLIN | POLLOUT)) { - int error = 0; + auto error = 0; socklen_t len = sizeof(error); auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len); @@ -2490,7 +3273,7 @@ inline Error wait_until_socket_is_ready(socket_t sock, time_t sec, if (ret == 0) { return Error::ConnectionTimeout; } if (ret > 0 && (FD_ISSET(sock, &fdsr) || FD_ISSET(sock, &fdsw))) { - int error = 0; + auto error = 0; socklen_t len = sizeof(error); auto res = getsockopt(sock, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len); @@ -2512,7 +3295,7 @@ inline bool is_socket_alive(socket_t sock) { return detail::read_socket(sock, &buf[0], sizeof(buf), MSG_PEEK) > 0; } -class SocketStream : public Stream { +class SocketStream final : public Stream { public: SocketStream(socket_t sock, time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, time_t write_timeout_usec); @@ -2537,11 +3320,11 @@ private: size_t read_buff_off_ = 0; size_t read_buff_content_size_ = 0; - static const size_t read_buff_size_ = 1024 * 4; + static const size_t read_buff_size_ = 1024l * 4; }; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT -class SSLSocketStream : public Stream { +class SSLSocketStream final : public Stream { public: SSLSocketStream(socket_t sock, SSL *ssl, time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, @@ -2566,23 +3349,37 @@ private: }; #endif -inline bool keep_alive(socket_t sock, time_t keep_alive_timeout_sec) { +inline bool keep_alive(const std::atomic &svr_sock, socket_t sock, + time_t keep_alive_timeout_sec) { using namespace std::chrono; - auto start = steady_clock::now(); + + const auto interval_usec = + CPPHTTPLIB_KEEPALIVE_TIMEOUT_CHECK_INTERVAL_USECOND; + + // Avoid expensive `steady_clock::now()` call for the first time + if (select_read(sock, 0, interval_usec) > 0) { return true; } + + const auto start = steady_clock::now() - microseconds{interval_usec}; + const auto timeout = seconds{keep_alive_timeout_sec}; + while (true) { - auto val = select_read(sock, 0, 10000); + if (svr_sock == INVALID_SOCKET) { + break; // Server socket is closed + } + + auto val = select_read(sock, 0, interval_usec); if (val < 0) { - return false; + break; // Ssocket error } else if (val == 0) { - auto current = steady_clock::now(); - auto duration = duration_cast(current - start); - auto timeout = keep_alive_timeout_sec * 1000; - if (duration.count() > timeout) { return false; } - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + if (steady_clock::now() - start > timeout) { + break; // Timeout + } } else { - return true; + return true; // Ready for read } } + + return false; } template @@ -2593,8 +3390,7 @@ process_server_socket_core(const std::atomic &svr_sock, socket_t sock, assert(keep_alive_max_count > 0); auto ret = false; auto count = keep_alive_max_count; - while (svr_sock != INVALID_SOCKET && count > 0 && - keep_alive(sock, keep_alive_timeout_sec)) { + while (count > 0 && keep_alive(svr_sock, sock, keep_alive_timeout_sec)) { auto close_connection = count == 1; auto connection_closed = false; ret = callback(close_connection, connection_closed); @@ -2638,10 +3434,29 @@ inline int shutdown_socket(socket_t sock) { #endif } +inline std::string escape_abstract_namespace_unix_domain(const std::string &s) { + if (s.size() > 1 && s[0] == '\0') { + auto ret = s; + ret[0] = '@'; + return ret; + } + return s; +} + +inline std::string +unescape_abstract_namespace_unix_domain(const std::string &s) { + if (s.size() > 1 && s[0] == '@') { + auto ret = s; + ret[0] = '\0'; + return ret; + } + return s; +} + template socket_t create_socket(const std::string &host, const std::string &ip, int port, int address_family, int socket_flags, bool tcp_nodelay, - SocketOptions socket_options, + bool ipv6_v6only, SocketOptions socket_options, BindOrConnect bind_or_connect) { // Get address info const char *node = nullptr; @@ -2650,7 +3465,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, memset(&hints, 0, sizeof(struct addrinfo)); hints.ai_socktype = SOCK_STREAM; - hints.ai_protocol = 0; + hints.ai_protocol = IPPROTO_IP; if (!ip.empty()) { node = ip.c_str(); @@ -2666,22 +3481,34 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, #ifndef _WIN32 if (hints.ai_family == AF_UNIX) { const auto addrlen = host.length(); - if (addrlen > sizeof(sockaddr_un::sun_path)) return INVALID_SOCKET; + if (addrlen > sizeof(sockaddr_un::sun_path)) { return INVALID_SOCKET; } +#ifdef SOCK_CLOEXEC + auto sock = socket(hints.ai_family, hints.ai_socktype | SOCK_CLOEXEC, + hints.ai_protocol); +#else auto sock = socket(hints.ai_family, hints.ai_socktype, hints.ai_protocol); +#endif + if (sock != INVALID_SOCKET) { sockaddr_un addr{}; addr.sun_family = AF_UNIX; - std::copy(host.begin(), host.end(), addr.sun_path); + + auto unescaped_host = unescape_abstract_namespace_unix_domain(host); + std::copy(unescaped_host.begin(), unescaped_host.end(), addr.sun_path); hints.ai_addr = reinterpret_cast(&addr); hints.ai_addrlen = static_cast( sizeof(addr) - sizeof(addr.sun_path) + addrlen); +#ifndef SOCK_CLOEXEC fcntl(sock, F_SETFD, FD_CLOEXEC); +#endif + if (socket_options) { socket_options(sock); } - if (!bind_or_connect(sock, hints)) { + bool dummy; + if (!bind_or_connect(sock, hints, dummy)) { close_socket(sock); sock = INVALID_SOCKET; } @@ -2698,6 +3525,7 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, #endif return INVALID_SOCKET; } + auto se = detail::scope_exit([&] { freeaddrinfo(result); }); for (auto rp = result; rp; rp = rp->ai_next) { // Create a socket @@ -2723,11 +3551,18 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol); } #else + +#ifdef SOCK_CLOEXEC + auto sock = + socket(rp->ai_family, rp->ai_socktype | SOCK_CLOEXEC, rp->ai_protocol); +#else auto sock = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol); +#endif + #endif if (sock == INVALID_SOCKET) { continue; } -#ifndef _WIN32 +#if !defined _WIN32 && !defined SOCK_CLOEXEC if (fcntl(sock, F_SETFD, FD_CLOEXEC) == -1) { close_socket(sock); continue; @@ -2735,29 +3570,38 @@ socket_t create_socket(const std::string &host, const std::string &ip, int port, #endif if (tcp_nodelay) { - int yes = 1; - setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&yes), - sizeof(yes)); + auto opt = 1; +#ifdef _WIN32 + setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, + reinterpret_cast(&opt), sizeof(opt)); +#else + setsockopt(sock, IPPROTO_TCP, TCP_NODELAY, + reinterpret_cast(&opt), sizeof(opt)); +#endif + } + + if (rp->ai_family == AF_INET6) { + auto opt = ipv6_v6only ? 1 : 0; +#ifdef _WIN32 + setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, + reinterpret_cast(&opt), sizeof(opt)); +#else + setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, + reinterpret_cast(&opt), sizeof(opt)); +#endif } if (socket_options) { socket_options(sock); } - if (rp->ai_family == AF_INET6) { - int no = 0; - setsockopt(sock, IPPROTO_IPV6, IPV6_V6ONLY, reinterpret_cast(&no), - sizeof(no)); - } - // bind or connect - if (bind_or_connect(sock, *rp)) { - freeaddrinfo(result); - return sock; - } + auto quit = false; + if (bind_or_connect(sock, *rp, quit)) { return sock; } close_socket(sock); + + if (quit) { break; } } - freeaddrinfo(result); return INVALID_SOCKET; } @@ -2790,6 +3634,7 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) { hints.ai_protocol = 0; if (getaddrinfo(host.c_str(), "0", &hints, &result)) { return false; } + auto se = detail::scope_exit([&] { freeaddrinfo(result); }); auto ret = false; for (auto rp = result; rp; rp = rp->ai_next) { @@ -2800,11 +3645,10 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) { } } - freeaddrinfo(result); return ret; } -#if !defined _WIN32 && !defined ANDROID && !defined _AIX +#if !defined _WIN32 && !defined ANDROID && !defined _AIX && !defined __MVS__ #define USE_IF2IP #endif @@ -2812,6 +3656,8 @@ inline bool bind_ip_address(socket_t sock, const std::string &host) { inline std::string if2ip(int address_family, const std::string &ifn) { struct ifaddrs *ifap; getifaddrs(&ifap); + auto se = detail::scope_exit([&] { freeifaddrs(ifap); }); + std::string addr_candidate; for (auto ifa = ifap; ifa; ifa = ifa->ifa_next) { if (ifa->ifa_addr && ifn == ifa->ifa_name && @@ -2821,7 +3667,6 @@ inline std::string if2ip(int address_family, const std::string &ifn) { auto sa = reinterpret_cast(ifa->ifa_addr); char buf[INET_ADDRSTRLEN]; if (inet_ntop(AF_INET, &sa->sin_addr, buf, INET_ADDRSTRLEN)) { - freeifaddrs(ifap); return std::string(buf, INET_ADDRSTRLEN); } } else if (ifa->ifa_addr->sa_family == AF_INET6) { @@ -2834,7 +3679,6 @@ inline std::string if2ip(int address_family, const std::string &ifn) { if (s6_addr_head == 0xfc || s6_addr_head == 0xfd) { addr_candidate = std::string(buf, INET6_ADDRSTRLEN); } else { - freeifaddrs(ifap); return std::string(buf, INET6_ADDRSTRLEN); } } @@ -2842,25 +3686,26 @@ inline std::string if2ip(int address_family, const std::string &ifn) { } } } - freeifaddrs(ifap); return addr_candidate; } #endif inline socket_t create_client_socket( const std::string &host, const std::string &ip, int port, - int address_family, bool tcp_nodelay, SocketOptions socket_options, - time_t connection_timeout_sec, time_t connection_timeout_usec, - time_t read_timeout_sec, time_t read_timeout_usec, time_t write_timeout_sec, + int address_family, bool tcp_nodelay, bool ipv6_v6only, + SocketOptions socket_options, time_t connection_timeout_sec, + time_t connection_timeout_usec, time_t read_timeout_sec, + time_t read_timeout_usec, time_t write_timeout_sec, time_t write_timeout_usec, const std::string &intf, Error &error) { auto sock = create_socket( - host, ip, port, address_family, 0, tcp_nodelay, std::move(socket_options), - [&](socket_t sock2, struct addrinfo &ai) -> bool { + host, ip, port, address_family, 0, tcp_nodelay, ipv6_v6only, + std::move(socket_options), + [&](socket_t sock2, struct addrinfo &ai, bool &quit) -> bool { if (!intf.empty()) { #ifdef USE_IF2IP auto ip_from_if = if2ip(address_family, intf); if (ip_from_if.empty()) { ip_from_if = intf; } - if (!bind_ip_address(sock2, ip_from_if.c_str())) { + if (!bind_ip_address(sock2, ip_from_if)) { error = Error::BindIPAddress; return false; } @@ -2879,7 +3724,10 @@ inline socket_t create_client_socket( } error = wait_until_socket_is_ready(sock2, connection_timeout_sec, connection_timeout_usec); - if (error != Error::Success) { return false; } + if (error != Error::Success) { + if (error == Error::ConnectionTimeout) { quit = true; } + return false; + } } set_nonblocking(sock2, false); @@ -2888,13 +3736,14 @@ inline socket_t create_client_socket( #ifdef _WIN32 auto timeout = static_cast(read_timeout_sec * 1000 + read_timeout_usec / 1000); - setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(read_timeout_sec); tv.tv_usec = static_cast(read_timeout_usec); - setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock2, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } { @@ -2902,13 +3751,14 @@ inline socket_t create_client_socket( #ifdef _WIN32 auto timeout = static_cast(write_timeout_sec * 1000 + write_timeout_usec / 1000); - setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(write_timeout_sec); tv.tv_usec = static_cast(write_timeout_usec); - setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock2, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } @@ -3002,24 +3852,26 @@ inline unsigned int str2tag(const std::string &s) { namespace udl { -inline constexpr unsigned int operator"" _t(const char *s, size_t l) { +inline constexpr unsigned int operator""_t(const char *s, size_t l) { return str2tag_core(s, l, 0); } } // namespace udl -inline const char * +inline std::string find_content_type(const std::string &path, - const std::map &user_data) { + const std::map &user_data, + const std::string &default_content_type) { auto ext = file_extension(path); auto it = user_data.find(ext); - if (it != user_data.end()) { return it->second.c_str(); } + if (it != user_data.end()) { return it->second; } using udl::operator""_t; switch (str2tag(ext)) { - default: return nullptr; + default: return default_content_type; + case "css"_t: return "text/css"; case "csv"_t: return "text/csv"; case "htm"_t: @@ -3072,76 +3924,6 @@ find_content_type(const std::string &path, } } -inline const char *status_message(int status) { - switch (status) { - case 100: return "Continue"; - case 101: return "Switching Protocol"; - case 102: return "Processing"; - case 103: return "Early Hints"; - case 200: return "OK"; - case 201: return "Created"; - case 202: return "Accepted"; - case 203: return "Non-Authoritative Information"; - case 204: return "No Content"; - case 205: return "Reset Content"; - case 206: return "Partial Content"; - case 207: return "Multi-Status"; - case 208: return "Already Reported"; - case 226: return "IM Used"; - case 300: return "Multiple Choice"; - case 301: return "Moved Permanently"; - case 302: return "Found"; - case 303: return "See Other"; - case 304: return "Not Modified"; - case 305: return "Use Proxy"; - case 306: return "unused"; - case 307: return "Temporary Redirect"; - case 308: return "Permanent Redirect"; - case 400: return "Bad Request"; - case 401: return "Unauthorized"; - case 402: return "Payment Required"; - case 403: return "Forbidden"; - case 404: return "Not Found"; - case 405: return "Method Not Allowed"; - case 406: return "Not Acceptable"; - case 407: return "Proxy Authentication Required"; - case 408: return "Request Timeout"; - case 409: return "Conflict"; - case 410: return "Gone"; - case 411: return "Length Required"; - case 412: return "Precondition Failed"; - case 413: return "Payload Too Large"; - case 414: return "URI Too Long"; - case 415: return "Unsupported Media Type"; - case 416: return "Range Not Satisfiable"; - case 417: return "Expectation Failed"; - case 418: return "I'm a teapot"; - case 421: return "Misdirected Request"; - case 422: return "Unprocessable Entity"; - case 423: return "Locked"; - case 424: return "Failed Dependency"; - case 425: return "Too Early"; - case 426: return "Upgrade Required"; - case 428: return "Precondition Required"; - case 429: return "Too Many Requests"; - case 431: return "Request Header Fields Too Large"; - case 451: return "Unavailable For Legal Reasons"; - case 501: return "Not Implemented"; - case 502: return "Bad Gateway"; - case 503: return "Service Unavailable"; - case 504: return "Gateway Timeout"; - case 505: return "HTTP Version Not Supported"; - case 506: return "Variant Also Negotiates"; - case 507: return "Insufficient Storage"; - case 508: return "Loop Detected"; - case 510: return "Not Extended"; - case 511: return "Network Authentication Required"; - - default: - case 500: return "Internal Server Error"; - } -} - inline bool can_compress_content_type(const std::string &content_type) { using udl::operator""_t; @@ -3155,8 +3937,9 @@ inline bool can_compress_content_type(const std::string &content_type) { case "application/protobuf"_t: case "application/xhtml+xml"_t: return true; - default: - return !content_type.rfind("text/", 0) && tag != "text/event-stream"_t; + case "text/event-stream"_t: return false; + + default: return !content_type.rfind("text/", 0); } } @@ -3218,7 +4001,7 @@ inline bool gzip_compressor::compress(const char *data, size_t data_length, data += strm_.avail_in; auto flush = (last && data_length == 0) ? Z_FINISH : Z_NO_FLUSH; - int ret = Z_OK; + auto ret = Z_OK; std::array buff{}; do { @@ -3262,7 +4045,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length, Callback callback) { assert(is_valid_); - int ret = Z_OK; + auto ret = Z_OK; do { constexpr size_t max_avail_in = @@ -3276,16 +4059,12 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length, data += strm_.avail_in; std::array buff{}; - while (strm_.avail_in > 0) { + while (strm_.avail_in > 0 && ret == Z_OK) { strm_.avail_out = static_cast(buff.size()); strm_.next_out = reinterpret_cast(buff.data()); - auto prev_avail_in = strm_.avail_in; - ret = inflate(&strm_, Z_NO_FLUSH); - if (prev_avail_in - strm_.avail_in == 0) { return false; } - assert(ret != Z_STREAM_ERROR); switch (ret) { case Z_NEED_DICT: @@ -3298,7 +4077,7 @@ inline bool gzip_decompressor::decompress(const char *data, size_t data_length, } } - if (ret != Z_OK && ret != Z_STREAM_END) return false; + if (ret != Z_OK && ret != Z_STREAM_END) { return false; } } while (data_length > 0); @@ -3367,7 +4146,7 @@ inline bool brotli_decompressor::decompress(const char *data, return 0; } - const uint8_t *next_in = (const uint8_t *)data; + auto next_in = reinterpret_cast(data); size_t avail_in = data_length; size_t total_out; @@ -3397,8 +4176,8 @@ inline bool has_header(const Headers &headers, const std::string &key) { } inline const char *get_header_value(const Headers &headers, - const std::string &key, size_t id, - const char *def) { + const std::string &key, const char *def, + size_t id) { auto rng = headers.equal_range(key); auto it = rng.first; std::advance(it, static_cast(id)); @@ -3406,14 +4185,6 @@ inline const char *get_header_value(const Headers &headers, return def; } -inline bool compare_case_ignore(const std::string &a, const std::string &b) { - if (a.size() != b.size()) { return false; } - for (size_t i = 0; i < b.size(); i++) { - if (::tolower(a[i]) != ::tolower(b[i])) { return false; } - } - return true; -} - template inline bool parse_header(const char *beg, const char *end, T fn) { // Skip trailing spaces and tabs. @@ -3436,12 +4207,27 @@ inline bool parse_header(const char *beg, const char *end, T fn) { p++; } - if (p < end) { + if (p <= end) { + auto key_len = key_end - beg; + if (!key_len) { return false; } + auto key = std::string(beg, key_end); - auto val = compare_case_ignore(key, "Location") + auto val = case_ignore::equal(key, "Location") ? std::string(p, end) : decode_url(std::string(p, end), false); - fn(std::move(key), std::move(val)); + + // NOTE: From RFC 9110: + // Field values containing CR, LF, or NUL characters are + // invalid and dangerous, due to the varying ways that + // implementations might parse and interpret those + // characters; a recipient of CR, LF, or NUL within a field + // value MUST either reject the message or replace each of + // those characters with SP before further processing or + // forwarding of that message. + static const std::string CR_LF_NUL("\r\n\0", 3); + if (val.find_first_of(CR_LF_NUL) != std::string::npos) { return false; } + + fn(key, val); return true; } @@ -3461,27 +4247,27 @@ inline bool read_headers(Stream &strm, Headers &headers) { if (line_reader.end_with_crlf()) { // Blank line indicates end of headers. if (line_reader.size() == 2) { break; } -#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR } else { +#ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR // Blank line indicates end of headers. if (line_reader.size() == 1) { break; } line_terminator_len = 1; - } #else - } else { continue; // Skip invalid line. - } #endif + } if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; } // Exclude line terminator auto end = line_reader.ptr() + line_reader.size() - line_terminator_len; - parse_header(line_reader.ptr(), end, - [&](std::string &&key, std::string &&val) { - headers.emplace(std::move(key), std::move(val)); - }); + if (!parse_header(line_reader.ptr(), end, + [&](const std::string &key, std::string &val) { + headers.emplace(key, val); + })) { + return false; + } } return true; @@ -3526,11 +4312,7 @@ inline bool read_content_without_length(Stream &strm, uint64_t r = 0; for (;;) { auto n = strm.read(buf, CPPHTTPLIB_RECV_BUFSIZ); - if (n < 0) { - return false; - } else if (n == 0) { - return true; - } + if (n <= 0) { return true; } if (!out(buf, static_cast(n), r, 0)) { return false; } r += static_cast(n); @@ -3566,17 +4348,28 @@ inline bool read_content_chunked(Stream &strm, T &x, if (!line_reader.getline()) { return false; } - if (strcmp(line_reader.ptr(), "\r\n")) { return false; } + if (strcmp(line_reader.ptr(), "\r\n") != 0) { return false; } if (!line_reader.getline()) { return false; } } assert(chunk_len == 0); - // Trailer - if (!line_reader.getline()) { return false; } + // NOTE: In RFC 9112, '7.1 Chunked Transfer Coding' mentiones "The chunked + // transfer coding is complete when a chunk with a chunk-size of zero is + // received, possibly followed by a trailer section, and finally terminated by + // an empty line". https://www.rfc-editor.org/rfc/rfc9112.html#section-7.1 + // + // In '7.1.3. Decoding Chunked', however, the pseudo-code in the section + // does't care for the existence of the final CRLF. In other words, it seems + // to be ok whether the final CRLF exists or not in the chunked data. + // https://www.rfc-editor.org/rfc/rfc9112.html#section-7.1.3 + // + // According to the reference code in RFC 9112, cpp-htpplib now allows + // chuncked transfer coding data without the final CRLF. + if (!line_reader.getline()) { return true; } - while (strcmp(line_reader.ptr(), "\r\n")) { + while (strcmp(line_reader.ptr(), "\r\n") != 0) { if (line_reader.size() > CPPHTTPLIB_HEADER_MAX_LENGTH) { return false; } // Exclude line terminator @@ -3584,8 +4377,8 @@ inline bool read_content_chunked(Stream &strm, T &x, auto end = line_reader.ptr() + line_reader.size() - line_terminator_len; parse_header(line_reader.ptr(), end, - [&](std::string &&key, std::string &&val) { - x.headers.emplace(std::move(key), std::move(val)); + [&](const std::string &key, const std::string &val) { + x.headers.emplace(key, val); }); if (!line_reader.getline()) { return false; } @@ -3595,8 +4388,8 @@ inline bool read_content_chunked(Stream &strm, T &x, } inline bool is_chunked_transfer_encoding(const Headers &headers) { - return !strcasecmp(get_header_value(headers, "Transfer-Encoding", 0, ""), - "chunked"); + return case_ignore::equal( + get_header_value(headers, "Transfer-Encoding", "", 0), "chunked"); } template @@ -3611,14 +4404,14 @@ bool prepare_content_receiver(T &x, int &status, #ifdef CPPHTTPLIB_ZLIB_SUPPORT decompressor = detail::make_unique(); #else - status = 415; + status = StatusCode::UnsupportedMediaType_415; return false; #endif } else if (encoding.find("br") != std::string::npos) { #ifdef CPPHTTPLIB_BROTLI_SUPPORT decompressor = detail::make_unique(); #else - status = 415; + status = StatusCode::UnsupportedMediaType_415; return false; #endif } @@ -3634,7 +4427,7 @@ bool prepare_content_receiver(T &x, int &status, }; return callback(std::move(out)); } else { - status = 500; + status = StatusCode::InternalServerError_500; return false; } } @@ -3662,8 +4455,14 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status, } else if (!has_header(x.headers, "Content-Length")) { ret = read_content_without_length(strm, out); } else { - auto len = get_header_value(x.headers, "Content-Length"); - if (len > payload_max_length) { + auto is_invalid_value = false; + auto len = get_header_value_u64(x.headers, "Content-Length", + std::numeric_limits::max(), + 0, is_invalid_value); + + if (is_invalid_value) { + ret = false; + } else if (len > payload_max_length) { exceed_payload_max_length = true; skip_content_with_length(strm, len); ret = false; @@ -3672,16 +4471,42 @@ bool read_content(Stream &strm, T &x, size_t payload_max_length, int &status, } } - if (!ret) { status = exceed_payload_max_length ? 413 : 400; } + if (!ret) { + status = exceed_payload_max_length ? StatusCode::PayloadTooLarge_413 + : StatusCode::BadRequest_400; + } return ret; }); -} // namespace detail +} + +inline ssize_t write_request_line(Stream &strm, const std::string &method, + const std::string &path) { + std::string s = method; + s += " "; + s += path; + s += " HTTP/1.1\r\n"; + return strm.write(s.data(), s.size()); +} + +inline ssize_t write_response_line(Stream &strm, int status) { + std::string s = "HTTP/1.1 "; + s += std::to_string(status); + s += " "; + s += httplib::status_message(status); + s += "\r\n"; + return strm.write(s.data(), s.size()); +} inline ssize_t write_headers(Stream &strm, const Headers &headers) { ssize_t write_len = 0; for (const auto &x : headers) { - auto len = - strm.write_format("%s: %s\r\n", x.first.c_str(), x.second.c_str()); + std::string s; + s = x.first; + s += ": "; + s += x.second; + s += "\r\n"; + + auto len = strm.write(s.data(), s.size()); if (len < 0) { return len; } write_len += len; } @@ -3720,6 +4545,8 @@ inline bool write_content(Stream &strm, const ContentProvider &content_provider, return ok; }; + data_sink.is_writable = [&]() -> bool { return strm.is_writable(); }; + while (offset < end_offset && !is_shutting_down()) { if (!strm.is_writable()) { error = Error::Write; @@ -3764,6 +4591,8 @@ write_content_without_length(Stream &strm, return ok; }; + data_sink.is_writable = [&]() -> bool { return strm.is_writable(); }; + data_sink.done = [&](void) { data_available = false; }; while (data_available && !is_shutting_down()) { @@ -3814,6 +4643,8 @@ write_content_chunked(Stream &strm, const ContentProvider &content_provider, return ok; }; + data_sink.is_writable = [&]() -> bool { return strm.is_writable(); }; + auto done_with_trailer = [&](const Headers *trailer) { if (!ok) { return; } @@ -3898,7 +4729,8 @@ inline bool redirect(T &cli, Request &req, Response &res, new_req.path = path; new_req.redirect_count_ -= 1; - if (res.status == 303 && (req.method != "GET" && req.method != "HEAD")) { + if (res.status == StatusCode::SeeOther_303 && + (req.method != "GET" && req.method != "HEAD")) { new_req.method = "GET"; new_req.body.clear(); new_req.headers.clear(); @@ -3910,7 +4742,8 @@ inline bool redirect(T &cli, Request &req, Response &res, if (ret) { req = new_req; res = new_res; - res.location = location; + + if (res.location.empty()) { res.location = location; } } return ret; } @@ -3927,9 +4760,47 @@ inline std::string params_to_query_str(const Params ¶ms) { return query; } -inline void parse_query_text(const std::string &s, Params ¶ms) { +inline void parse_query_text(const char *data, std::size_t size, + Params ¶ms) { std::set cache; - split(s.data(), s.data() + s.size(), '&', [&](const char *b, const char *e) { + split(data, data + size, '&', [&](const char *b, const char *e) { + std::string kv(b, e); + if (cache.find(kv) != cache.end()) { return; } + cache.insert(std::move(kv)); + + std::string key; + std::string val; + divide(b, static_cast(e - b), '=', + [&](const char *lhs_data, std::size_t lhs_size, const char *rhs_data, + std::size_t rhs_size) { + key.assign(lhs_data, lhs_size); + val.assign(rhs_data, rhs_size); + }); + + if (!key.empty()) { + params.emplace(decode_url(key, true), decode_url(val, true)); + } + }); +} + +inline void parse_query_text(const std::string &s, Params ¶ms) { + parse_query_text(s.data(), s.size(), params); +} + +inline bool parse_multipart_boundary(const std::string &content_type, + std::string &boundary) { + auto boundary_keyword = "boundary="; + auto pos = content_type.find(boundary_keyword); + if (pos == std::string::npos) { return false; } + auto end = content_type.find(';', pos); + auto beg = pos + strlen(boundary_keyword); + boundary = trim_double_quotes_copy(content_type.substr(beg, end - beg)); + return !boundary.empty(); +} + +inline void parse_disposition_params(const std::string &s, Params ¶ms) { + std::set cache; + split(s.data(), s.data() + s.size(), ';', [&](const char *b, const char *e) { std::string kv(b, e); if (cache.find(kv) != cache.end()) { return; } cache.insert(kv); @@ -3945,60 +4816,55 @@ inline void parse_query_text(const std::string &s, Params ¶ms) { }); if (!key.empty()) { - params.emplace(decode_url(key, true), decode_url(val, true)); + params.emplace(trim_double_quotes_copy((key)), + trim_double_quotes_copy((val))); } }); } -inline bool parse_multipart_boundary(const std::string &content_type, - std::string &boundary) { - auto boundary_keyword = "boundary="; - auto pos = content_type.find(boundary_keyword); - if (pos == std::string::npos) { return false; } - auto end = content_type.find(';', pos); - auto beg = pos + strlen(boundary_keyword); - boundary = content_type.substr(beg, end - beg); - if (boundary.length() >= 2 && boundary.front() == '"' && - boundary.back() == '"') { - boundary = boundary.substr(1, boundary.size() - 2); - } - return !boundary.empty(); -} - #ifdef CPPHTTPLIB_NO_EXCEPTIONS inline bool parse_range_header(const std::string &s, Ranges &ranges) { #else inline bool parse_range_header(const std::string &s, Ranges &ranges) try { #endif - static auto re_first_range = std::regex(R"(bytes=(\d*-\d*(?:,\s*\d*-\d*)*))"); - std::smatch m; - if (std::regex_match(s, m, re_first_range)) { - auto pos = static_cast(m.position(1)); - auto len = static_cast(m.length(1)); - bool all_valid_ranges = true; + auto is_valid = [](const std::string &str) { + return std::all_of(str.cbegin(), str.cend(), + [](unsigned char c) { return std::isdigit(c); }); + }; + + if (s.size() > 7 && s.compare(0, 6, "bytes=") == 0) { + const auto pos = static_cast(6); + const auto len = static_cast(s.size() - 6); + auto all_valid_ranges = true; split(&s[pos], &s[pos + len], ',', [&](const char *b, const char *e) { - if (!all_valid_ranges) return; - static auto re_another_range = std::regex(R"(\s*(\d*)-(\d*))"); - std::cmatch cm; - if (std::regex_match(b, e, cm, re_another_range)) { - ssize_t first = -1; - if (!cm.str(1).empty()) { - first = static_cast(std::stoll(cm.str(1))); - } + if (!all_valid_ranges) { return; } - ssize_t last = -1; - if (!cm.str(2).empty()) { - last = static_cast(std::stoll(cm.str(2))); - } - - if (first != -1 && last != -1 && first > last) { - all_valid_ranges = false; - return; - } - ranges.emplace_back(std::make_pair(first, last)); + const auto it = std::find(b, e, '-'); + if (it == e) { + all_valid_ranges = false; + return; } + + const auto lhs = std::string(b, it); + const auto rhs = std::string(it + 1, e); + if (!is_valid(lhs) || !is_valid(rhs)) { + all_valid_ranges = false; + return; + } + + const auto first = + static_cast(lhs.empty() ? -1 : std::stoll(lhs)); + const auto last = + static_cast(rhs.empty() ? -1 : std::stoll(rhs)); + if ((first == -1 && last == -1) || + (first != -1 && last != -1 && first > last)) { + all_valid_ranges = false; + return; + } + + ranges.emplace_back(first, last); }); - return all_valid_ranges; + return all_valid_ranges && !ranges.empty(); } return false; #ifdef CPPHTTPLIB_NO_EXCEPTIONS @@ -4022,11 +4888,6 @@ public: bool parse(const char *buf, size_t n, const ContentReceiver &content_callback, const MultipartContentHeader &header_callback) { - // TODO: support 'filename*' - static const std::regex re_content_disposition( - R"~(^Content-Disposition:\s*form-data;\s*name="(.*?)"(?:;\s*filename="(.*?)")?(?:;\s*filename\*=\S+)?\s*$)~", - std::regex_constants::icase); - buf_append(buf, n); while (buf_size() > 0) { @@ -4059,18 +4920,54 @@ public: break; } - static const std::string header_name = "content-type:"; const auto header = buf_head(pos); - if (start_with_case_ignore(header, header_name)) { - file_.content_type = trim_copy(header.substr(header_name.size())); + + if (!parse_header(header.data(), header.data() + header.size(), + [&](const std::string &, const std::string &) {})) { + is_valid_ = false; + return false; + } + + static const std::string header_content_type = "Content-Type:"; + + if (start_with_case_ignore(header, header_content_type)) { + file_.content_type = + trim_copy(header.substr(header_content_type.size())); } else { + static const std::regex re_content_disposition( + R"~(^Content-Disposition:\s*form-data;\s*(.*)$)~", + std::regex_constants::icase); + std::smatch m; if (std::regex_match(header, m, re_content_disposition)) { - file_.name = m[1]; - file_.filename = m[2]; - } else { - is_valid_ = false; - return false; + Params params; + parse_disposition_params(m[1], params); + + auto it = params.find("name"); + if (it != params.end()) { + file_.name = it->second; + } else { + is_valid_ = false; + return false; + } + + it = params.find("filename"); + if (it != params.end()) { file_.filename = it->second; } + + it = params.find("filename*"); + if (it != params.end()) { + // Only allow UTF-8 enconnding... + static const std::regex re_rfc5987_encoding( + R"~(^UTF-8''(.+?)$)~", std::regex_constants::icase); + + std::smatch m2; + if (std::regex_match(it->second, m2, re_rfc5987_encoding)) { + file_.filename = decode_url(m2[1], false); // override... + } else { + is_valid_ = false; + return false; + } + } } } buf_erase(pos + crlf_.size()); @@ -4108,9 +5005,9 @@ public: buf_erase(crlf_.size()); state_ = 1; } else { - if (dash_crlf_.size() > buf_size()) { return true; } - if (buf_start_with(dash_crlf_)) { - buf_erase(dash_crlf_.size()); + if (dash_.size() > buf_size()) { return true; } + if (buf_start_with(dash_)) { + buf_erase(dash_.size()); is_valid_ = true; buf_erase(buf_size()); // Remove epilogue } else { @@ -4136,14 +5033,15 @@ private: const std::string &b) const { if (a.size() < b.size()) { return false; } for (size_t i = 0; i < b.size(); i++) { - if (::tolower(a[i]) != ::tolower(b[i])) { return false; } + if (case_ignore::to_lower(a[i]) != case_ignore::to_lower(b[i])) { + return false; + } } return true; } const std::string dash_ = "--"; const std::string crlf_ = "\r\n"; - const std::string dash_crlf_ = "--\r\n"; std::string boundary_; std::string dash_boundary_crlf_; std::string crlf_dash_boundary_; @@ -4220,38 +5118,32 @@ private: size_t buf_epos_ = 0; }; -inline std::string to_lower(const char *beg, const char *end) { - std::string out; - auto it = beg; - while (it != end) { - out += static_cast(::tolower(*it)); - it++; - } - return out; -} - -inline std::string make_multipart_data_boundary() { +inline std::string random_string(size_t length) { static const char data[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; // std::random_device might actually be deterministic on some // platforms, but due to lack of support in the c++ standard library, // doing better requires either some ugly hacks or breaking portability. - std::random_device seed_gen; + static std::random_device seed_gen; // Request 128 bits of entropy for initialization - std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(), seed_gen()}; - std::mt19937 engine(seed_sequence); + static std::seed_seq seed_sequence{seed_gen(), seed_gen(), seed_gen(), + seed_gen()}; - std::string result = "--cpp-httplib-multipart-data-"; + static std::mt19937 engine(seed_sequence); - for (auto i = 0; i < 16; i++) { + std::string result; + for (size_t i = 0; i < length; i++) { result += data[engine() % (sizeof(data) - 1)]; } - return result; } +inline std::string make_multipart_data_boundary() { + return "--cpp-httplib-multipart-data-" + detail::random_string(16); +} + inline bool is_multipart_boundary_chars_valid(const std::string &boundary) { auto valid = true; for (size_t i = 0; i < boundary.size(); i++) { @@ -4304,48 +5196,107 @@ serialize_multipart_formdata(const MultipartFormDataItems &items, body += item.content + serialize_multipart_formdata_item_end(); } - if (finish) body += serialize_multipart_formdata_finish(boundary); + if (finish) { body += serialize_multipart_formdata_finish(boundary); } return body; } +inline bool range_error(Request &req, Response &res) { + if (!req.ranges.empty() && 200 <= res.status && res.status < 300) { + ssize_t contant_len = static_cast( + res.content_length_ ? res.content_length_ : res.body.size()); + + ssize_t prev_first_pos = -1; + ssize_t prev_last_pos = -1; + size_t overwrapping_count = 0; + + // NOTE: The following Range check is based on '14.2. Range' in RFC 9110 + // 'HTTP Semantics' to avoid potential denial-of-service attacks. + // https://www.rfc-editor.org/rfc/rfc9110#section-14.2 + + // Too many ranges + if (req.ranges.size() > CPPHTTPLIB_RANGE_MAX_COUNT) { return true; } + + for (auto &r : req.ranges) { + auto &first_pos = r.first; + auto &last_pos = r.second; + + if (first_pos == -1 && last_pos == -1) { + first_pos = 0; + last_pos = contant_len; + } + + if (first_pos == -1) { + first_pos = contant_len - last_pos; + last_pos = contant_len - 1; + } + + // NOTE: RFC-9110 '14.1.2. Byte Ranges': + // A client can limit the number of bytes requested without knowing the + // size of the selected representation. If the last-pos value is absent, + // or if the value is greater than or equal to the current length of the + // representation data, the byte range is interpreted as the remainder of + // the representation (i.e., the server replaces the value of last-pos + // with a value that is one less than the current length of the selected + // representation). + // https://www.rfc-editor.org/rfc/rfc9110.html#section-14.1.2-6 + if (last_pos == -1 || last_pos >= contant_len) { + last_pos = contant_len - 1; + } + + // Range must be within content length + if (!(0 <= first_pos && first_pos <= last_pos && + last_pos <= contant_len - 1)) { + return true; + } + + // Ranges must be in ascending order + if (first_pos <= prev_first_pos) { return true; } + + // Request must not have more than two overlapping ranges + if (first_pos <= prev_last_pos) { + overwrapping_count++; + if (overwrapping_count > 2) { return true; } + } + + prev_first_pos = (std::max)(prev_first_pos, first_pos); + prev_last_pos = (std::max)(prev_last_pos, last_pos); + } + } + + return false; +} + inline std::pair -get_range_offset_and_length(const Request &req, size_t content_length, - size_t index) { - auto r = req.ranges[index]; - - if (r.first == -1 && r.second == -1) { - return std::make_pair(0, content_length); - } - - auto slen = static_cast(content_length); - - if (r.first == -1) { - r.first = (std::max)(static_cast(0), slen - r.second); - r.second = slen - 1; - } - - if (r.second == -1) { r.second = slen - 1; } +get_range_offset_and_length(Range r, size_t content_length) { + assert(r.first != -1 && r.second != -1); + assert(0 <= r.first && r.first < static_cast(content_length)); + assert(r.first <= r.second && + r.second < static_cast(content_length)); + (void)(content_length); return std::make_pair(r.first, static_cast(r.second - r.first) + 1); } -inline std::string make_content_range_header_field(size_t offset, size_t length, - size_t content_length) { +inline std::string make_content_range_header_field( + const std::pair &offset_and_length, size_t content_length) { + auto st = offset_and_length.first; + auto ed = st + offset_and_length.second - 1; + std::string field = "bytes "; - field += std::to_string(offset); + field += std::to_string(st); field += "-"; - field += std::to_string(offset + length - 1); + field += std::to_string(ed); field += "/"; field += std::to_string(content_length); return field; } template -bool process_multipart_ranges_data(const Request &req, Response &res, +bool process_multipart_ranges_data(const Request &req, const std::string &boundary, const std::string &content_type, - SToken stoken, CToken ctoken, - Content content) { + size_t content_length, SToken stoken, + CToken ctoken, Content content) { for (size_t i = 0; i < req.ranges.size(); i++) { ctoken("--"); stoken(boundary); @@ -4356,50 +5307,51 @@ bool process_multipart_ranges_data(const Request &req, Response &res, ctoken("\r\n"); } - auto offsets = get_range_offset_and_length(req, res.body.size(), i); - auto offset = offsets.first; - auto length = offsets.second; + auto offset_and_length = + get_range_offset_and_length(req.ranges[i], content_length); ctoken("Content-Range: "); - stoken(make_content_range_header_field(offset, length, res.body.size())); + stoken(make_content_range_header_field(offset_and_length, content_length)); ctoken("\r\n"); ctoken("\r\n"); - if (!content(offset, length)) { return false; } + + if (!content(offset_and_length.first, offset_and_length.second)) { + return false; + } ctoken("\r\n"); } ctoken("--"); stoken(boundary); - ctoken("--\r\n"); + ctoken("--"); return true; } -inline bool make_multipart_ranges_data(const Request &req, Response &res, +inline void make_multipart_ranges_data(const Request &req, Response &res, const std::string &boundary, const std::string &content_type, + size_t content_length, std::string &data) { - return process_multipart_ranges_data( - req, res, boundary, content_type, + process_multipart_ranges_data( + req, boundary, content_type, content_length, [&](const std::string &token) { data += token; }, [&](const std::string &token) { data += token; }, [&](size_t offset, size_t length) { - if (offset < res.body.size()) { - data += res.body.substr(offset, length); - return true; - } - return false; + assert(offset + length <= content_length); + data += res.body.substr(offset, length); + return true; }); } -inline size_t -get_multipart_ranges_data_length(const Request &req, Response &res, - const std::string &boundary, - const std::string &content_type) { +inline size_t get_multipart_ranges_data_length(const Request &req, + const std::string &boundary, + const std::string &content_type, + size_t content_length) { size_t data_length = 0; process_multipart_ranges_data( - req, res, boundary, content_type, + req, boundary, content_type, content_length, [&](const std::string &token) { data_length += token.size(); }, [&](const std::string &token) { data_length += token.size(); }, [&](size_t /*offset*/, size_t length) { @@ -4411,13 +5363,13 @@ get_multipart_ranges_data_length(const Request &req, Response &res, } template -inline bool write_multipart_ranges_data(Stream &strm, const Request &req, - Response &res, - const std::string &boundary, - const std::string &content_type, - const T &is_shutting_down) { +inline bool +write_multipart_ranges_data(Stream &strm, const Request &req, Response &res, + const std::string &boundary, + const std::string &content_type, + size_t content_length, const T &is_shutting_down) { return process_multipart_ranges_data( - req, res, boundary, content_type, + req, boundary, content_type, content_length, [&](const std::string &token) { strm.write(token); }, [&](const std::string &token) { strm.write(token); }, [&](size_t offset, size_t length) { @@ -4426,18 +5378,6 @@ inline bool write_multipart_ranges_data(Stream &strm, const Request &req, }); } -inline std::pair -get_range_offset_and_length(const Request &req, const Response &res, - size_t index) { - auto r = req.ranges[index]; - - if (r.second == -1) { - r.second = static_cast(res.content_length_) - 1; - } - - return std::make_pair(r.first, r.second - r.first + 1); -} - inline bool expect_content(const Request &req) { if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH" || req.method == "PRI" || req.method == "DELETE") { @@ -4471,7 +5411,7 @@ inline std::string message_digest(const std::string &s, const EVP_MD *algo) { std::stringstream ss; for (auto i = 0u; i < hash_length; ++i) { ss << std::hex << std::setw(2) << std::setfill('0') - << (unsigned int)hash[i]; + << static_cast(hash[i]); } return ss.str(); @@ -4564,7 +5504,7 @@ inline bool retrieve_root_certs_from_keychain(CFObjectPtr &certs) { inline bool add_certs_to_x509_store(CFArrayRef certs, X509_STORE *store) { auto result = false; - for (int i = 0; i < CFArrayGetCount(certs); ++i) { + for (auto i = 0; i < CFArrayGetCount(certs); ++i) { const auto cert = reinterpret_cast( CFArrayGetValueAtIndex(certs, i)); @@ -4707,7 +5647,7 @@ inline bool parse_www_authenticate(const Response &res, s = s.substr(pos + 1); auto beg = std::sregex_iterator(s.begin(), s.end(), re); for (auto i = beg; i != std::sregex_iterator(); ++i) { - auto m = *i; + const auto &m = *i; auto key = s.substr(static_cast(m.position(1)), static_cast(m.length(1))); auto val = m.length(2) > 0 @@ -4724,20 +5664,6 @@ inline bool parse_www_authenticate(const Response &res, return false; } -// https://stackoverflow.com/questions/440133/how-do-i-create-a-random-alpha-numeric-string-in-c/440240#answer-440240 -inline std::string random_string(size_t length) { - auto randchar = []() -> char { - const char charset[] = "0123456789" - "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - "abcdefghijklmnopqrstuvwxyz"; - const size_t max_index = (sizeof(charset) - 1); - return charset[static_cast(std::rand()) % max_index]; - }; - std::string str(length, 0); - std::generate_n(str.begin(), length, randchar); - return str; -} - class ContentProviderAdapter { public: explicit ContentProviderAdapter( @@ -4777,19 +5703,18 @@ inline void hosted_at(const std::string &hostname, #endif return; } + auto se = detail::scope_exit([&] { freeaddrinfo(result); }); for (auto rp = result; rp; rp = rp->ai_next) { const auto &addr = *reinterpret_cast(rp->ai_addr); std::string ip; - int dummy = -1; + auto dummy = -1; if (detail::get_ip_and_port(addr, sizeof(struct sockaddr_storage), ip, dummy)) { addrs.push_back(ip); } } - - freeaddrinfo(result); } inline std::string append_query_params(const std::string &path, @@ -4802,10 +5727,11 @@ inline std::string append_query_params(const std::string &path, } // Header utilities -inline std::pair make_range_header(Ranges ranges) { +inline std::pair +make_range_header(const Ranges &ranges) { std::string field = "bytes="; auto i = 0; - for (auto r : ranges) { + for (const auto &r : ranges) { if (i != 0) { field += ", "; } if (r.first != -1) { field += std::to_string(r.first); } field += '-'; @@ -4837,8 +5763,8 @@ inline bool Request::has_header(const std::string &key) const { } inline std::string Request::get_header_value(const std::string &key, - size_t id) const { - return detail::get_header_value(headers, key, id, ""); + const char *def, size_t id) const { + return detail::get_header_value(headers, key, def, id); } inline size_t Request::get_header_value_count(const std::string &key) const { @@ -4848,7 +5774,8 @@ inline size_t Request::get_header_value_count(const std::string &key) const { inline void Request::set_header(const std::string &key, const std::string &val) { - if (!detail::has_crlf(key) && !detail::has_crlf(val)) { + if (detail::fields::is_field_name(key) && + detail::fields::is_field_value(val)) { headers.emplace(key, val); } } @@ -4902,8 +5829,9 @@ inline bool Response::has_header(const std::string &key) const { } inline std::string Response::get_header_value(const std::string &key, + const char *def, size_t id) const { - return detail::get_header_value(headers, key, id, ""); + return detail::get_header_value(headers, key, def, id); } inline size_t Response::get_header_value_count(const std::string &key) const { @@ -4913,18 +5841,19 @@ inline size_t Response::get_header_value_count(const std::string &key) const { inline void Response::set_header(const std::string &key, const std::string &val) { - if (!detail::has_crlf(key) && !detail::has_crlf(val)) { + if (detail::fields::is_field_name(key) && + detail::fields::is_field_value(val)) { headers.emplace(key, val); } } inline void Response::set_redirect(const std::string &url, int stat) { - if (!detail::has_crlf(url)) { + if (detail::fields::is_field_value(url)) { set_header("Location", url); if (300 <= stat && stat < 400) { this->status = stat; } else { - this->status = 302; + this->status = StatusCode::Found_302; } } } @@ -4943,13 +5872,22 @@ inline void Response::set_content(const std::string &s, set_content(s.data(), s.size(), content_type); } +inline void Response::set_content(std::string &&s, + const std::string &content_type) { + body = std::move(s); + + auto rng = headers.equal_range("Content-Type"); + headers.erase(rng.first, rng.second); + set_header("Content-Type", content_type); +} + inline void Response::set_content_provider( size_t in_length, const std::string &content_type, ContentProvider provider, ContentProviderResourceReleaser resource_releaser) { set_header("Content-Type", content_type); content_length_ = in_length; if (in_length > 0) { content_provider_ = std::move(provider); } - content_provider_resource_releaser_ = resource_releaser; + content_provider_resource_releaser_ = std::move(resource_releaser); is_chunked_content_provider_ = false; } @@ -4959,7 +5897,7 @@ inline void Response::set_content_provider( set_header("Content-Type", content_type); content_length_ = 0; content_provider_ = detail::ContentProviderAdapter(std::move(provider)); - content_provider_resource_releaser_ = resource_releaser; + content_provider_resource_releaser_ = std::move(resource_releaser); is_chunked_content_provider_ = false; } @@ -4969,18 +5907,29 @@ inline void Response::set_chunked_content_provider( set_header("Content-Type", content_type); content_length_ = 0; content_provider_ = detail::ContentProviderAdapter(std::move(provider)); - content_provider_resource_releaser_ = resource_releaser; + content_provider_resource_releaser_ = std::move(resource_releaser); is_chunked_content_provider_ = true; } +inline void Response::set_file_content(const std::string &path, + const std::string &content_type) { + file_content_path_ = path; + file_content_content_type_ = content_type; +} + +inline void Response::set_file_content(const std::string &path) { + file_content_path_ = path; +} + // Result implementation inline bool Result::has_request_header(const std::string &key) const { return request_headers_.find(key) != request_headers_.end(); } inline std::string Result::get_request_header_value(const std::string &key, + const char *def, size_t id) const { - return detail::get_header_value(request_headers_, key, id, ""); + return detail::get_header_value(request_headers_, key, def, id); } inline size_t @@ -5010,7 +5959,7 @@ inline SocketStream::SocketStream(socket_t sock, time_t read_timeout_sec, write_timeout_sec_(write_timeout_sec), write_timeout_usec_(write_timeout_usec), read_buff_(read_buff_size_, 0) {} -inline SocketStream::~SocketStream() {} +inline SocketStream::~SocketStream() = default; inline bool SocketStream::is_readable() const { return select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; @@ -5120,6 +6069,102 @@ inline socket_t BufferStream::socket() const { return 0; } inline const std::string &BufferStream::get_buffer() const { return buffer; } +inline PathParamsMatcher::PathParamsMatcher(const std::string &pattern) { + static constexpr char marker[] = "/:"; + + // One past the last ending position of a path param substring + std::size_t last_param_end = 0; + +#ifndef CPPHTTPLIB_NO_EXCEPTIONS + // Needed to ensure that parameter names are unique during matcher + // construction + // If exceptions are disabled, only last duplicate path + // parameter will be set + std::unordered_set param_name_set; +#endif + + while (true) { + const auto marker_pos = pattern.find( + marker, last_param_end == 0 ? last_param_end : last_param_end - 1); + if (marker_pos == std::string::npos) { break; } + + static_fragments_.push_back( + pattern.substr(last_param_end, marker_pos - last_param_end + 1)); + + const auto param_name_start = marker_pos + 2; + + auto sep_pos = pattern.find(separator, param_name_start); + if (sep_pos == std::string::npos) { sep_pos = pattern.length(); } + + auto param_name = + pattern.substr(param_name_start, sep_pos - param_name_start); + +#ifndef CPPHTTPLIB_NO_EXCEPTIONS + if (param_name_set.find(param_name) != param_name_set.cend()) { + std::string msg = "Encountered path parameter '" + param_name + + "' multiple times in route pattern '" + pattern + "'."; + throw std::invalid_argument(msg); + } +#endif + + param_names_.push_back(std::move(param_name)); + + last_param_end = sep_pos + 1; + } + + if (last_param_end < pattern.length()) { + static_fragments_.push_back(pattern.substr(last_param_end)); + } +} + +inline bool PathParamsMatcher::match(Request &request) const { + request.matches = std::smatch(); + request.path_params.clear(); + request.path_params.reserve(param_names_.size()); + + // One past the position at which the path matched the pattern last time + std::size_t starting_pos = 0; + for (size_t i = 0; i < static_fragments_.size(); ++i) { + const auto &fragment = static_fragments_[i]; + + if (starting_pos + fragment.length() > request.path.length()) { + return false; + } + + // Avoid unnecessary allocation by using strncmp instead of substr + + // comparison + if (std::strncmp(request.path.c_str() + starting_pos, fragment.c_str(), + fragment.length()) != 0) { + return false; + } + + starting_pos += fragment.length(); + + // Should only happen when we have a static fragment after a param + // Example: '/users/:id/subscriptions' + // The 'subscriptions' fragment here does not have a corresponding param + if (i >= param_names_.size()) { continue; } + + auto sep_pos = request.path.find(separator, starting_pos); + if (sep_pos == std::string::npos) { sep_pos = request.path.length(); } + + const auto ¶m_name = param_names_[i]; + + request.path_params.emplace( + param_name, request.path.substr(starting_pos, sep_pos - starting_pos)); + + // Mark everything up to '/' as matched + starting_pos = sep_pos + 1; + } + // Returns false if the path is longer than the pattern + return starting_pos >= request.path.length(); +} + +inline bool RegexMatcher::match(Request &request) const { + request.path_params.clear(); + return std::regex_match(request.path, request.matches, regex_); +} + } // namespace detail // HTTP server implementation @@ -5131,69 +6176,72 @@ inline Server::Server() #endif } -inline Server::~Server() {} +inline Server::~Server() = default; + +inline std::unique_ptr +Server::make_matcher(const std::string &pattern) { + if (pattern.find("/:") != std::string::npos) { + return detail::make_unique(pattern); + } else { + return detail::make_unique(pattern); + } +} inline Server &Server::Get(const std::string &pattern, Handler handler) { - get_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + get_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Post(const std::string &pattern, Handler handler) { - post_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + post_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Post(const std::string &pattern, HandlerWithContentReader handler) { - post_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + post_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Put(const std::string &pattern, Handler handler) { - put_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + put_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Put(const std::string &pattern, HandlerWithContentReader handler) { - put_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + put_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Patch(const std::string &pattern, Handler handler) { - patch_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + patch_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Patch(const std::string &pattern, HandlerWithContentReader handler) { - patch_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + patch_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Delete(const std::string &pattern, Handler handler) { - delete_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + delete_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } inline Server &Server::Delete(const std::string &pattern, HandlerWithContentReader handler) { - delete_handlers_for_content_reader_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + delete_handlers_for_content_reader_.emplace_back(make_matcher(pattern), + std::move(handler)); return *this; } inline Server &Server::Options(const std::string &pattern, Handler handler) { - options_handlers_.push_back( - std::make_pair(std::regex(pattern), std::move(handler))); + options_handlers_.emplace_back(make_matcher(pattern), std::move(handler)); return *this; } @@ -5204,7 +6252,8 @@ inline bool Server::set_base_dir(const std::string &dir, inline bool Server::set_mount_point(const std::string &mount_point, const std::string &dir, Headers headers) { - if (detail::is_dir(dir)) { + detail::FileStat stat(dir); + if (stat.is_dir()) { std::string mnt = !mount_point.empty() ? mount_point : "/"; if (!mnt.empty() && mnt[0] == '/') { base_dirs_.push_back({mnt, dir, std::move(headers)}); @@ -5231,17 +6280,24 @@ Server::set_file_extension_and_mimetype_mapping(const std::string &ext, return *this; } +inline Server &Server::set_default_file_mimetype(const std::string &mime) { + default_file_mimetype_ = mime; + return *this; +} + inline Server &Server::set_file_request_handler(Handler handler) { file_request_handler_ = std::move(handler); return *this; } -inline Server &Server::set_error_handler(HandlerWithResponse handler) { +inline Server &Server::set_error_handler_core(HandlerWithResponse handler, + std::true_type) { error_handler_ = std::move(handler); return *this; } -inline Server &Server::set_error_handler(Handler handler) { +inline Server &Server::set_error_handler_core(Handler handler, + std::false_type) { error_handler_ = [handler](const Request &req, Response &res) { handler(req, res); return HandlerResponse::Handled; @@ -5272,7 +6328,6 @@ inline Server &Server::set_logger(Logger logger) { inline Server & Server::set_expect_100_continue_handler(Expect100ContinueHandler handler) { expect_100_continue_handler_ = std::move(handler); - return *this; } @@ -5286,6 +6341,11 @@ inline Server &Server::set_tcp_nodelay(bool on) { return *this; } +inline Server &Server::set_ipv6_v6only(bool on) { + ipv6_v6only_ = on; + return *this; +} + inline Server &Server::set_socket_options(SocketOptions socket_options) { socket_options_ = std::move(socket_options); return *this; @@ -5296,6 +6356,12 @@ inline Server &Server::set_default_headers(Headers headers) { return *this; } +inline Server &Server::set_header_writer( + std::function const &writer) { + header_writer_ = writer; + return *this; +} + inline Server &Server::set_keep_alive_max_count(size_t count) { keep_alive_max_count_ = count; return *this; @@ -5331,28 +6397,27 @@ inline Server &Server::set_payload_max_length(size_t length) { inline bool Server::bind_to_port(const std::string &host, int port, int socket_flags) { - if (bind_internal(host, port, socket_flags) < 0) return false; - return true; + auto ret = bind_internal(host, port, socket_flags); + if (ret == -1) { is_decommisioned = true; } + return ret >= 0; } inline int Server::bind_to_any_port(const std::string &host, int socket_flags) { - return bind_internal(host, 0, socket_flags); + auto ret = bind_internal(host, 0, socket_flags); + if (ret == -1) { is_decommisioned = true; } + return ret; } -inline bool Server::listen_after_bind() { - auto se = detail::scope_exit([&]() { done_ = true; }); - return listen_internal(); -} +inline bool Server::listen_after_bind() { return listen_internal(); } inline bool Server::listen(const std::string &host, int port, int socket_flags) { - auto se = detail::scope_exit([&]() { done_ = true; }); return bind_to_port(host, port, socket_flags) && listen_internal(); } inline bool Server::is_running() const { return is_running_; } inline void Server::wait_until_ready() const { - while (!is_running() && !done_) { + while (!is_running_ && !is_decommisioned) { std::this_thread::sleep_for(std::chrono::milliseconds{1}); } } @@ -5364,9 +6429,12 @@ inline void Server::stop() { detail::shutdown_socket(sock); detail::close_socket(sock); } + is_decommisioned = false; } -inline bool Server::parse_request_line(const char *s, Request &req) { +inline void Server::decommission() { is_decommisioned = true; } + +inline bool Server::parse_request_line(const char *s, Request &req) const { auto len = strlen(s); if (len < 2 || s[len - 2] != '\r' || s[len - 1] != '\n') { return false; } len -= 2; @@ -5404,33 +6472,23 @@ inline bool Server::parse_request_line(const char *s, Request &req) { } } - size_t count = 0; - - detail::split(req.target.data(), req.target.data() + req.target.size(), '?', - [&](const char *b, const char *e) { - switch (count) { - case 0: - req.path = detail::decode_url(std::string(b, e), false); - break; - case 1: { - if (e - b > 0) { - detail::parse_query_text(std::string(b, e), req.params); - } - break; - } - default: break; - } - count++; - }); - - if (count > 2) { return false; } + detail::divide(req.target, '?', + [&](const char *lhs_data, std::size_t lhs_size, + const char *rhs_data, std::size_t rhs_size) { + req.path = detail::decode_url( + std::string(lhs_data, lhs_size), false); + detail::parse_query_text(rhs_data, rhs_size, req.params); + }); } return true; } inline bool Server::write_response(Stream &strm, bool close_connection, - const Request &req, Response &res) { + Request &req, Response &res) { + // NOTE: `req.ranges` should be empty, otherwise it will be applied + // incorrectly to the error content. + req.ranges.clear(); return write_response_core(strm, close_connection, req, res, false); } @@ -5459,23 +6517,24 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection, if (close_connection || req.get_header_value("Connection") == "close") { res.set_header("Connection", "close"); } else { - std::stringstream ss; - ss << "timeout=" << keep_alive_timeout_sec_ - << ", max=" << keep_alive_max_count_; - res.set_header("Keep-Alive", ss.str()); + std::string s = "timeout="; + s += std::to_string(keep_alive_timeout_sec_); + s += ", max="; + s += std::to_string(keep_alive_max_count_); + res.set_header("Keep-Alive", s); } - if (!res.has_header("Content-Type") && - (!res.body.empty() || res.content_length_ > 0 || res.content_provider_)) { + if ((!res.body.empty() || res.content_length_ > 0 || res.content_provider_) && + !res.has_header("Content-Type")) { res.set_header("Content-Type", "text/plain"); } - if (!res.has_header("Content-Length") && res.body.empty() && - !res.content_length_ && !res.content_provider_) { + if (res.body.empty() && !res.content_length_ && !res.content_provider_ && + !res.has_header("Content-Length")) { res.set_header("Content-Length", "0"); } - if (!res.has_header("Accept-Ranges") && req.method == "HEAD") { + if (req.method == "HEAD" && !res.has_header("Accept-Ranges")) { res.set_header("Accept-Ranges", "bytes"); } @@ -5484,13 +6543,8 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection, // Response line and headers { detail::BufferStream bstrm; - - if (!bstrm.write_format("HTTP/1.1 %d %s\r\n", res.status, - detail::status_message(res.status))) { - return false; - } - - if (!detail::write_headers(bstrm, res.headers)) { return false; } + if (!detail::write_response_line(bstrm, res.status)) { return false; } + if (!header_writer_(bstrm, res.headers)) { return false; } // Flush buffer auto &data = bstrm.get_buffer(); @@ -5508,7 +6562,6 @@ inline bool Server::write_response_core(Stream &strm, bool close_connection, if (write_content_with_provider(strm, req, res, boundary, content_type)) { res.content_provider_success_ = true; } else { - res.content_provider_success_ = false; ret = false; } } @@ -5533,15 +6586,16 @@ Server::write_content_with_provider(Stream &strm, const Request &req, return detail::write_content(strm, res.content_provider_, 0, res.content_length_, is_shutting_down); } else if (req.ranges.size() == 1) { - auto offsets = - detail::get_range_offset_and_length(req, res.content_length_, 0); - auto offset = offsets.first; - auto length = offsets.second; - return detail::write_content(strm, res.content_provider_, offset, length, - is_shutting_down); + auto offset_and_length = detail::get_range_offset_and_length( + req.ranges[0], res.content_length_); + + return detail::write_content(strm, res.content_provider_, + offset_and_length.first, + offset_and_length.second, is_shutting_down); } else { return detail::write_multipart_ranges_data( - strm, req, res, boundary, content_type, is_shutting_down); + strm, req, res, boundary, content_type, res.content_length_, + is_shutting_down); } } else { if (res.is_chunked_content_provider_) { @@ -5598,7 +6652,7 @@ inline bool Server::read_content(Stream &strm, Request &req, Response &res) { const auto &content_type = req.get_header_value("Content-Type"); if (!content_type.find("application/x-www-form-urlencoded")) { if (req.body.size() > CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH) { - res.status = 413; // NOTE: should be 414? + res.status = StatusCode::PayloadTooLarge_413; // NOTE: should be 414? return false; } detail::parse_query_text(req.body, req.params); @@ -5617,10 +6671,11 @@ inline bool Server::read_content_with_content_receiver( std::move(multipart_receiver)); } -inline bool Server::read_content_core(Stream &strm, Request &req, Response &res, - ContentReceiver receiver, - MultipartContentHeader multipart_header, - ContentReceiver multipart_receiver) { +inline bool +Server::read_content_core(Stream &strm, Request &req, Response &res, + ContentReceiver receiver, + MultipartContentHeader multipart_header, + ContentReceiver multipart_receiver) const { detail::MultipartFormDataParser multipart_form_data_parser; ContentReceiverWithProgress out; @@ -5628,7 +6683,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res, const auto &content_type = req.get_header_value("Content-Type"); std::string boundary; if (!detail::parse_multipart_boundary(content_type, boundary)) { - res.status = 400; + res.status = StatusCode::BadRequest_400; return false; } @@ -5664,7 +6719,7 @@ inline bool Server::read_content_core(Stream &strm, Request &req, Response &res, if (req.is_multipart_form_data()) { if (!multipart_form_data_parser.is_valid()) { - res.status = 400; + res.status = StatusCode::BadRequest_400; return false; } } @@ -5682,18 +6737,34 @@ inline bool Server::handle_file_request(const Request &req, Response &res, auto path = entry.base_dir + sub_path; if (path.back() == '/') { path += "index.html"; } - if (detail::is_file(path)) { - detail::read_file(path, res.body); - auto type = - detail::find_content_type(path, file_extension_and_mimetype_map_); - if (type) { res.set_header("Content-Type", type); } + detail::FileStat stat(path); + + if (stat.is_dir()) { + res.set_redirect(sub_path + "/", StatusCode::MovedPermanently_301); + return true; + } + + if (stat.is_file()) { for (const auto &kv : entry.headers) { - res.set_header(kv.first.c_str(), kv.second); + res.set_header(kv.first, kv.second); } - res.status = req.has_header("Range") ? 206 : 200; + + auto mm = std::make_shared(path.c_str()); + if (!mm->is_open()) { return false; } + + res.set_content_provider( + mm->size(), + detail::find_content_type(path, file_extension_and_mimetype_map_, + default_file_mimetype_), + [mm](size_t offset, size_t length, DataSink &sink) -> bool { + sink.write(mm->data() + offset, length); + return true; + }); + if (!head && file_request_handler_) { file_request_handler_(req, res); } + return true; } } @@ -5708,8 +6779,8 @@ Server::create_server_socket(const std::string &host, int port, SocketOptions socket_options) const { return detail::create_socket( host, std::string(), port, address_family_, socket_flags, tcp_nodelay_, - std::move(socket_options), - [](socket_t sock, struct addrinfo &ai) -> bool { + ipv6_v6only_, std::move(socket_options), + [](socket_t sock, struct addrinfo &ai, bool & /*quit*/) -> bool { if (::bind(sock, ai.ai_addr, static_cast(ai.ai_addrlen))) { return false; } @@ -5720,6 +6791,8 @@ Server::create_server_socket(const std::string &host, int port, inline int Server::bind_internal(const std::string &host, int port, int socket_flags) { + if (is_decommisioned) { return -1; } + if (!is_valid()) { return -1; } svr_sock_ = create_server_socket(host, port, socket_flags, socket_options_); @@ -5745,6 +6818,8 @@ inline int Server::bind_internal(const std::string &host, int port, } inline bool Server::listen_internal() { + if (is_decommisioned) { return false; } + auto ret = true; is_running_ = true; auto se = detail::scope_exit([&]() { is_running_ = false; }); @@ -5765,13 +6840,22 @@ inline bool Server::listen_internal() { #ifndef _WIN32 } #endif + +#if defined _WIN32 + // sockets conneced via WASAccept inherit flags NO_HANDLE_INHERIT, + // OVERLAPPED + socket_t sock = WSAAccept(svr_sock_, nullptr, nullptr, nullptr, 0); +#elif defined SOCK_CLOEXEC + socket_t sock = accept4(svr_sock_, nullptr, nullptr, SOCK_CLOEXEC); +#else socket_t sock = accept(svr_sock_, nullptr, nullptr); +#endif if (sock == INVALID_SOCKET) { if (errno == EMFILE) { // The per-process limit of open file descriptors has been reached. // Try to accept new connections after a short sleep. - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds{1}); continue; } else if (errno == EINTR || errno == EAGAIN) { continue; @@ -5789,13 +6873,14 @@ inline bool Server::listen_internal() { #ifdef _WIN32 auto timeout = static_cast(read_timeout_sec_ * 1000 + read_timeout_usec_ / 1000); - setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(read_timeout_sec_); tv.tv_usec = static_cast(read_timeout_usec_); - setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } { @@ -5803,22 +6888,28 @@ inline bool Server::listen_internal() { #ifdef _WIN32 auto timeout = static_cast(write_timeout_sec_ * 1000 + write_timeout_usec_ / 1000); - setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&timeout, - sizeof(timeout)); + setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&timeout), sizeof(timeout)); #else timeval tv; tv.tv_sec = static_cast(write_timeout_sec_); tv.tv_usec = static_cast(write_timeout_usec_); - setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, (char *)&tv, sizeof(tv)); + setsockopt(sock, SOL_SOCKET, SO_SNDTIMEO, + reinterpret_cast(&tv), sizeof(tv)); #endif } - task_queue->enqueue([this, sock]() { process_and_close_socket(sock); }); + if (!task_queue->enqueue( + [this, sock]() { process_and_close_socket(sock); })) { + detail::shutdown_socket(sock); + detail::close_socket(sock); + } } task_queue->shutdown(); } + is_decommisioned = !ret; return ret; } @@ -5829,7 +6920,7 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) { } // File handler - bool is_head_request = req.method == "HEAD"; + auto is_head_request = req.method == "HEAD"; if ((req.method == "GET" || is_head_request) && handle_file_request(req, res, is_head_request)) { return true; @@ -5895,17 +6986,17 @@ inline bool Server::routing(Request &req, Response &res, Stream &strm) { return dispatch_request(req, res, patch_handlers_); } - res.status = 400; + res.status = StatusCode::BadRequest_400; return false; } inline bool Server::dispatch_request(Request &req, Response &res, - const Handlers &handlers) { + const Handlers &handlers) const { for (const auto &x : handlers) { - const auto &pattern = x.first; + const auto &matcher = x.first; const auto &handler = x.second; - if (std::regex_match(req.path, req.matches, pattern)) { + if (matcher->match(req)) { handler(req, res); return true; } @@ -5915,18 +7006,18 @@ inline bool Server::dispatch_request(Request &req, Response &res, inline void Server::apply_ranges(const Request &req, Response &res, std::string &content_type, - std::string &boundary) { - if (req.ranges.size() > 1) { - boundary = detail::make_multipart_data_boundary(); - + std::string &boundary) const { + if (req.ranges.size() > 1 && res.status == StatusCode::PartialContent_206) { auto it = res.headers.find("Content-Type"); if (it != res.headers.end()) { content_type = it->second; res.headers.erase(it); } - res.headers.emplace("Content-Type", - "multipart/byteranges; boundary=" + boundary); + boundary = detail::make_multipart_data_boundary(); + + res.set_header("Content-Type", + "multipart/byteranges; boundary=" + boundary); } auto type = detail::encoding_type(req, res); @@ -5934,19 +7025,20 @@ inline void Server::apply_ranges(const Request &req, Response &res, if (res.body.empty()) { if (res.content_length_ > 0) { size_t length = 0; - if (req.ranges.empty()) { + if (req.ranges.empty() || res.status != StatusCode::PartialContent_206) { length = res.content_length_; } else if (req.ranges.size() == 1) { - auto offsets = - detail::get_range_offset_and_length(req, res.content_length_, 0); - auto offset = offsets.first; - length = offsets.second; + auto offset_and_length = detail::get_range_offset_and_length( + req.ranges[0], res.content_length_); + + length = offset_and_length.second; + auto content_range = detail::make_content_range_header_field( - offset, length, res.content_length_); + offset_and_length, res.content_length_); res.set_header("Content-Range", content_range); } else { - length = detail::get_multipart_ranges_data_length(req, res, boundary, - content_type); + length = detail::get_multipart_ranges_data_length( + req, boundary, content_type, res.content_length_); } res.set_header("Content-Length", std::to_string(length)); } else { @@ -5962,31 +7054,25 @@ inline void Server::apply_ranges(const Request &req, Response &res, } } } else { - if (req.ranges.empty()) { + if (req.ranges.empty() || res.status != StatusCode::PartialContent_206) { ; } else if (req.ranges.size() == 1) { - auto offsets = - detail::get_range_offset_and_length(req, res.body.size(), 0); - auto offset = offsets.first; - auto length = offsets.second; + auto offset_and_length = + detail::get_range_offset_and_length(req.ranges[0], res.body.size()); + auto offset = offset_and_length.first; + auto length = offset_and_length.second; + auto content_range = detail::make_content_range_header_field( - offset, length, res.body.size()); + offset_and_length, res.body.size()); res.set_header("Content-Range", content_range); - if (offset < res.body.size()) { - res.body = res.body.substr(offset, length); - } else { - res.body.clear(); - res.status = 416; - } + + assert(offset + length <= res.body.size()); + res.body = res.body.substr(offset, length); } else { std::string data; - if (detail::make_multipart_ranges_data(req, res, boundary, content_type, - data)) { - res.body.swap(data); - } else { - res.body.clear(); - res.status = 416; - } + detail::make_multipart_ranges_data(req, res, boundary, content_type, + res.body.size(), data); + res.body.swap(data); } if (type != detail::EncodingType::None) { @@ -6025,12 +7111,12 @@ inline void Server::apply_ranges(const Request &req, Response &res, inline bool Server::dispatch_request_for_content_reader( Request &req, Response &res, ContentReader content_reader, - const HandlersForContentReader &handlers) { + const HandlersForContentReader &handlers) const { for (const auto &x : handlers) { - const auto &pattern = x.first; + const auto &matcher = x.first; const auto &handler = x.second; - if (std::regex_match(req.path, req.matches, pattern)) { + if (matcher->match(req)) { handler(req, res, content_reader); return true; } @@ -6039,7 +7125,9 @@ inline bool Server::dispatch_request_for_content_reader( } inline bool -Server::process_request(Stream &strm, bool close_connection, +Server::process_request(Stream &strm, const std::string &remote_addr, + int remote_port, const std::string &local_addr, + int local_port, bool close_connection, bool &connection_closed, const std::function &setup_request) { std::array buf{}; @@ -6050,15 +7138,10 @@ Server::process_request(Stream &strm, bool close_connection, if (!line_reader.getline()) { return false; } Request req; + Response res; - res.version = "HTTP/1.1"; - - for (const auto &header : default_headers_) { - if (res.headers.find(header.first) == res.headers.end()) { - res.headers.insert(header); - } - } + res.headers = default_headers_; #ifdef _WIN32 // TODO: Increase FD_SETSIZE statically (libzmq), dynamically (MySQL). @@ -6068,7 +7151,7 @@ Server::process_request(Stream &strm, bool close_connection, if (strm.socket() >= FD_SETSIZE) { Headers dummy; detail::read_headers(strm, dummy); - res.status = 500; + res.status = StatusCode::InternalServerError_500; return write_response(strm, close_connection, req, res); } #endif @@ -6078,14 +7161,14 @@ Server::process_request(Stream &strm, bool close_connection, if (line_reader.size() > CPPHTTPLIB_REQUEST_URI_MAX_LENGTH) { Headers dummy; detail::read_headers(strm, dummy); - res.status = 414; + res.status = StatusCode::UriTooLong_414; return write_response(strm, close_connection, req, res); } // Request line and headers if (!parse_request_line(line_reader.ptr(), req) || !detail::read_headers(strm, req.headers)) { - res.status = 400; + res.status = StatusCode::BadRequest_400; return write_response(strm, close_connection, req, res); } @@ -6098,18 +7181,20 @@ Server::process_request(Stream &strm, bool close_connection, connection_closed = true; } - strm.get_remote_ip_and_port(req.remote_addr, req.remote_port); + req.remote_addr = remote_addr; + req.remote_port = remote_port; req.set_header("REMOTE_ADDR", req.remote_addr); req.set_header("REMOTE_PORT", std::to_string(req.remote_port)); - strm.get_local_ip_and_port(req.local_addr, req.local_port); + req.local_addr = local_addr; + req.local_port = local_port; req.set_header("LOCAL_ADDR", req.local_addr); req.set_header("LOCAL_PORT", std::to_string(req.local_port)); if (req.has_header("Range")) { const auto &range_header_value = req.get_header_value("Range"); if (!detail::parse_range_header(range_header_value, req.ranges)) { - res.status = 416; + res.status = StatusCode::RangeNotSatisfiable_416; return write_response(strm, close_connection, req, res); } } @@ -6117,22 +7202,29 @@ Server::process_request(Stream &strm, bool close_connection, if (setup_request) { setup_request(req); } if (req.get_header_value("Expect") == "100-continue") { - auto status = 100; + int status = StatusCode::Continue_100; if (expect_100_continue_handler_) { status = expect_100_continue_handler_(req, res); } switch (status) { - case 100: - case 417: - strm.write_format("HTTP/1.1 %d %s\r\n\r\n", status, - detail::status_message(status)); + case StatusCode::Continue_100: + case StatusCode::ExpectationFailed_417: + detail::write_response_line(strm, status); + strm.write("\r\n"); break; - default: return write_response(strm, close_connection, req, res); + default: + connection_closed = true; + return write_response(strm, true, req, res); } } - // Rounting - bool routed = false; + // Setup `is_connection_closed` method + req.is_connection_closed = [&]() { + return !detail::is_socket_alive(strm.socket()); + }; + + // Routing + auto routed = false; #ifdef CPPHTTPLIB_NO_EXCEPTIONS routed = routing(req, res, strm); #else @@ -6144,7 +7236,7 @@ Server::process_request(Stream &strm, bool close_connection, exception_handler_(req, res, ep); routed = true; } else { - res.status = 500; + res.status = StatusCode::InternalServerError_500; std::string val; auto s = e.what(); for (size_t i = 0; s[i]; i++) { @@ -6162,17 +7254,55 @@ Server::process_request(Stream &strm, bool close_connection, exception_handler_(req, res, ep); routed = true; } else { - res.status = 500; + res.status = StatusCode::InternalServerError_500; res.set_header("EXCEPTION_WHAT", "UNKNOWN"); } } #endif - if (routed) { - if (res.status == -1) { res.status = req.ranges.empty() ? 200 : 206; } + if (res.status == -1) { + res.status = req.ranges.empty() ? StatusCode::OK_200 + : StatusCode::PartialContent_206; + } + + // Serve file content by using a content provider + if (!res.file_content_path_.empty()) { + const auto &path = res.file_content_path_; + auto mm = std::make_shared(path.c_str()); + if (!mm->is_open()) { + res.body.clear(); + res.content_length_ = 0; + res.content_provider_ = nullptr; + res.status = StatusCode::NotFound_404; + return write_response(strm, close_connection, req, res); + } + + auto content_type = res.file_content_content_type_; + if (content_type.empty()) { + content_type = detail::find_content_type( + path, file_extension_and_mimetype_map_, default_file_mimetype_); + } + + res.set_content_provider( + mm->size(), content_type, + [mm](size_t offset, size_t length, DataSink &sink) -> bool { + sink.write(mm->data() + offset, length); + return true; + }); + } + + if (detail::range_error(req, res)) { + res.body.clear(); + res.content_length_ = 0; + res.content_provider_ = nullptr; + res.status = StatusCode::RangeNotSatisfiable_416; + return write_response(strm, close_connection, req, res); + } + return write_response_with_content(strm, close_connection, req, res); } else { - if (res.status == -1) { res.status = 404; } + if (res.status == -1) { res.status = StatusCode::NotFound_404; } + return write_response(strm, close_connection, req, res); } } @@ -6180,12 +7310,21 @@ Server::process_request(Stream &strm, bool close_connection, inline bool Server::is_valid() const { return true; } inline bool Server::process_and_close_socket(socket_t sock) { + std::string remote_addr; + int remote_port = 0; + detail::get_remote_ip_and_port(sock, remote_addr, remote_port); + + std::string local_addr; + int local_port = 0; + detail::get_local_ip_and_port(sock, local_addr, local_port); + auto ret = detail::process_server_socket( svr_sock_, sock, keep_alive_max_count_, keep_alive_timeout_sec_, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, - [this](Stream &strm, bool close_connection, bool &connection_closed) { - return process_request(strm, close_connection, connection_closed, + [&](Stream &strm, bool close_connection, bool &connection_closed) { + return process_request(strm, remote_addr, remote_port, local_addr, + local_port, close_connection, connection_closed, nullptr); }); @@ -6204,8 +7343,8 @@ inline ClientImpl::ClientImpl(const std::string &host, int port) inline ClientImpl::ClientImpl(const std::string &host, int port, const std::string &client_cert_path, const std::string &client_key_path) - : host_(host), port_(port), - host_and_port_(adjust_host_string(host) + ":" + std::to_string(port)), + : host_(detail::escape_abstract_namespace_unix_domain(host)), port_(port), + host_and_port_(adjust_host_string(host_) + ":" + std::to_string(port)), client_cert_path_(client_cert_path), client_key_path_(client_key_path) {} inline ClientImpl::~ClientImpl() { @@ -6236,6 +7375,7 @@ inline void ClientImpl::copy_settings(const ClientImpl &rhs) { url_encode_ = rhs.url_encode_; address_family_ = rhs.address_family_; tcp_nodelay_ = rhs.tcp_nodelay_; + ipv6_v6only_ = rhs.ipv6_v6only_; socket_options_ = rhs.socket_options_; compress_ = rhs.compress_; decompress_ = rhs.decompress_; @@ -6256,6 +7396,8 @@ inline void ClientImpl::copy_settings(const ClientImpl &rhs) { #endif #ifdef CPPHTTPLIB_OPENSSL_SUPPORT server_certificate_verification_ = rhs.server_certificate_verification_; + server_hostname_verification_ = rhs.server_hostname_verification_; + server_certificate_verifier_ = rhs.server_certificate_verifier_; #endif logger_ = rhs.logger_; } @@ -6264,21 +7406,21 @@ inline socket_t ClientImpl::create_client_socket(Error &error) const { if (!proxy_host_.empty() && proxy_port_ != -1) { return detail::create_client_socket( proxy_host_, std::string(), proxy_port_, address_family_, tcp_nodelay_, - socket_options_, connection_timeout_sec_, connection_timeout_usec_, - read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, - write_timeout_usec_, interface_, error); + ipv6_v6only_, socket_options_, connection_timeout_sec_, + connection_timeout_usec_, read_timeout_sec_, read_timeout_usec_, + write_timeout_sec_, write_timeout_usec_, interface_, error); } // Check is custom IP specified for host_ std::string ip; auto it = addr_map_.find(host_); - if (it != addr_map_.end()) ip = it->second; + if (it != addr_map_.end()) { ip = it->second; } return detail::create_client_socket( - host_, ip, port_, address_family_, tcp_nodelay_, socket_options_, - connection_timeout_sec_, connection_timeout_usec_, read_timeout_sec_, - read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, interface_, - error); + host_, ip, port_, address_family_, tcp_nodelay_, ipv6_v6only_, + socket_options_, connection_timeout_sec_, connection_timeout_usec_, + read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, + write_timeout_usec_, interface_, error); } inline bool ClientImpl::create_and_connect_socket(Socket &socket, @@ -6297,7 +7439,7 @@ inline void ClientImpl::shutdown_ssl(Socket & /*socket*/, socket_requests_are_from_thread_ == std::this_thread::get_id()); } -inline void ClientImpl::shutdown_socket(Socket &socket) { +inline void ClientImpl::shutdown_socket(Socket &socket) const { if (socket.sock == INVALID_SOCKET) { return; } detail::shutdown_socket(socket.sock); } @@ -6322,7 +7464,7 @@ inline void ClientImpl::close_socket(Socket &socket) { } inline bool ClientImpl::read_response_line(Stream &strm, const Request &req, - Response &res) { + Response &res) const { std::array buf{}; detail::stream_line_reader line_reader(strm, buf.data(), buf.size()); @@ -6330,9 +7472,9 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req, if (!line_reader.getline()) { return false; } #ifdef CPPHTTPLIB_ALLOW_LF_AS_LINE_TERMINATOR - const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n"); -#else const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r?\n"); +#else + const static std::regex re("(HTTP/1\\.[01]) (\\d{3})(?: (.*?))?\r\n"); #endif std::cmatch m; @@ -6344,7 +7486,7 @@ inline bool ClientImpl::read_response_line(Stream &strm, const Request &req, res.reason = std::string(m[3]); // Ignore '100 Continue' - while (res.status == 100) { + while (res.status == StatusCode::Continue_100) { if (!line_reader.getline()) { return false; } // CRLF if (!line_reader.getline()) { return false; } // next response line @@ -6367,6 +7509,18 @@ inline bool ClientImpl::send(Request &req, Response &res, Error &error) { return ret; } +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT +inline bool ClientImpl::is_ssl_peer_could_be_closed(SSL *ssl) const { + detail::set_nonblocking(socket_.sock, true); + auto se = detail::scope_exit( + [&]() { detail::set_nonblocking(socket_.sock, false); }); + + char buf[1]; + return !SSL_peek(ssl, buf, 1) && + SSL_get_error(ssl, 0) == SSL_ERROR_ZERO_RETURN; +} +#endif + inline bool ClientImpl::send_(Request &req, Response &res, Error &error) { { std::lock_guard guard(socket_mutex_); @@ -6378,6 +7532,13 @@ inline bool ClientImpl::send_(Request &req, Response &res, Error &error) { auto is_alive = false; if (socket_.is_open()) { is_alive = detail::is_socket_alive(socket_.sock); + +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + if (is_alive && is_ssl()) { + if (is_ssl_peer_could_be_closed(socket_.ssl)) { is_alive = false; } + } +#endif + if (!is_alive) { // Attempt to avoid sigpipe by shutting down nongracefully if it seems // like the other side has already closed the connection Also, there @@ -6492,15 +7653,31 @@ inline bool ClientImpl::handle_request(Stream &strm, Request &req, if (!ret) { return false; } + if (res.get_header_value("Connection") == "close" || + (res.version == "HTTP/1.0" && res.reason != "Connection established")) { + // TODO this requires a not-entirely-obvious chain of calls to be correct + // for this to be safe. + + // This is safe to call because handle_request is only called by send_ + // which locks the request mutex during the process. It would be a bug + // to call it from a different thread since it's a thread-safety issue + // to do these things to the socket if another thread is using the socket. + std::lock_guard guard(socket_mutex_); + shutdown_ssl(socket_, true); + shutdown_socket(socket_); + close_socket(socket_); + } + if (300 < res.status && res.status < 400 && follow_location_) { req = req_save; ret = redirect(req, res, error); } #ifdef CPPHTTPLIB_OPENSSL_SUPPORT - if ((res.status == 401 || res.status == 407) && + if ((res.status == StatusCode::Unauthorized_401 || + res.status == StatusCode::ProxyAuthenticationRequired_407) && req.authorization_count_ < 5) { - auto is_proxy = res.status == 407; + auto is_proxy = res.status == StatusCode::ProxyAuthenticationRequired_407; const auto &username = is_proxy ? proxy_digest_auth_username_ : digest_auth_username_; const auto &password = @@ -6539,7 +7716,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { if (location.empty()) { return false; } const static std::regex re( - R"((?:(https?):)?(?://(?:\[([\d:]+)\]|([^:/?#]+))(?::(\d+))?)?([^?#]*)(\?[^#]*)?(?:#.*)?)"); + R"((?:(https?):)?(?://(?:\[([a-fA-F\d:]+)\]|([^:/?#]+))(?::(\d+))?)?([^?#]*)(\?[^#]*)?(?:#.*)?)"); std::smatch m; if (!std::regex_match(location, m, re)) { return false; } @@ -6571,7 +7748,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { } else { if (next_scheme == "https") { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT - SSLClient cli(next_host.c_str(), next_port); + SSLClient cli(next_host, next_port); cli.copy_settings(*this); if (ca_cert_store_) { cli.set_ca_cert_store(ca_cert_store_); } return detail::redirect(cli, req, res, path, location, error); @@ -6579,7 +7756,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { return false; #endif } else { - ClientImpl cli(next_host.c_str(), next_port); + ClientImpl cli(next_host, next_port); cli.copy_settings(*this); return detail::redirect(cli, req, res, path, location, error); } @@ -6588,7 +7765,7 @@ inline bool ClientImpl::redirect(Request &req, Response &res, Error &error) { inline bool ClientImpl::write_content_with_provider(Stream &strm, const Request &req, - Error &error) { + Error &error) const { auto is_shutting_down = []() { return false; }; if (req.is_chunked_content_provider_) { @@ -6616,57 +7793,71 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req, // Prepare additional headers if (close_connection) { if (!req.has_header("Connection")) { - req.headers.emplace("Connection", "close"); + req.set_header("Connection", "close"); } } if (!req.has_header("Host")) { if (is_ssl()) { if (port_ == 443) { - req.headers.emplace("Host", host_); + req.set_header("Host", host_); } else { - req.headers.emplace("Host", host_and_port_); + req.set_header("Host", host_and_port_); } } else { if (port_ == 80) { - req.headers.emplace("Host", host_); + req.set_header("Host", host_); } else { - req.headers.emplace("Host", host_and_port_); + req.set_header("Host", host_and_port_); } } } - if (!req.has_header("Accept")) { req.headers.emplace("Accept", "*/*"); } + if (!req.has_header("Accept")) { req.set_header("Accept", "*/*"); } + + if (!req.content_receiver) { + if (!req.has_header("Accept-Encoding")) { + std::string accept_encoding; +#ifdef CPPHTTPLIB_BROTLI_SUPPORT + accept_encoding = "br"; +#endif +#ifdef CPPHTTPLIB_ZLIB_SUPPORT + if (!accept_encoding.empty()) { accept_encoding += ", "; } + accept_encoding += "gzip, deflate"; +#endif + req.set_header("Accept-Encoding", accept_encoding); + } #ifndef CPPHTTPLIB_NO_DEFAULT_USER_AGENT - if (!req.has_header("User-Agent")) { - auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION; - req.headers.emplace("User-Agent", agent); - } + if (!req.has_header("User-Agent")) { + auto agent = std::string("cpp-httplib/") + CPPHTTPLIB_VERSION; + req.set_header("User-Agent", agent); + } #endif + }; if (req.body.empty()) { if (req.content_provider_) { if (!req.is_chunked_content_provider_) { if (!req.has_header("Content-Length")) { auto length = std::to_string(req.content_length_); - req.headers.emplace("Content-Length", length); + req.set_header("Content-Length", length); } } } else { if (req.method == "POST" || req.method == "PUT" || req.method == "PATCH") { - req.headers.emplace("Content-Length", "0"); + req.set_header("Content-Length", "0"); } } } else { if (!req.has_header("Content-Type")) { - req.headers.emplace("Content-Type", "text/plain"); + req.set_header("Content-Type", "text/plain"); } if (!req.has_header("Content-Length")) { auto length = std::to_string(req.body.size()); - req.headers.emplace("Content-Length", length); + req.set_header("Content-Length", length); } } @@ -6703,10 +7894,16 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req, { detail::BufferStream bstrm; - const auto &path = url_encode_ ? detail::encode_url(req.path) : req.path; - bstrm.write_format("%s %s HTTP/1.1\r\n", req.method.c_str(), path.c_str()); + const auto &path_with_query = + req.params.empty() ? req.path + : append_query_params(req.path, req.params); - detail::write_headers(bstrm, req.headers); + const auto &path = + url_encode_ ? detail::encode_url(path_with_query) : path_with_query; + + detail::write_request_line(bstrm, req.method, path); + + header_writer_(bstrm, req.headers); // Flush buffer auto &data = bstrm.get_buffer(); @@ -6734,12 +7931,10 @@ inline std::unique_ptr ClientImpl::send_with_content_provider( ContentProvider content_provider, ContentProviderWithoutLength content_provider_without_length, const std::string &content_type, Error &error) { - if (!content_type.empty()) { - req.headers.emplace("Content-Type", content_type); - } + if (!content_type.empty()) { req.set_header("Content-Type", content_type); } #ifdef CPPHTTPLIB_ZLIB_SUPPORT - if (compress_) { req.headers.emplace("Content-Encoding", "gzip"); } + if (compress_) { req.set_header("Content-Encoding", "gzip"); } #endif #ifdef CPPHTTPLIB_ZLIB_SUPPORT @@ -6800,10 +7995,9 @@ inline std::unique_ptr ClientImpl::send_with_content_provider( req.content_provider_ = detail::ContentProviderAdapter( std::move(content_provider_without_length)); req.is_chunked_content_provider_ = true; - req.headers.emplace("Transfer-Encoding", "chunked"); + req.set_header("Transfer-Encoding", "chunked"); } else { req.body.assign(body, content_length); - ; } } @@ -6815,11 +8009,12 @@ inline Result ClientImpl::send_with_content_provider( const std::string &method, const std::string &path, const Headers &headers, const char *body, size_t content_length, ContentProvider content_provider, ContentProviderWithoutLength content_provider_without_length, - const std::string &content_type) { + const std::string &content_type, Progress progress) { Request req; req.method = method; req.headers = headers; req.path = path; + req.progress = progress; auto error = Error::Success; @@ -6846,9 +8041,7 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, if (is_ssl()) { auto is_proxy_enabled = !proxy_host_.empty() && proxy_port_ != -1; if (!is_proxy_enabled) { - char buf[1]; - if (SSL_peek(socket_.ssl, buf, 1) == 0 && - SSL_get_error(socket_.ssl, 0) == SSL_ERROR_ZERO_RETURN) { + if (is_ssl_peer_could_be_closed(socket_.ssl)) { error = Error::SSLPeerCouldBeClosed_; return false; } @@ -6864,8 +8057,11 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, } // Body - if ((res.status != 204) && req.method != "HEAD" && req.method != "CONNECT") { - auto redirect = 300 < res.status && res.status < 400 && follow_location_; + if ((res.status != StatusCode::NoContent_204) && req.method != "HEAD" && + req.method != "CONNECT") { + auto redirect = 300 < res.status && res.status < 400 && + res.status != StatusCode::NotModified_304 && + follow_location_; if (req.response_handler && !redirect) { if (!req.response_handler(res)) { @@ -6886,9 +8082,7 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, : static_cast( [&](const char *buf, size_t n, uint64_t /*off*/, uint64_t /*len*/) { - if (res.body.size() + n > res.body.max_size()) { - return false; - } + assert(res.body.size() + n <= res.body.max_size()); res.body.append(buf, n); return true; }); @@ -6900,31 +8094,26 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, return ret; }; - int dummy_status; - if (!detail::read_content(strm, res, (std::numeric_limits::max)(), - dummy_status, std::move(progress), std::move(out), - decompress_)) { - if (error != Error::Canceled) { error = Error::Read; } - return false; + if (res.has_header("Content-Length")) { + if (!req.content_receiver) { + auto len = res.get_header_value_u64("Content-Length"); + if (len > res.body.max_size()) { + error = Error::Read; + return false; + } + res.body.reserve(static_cast(len)); + } } - } - if (res.get_header_value("Connection") == "close" || - (res.version == "HTTP/1.0" && res.reason != "Connection established")) { - // TODO this requires a not-entirely-obvious chain of calls to be correct - // for this to be safe. Maybe a code refactor (such as moving this out to - // the send function and getting rid of the recursiveness of the mutex) - // could make this more obvious. - - // This is safe to call because process_request is only called by - // handle_request which is only called by send, which locks the request - // mutex during the process. It would be a bug to call it from a different - // thread since it's a thread-safety issue to do these things to the socket - // if another thread is using the socket. - std::lock_guard guard(socket_mutex_); - shutdown_ssl(socket_, true); - shutdown_socket(socket_); - close_socket(socket_); + if (res.status != StatusCode::NotModified_304) { + int dummy_status; + if (!detail::read_content(strm, res, (std::numeric_limits::max)(), + dummy_status, std::move(progress), + std::move(out), decompress_)) { + if (error != Error::Canceled) { error = Error::Read; } + return false; + } + } } // Log @@ -6935,13 +8124,14 @@ inline bool ClientImpl::process_request(Stream &strm, Request &req, inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider( const std::string &boundary, const MultipartFormDataItems &items, - const MultipartFormDataProviderItems &provider_items) { - size_t cur_item = 0, cur_start = 0; + const MultipartFormDataProviderItems &provider_items) const { + size_t cur_item = 0; + size_t cur_start = 0; // cur_item and cur_start are copied to within the std::function and maintain // state between successive calls return [&, cur_item, cur_start](size_t offset, DataSink &sink) mutable -> bool { - if (!offset && items.size()) { + if (!offset && !items.empty()) { sink.os << detail::serialize_multipart_formdata(items, boundary, false); return true; } else if (cur_item < provider_items.size()) { @@ -6954,12 +8144,13 @@ inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider( } DataSink cur_sink; - bool has_data = true; + auto has_data = true; cur_sink.write = sink.write; cur_sink.done = [&]() { has_data = false; }; - if (!provider_items[cur_item].provider(offset - cur_start, cur_sink)) + if (!provider_items[cur_item].provider(offset - cur_start, cur_sink)) { return false; + } if (!has_data) { sink.os << detail::serialize_multipart_formdata_item_end(); @@ -7078,14 +8269,15 @@ inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, if (params.empty()) { return Get(path, headers); } std::string path_with_query = append_query_params(path, params); - return Get(path_with_query.c_str(), headers, progress); + return Get(path_with_query, headers, std::move(progress)); } inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, const Headers &headers, ContentReceiver content_receiver, Progress progress) { - return Get(path, params, headers, nullptr, content_receiver, progress); + return Get(path, params, headers, nullptr, std::move(content_receiver), + std::move(progress)); } inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, @@ -7094,12 +8286,13 @@ inline Result ClientImpl::Get(const std::string &path, const Params ¶ms, ContentReceiver content_receiver, Progress progress) { if (params.empty()) { - return Get(path, headers, response_handler, content_receiver, progress); + return Get(path, headers, std::move(response_handler), + std::move(content_receiver), std::move(progress)); } std::string path_with_query = append_query_params(path, params); - return Get(path_with_query.c_str(), headers, response_handler, - content_receiver, progress); + return Get(path_with_query, headers, std::move(response_handler), + std::move(content_receiver), std::move(progress)); } inline Result ClientImpl::Head(const std::string &path) { @@ -7128,14 +8321,22 @@ inline Result ClientImpl::Post(const std::string &path, inline Result ClientImpl::Post(const std::string &path, const char *body, size_t content_length, const std::string &content_type) { - return Post(path, Headers(), body, content_length, content_type); + return Post(path, Headers(), body, content_length, content_type, nullptr); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return send_with_content_provider("POST", path, headers, body, content_length, - nullptr, nullptr, content_type); + nullptr, nullptr, content_type, nullptr); +} + +inline Result ClientImpl::Post(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("POST", path, headers, body, content_length, + nullptr, nullptr, content_type, progress); } inline Result ClientImpl::Post(const std::string &path, const std::string &body, @@ -7143,12 +8344,27 @@ inline Result ClientImpl::Post(const std::string &path, const std::string &body, return Post(path, Headers(), body, content_type); } +inline Result ClientImpl::Post(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return Post(path, Headers(), body, content_type, progress); +} + inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return send_with_content_provider("POST", path, headers, body.data(), - body.size(), nullptr, nullptr, - content_type); + body.size(), nullptr, nullptr, content_type, + nullptr); +} + +inline Result ClientImpl::Post(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("POST", path, headers, body.data(), + body.size(), nullptr, nullptr, content_type, + progress); } inline Result ClientImpl::Post(const std::string &path, const Params ¶ms) { @@ -7174,14 +8390,15 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const std::string &content_type) { return send_with_content_provider("POST", path, headers, nullptr, content_length, std::move(content_provider), - nullptr, content_type); + nullptr, content_type, nullptr); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, ContentProviderWithoutLength content_provider, const std::string &content_type) { return send_with_content_provider("POST", path, headers, nullptr, 0, nullptr, - std::move(content_provider), content_type); + std::move(content_provider), content_type, + nullptr); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, @@ -7190,6 +8407,13 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, return Post(path, headers, query, "application/x-www-form-urlencoded"); } +inline Result ClientImpl::Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + auto query = detail::params_to_query_str(params); + return Post(path, headers, query, "application/x-www-form-urlencoded", + progress); +} + inline Result ClientImpl::Post(const std::string &path, const MultipartFormDataItems &items) { return Post(path, Headers(), items); @@ -7201,7 +8425,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const auto &content_type = detail::serialize_multipart_formdata_get_content_type(boundary); const auto &body = detail::serialize_multipart_formdata(items, boundary); - return Post(path, headers, body, content_type.c_str()); + return Post(path, headers, body, content_type); } inline Result ClientImpl::Post(const std::string &path, const Headers &headers, @@ -7214,7 +8438,7 @@ inline Result ClientImpl::Post(const std::string &path, const Headers &headers, const auto &content_type = detail::serialize_multipart_formdata_get_content_type(boundary); const auto &body = detail::serialize_multipart_formdata(items, boundary); - return Post(path, headers, body, content_type.c_str()); + return Post(path, headers, body, content_type); } inline Result @@ -7227,7 +8451,7 @@ ClientImpl::Post(const std::string &path, const Headers &headers, return send_with_content_provider( "POST", path, headers, nullptr, 0, nullptr, get_multipart_content_provider(boundary, items, provider_items), - content_type); + content_type, nullptr); } inline Result ClientImpl::Put(const std::string &path) { @@ -7244,7 +8468,15 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, body, content_length, - nullptr, nullptr, content_type); + nullptr, nullptr, content_type, nullptr); +} + +inline Result ClientImpl::Put(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("PUT", path, headers, body, content_length, + nullptr, nullptr, content_type, progress); } inline Result ClientImpl::Put(const std::string &path, const std::string &body, @@ -7252,12 +8484,27 @@ inline Result ClientImpl::Put(const std::string &path, const std::string &body, return Put(path, Headers(), body, content_type); } +inline Result ClientImpl::Put(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return Put(path, Headers(), body, content_type, progress); +} + inline Result ClientImpl::Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, body.data(), - body.size(), nullptr, nullptr, - content_type); + body.size(), nullptr, nullptr, content_type, + nullptr); +} + +inline Result ClientImpl::Put(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return send_with_content_provider("PUT", path, headers, body.data(), + body.size(), nullptr, nullptr, content_type, + progress); } inline Result ClientImpl::Put(const std::string &path, size_t content_length, @@ -7279,14 +8526,15 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, nullptr, content_length, std::move(content_provider), - nullptr, content_type); + nullptr, content_type, nullptr); } inline Result ClientImpl::Put(const std::string &path, const Headers &headers, ContentProviderWithoutLength content_provider, const std::string &content_type) { return send_with_content_provider("PUT", path, headers, nullptr, 0, nullptr, - std::move(content_provider), content_type); + std::move(content_provider), content_type, + nullptr); } inline Result ClientImpl::Put(const std::string &path, const Params ¶ms) { @@ -7299,6 +8547,13 @@ inline Result ClientImpl::Put(const std::string &path, const Headers &headers, return Put(path, headers, query, "application/x-www-form-urlencoded"); } +inline Result ClientImpl::Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + auto query = detail::params_to_query_str(params); + return Put(path, headers, query, "application/x-www-form-urlencoded", + progress); +} + inline Result ClientImpl::Put(const std::string &path, const MultipartFormDataItems &items) { return Put(path, Headers(), items); @@ -7336,7 +8591,7 @@ ClientImpl::Put(const std::string &path, const Headers &headers, return send_with_content_provider( "PUT", path, headers, nullptr, 0, nullptr, get_multipart_content_provider(boundary, items, provider_items), - content_type); + content_type, nullptr); } inline Result ClientImpl::Patch(const std::string &path) { return Patch(path, std::string(), std::string()); @@ -7348,12 +8603,26 @@ inline Result ClientImpl::Patch(const std::string &path, const char *body, return Patch(path, Headers(), body, content_length, content_type); } +inline Result ClientImpl::Patch(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return Patch(path, Headers(), body, content_length, content_type, progress); +} + inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { + return Patch(path, headers, body, content_length, content_type, nullptr); +} + +inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { return send_with_content_provider("PATCH", path, headers, body, content_length, nullptr, nullptr, - content_type); + content_type, progress); } inline Result ClientImpl::Patch(const std::string &path, @@ -7362,12 +8631,26 @@ inline Result ClientImpl::Patch(const std::string &path, return Patch(path, Headers(), body, content_type); } +inline Result ClientImpl::Patch(const std::string &path, + const std::string &body, + const std::string &content_type, + Progress progress) { + return Patch(path, Headers(), body, content_type, progress); +} + inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { + return Patch(path, headers, body, content_type, nullptr); +} + +inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { return send_with_content_provider("PATCH", path, headers, body.data(), - body.size(), nullptr, nullptr, - content_type); + body.size(), nullptr, nullptr, content_type, + progress); } inline Result ClientImpl::Patch(const std::string &path, size_t content_length, @@ -7389,14 +8672,15 @@ inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, const std::string &content_type) { return send_with_content_provider("PATCH", path, headers, nullptr, content_length, std::move(content_provider), - nullptr, content_type); + nullptr, content_type, nullptr); } inline Result ClientImpl::Patch(const std::string &path, const Headers &headers, ContentProviderWithoutLength content_provider, const std::string &content_type) { return send_with_content_provider("PATCH", path, headers, nullptr, 0, nullptr, - std::move(content_provider), content_type); + std::move(content_provider), content_type, + nullptr); } inline Result ClientImpl::Delete(const std::string &path) { @@ -7414,18 +8698,32 @@ inline Result ClientImpl::Delete(const std::string &path, const char *body, return Delete(path, Headers(), body, content_length, content_type); } +inline Result ClientImpl::Delete(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return Delete(path, Headers(), body, content_length, content_type, progress); +} + inline Result ClientImpl::Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { + return Delete(path, headers, body, content_length, content_type, nullptr); +} + +inline Result ClientImpl::Delete(const std::string &path, + const Headers &headers, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { Request req; req.method = "DELETE"; req.headers = headers; req.path = path; + req.progress = progress; - if (!content_type.empty()) { - req.headers.emplace("Content-Type", content_type); - } + if (!content_type.empty()) { req.set_header("Content-Type", content_type); } req.body.assign(body, content_length); return send_(std::move(req)); @@ -7437,6 +8735,14 @@ inline Result ClientImpl::Delete(const std::string &path, return Delete(path, Headers(), body.data(), body.size(), content_type); } +inline Result ClientImpl::Delete(const std::string &path, + const std::string &body, + const std::string &content_type, + Progress progress) { + return Delete(path, Headers(), body.data(), body.size(), content_type, + progress); +} + inline Result ClientImpl::Delete(const std::string &path, const Headers &headers, const std::string &body, @@ -7444,6 +8750,15 @@ inline Result ClientImpl::Delete(const std::string &path, return Delete(path, headers, body.data(), body.size(), content_type); } +inline Result ClientImpl::Delete(const std::string &path, + const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return Delete(path, headers, body.data(), body.size(), content_type, + progress); +} + inline Result ClientImpl::Options(const std::string &path) { return Options(path, Headers()); } @@ -7458,13 +8773,6 @@ inline Result ClientImpl::Options(const std::string &path, return send_(std::move(req)); } -inline size_t ClientImpl::is_socket_open() const { - std::lock_guard guard(socket_mutex_); - return socket_.is_open(); -} - -inline socket_t ClientImpl::socket() const { return socket_.sock; } - inline void ClientImpl::stop() { std::lock_guard guard(socket_mutex_); @@ -7488,6 +8796,17 @@ inline void ClientImpl::stop() { close_socket(socket_); } +inline std::string ClientImpl::host() const { return host_; } + +inline int ClientImpl::port() const { return port_; } + +inline size_t ClientImpl::is_socket_open() const { + std::lock_guard guard(socket_mutex_); + return socket_.is_open(); +} + +inline socket_t ClientImpl::socket() const { return socket_.sock; } + inline void ClientImpl::set_connection_timeout(time_t sec, time_t usec) { connection_timeout_sec_ = sec; connection_timeout_usec_ = usec; @@ -7536,12 +8855,19 @@ inline void ClientImpl::set_default_headers(Headers headers) { default_headers_ = std::move(headers); } +inline void ClientImpl::set_header_writer( + std::function const &writer) { + header_writer_ = writer; +} + inline void ClientImpl::set_address_family(int family) { address_family_ = family; } inline void ClientImpl::set_tcp_nodelay(bool on) { tcp_nodelay_ = on; } +inline void ClientImpl::set_ipv6_v6only(bool on) { ipv6_v6only_ = on; } + inline void ClientImpl::set_socket_options(SocketOptions socket_options) { socket_options_ = std::move(socket_options); } @@ -7575,9 +8901,7 @@ inline void ClientImpl::set_proxy_digest_auth(const std::string &username, proxy_digest_auth_username_ = username; proxy_digest_auth_password_ = password; } -#endif -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT inline void ClientImpl::set_ca_cert_path(const std::string &ca_cert_file_path, const std::string &ca_cert_dir_path) { ca_cert_file_path_ = ca_cert_file_path; @@ -7589,12 +8913,43 @@ inline void ClientImpl::set_ca_cert_store(X509_STORE *ca_cert_store) { ca_cert_store_ = ca_cert_store; } } -#endif -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT +inline X509_STORE *ClientImpl::create_ca_cert_store(const char *ca_cert, + std::size_t size) const { + auto mem = BIO_new_mem_buf(ca_cert, static_cast(size)); + auto se = detail::scope_exit([&] { BIO_free_all(mem); }); + if (!mem) { return nullptr; } + + auto inf = PEM_X509_INFO_read_bio(mem, nullptr, nullptr, nullptr); + if (!inf) { return nullptr; } + + auto cts = X509_STORE_new(); + if (cts) { + for (auto i = 0; i < static_cast(sk_X509_INFO_num(inf)); i++) { + auto itmp = sk_X509_INFO_value(inf, i); + if (!itmp) { continue; } + + if (itmp->x509) { X509_STORE_add_cert(cts, itmp->x509); } + if (itmp->crl) { X509_STORE_add_crl(cts, itmp->crl); } + } + } + + sk_X509_INFO_pop_free(inf, X509_INFO_free); + return cts; +} + inline void ClientImpl::enable_server_certificate_verification(bool enabled) { server_certificate_verification_ = enabled; } + +inline void ClientImpl::enable_server_hostname_verification(bool enabled) { + server_hostname_verification_ = enabled; +} + +inline void ClientImpl::set_server_certificate_verifier( + std::function verifier) { + server_certificate_verifier_ = verifier; +} #endif inline void ClientImpl::set_logger(Logger logger) { @@ -7638,13 +8993,30 @@ inline SSL *ssl_new(socket_t sock, SSL_CTX *ctx, std::mutex &ctx_mutex, return ssl; } -inline void ssl_delete(std::mutex &ctx_mutex, SSL *ssl, +inline void ssl_delete(std::mutex &ctx_mutex, SSL *ssl, socket_t sock, bool shutdown_gracefully) { // sometimes we may want to skip this to try to avoid SIGPIPE if we know // the remote has closed the network connection // Note that it is not always possible to avoid SIGPIPE, this is merely a // best-efforts. - if (shutdown_gracefully) { SSL_shutdown(ssl); } + if (shutdown_gracefully) { +#ifdef _WIN32 + (void)(sock); + SSL_shutdown(ssl); +#else + timeval tv; + tv.tv_sec = 1; + tv.tv_usec = 0; + setsockopt(sock, SOL_SOCKET, SO_RCVTIMEO, + reinterpret_cast(&tv), sizeof(tv)); + + auto ret = SSL_shutdown(ssl); + while (ret == 0) { + std::this_thread::sleep_for(std::chrono::milliseconds{100}); + ret = SSL_shutdown(ssl); + } +#endif + } std::lock_guard guard(ctx_mutex); SSL_free(ssl); @@ -7655,7 +9027,7 @@ bool ssl_connect_or_accept_nonblocking(socket_t sock, SSL *ssl, U ssl_connect_or_accept, time_t timeout_sec, time_t timeout_usec) { - int res = 0; + auto res = 0; while ((res = ssl_connect_or_accept(ssl)) != 1) { auto err = SSL_get_error(ssl, res); switch (err) { @@ -7718,7 +9090,7 @@ inline SSLSocketStream::SSLSocketStream(socket_t sock, SSL *ssl, SSL_clear_mode(ssl, SSL_MODE_AUTO_RETRY); } -inline SSLSocketStream::~SSLSocketStream() {} +inline SSLSocketStream::~SSLSocketStream() = default; inline bool SSLSocketStream::is_readable() const { return detail::select_read(sock_, read_timeout_sec_, read_timeout_usec_) > 0; @@ -7736,7 +9108,7 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) { auto ret = SSL_read(ssl_, ptr, static_cast(size)); if (ret < 0) { auto err = SSL_get_error(ssl_, ret); - int n = 1000; + auto n = 1000; #ifdef _WIN32 while (--n >= 0 && (err == SSL_ERROR_WANT_READ || (err == SSL_ERROR_SYSCALL && @@ -7747,7 +9119,7 @@ inline ssize_t SSLSocketStream::read(char *ptr, size_t size) { if (SSL_pending(ssl_) > 0) { return SSL_read(ssl_, ptr, static_cast(size)); } else if (is_readable()) { - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds{10}); ret = SSL_read(ssl_, ptr, static_cast(size)); if (ret >= 0) { return ret; } err = SSL_get_error(ssl_, ret); @@ -7769,7 +9141,7 @@ inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) { auto ret = SSL_write(ssl_, ptr, static_cast(handle_size)); if (ret < 0) { auto err = SSL_get_error(ssl_, ret); - int n = 1000; + auto n = 1000; #ifdef _WIN32 while (--n >= 0 && (err == SSL_ERROR_WANT_WRITE || (err == SSL_ERROR_SYSCALL && @@ -7778,7 +9150,7 @@ inline ssize_t SSLSocketStream::write(const char *ptr, size_t size) { while (--n >= 0 && err == SSL_ERROR_WANT_WRITE) { #endif if (is_writable()) { - std::this_thread::sleep_for(std::chrono::milliseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds{10}); ret = SSL_write(ssl_, ptr, static_cast(handle_size)); if (ret >= 0) { return ret; } err = SSL_get_error(ssl_, ret); @@ -7820,17 +9192,18 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path, SSL_OP_NO_COMPRESSION | SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION); - SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION); + SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION); - // add default password callback before opening encrypted private key if (private_key_password != nullptr && (private_key_password[0] != '\0')) { - SSL_CTX_set_default_passwd_cb_userdata(ctx_, - (char *)private_key_password); + SSL_CTX_set_default_passwd_cb_userdata( + ctx_, + reinterpret_cast(const_cast(private_key_password))); } if (SSL_CTX_use_certificate_chain_file(ctx_, cert_path) != 1 || SSL_CTX_use_PrivateKey_file(ctx_, private_key_path, SSL_FILETYPE_PEM) != - 1) { + 1 || + SSL_CTX_check_private_key(ctx_) != 1) { SSL_CTX_free(ctx_); ctx_ = nullptr; } else if (client_ca_cert_file_path || client_ca_cert_dir_path) { @@ -7852,7 +9225,7 @@ inline SSLServer::SSLServer(X509 *cert, EVP_PKEY *private_key, SSL_OP_NO_COMPRESSION | SSL_OP_NO_SESSION_RESUMPTION_ON_RENEGOTIATION); - SSL_CTX_set_min_proto_version(ctx_, TLS1_1_VERSION); + SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION); if (SSL_CTX_use_certificate(ctx_, cert) != 1 || SSL_CTX_use_PrivateKey(ctx_, private_key) != 1) { @@ -7886,6 +9259,19 @@ inline bool SSLServer::is_valid() const { return ctx_; } inline SSL_CTX *SSLServer::ssl_context() const { return ctx_; } +inline void SSLServer::update_certs(X509 *cert, EVP_PKEY *private_key, + X509_STORE *client_ca_cert_store) { + + std::lock_guard guard(ctx_mutex_); + + SSL_CTX_use_certificate(ctx_, cert); + SSL_CTX_use_PrivateKey(ctx_, private_key); + + if (client_ca_cert_store != nullptr) { + SSL_CTX_set_cert_store(ctx_, client_ca_cert_store); + } +} + inline bool SSLServer::process_and_close_socket(socket_t sock) { auto ssl = detail::ssl_new( sock, ctx_, ctx_mutex_, @@ -7897,20 +9283,29 @@ inline bool SSLServer::process_and_close_socket(socket_t sock) { auto ret = false; if (ssl) { + std::string remote_addr; + int remote_port = 0; + detail::get_remote_ip_and_port(sock, remote_addr, remote_port); + + std::string local_addr; + int local_port = 0; + detail::get_local_ip_and_port(sock, local_addr, local_port); + ret = detail::process_server_socket_ssl( svr_sock_, ssl, sock, keep_alive_max_count_, keep_alive_timeout_sec_, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, - [this, ssl](Stream &strm, bool close_connection, - bool &connection_closed) { - return process_request(strm, close_connection, connection_closed, + [&](Stream &strm, bool close_connection, bool &connection_closed) { + return process_request(strm, remote_addr, remote_port, local_addr, + local_port, close_connection, + connection_closed, [&](Request &req) { req.ssl = ssl; }); }); // Shutdown gracefully if the result seemed successful, non-gracefully if // the connection appeared to be closed. const bool shutdown_gracefully = ret; - detail::ssl_delete(ctx_mutex_, ssl, shutdown_gracefully); + detail::ssl_delete(ctx_mutex_, ssl, sock, shutdown_gracefully); } detail::shutdown_socket(sock); @@ -7927,16 +9322,25 @@ inline SSLClient::SSLClient(const std::string &host, int port) inline SSLClient::SSLClient(const std::string &host, int port, const std::string &client_cert_path, - const std::string &client_key_path) + const std::string &client_key_path, + const std::string &private_key_password) : ClientImpl(host, port, client_cert_path, client_key_path) { ctx_ = SSL_CTX_new(TLS_client_method()); + SSL_CTX_set_min_proto_version(ctx_, TLS1_2_VERSION); + detail::split(&host_[0], &host_[host_.size()], '.', [&](const char *b, const char *e) { - host_components_.emplace_back(std::string(b, e)); + host_components_.emplace_back(b, e); }); if (!client_cert_path.empty() && !client_key_path.empty()) { + if (!private_key_password.empty()) { + SSL_CTX_set_default_passwd_cb_userdata( + ctx_, reinterpret_cast( + const_cast(private_key_password.c_str()))); + } + if (SSL_CTX_use_certificate_file(ctx_, client_cert_path.c_str(), SSL_FILETYPE_PEM) != 1 || SSL_CTX_use_PrivateKey_file(ctx_, client_key_path.c_str(), @@ -7948,16 +9352,23 @@ inline SSLClient::SSLClient(const std::string &host, int port, } inline SSLClient::SSLClient(const std::string &host, int port, - X509 *client_cert, EVP_PKEY *client_key) + X509 *client_cert, EVP_PKEY *client_key, + const std::string &private_key_password) : ClientImpl(host, port) { ctx_ = SSL_CTX_new(TLS_client_method()); detail::split(&host_[0], &host_[host_.size()], '.', [&](const char *b, const char *e) { - host_components_.emplace_back(std::string(b, e)); + host_components_.emplace_back(b, e); }); if (client_cert != nullptr && client_key != nullptr) { + if (!private_key_password.empty()) { + SSL_CTX_set_default_passwd_cb_userdata( + ctx_, reinterpret_cast( + const_cast(private_key_password.c_str()))); + } + if (SSL_CTX_use_certificate(ctx_, client_cert) != 1 || SSL_CTX_use_PrivateKey(ctx_, client_key) != 1) { SSL_CTX_free(ctx_); @@ -7989,6 +9400,11 @@ inline void SSLClient::set_ca_cert_store(X509_STORE *ca_cert_store) { } } +inline void SSLClient::load_ca_cert_store(const char *ca_cert, + std::size_t size) { + set_ca_cert_store(ClientImpl::create_ca_cert_store(ca_cert, size)); +} + inline long SSLClient::get_openssl_verify_result() const { return verify_result_; } @@ -8003,14 +9419,14 @@ inline bool SSLClient::create_and_connect_socket(Socket &socket, Error &error) { inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, bool &success, Error &error) { success = true; - Response res2; + Response proxy_res; if (!detail::process_client_socket( socket.sock, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) { Request req2; req2.method = "CONNECT"; req2.path = host_and_port_; - return process_request(strm, req2, res2, false, error); + return process_request(strm, req2, proxy_res, false, error); })) { // Thread-safe to close everything because we are assuming there are no // requests in flight @@ -8021,12 +9437,12 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, return false; } - if (res2.status == 407) { + if (proxy_res.status == StatusCode::ProxyAuthenticationRequired_407) { if (!proxy_digest_auth_username_.empty() && !proxy_digest_auth_password_.empty()) { std::map auth; - if (detail::parse_www_authenticate(res2, auth, true)) { - Response res3; + if (detail::parse_www_authenticate(proxy_res, auth, true)) { + proxy_res = Response(); if (!detail::process_client_socket( socket.sock, read_timeout_sec_, read_timeout_usec_, write_timeout_sec_, write_timeout_usec_, [&](Stream &strm) { @@ -8037,7 +9453,7 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, req3, auth, 1, detail::random_string(10), proxy_digest_auth_username_, proxy_digest_auth_password_, true)); - return process_request(strm, req3, res3, false, error); + return process_request(strm, req3, proxy_res, false, error); })) { // Thread-safe to close everything because we are assuming there are // no requests in flight @@ -8048,17 +9464,28 @@ inline bool SSLClient::connect_with_proxy(Socket &socket, Response &res, return false; } } - } else { - res = res2; - return false; } } + // If status code is not 200, proxy request is failed. + // Set error to ProxyConnection and return proxy response + // as the response of the request + if (proxy_res.status != StatusCode::OK_200) { + error = Error::ProxyConnection; + res = std::move(proxy_res); + // Thread-safe to close everything because we are assuming there are + // no requests in flight + shutdown_ssl(socket, true); + shutdown_socket(socket); + close_socket(socket); + return false; + } + return true; } inline bool SSLClient::load_certs() { - bool ret = true; + auto ret = true; std::call_once(initialize_cert_, [&]() { std::lock_guard guard(ctx_mutex_); @@ -8109,32 +9536,47 @@ inline bool SSLClient::initialize_ssl(Socket &socket, Error &error) { } if (server_certificate_verification_) { - verify_result_ = SSL_get_verify_result(ssl2); + if (server_certificate_verifier_) { + if (!server_certificate_verifier_(ssl2)) { + error = Error::SSLServerVerification; + return false; + } + } else { + verify_result_ = SSL_get_verify_result(ssl2); - if (verify_result_ != X509_V_OK) { - error = Error::SSLServerVerification; - return false; + if (verify_result_ != X509_V_OK) { + error = Error::SSLServerVerification; + return false; + } + + auto server_cert = SSL_get1_peer_certificate(ssl2); + auto se = detail::scope_exit([&] { X509_free(server_cert); }); + + if (server_cert == nullptr) { + error = Error::SSLServerVerification; + return false; + } + + if (server_hostname_verification_) { + if (!verify_host(server_cert)) { + error = Error::SSLServerHostnameVerification; + return false; + } + } } - - auto server_cert = SSL_get1_peer_certificate(ssl2); - - if (server_cert == nullptr) { - error = Error::SSLServerVerification; - return false; - } - - if (!verify_host(server_cert)) { - X509_free(server_cert); - error = Error::SSLServerVerification; - return false; - } - X509_free(server_cert); } return true; }, [&](SSL *ssl2) { +#if defined(OPENSSL_IS_BORINGSSL) SSL_set_tlsext_host_name(ssl2, host_.c_str()); +#else + // NOTE: Direct call instead of using the OpenSSL macro to suppress + // -Wold-style-cast warning + SSL_ctrl(ssl2, SSL_CTRL_SET_TLSEXT_HOSTNAME, TLSEXT_NAMETYPE_host_name, + static_cast(const_cast(host_.c_str()))); +#endif return true; }); @@ -8159,7 +9601,8 @@ inline void SSLClient::shutdown_ssl_impl(Socket &socket, return; } if (socket.ssl) { - detail::ssl_delete(ctx_mutex_, socket.ssl, shutdown_gracefully); + detail::ssl_delete(ctx_mutex_, socket.ssl, socket.sock, + shutdown_gracefully); socket.ssl = nullptr; } assert(socket.ssl == nullptr); @@ -8208,8 +9651,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { auto type = GEN_DNS; - struct in6_addr addr6; - struct in_addr addr; + struct in6_addr addr6{}; + struct in_addr addr{}; size_t addr_len = 0; #ifndef __MINGW32__ @@ -8234,8 +9677,9 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { for (decltype(count) i = 0; i < count && !dsn_matched; i++) { auto val = sk_GENERAL_NAME_value(alt_names, i); if (val->type == type) { - auto name = (const char *)ASN1_STRING_get0_data(val->d.ia5); - auto name_len = (size_t)ASN1_STRING_length(val->d.ia5); + auto name = + reinterpret_cast(ASN1_STRING_get0_data(val->d.ia5)); + auto name_len = static_cast(ASN1_STRING_length(val->d.ia5)); switch (type) { case GEN_DNS: dsn_matched = check_host_name(name, name_len); break; @@ -8253,7 +9697,8 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const { if (dsn_matched || ip_matched) { ret = true; } } - GENERAL_NAMES_free((STACK_OF(GENERAL_NAME) *)alt_names); + GENERAL_NAMES_free(const_cast( + reinterpret_cast(alt_names))); return ret; } @@ -8282,7 +9727,7 @@ inline bool SSLClient::check_host_name(const char *pattern, std::vector pattern_components; detail::split(&pattern[0], &pattern[pattern_len], '.', [&](const char *b, const char *e) { - pattern_components.emplace_back(std::string(b, e)); + pattern_components.emplace_back(b, e); }); if (host_components_.size() != pattern_components.size()) { return false; } @@ -8310,7 +9755,7 @@ inline Client::Client(const std::string &scheme_host_port, const std::string &client_cert_path, const std::string &client_key_path) { const static std::regex re( - R"((?:([a-z]+):\/\/)?(?:\[([\d:]+)\]|([^:/?#]+))(?::(\d+))?)"); + R"((?:([a-z]+):\/\/)?(?:\[([a-fA-F\d:]+)\]|([^:/?#]+))(?::(\d+))?)"); std::smatch m; if (std::regex_match(scheme_host_port, m, re)) { @@ -8347,10 +9792,12 @@ inline Client::Client(const std::string &scheme_host_port, client_key_path); } } else { + // NOTE: Update TEST(UniversalClientImplTest, Ipv6LiteralAddress) + // if port param below changes. cli_ = detail::make_unique(scheme_host_port, 80, client_cert_path, client_key_path); } -} +} // namespace detail inline Client::Client(const std::string &host, int port) : cli_(detail::make_unique(host, port)) {} @@ -8361,7 +9808,7 @@ inline Client::Client(const std::string &host, int port, : cli_(detail::make_unique(host, port, client_cert_path, client_key_path)) {} -inline Client::~Client() {} +inline Client::~Client() = default; inline bool Client::is_valid() const { return cli_ != nullptr && cli_->is_valid(); @@ -8421,19 +9868,20 @@ inline Result Client::Get(const std::string &path, const Headers &headers, } inline Result Client::Get(const std::string &path, const Params ¶ms, const Headers &headers, Progress progress) { - return cli_->Get(path, params, headers, progress); + return cli_->Get(path, params, headers, std::move(progress)); } inline Result Client::Get(const std::string &path, const Params ¶ms, const Headers &headers, ContentReceiver content_receiver, Progress progress) { - return cli_->Get(path, params, headers, content_receiver, progress); + return cli_->Get(path, params, headers, std::move(content_receiver), + std::move(progress)); } inline Result Client::Get(const std::string &path, const Params ¶ms, const Headers &headers, ResponseHandler response_handler, ContentReceiver content_receiver, Progress progress) { - return cli_->Get(path, params, headers, response_handler, content_receiver, - progress); + return cli_->Get(path, params, headers, std::move(response_handler), + std::move(content_receiver), std::move(progress)); } inline Result Client::Head(const std::string &path) { return cli_->Head(path); } @@ -8455,15 +9903,30 @@ inline Result Client::Post(const std::string &path, const Headers &headers, const std::string &content_type) { return cli_->Post(path, headers, body, content_length, content_type); } +inline Result Client::Post(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress) { + return cli_->Post(path, headers, body, content_length, content_type, + progress); +} inline Result Client::Post(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Post(path, body, content_type); } +inline Result Client::Post(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Post(path, body, content_type, progress); +} inline Result Client::Post(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Post(path, headers, body, content_type); } +inline Result Client::Post(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Post(path, headers, body, content_type, progress); +} inline Result Client::Post(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type) { @@ -8494,6 +9957,10 @@ inline Result Client::Post(const std::string &path, const Headers &headers, const Params ¶ms) { return cli_->Post(path, headers, params); } +inline Result Client::Post(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + return cli_->Post(path, headers, params, progress); +} inline Result Client::Post(const std::string &path, const MultipartFormDataItems &items) { return cli_->Post(path, items); @@ -8524,15 +9991,29 @@ inline Result Client::Put(const std::string &path, const Headers &headers, const std::string &content_type) { return cli_->Put(path, headers, body, content_length, content_type); } +inline Result Client::Put(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, Progress progress) { + return cli_->Put(path, headers, body, content_length, content_type, progress); +} inline Result Client::Put(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Put(path, body, content_type); } +inline Result Client::Put(const std::string &path, const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Put(path, body, content_type, progress); +} inline Result Client::Put(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Put(path, headers, body, content_type); } +inline Result Client::Put(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, Progress progress) { + return cli_->Put(path, headers, body, content_type, progress); +} inline Result Client::Put(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type) { @@ -8563,6 +10044,10 @@ inline Result Client::Put(const std::string &path, const Headers &headers, const Params ¶ms) { return cli_->Put(path, headers, params); } +inline Result Client::Put(const std::string &path, const Headers &headers, + const Params ¶ms, Progress progress) { + return cli_->Put(path, headers, params, progress); +} inline Result Client::Put(const std::string &path, const MultipartFormDataItems &items) { return cli_->Put(path, items); @@ -8590,20 +10075,44 @@ inline Result Client::Patch(const std::string &path, const char *body, const std::string &content_type) { return cli_->Patch(path, body, content_length, content_type); } +inline Result Client::Patch(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, body, content_length, content_type, progress); +} inline Result Client::Patch(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return cli_->Patch(path, headers, body, content_length, content_type); } +inline Result Client::Patch(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, headers, body, content_length, content_type, + progress); +} inline Result Client::Patch(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Patch(path, body, content_type); } +inline Result Client::Patch(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, body, content_type, progress); +} inline Result Client::Patch(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Patch(path, headers, body, content_type); } +inline Result Client::Patch(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Patch(path, headers, body, content_type, progress); +} inline Result Client::Patch(const std::string &path, size_t content_length, ContentProvider content_provider, const std::string &content_type) { @@ -8638,20 +10147,44 @@ inline Result Client::Delete(const std::string &path, const char *body, const std::string &content_type) { return cli_->Delete(path, body, content_length, content_type); } +inline Result Client::Delete(const std::string &path, const char *body, + size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, body, content_length, content_type, progress); +} inline Result Client::Delete(const std::string &path, const Headers &headers, const char *body, size_t content_length, const std::string &content_type) { return cli_->Delete(path, headers, body, content_length, content_type); } +inline Result Client::Delete(const std::string &path, const Headers &headers, + const char *body, size_t content_length, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, headers, body, content_length, content_type, + progress); +} inline Result Client::Delete(const std::string &path, const std::string &body, const std::string &content_type) { return cli_->Delete(path, body, content_type); } +inline Result Client::Delete(const std::string &path, const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, body, content_type, progress); +} inline Result Client::Delete(const std::string &path, const Headers &headers, const std::string &body, const std::string &content_type) { return cli_->Delete(path, headers, body, content_type); } +inline Result Client::Delete(const std::string &path, const Headers &headers, + const std::string &body, + const std::string &content_type, + Progress progress) { + return cli_->Delete(path, headers, body, content_type, progress); +} inline Result Client::Options(const std::string &path) { return cli_->Options(path); } @@ -8665,12 +10198,16 @@ inline bool Client::send(Request &req, Response &res, Error &error) { inline Result Client::send(const Request &req) { return cli_->send(req); } +inline void Client::stop() { cli_->stop(); } + +inline std::string Client::host() const { return cli_->host(); } + +inline int Client::port() const { return cli_->port(); } + inline size_t Client::is_socket_open() const { return cli_->is_socket_open(); } inline socket_t Client::socket() const { return cli_->socket(); } -inline void Client::stop() { cli_->stop(); } - inline void Client::set_hostname_addr_map(std::map addr_map) { cli_->set_hostname_addr_map(std::move(addr_map)); @@ -8680,6 +10217,11 @@ inline void Client::set_default_headers(Headers headers) { cli_->set_default_headers(std::move(headers)); } +inline void Client::set_header_writer( + std::function const &writer) { + cli_->set_header_writer(writer); +} + inline void Client::set_address_family(int family) { cli_->set_address_family(family); } @@ -8752,9 +10294,20 @@ inline void Client::set_proxy_digest_auth(const std::string &username, inline void Client::enable_server_certificate_verification(bool enabled) { cli_->enable_server_certificate_verification(enabled); } + +inline void Client::enable_server_hostname_verification(bool enabled) { + cli_->enable_server_hostname_verification(enabled); +} + +inline void Client::set_server_certificate_verifier( + std::function verifier) { + cli_->set_server_certificate_verifier(verifier); +} #endif -inline void Client::set_logger(Logger logger) { cli_->set_logger(logger); } +inline void Client::set_logger(Logger logger) { + cli_->set_logger(std::move(logger)); +} #ifdef CPPHTTPLIB_OPENSSL_SUPPORT inline void Client::set_ca_cert_path(const std::string &ca_cert_file_path, @@ -8770,6 +10323,10 @@ inline void Client::set_ca_cert_store(X509_STORE *ca_cert_store) { } } +inline void Client::load_ca_cert_store(const char *ca_cert, std::size_t size) { + set_ca_cert_store(cli_->create_ca_cert_store(ca_cert, size)); +} + inline long Client::get_openssl_verify_result() const { if (is_ssl_) { return static_cast(*cli_).get_openssl_verify_result(); diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp deleted file mode 100644 index 20551520e..000000000 --- a/examples/server/index.html.hpp +++ /dev/null @@ -1,2791 +0,0 @@ -unsigned char index_html[] = { - 0x3c, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a, 0x0a, 0x3c, 0x68, 0x65, 0x61, - 0x64, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20, 0x63, - 0x68, 0x61, 0x72, 0x73, 0x65, 0x74, 0x3d, 0x22, 0x55, 0x54, 0x46, 0x2d, - 0x38, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20, - 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x76, 0x69, 0x65, 0x77, 0x70, 0x6f, - 0x72, 0x74, 0x22, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3d, - 0x22, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3d, 0x64, 0x65, 0x76, 0x69, 0x63, - 0x65, 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x2c, 0x20, 0x69, 0x6e, 0x69, - 0x74, 0x69, 0x61, 0x6c, 0x2d, 0x73, 0x63, 0x61, 0x6c, 0x65, 0x3d, 0x31, - 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x69, 0x6d, 0x75, 0x6d, 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--- a/examples/server/json-schema-to-grammar.mjs.hpp +++ /dev/null @@ -1,311 +0,0 @@ -unsigned char json_schema_to_grammar_mjs[] = { - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x53, 0x50, 0x41, 0x43, 0x45, 0x5f, - 0x52, 0x55, 0x4c, 0x45, 0x20, 0x3d, 0x20, 0x27, 0x22, 0x20, 0x22, 0x3f, - 0x27, 0x3b, 0x0a, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x50, 0x52, - 0x49, 0x4d, 0x49, 0x54, 0x49, 0x56, 0x45, 0x5f, 0x52, 0x55, 0x4c, 0x45, - 0x53, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x62, 0x6f, 0x6f, 0x6c, - 0x65, 0x61, 0x6e, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x74, 0x72, 0x75, 0x65, - 0x22, 0x20, 0x7c, 0x20, 0x22, 0x66, 0x61, 0x6c, 0x73, 0x65, 0x22, 0x29, - 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x6e, - 0x75, 0x6d, 0x62, 0x65, 0x72, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x2d, 0x22, - 0x3f, 0x20, 0x28, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 0x20, 0x7c, 0x20, 0x5b, - 0x31, 0x2d, 0x39, 0x5d, 0x20, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 0x2a, 0x29, - 0x29, 0x20, 0x28, 0x22, 0x2e, 0x22, 0x20, 0x5b, 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diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp deleted file mode 100644 index ff4ad6994..000000000 --- a/examples/server/oai.hpp +++ /dev/null @@ -1,225 +0,0 @@ -#pragma once - -#include -#include -#include -#include -#include -#include - -#include "json.hpp" -#include "utils.hpp" - -#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" - -using json = nlohmann::json; - -inline static json oaicompat_completion_params_parse( - const struct llama_model * model, - const json &body, /* openai api json semantics */ - const std::string &chat_template) -{ - json llama_params; - - llama_params["__oaicompat"] = true; - - // Map OpenAI parameters to llama.cpp parameters - // - // For parameters that are defined by the OpenAI documentation (e.g. - // temperature), we explicitly specify OpenAI's intended default; we - // need to do that because sometimes OpenAI disagrees with llama.cpp - // - // https://platform.openai.com/docs/api-reference/chat/create - llama_sampling_params default_sparams; - llama_params["model"] = json_value(body, "model", std::string("unknown")); - llama_params["prompt"] = format_chat(model, chat_template, body["messages"]); - llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); - llama_params["temperature"] = json_value(body, "temperature", 0.0); - llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); - llama_params["top_p"] = json_value(body, "top_p", 1.0); - llama_params["n_predict"] = json_value(body, "max_tokens", -1); - llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); - llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); - llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); - llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); - llama_params["stream"] = json_value(body, "stream", false); - llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); - llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); - llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); - llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); - llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); - llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); - - if (body.count("grammar") != 0) { - llama_params["grammar"] = json_value(body, "grammar", json::object()); - } - - // Handle 'stop' field - if (body.contains("stop") && body["stop"].is_string()) { - llama_params["stop"] = json::array({body["stop"].get()}); - } else { - llama_params["stop"] = json_value(body, "stop", json::array()); - } - - // Ensure there is ChatML-specific end sequence among stop words - llama_params["stop"].push_back("<|im_end|>"); - - return llama_params; -} - -inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false) -{ - json result = response.result_json; - - bool stopped_word = result.count("stopped_word") != 0; - bool stopped_eos = json_value(result, "stopped_eos", false); - int num_tokens_predicted = json_value(result, "tokens_predicted", 0); - int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason = "length"; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - - json choices = - streaming ? json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}) - : json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"message", json{{"content", content}, - {"role", "assistant"}}}}}); - - std::time_t t = std::time(0); - - json res = - json{{"choices", choices}, - {"created", t}, - {"model", - json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, - {"usage", - json{{"completion_tokens", num_tokens_predicted}, - {"prompt_tokens", num_prompt_tokens}, - {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, - {"id", gen_chatcmplid()}}; - - if (server_verbose) { - res["__verbose"] = result; - } - - if (result.contains("completion_probabilities")) { - res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); - } - - return res; -} - -// return value is vector as there is one case where we might need to generate two responses -inline static std::vector format_partial_response_oaicompat(const task_result &response) { - json result = response.result_json; - - if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { - return std::vector({response.result_json}); - } - - bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; - std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); - - bool stopped_word = json_value(result, "stopped_word", false); - bool stopped_eos = json_value(result, "stopped_eos", false); - bool stopped_limit = json_value(result, "stopped_limit", false); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - if (stopped_limit) { - finish_reason = "length"; - } - - std::time_t t = std::time(0); - - json choices; - - if (!finish_reason.empty()) { - choices = json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}); - } else { - if (first) { - if (content.empty()) { - choices = json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{{"role", "assistant"}}}}}); - } else { - // We have to send this as two updates to conform to openai behavior - json initial_ret = json{{"choices", json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"role", "assistant"} - }}}})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - json second_ret = json{ - {"choices", json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"content", content}}} - }})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({initial_ret, second_ret}); - } - } else { - // Some idiosyncrasy in task processing logic makes several trailing calls - // with empty content, we ignore these at the calee site. - if (content.empty()) { - return std::vector({json::object()}); - } - - choices = json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", - json{ - {"content", content}, - }}, - }}); - } - } - - json ret = json{{"choices", choices}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({ret}); -} - -inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings) -{ - json res = - json{ - {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", "list"}, - {"usage", - json{{"prompt_tokens", 0}, - {"total_tokens", 0}}}, - {"data", embeddings} - }; - return res; -} - diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz new file mode 100644 index 000000000..141e80920 Binary files /dev/null and b/examples/server/public/index.html.gz differ diff --git a/examples/server/public/index.js b/examples/server/public/index.js deleted file mode 100644 index 9db5a9d9f..000000000 --- a/examples/server/public/index.js +++ /dev/null @@ -1 +0,0 @@ -function t(){throw new Error("Cycle detected")}function n(){if(u>1){u--;return}let t,n=!1;while(void 0!==_){let i=_;_=void 0;f++;while(void 0!==i){const _=i.o;i.o=void 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([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', - integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', - string: ` "\\"" ( - [^"\\\\] | - "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\\"" space`, - null: '"null" space', -}; - -const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; -const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; -const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; - -export class SchemaConverter { - constructor(propOrder) { - this._propOrder = propOrder || {}; - this._rules = new Map(); - this._rules.set('space', SPACE_RULE); - } - - _formatLiteral(literal) { - const escaped = JSON.stringify(literal).replace( - GRAMMAR_LITERAL_ESCAPE_RE, - m => GRAMMAR_LITERAL_ESCAPES[m] - ); - return `"${escaped}"`; - } - - _addRule(name, rule) { - let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); - let key = escName; - - if (this._rules.has(escName)) { - if (this._rules.get(escName) === rule) { - return key; - } - - let i = 0; - while (this._rules.has(`${escName}${i}`)) { - i += 1; - } - key = `${escName}${i}`; - } - - this._rules.set(key, rule); - return key; - } - - visit(schema, name) { - const schemaType = schema.type; - const ruleName = name || 'root'; - - if (schema.oneOf || schema.anyOf) { - const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => - this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) - ).join(' | '); - - return this._addRule(ruleName, rule); - } else if ('const' in schema) { - return this._addRule(ruleName, this._formatLiteral(schema.const)); - } else if ('enum' in schema) { - const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); - return this._addRule(ruleName, rule); - } else if (schemaType === 'object' && 'properties' in schema) { - // TODO: `required` keyword (from python implementation) - const propOrder = this._propOrder; - const propPairs = Object.entries(schema.properties).sort((a, b) => { - // sort by position in prop_order (if specified) then by key - const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; - const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; - return orderA - orderB || a[0].localeCompare(b[0]); - }); - - let rule = '"{" space'; - propPairs.forEach(([propName, propSchema], i) => { - const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); - if (i > 0) { - rule += ' "," space'; - } - rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; - }); - rule += ' "}" space'; - - return this._addRule(ruleName, rule); - } else if (schemaType === 'array' && 'items' in schema) { - // TODO `prefixItems` keyword (from python implementation) - const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); - const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; - return this._addRule(ruleName, rule); - } else { - if (!PRIMITIVE_RULES[schemaType]) { - throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); - } - return this._addRule( - ruleName === 'root' ? 'root' : schemaType, - PRIMITIVE_RULES[schemaType] - ); - } - } - - formatGrammar() { - let grammar = ''; - this._rules.forEach((rule, name) => { - grammar += `${name} ::= ${rule}\n`; - }); - return grammar; - } -} diff --git a/examples/server/public/loading.html b/examples/server/public/loading.html new file mode 100644 index 000000000..c3fd19a0f --- /dev/null +++ b/examples/server/public/loading.html @@ -0,0 +1,12 @@ + + + + + + +
+ The model is loading. Please wait.
+ The user interface will appear soon. +
+ + diff --git a/examples/server/public_legacy/colorthemes.css b/examples/server/public_legacy/colorthemes.css new file mode 100755 index 000000000..b1e2b8b70 --- /dev/null +++ b/examples/server/public_legacy/colorthemes.css @@ -0,0 +1,402 @@ +@import url("theme-snowstorm.css"); +@import url("theme-polarnight.css"); +@import url("theme-ketivah.css"); +@import url("theme-mangotango.css"); +@import url("theme-playground.css"); +@import url("theme-beeninorder.css"); + +:root { +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); +} + +/* + +.theme-template { + + + If light theme: should go from bright to darker + If dark theme: should go from dark to brighter + ideally this should not be anything but steps of + gray or slightly variants from it + + --primary-color-1: #2E3440; + --primary-color-2: #3B4252; + --primary-color-3: #434C5E; + --primary-color-4: #4C566A; + + + + If light theme: should go from dark to brighter + If dark theme: should go from bright to darker + ideally this should not be anything but steps of + gray or slightly variants from it + + --secondary-color-1: #ECEFF4; + --secondary-color-2: #E5E9F0; + --secondary-color-3: #D8DEE9; + --secondary-color-4: #C8CED9; + + + + Choose wisely nuance colors. It is not easy to find + 4 harmonizing nuance colors. But keep in mind, that + only one accent color could work too. + + --theme-nuance-color-1: #8FBCBB; + --theme-nuance-color-2: #88C0D0; + --theme-nuance-color-3: #81A1C1; + --theme-nuance-color-4: #5E81AC; + + + + adapt the color red, orange, yellow, green, + purple to the 'mood' of your overall design + e.g is it low-contrast? vibrant? dynamic? etc + + --theme-red-color: #BF616A; + --theme-orange-color: #D08770; + --theme-yellow-color: #EBCB8B; + --theme-green-color: #A3BE8C; + --theme-purple-color: #B48EAD; + + + +NOTE: comment all those line `--- ...` out +------------------------------------------------ +--background-color-1: +--background-color-2: +--background-color-3: +--background-color-4: + +--border-color-1: +--border-color-2: +--border-color-3: + +--border-focus-color: +--border-focus-shadow: + +--text-color-plain: +--text-color-subtile-1: +--text-color-subtile-2: + +--code-background-color: +--code-text-color: + +--ui-range-thumb-color: +--ui-range-thumb-border: + +--textarea-border-color: + + + +------------------------------------------- +--button-alert-text-hover: +--button-alert-color-hover: +--button-alert-border-hover: + +--button-alert-text-active: +--button-alert-color-active: +--button-alert-border-active: + + + +----------- PRIMARY ----------------------- +--button should immediately catch the eye-- + +--button-primary-text: +--button-primary-color: +--button-primary-border: + + +---------hover---------- +--button-primary-text-hover: +--button-primary-color-hover: +--button-primary-border-hover: + + +---------active--------- +--button-primary-text-active: +--button-primary-color-active: +--button-primary-border-active: + + + +------------ SECONDARY ------------------------ +--button should NOT immediately catch the eye-- + +--button-secondary-text: +--button-secondary-color: +--button-secondary-border: + + +---------hover---------- +--button-secondary-text-hover: +--button-secondary-color-hover: +--button-secondary-border-hover: + + +---------active--------- +--button-secondary-text-active: +--button-secondary-color-active: +--button-secondary-border-active: + + + +---------- TERTIARY ----------------------- +---------- disabled buttons --------------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + + +---------hover---------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + +} + +*/ diff --git a/examples/server/public/completion.js b/examples/server/public_legacy/completion.js similarity index 84% rename from examples/server/public/completion.js rename to examples/server/public_legacy/completion.js index ab38a7b40..30df7c2fa 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public_legacy/completion.js @@ -21,6 +21,7 @@ let generation_settings = null; // export async function* llama(prompt, params = {}, config = {}) { let controller = config.controller; + const api_url = config.api_url?.replace(/\/+$/, '') || ""; if (!controller) { controller = new AbortController(); @@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) { const completionParams = { ...paramDefaults, ...params, prompt }; - const response = await fetch("/completion", { + const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, { method: 'POST', body: JSON.stringify(completionParams), headers: { @@ -77,7 +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); @@ -96,18 +102,18 @@ export async function* llama(prompt, params = {}, config = {}) { } } if (result.error) { - result.error = JSON.parse(result.error); - if (result.error.content.includes('slot unavailable')) { - // Throw an error to be caught by upstream callers - throw new Error('slot unavailable'); - } else { - console.error(`llama.cpp error: ${result.error.content}`); + try { + result.error = JSON.parse(result.error); + if (result.error.message.includes('slot unavailable')) { + // Throw an error to be caught by upstream callers + throw new Error('slot unavailable'); + } else { + console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`); + } + } catch(e) { + console.error(`llama.cpp error ${result.error}`) } } - if (result.error) { - result.error = JSON.parse(result.error); - console.error(`llama.cpp error: ${result.error.content}`); - } } } } @@ -193,9 +199,10 @@ export const llamaComplete = async (params, controller, callback) => { } // Get the model info from the server. This is useful for getting the context window and so on. -export const llamaModelInfo = async () => { +export const llamaModelInfo = async (config = {}) => { if (!generation_settings) { - const props = await fetch("/props").then(r => r.json()); + const api_url = config.api_url?.replace(/\/+$/, '') || ""; + const props = await fetch(`${api_url}/props`).then(r => r.json()); generation_settings = props.default_generation_settings; } return generation_settings; diff --git a/examples/server/public_legacy/favicon.ico b/examples/server/public_legacy/favicon.ico new file mode 100644 index 000000000..89e154a0a Binary files /dev/null and b/examples/server/public_legacy/favicon.ico differ diff --git a/examples/server/public_legacy/index-new.html b/examples/server/public_legacy/index-new.html new file mode 100644 index 000000000..cbfbbdf28 --- /dev/null +++ b/examples/server/public_legacy/index-new.html @@ -0,0 +1,1190 @@ + + + + + + + + + llama.cpp - chat + + + + + + + + + +
+ +
+
+ + + diff --git a/examples/server/public_legacy/index.html b/examples/server/public_legacy/index.html new file mode 100644 index 000000000..75f39330a --- /dev/null +++ b/examples/server/public_legacy/index.html @@ -0,0 +1,1301 @@ + + + + + + llama.cpp - chat + + + + + + + +
+ +
+
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was ported from json_schema_to_grammar.py, please fix bugs / add features there first. +const SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}'; + +function _buildRepetition(itemRule, minItems, maxItems, opts={}) { + if (minItems === 0 && maxItems === 1) { + return `${itemRule}?`; + } + + + const separatorRule = opts.separatorRule ?? ''; + const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false + + if (separatorRule === '') { + if (minItems === 1 && maxItems === undefined) { + return `${itemRule}+`; + } else if (minItems === 0 && maxItems === undefined) { + return `${itemRule}*`; + } else { + return `${itemRule}{${minItems},${maxItems !== undefined ? maxItems : ''}}`; + } + } + + const result = itemRule + ' ' + _buildRepetition(`(${separatorRule} ${itemRule})`, minItems > 0 ? minItems - 1 : 0, maxItems !== undefined ? maxItems - 1 : undefined); + return minItems === 0 ? `(${result})?` : result; +} + +function _generateMinMaxInt(minValue, maxValue, out, decimalsLeft = 16, topLevel = true) { + const hasMin = minValue !== null; + const hasMax = maxValue !== null; + + function digitRange(fromChar, toChar) { + out.push("["); + if (fromChar === toChar) { + out.push(fromChar); + } else { + out.push(fromChar); + out.push("-"); + out.push(toChar); + } + out.push("]"); + } + + function moreDigits(minDigits, maxDigits) { + out.push("[0-9]"); + if (minDigits === maxDigits && minDigits === 1) { + return; + } + out.push("{"); + out.push(minDigits.toString()); + if (maxDigits !== minDigits) { + out.push(","); + if (maxDigits !== Number.MAX_SAFE_INTEGER) { + out.push(maxDigits.toString()); + } + } + out.push("}"); + } + + function uniformRange(fromStr, toStr) { + let i = 0; + while (i < fromStr.length && fromStr[i] === toStr[i]) { + i++; + } + if (i > 0) { + out.push("\""); + out.push(fromStr.slice(0, i)); + out.push("\""); + } + if (i < fromStr.length) { + if (i > 0) { + out.push(" "); + } + const subLen = fromStr.length - i - 1; + if (subLen > 0) { + const fromSub = fromStr.slice(i + 1); + const toSub = toStr.slice(i + 1); + const subZeros = "0".repeat(subLen); + const subNines = "9".repeat(subLen); + + let toReached = false; + out.push("("); + if (fromSub === subZeros) { + digitRange(fromStr[i], String.fromCharCode(toStr.charCodeAt(i) - 1)); + out.push(" "); + moreDigits(subLen, subLen); + } else { + out.push("["); + out.push(fromStr[i]); + out.push("] "); + out.push("("); + uniformRange(fromSub, subNines); + out.push(")"); + if (fromStr.charCodeAt(i) < toStr.charCodeAt(i) - 1) { + out.push(" | "); + if (toSub === subNines) { + digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), toStr[i]); + toReached = true; + } else { + digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), String.fromCharCode(toStr.charCodeAt(i) - 1)); + } + out.push(" "); + moreDigits(subLen, subLen); + } + } + if (!toReached) { + out.push(" | "); + digitRange(toStr[i], toStr[i]); + out.push(" "); + uniformRange(subZeros, toSub); + } + out.push(")"); + } else { + out.push("["); + out.push(fromStr[i]); + out.push("-"); + out.push(toStr[i]); + out.push("]"); + } + } + } + + if (hasMin && hasMax) { + if (minValue < 0 && maxValue < 0) { + out.push("\"-\" ("); + _generateMinMaxInt(-maxValue, -minValue, out, decimalsLeft, true); + out.push(")"); + return; + } + + if (minValue < 0) { + out.push("\"-\" ("); + _generateMinMaxInt(0, -minValue, out, decimalsLeft, true); + out.push(") | "); + minValue = 0; + } + + let minS = minValue.toString(); + const maxS = maxValue.toString(); + const minDigits = minS.length; + const maxDigits = maxS.length; + + for (let digits = minDigits; digits < maxDigits; digits++) { + uniformRange(minS, "9".repeat(digits)); + minS = "1" + "0".repeat(digits); + out.push(" | "); + } + uniformRange(minS, maxS); + return; + } + + const lessDecimals = Math.max(decimalsLeft - 1, 1); + + if (hasMin) { + if (minValue < 0) { + out.push("\"-\" ("); + _generateMinMaxInt(null, -minValue, out, decimalsLeft, false); + out.push(") | [0] | [1-9] "); + moreDigits(0, decimalsLeft - 1); + } else if (minValue === 0) { + if (topLevel) { + out.push("[0] | [1-9] "); + moreDigits(0, lessDecimals); + } else { + moreDigits(1, decimalsLeft); + } + } else if (minValue <= 9) { + const c = minValue.toString(); + const range_start = topLevel ? '1' : '0'; + if (c > range_start) { + digitRange(range_start, String.fromCharCode(c.charCodeAt(0) - 1)); + out.push(" "); + moreDigits(1, lessDecimals); + out.push(" | "); + } + digitRange(c, "9"); + out.push(" "); + moreDigits(0, lessDecimals); + } else { + const minS = minValue.toString(); + const length = minS.length; + const c = minS[0]; + + if (c > "1") { + digitRange(topLevel ? "1" : "0", String.fromCharCode(c.charCodeAt(0) - 1)); + out.push(" "); + moreDigits(length, lessDecimals); + out.push(" | "); + } + digitRange(c, c); + out.push(" ("); + _generateMinMaxInt(parseInt(minS.slice(1)), null, out, lessDecimals, false); + out.push(")"); + if (c < "9") { + out.push(" | "); + digitRange(String.fromCharCode(c.charCodeAt(0) + 1), "9"); + out.push(" "); + moreDigits(length - 1, lessDecimals); + } + } + return; + } + + if (hasMax) { + if (maxValue >= 0) { + if (topLevel) { + out.push("\"-\" [1-9] "); + moreDigits(0, lessDecimals); + out.push(" | "); + } + _generateMinMaxInt(0, maxValue, out, decimalsLeft, true); + } else { + out.push("\"-\" ("); + _generateMinMaxInt(-maxValue, null, out, decimalsLeft, false); + out.push(")"); + } + return; + } + + throw new Error("At least one of minValue or maxValue must be set"); +} + +class BuiltinRule { + constructor(content, deps) { + this.content = content; + this.deps = deps || []; + } +} + +const PRIMITIVE_RULES = { + boolean : new BuiltinRule('("true" | "false") space', []), + 'decimal-part' : new BuiltinRule('[0-9]{1,16}', []), + 'integral-part': new BuiltinRule('[0] | [1-9] [0-9]{0,15}', []), + number : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), + integer : new BuiltinRule('("-"? integral-part) space', ['integral-part']), + value : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), + object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), + array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), + uuid : new BuiltinRule('"\\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\\"" space', []), + char : new BuiltinRule(`[^"\\\\\\x7F\\x00-\\x1F] | [\\\\] (["\\\\bfnrt] | "u" [0-9a-fA-F]{4})`, []), + string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']), + null : new BuiltinRule('"null" space', []), +}; + +// TODO: support "uri", "email" string formats +const STRING_FORMAT_RULES = { + 'date' : new BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), + 'time' : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), + 'date-time' : new BuiltinRule('date "T" time', ['date', 'time']), + 'date-string' : new BuiltinRule('"\\"" date "\\"" space', ['date']), + 'time-string' : new BuiltinRule('"\\"" time "\\"" space', ['time']), + 'date-time-string': new BuiltinRule('"\\"" date-time "\\"" space', ['date-time']), +} + +const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES}; + +const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; +const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; +const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g; +const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' }; + +const NON_LITERAL_SET = new Set('|.()[]{}*+?'); +const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('^$.[]()|{}*+?'); + +export class SchemaConverter { + constructor(options) { + this._propOrder = options.prop_order || {}; + this._allowFetch = options.allow_fetch || false; + this._dotall = options.dotall || false; + this._rules = {'space': SPACE_RULE}; + this._refs = {}; + this._refsBeingResolved = new Set(); + } + + _formatLiteral(literal) { + const escaped = literal.replace( + GRAMMAR_LITERAL_ESCAPE_RE, + m => GRAMMAR_LITERAL_ESCAPES[m] + ); + return `"${escaped}"`; + } + + _formatRangeChar(literal) { + return JSON.stringify(literal).slice(1, -1).replace( + GRAMMAR_RANGE_LITERAL_ESCAPE_RE, + m => GRAMMAR_LITERAL_ESCAPES[m] + ); + } + + _addRule(name, rule) { + let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); + let key = escName; + + if (escName in this._rules) { + if (this._rules[escName] === rule) { + return key; + } + + let i = 0; + while ((`${escName}${i}` in this._rules) && (this._rules[`${escName}${i}`] !== rule)) { + i += 1; + } + key = `${escName}${i}`; + } + + this._rules[key] = rule; + return key; + } + + async resolveRefs(schema, url) { + const visit = async (n) => { + if (Array.isArray(n)) { + return Promise.all(n.map(visit)); + } else if (typeof n === 'object' && n !== null) { + let ref = n.$ref; + let target; + if (ref !== undefined && !this._refs[ref]) { + if (ref.startsWith('https://')) { + if (!this._allowFetch) { + throw new Error('Fetching remote schemas is not allowed (use --allow-fetch for force)'); + } + const fetch = (await import('node-fetch')).default; + + const fragSplit = ref.split('#'); + const baseUrl = fragSplit[0]; + + target = this._refs[baseUrl]; + if (!target) { + target = await this.resolveRefs(await fetch(ref).then(res => res.json()), baseUrl); + this._refs[baseUrl] = target; + } + + if (fragSplit.length === 1 || fragSplit[fragSplit.length - 1] === '') { + return target; + } + } else if (ref.startsWith('#/')) { + target = schema; + ref = `${url}${ref}`; + n.$ref = ref; + } else { + throw new Error(`Unsupported ref ${ref}`); + } + + const selectors = ref.split('#')[1].split('/').slice(1); + for (const sel of selectors) { + if (!target || !(sel in target)) { + throw new Error(`Error resolving ref ${ref}: ${sel} not in ${JSON.stringify(target)}`); + } + target = target[sel]; + } + + this._refs[ref] = target; + } else { + await Promise.all(Object.values(n).map(visit)); + } + } + + return n; + }; + + return visit(schema); + } + + _generateUnionRule(name, altSchemas) { + return altSchemas + .map((altSchema, i) => this.visit(altSchema, `${name ?? ''}${name ? '-' : 'alternative-'}${i}`)) + .join(' | '); + } + + _visitPattern(pattern, name) { + if (!pattern.startsWith('^') || !pattern.endsWith('$')) { + throw new Error('Pattern must start with "^" and end with "$"'); + } + pattern = pattern.slice(1, -1); + const subRuleIds = {}; + + let i = 0; + const length = pattern.length; + + const getDot = () => { + let rule; + if (this._dotall) { + rule = '[\\U00000000-\\U0010FFFF]'; + } else { + // Accept any character... except \n and \r line break chars (\x0A and \xOD) + rule = '[^\\x0A\\x0D]'; + } + return this._addRule('dot', rule); + }; + + + const toRule = ([s, isLiteral]) => isLiteral ? "\"" + s + "\"" : s; + + const transform = () => { + const start = i; + // For each component of this sequence, store its string representation and whether it's a literal. + // We only need a flat structure here to apply repetition operators to the last item, and + // to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially + // (GBNF's syntax is luckily very close to regular expressions!) + const seq = []; + + const joinSeq = () => { + const ret = []; + for (const [isLiteral, g] of groupBy(seq, x => x[1])) { + if (isLiteral) { + ret.push([[...g].map(x => x[0]).join(''), true]); + } else { + ret.push(...g); + } + } + if (ret.length === 1) { + return ret[0]; + } + return [ret.map(x => toRule(x)).join(' '), false]; + }; + + while (i < length) { + const c = pattern[i]; + if (c === '.') { + seq.push([getDot(), false]); + i += 1; + } else if (c === '(') { + i += 1; + if (i < length) { + if (pattern[i] === '?') { + throw new Error(`Unsupported pattern syntax "${pattern[i]}" at index ${i} of /${pattern}/`); + } + } + seq.push([`(${toRule(transform())})`, false]); + } else if (c === ')') { + i += 1; + if (start <= 0 || pattern[start - 1] !== '(') { + throw new Error(`Unbalanced parentheses; start = ${start}, i = ${i}, pattern = ${pattern}`); + } + return joinSeq(); + } else if (c === '[') { + let squareBrackets = c; + i += 1; + while (i < length && pattern[i] !== ']') { + if (pattern[i] === '\\') { + squareBrackets += pattern.slice(i, i + 2); + i += 2; + } else { + squareBrackets += pattern[i]; + i += 1; + } + } + if (i >= length) { + throw new Error(`Unbalanced square brackets; start = ${start}, i = ${i}, pattern = ${pattern}`); + } + squareBrackets += ']'; + i += 1; + seq.push([squareBrackets, false]); + } else if (c === '|') { + seq.push(['|', false]); + i += 1; + } else if (c === '*' || c === '+' || c === '?') { + seq[seq.length - 1] = [toRule(seq[seq.length - 1]) + c, false]; + i += 1; + } else if (c === '{') { + let curlyBrackets = c; + i += 1; + while (i < length && pattern[i] !== '}') { + curlyBrackets += pattern[i]; + i += 1; + } + if (i >= length) { + throw new Error(`Unbalanced curly brackets; start = ${start}, i = ${i}, pattern = ${pattern}`); + } + curlyBrackets += '}'; + i += 1; + const nums = curlyBrackets.slice(1, -1).split(',').map(s => s.trim()); + let minTimes, maxTimes; + if (nums.length === 1) { + minTimes = parseInt(nums[0], 10); + maxTimes = minTimes; + } else { + if (nums.length !== 2) { + throw new Error(`Invalid quantifier ${curlyBrackets}`); + } + minTimes = nums[0] ? parseInt(nums[0], 10) : 0; + maxTimes = nums[1] ? parseInt(nums[1], 10) : Infinity; + } + + let [sub, subIsLiteral] = seq[seq.length - 1]; + + if (!subIsLiteral) { + let id = subRuleIds[sub]; + if (id === undefined) { + id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub); + subRuleIds[sub] = id; + } + sub = id; + } + + seq[seq.length - 1] = [ + _buildRepetition(subIsLiteral ? `"${sub}"` : sub, minTimes, maxTimes, {itemRuleIsLiteral: subIsLiteral}), + false + ]; + } else { + let literal = ''; + while (i < length) { + if (pattern[i] === '\\' && i < length - 1) { + const next = pattern[i + 1]; + if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.has(next)) { + i += 1; + literal += pattern[i]; + i += 1; + } else { + literal += pattern.slice(i, i + 2); + i += 2; + } + } else if (pattern[i] === '"') { + literal += '\\"'; + i += 1; + } else if (!NON_LITERAL_SET.has(pattern[i]) && + (i === length - 1 || literal === '' || pattern[i + 1] === '.' || !NON_LITERAL_SET.has(pattern[i+1]))) { + literal += pattern[i]; + i += 1; + } else { + break; + } + } + if (literal !== '') { + seq.push([literal, true]); + } + } + } + + return joinSeq(); + }; + + return this._addRule(name, "\"\\\"\" (" + toRule(transform()) + ") \"\\\"\" space") + } + + _notStrings(strings) { + class TrieNode { + constructor() { + this.children = {}; + this.isEndOfString = false; + } + + insert(str) { + let node = this; + for (const c of str) { + node = node.children[c] = node.children[c] || new TrieNode(); + } + node.isEndOfString = true; + } + } + + const trie = new TrieNode(); + for (const s of strings) { + trie.insert(s); + } + + const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']); + const out = ['["] ( ']; + + const visit = (node) => { + const rejects = []; + let first = true; + for (const c of Object.keys(node.children).sort()) { + const child = node.children[c]; + rejects.push(c); + if (first) { + first = false; + } else { + out.push(' | '); + } + out.push(`[${c}]`); + if (Object.keys(child.children).length > 0) { + out.push(' ('); + visit(child); + out.push(')'); + } else if (child.isEndOfString) { + out.push(` ${charRuleName}+`); + } + } + if (Object.keys(node.children).length > 0) { + if (!first) { + out.push(' | '); + } + out.push(`[^"${rejects.join('')}] ${charRuleName}*`); + } + }; + + visit(trie); + + out.push(` )${trie.isEndOfString ? '' : '?'} ["] space`); + return out.join(''); + } + + _resolveRef(ref) { + let refName = ref.split('/').pop(); + if (!(refName in this._rules) && !this._refsBeingResolved.has(ref)) { + this._refsBeingResolved.add(ref); + const resolved = this._refs[ref]; + refName = this.visit(resolved, refName); + this._refsBeingResolved.delete(ref); + } + return refName; + } + + _generateConstantRule(value) { + return this._formatLiteral(JSON.stringify(value)); + } + + visit(schema, name) { + const schemaType = schema.type; + const schemaFormat = schema.format; + const ruleName = name in RESERVED_NAMES ? name + '-' : name == '' ? 'root' : name; + + const ref = schema.$ref; + if (ref !== undefined) { + return this._addRule(ruleName, this._resolveRef(ref)); + } else if (schema.oneOf || schema.anyOf) { + return this._addRule(ruleName, this._generateUnionRule(name, schema.oneOf || schema.anyOf)); + } else if (Array.isArray(schemaType)) { + return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({...schema, type: t})))); + } else if ('const' in schema) { + return this._addRule(ruleName, this._generateConstantRule(schema.const) + ' space'); + } else if ('enum' in schema) { + const rule = '(' + schema.enum.map(v => this._generateConstantRule(v)).join(' | ') + ') space'; + return this._addRule(ruleName, rule); + } else if ((schemaType === undefined || schemaType === 'object') && + ('properties' in schema || + ('additionalProperties' in schema && schema.additionalProperties !== true))) { + const required = new Set(schema.required || []); + const properties = Object.entries(schema.properties ?? {}); + return this._addRule(ruleName, this._buildObjectRule(properties, required, name, schema.additionalProperties)); + } else if ((schemaType === undefined || schemaType === 'object') && 'allOf' in schema) { + const required = new Set(); + const properties = []; + const addComponent = (compSchema, isRequired) => { + const ref = compSchema.$ref; + if (ref !== undefined) { + compSchema = this._refs[ref]; + } + + if ('properties' in compSchema) { + for (const [propName, propSchema] of Object.entries(compSchema.properties)) { + properties.push([propName, propSchema]); + if (isRequired) { + required.add(propName); + } + } + } + }; + + for (const t of schema.allOf) { + if ('anyOf' in t) { + for (const tt of t.anyOf) { + addComponent(tt, false); + } + } else { + addComponent(t, true); + } + } + + return this._addRule(ruleName, this._buildObjectRule(properties, required, name, null)); + } else if ((schemaType === undefined || schemaType === 'array') && ('items' in schema || 'prefixItems' in schema)) { + const items = schema.items ?? schema.prefixItems; + if (Array.isArray(items)) { + return this._addRule( + ruleName, + '"[" space ' + + items.map((item, i) => this.visit(item, `${name ?? ''}${name ? '-' : ''}tuple-${i}`)).join(' "," space ') + + ' "]" space' + ); + } else { + const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`); + const minItems = schema.minItems || 0; + const maxItems = schema.maxItems; + return this._addRule(ruleName, '"[" space ' + _buildRepetition(itemRuleName, minItems, maxItems, {separatorRule: '"," space'}) + ' "]" space'); + } + } else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) { + return this._visitPattern(schema.pattern, ruleName); + } else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) { + return this._addPrimitive( + ruleName === 'root' ? 'root' : schemaFormat, + PRIMITIVE_RULES['uuid'] + ); + } else if ((schemaType === undefined || schemaType === 'string') && `${schema.format}-string` in STRING_FORMAT_RULES) { + const primName = `${schema.format}-string` + return this._addRule(ruleName, this._addPrimitive(primName, STRING_FORMAT_RULES[primName])); + } else if (schemaType === 'string' && ('minLength' in schema || 'maxLength' in schema)) { + const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']); + const minLen = schema.minLength || 0; + const maxLen = schema.maxLength; + return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space'); + } else if (schemaType === 'integer' && ('minimum' in schema || 'exclusiveMinimum' in schema || 'maximum' in schema || 'exclusiveMaximum' in schema)) { + let minValue = null; + let maxValue = null; + if ('minimum' in schema) { + minValue = schema.minimum; + } else if ('exclusiveMinimum' in schema) { + minValue = schema.exclusiveMinimum + 1; + } + if ('maximum' in schema) { + maxValue = schema.maximum; + } else if ('exclusiveMaximum' in schema) { + maxValue = schema.exclusiveMaximum - 1; + } + + const out = ["("]; + _generateMinMaxInt(minValue, maxValue, out); + out.push(") space"); + return this._addRule(ruleName, out.join('')); + } else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) { + return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object'])); + } else { + if (!(schemaType in PRIMITIVE_RULES)) { + throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); + } + // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero + return this._addPrimitive(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]); + } + } + + _addPrimitive(name, rule) { + let n = this._addRule(name, rule.content); + for (const dep of rule.deps) { + const depRule = PRIMITIVE_RULES[dep] || STRING_FORMAT_RULES[dep]; + if (!depRule) { + throw new Error(`Rule ${dep} not known`); + } + if (!(dep in this._rules)) { + this._addPrimitive(dep, depRule); + } + } + return n; + } + + _buildObjectRule(properties, required, name, additionalProperties) { + const propOrder = this._propOrder; + // sort by position in prop_order (if specified) then by original order + const sortedProps = properties.map(([k]) => k).sort((a, b) => { + const orderA = propOrder[a] || Infinity; + const orderB = propOrder[b] || Infinity; + return orderA - orderB || properties.findIndex(([k]) => k === a) - properties.findIndex(([k]) => k === b); + }); + + const propKvRuleNames = {}; + for (const [propName, propSchema] of properties) { + const propRuleName = this.visit(propSchema, `${name ?? ''}${name ? '-' : ''}${propName}`); + propKvRuleNames[propName] = this._addRule( + `${name ?? ''}${name ? '-' : ''}${propName}-kv`, + `${this._formatLiteral(JSON.stringify(propName))} space ":" space ${propRuleName}` + ); + } + const requiredProps = sortedProps.filter(k => required.has(k)); + const optionalProps = sortedProps.filter(k => !required.has(k)); + + if (additionalProperties) { + const subName = `${name ?? ''}${name ? '-' : ''}additional`; + const valueRule = + additionalProperties != null && typeof additionalProperties === 'object' ? this.visit(additionalProperties, `${subName}-value`) + : this._addPrimitive('value', PRIMITIVE_RULES['value']); + + const key_rule = + sortedProps.length === 0 ? this._addPrimitive('string', PRIMITIVE_RULES['string']) + : this._addRule(`${subName}-k`, this._notStrings(sortedProps)); + + propKvRuleNames['*'] = this._addRule( + `${subName}-kv`, + `${key_rule} ":" space ${valueRule}`); + optionalProps.push('*'); + } + + let rule = '"{" space '; + rule += requiredProps.map(k => propKvRuleNames[k]).join(' "," space '); + + if (optionalProps.length > 0) { + rule += ' ('; + if (requiredProps.length > 0) { + rule += ' "," space ( '; + } + + const getRecursiveRefs = (ks, firstIsOptional) => { + const [k, ...rest] = ks; + const kvRuleName = propKvRuleNames[k]; + let res; + const commaRef = `( "," space ${kvRuleName} )`; + if (firstIsOptional) { + res = commaRef + (k === '*' ? '*' : '?'); + } else { + res = kvRuleName + (k === '*' ? ' ' + commaRef + '*' : ''); + } + if (rest.length > 0) { + res += ' ' + this._addRule( + `${name ?? ''}${name ? '-' : ''}${k}-rest`, + getRecursiveRefs(rest, true) + ); + } + return res; + }; + + rule += optionalProps.map((_, i) => getRecursiveRefs(optionalProps.slice(i), false)).join(' | '); + if (requiredProps.length > 0) { + rule += ' )'; + } + rule += ' )?'; + } + + rule += ' "}" space'; + + return rule; + } + + formatGrammar() { + let grammar = ''; + for (const [name, rule] of Object.entries(this._rules).sort(([a], [b]) => a.localeCompare(b))) { + grammar += `${name} ::= ${rule}\n`; + } + return grammar; + } +} + +// Helper function to group elements by a key function +function* groupBy(iterable, keyFn) { + let lastKey = null; + let group = []; + for (const element of iterable) { + const key = keyFn(element); + if (lastKey !== null && key !== lastKey) { + yield [lastKey, group]; + group = []; + } + group.push(element); + lastKey = key; + } + if (group.length > 0) { + yield [lastKey, group]; + } +} 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_legacy/prompt-formats.js b/examples/server/public_legacy/prompt-formats.js new file mode 100644 index 000000000..73ddb7187 --- /dev/null +++ b/examples/server/public_legacy/prompt-formats.js @@ -0,0 +1,331 @@ +// extended list +export const promptFormats = { + "alpaca": { + template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "chatml": { + template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`, + + historyTemplate: `<|im_start|>{{name}}\n{{message}}`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "<|im_end|>\n", + + stops: "" + }, + + // ---------------------------- + + "commandr": { + template: `<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`, + + historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`, + + char: "CHATBOT_TOKEN", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER_TOKEN", + userMsgPrefix: "", + userMsgSuffix: "<|END_OF_TURN_TOKEN|>", + + stops: "" + }, + // ref: https://docs.cohere.com/docs/prompting-command-r + + // ---------------------------- + + "llama2": { + template: `[INST] <>\n{{prompt}}\n<>\n\nTest Message [/INST] Test Successfull {{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "[INST] ", + userMsgSuffix: " [/INST]", + + stops: "" + }, + // ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2 + + // ---------------------------- + + "llama3": { + template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`, + + historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|eot_id|>" + }, + // ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3 + + // ---------------------------- + + "openchat": { + template: `{{history}}{{char}}`, + + historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "phi3": { + template: `{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|end|>" + }, + // ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format + + // ---------------------------- + + "vicuna": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}\n`, + + char: "ASSISTANT", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + // ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1 + + // ---------------------------- + + "deepseekCoder": { + template: `{{prompt}}{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|EOT|>" + }, + + // ---------------------------- + + "med42": { + template: `<|system|>: {{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>: {{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "prompter", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "neuralchat": { + template: `### System:\n{{prompt}}\n{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "nousHermes": { + template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Input", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "openchatMath": { + template: `{{history}}{{char}}`, + + historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "orion": { + template: `Human: Test Message\n\nAssistant: Test Successful{{history}}{{char}}:`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant ", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "\n\n", + + stops: "" + }, + + // ---------------------------- + + "sauerkraut": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: ` + {{name}}: {{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "starlingCode": { + template: `{{history}}{{char}}`, + + historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "yi34b": { + template: `{{history}} {{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "zephyr": { + template: `<|system|>\n{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + } + }; diff --git a/examples/server/public_legacy/style.css b/examples/server/public_legacy/style.css new file mode 100644 index 000000000..087cc62da --- /dev/null +++ b/examples/server/public_legacy/style.css @@ -0,0 +1,954 @@ +@import url("colorthemes.css"); + +body { + font-family: 'Arial', sans-serif; + font-size: 90%; + background-color: var(--background-color-1); + color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */ + max-width: 600px; + min-width: 300px; + line-height: 1.2; + margin: 0 auto; + padding: 0 0.5em; + transition: background-color 0.3s; +} + +::selection { + color: var(--button-primary-text) ; + background: var(--button-primary-color); +} + +code, pre code { + font-family: 'Courier New', monospace; +} + +#container { + margin: 0em auto; + display: flex; + flex-direction: column; + justify-content: space-between; + height: 100%; +} + +main { + margin: 3px; + display: flex; + flex-direction: column; + justify-content: space-between; + gap: 1em; + flex-grow: 1; + overflow-y: auto; + border: 1px solid var(--border-color-3); + border-radius: 5px; + padding: 0.5em; +} + +p { + overflow-wrap: break-word; + word-wrap: break-word; + hyphens: auto; + margin-top: 0.5em; + margin-bottom: 0.5em; +} + +#write form { + margin: 1em 0 0 0; + display: flex; + flex-direction: column; + gap: 0.5em; + align-items: stretch; +} + +.right { + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + margin-bottom: 30px; +} + +.two-columns { + width: 97%; + max-width: 97%; + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1em; + position: relative; +} + +.json-schema-controls { + margin-top: 10px; + width: 100%; + max-width: 100%; + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.json-schema-controls > * { + flex: 1; +} + +/* titles of the details-summary boxes */ +.summary-title { + font-weight: 600; + font-size: x-small; + color: var(--text-color-subtile-1); + text-transform: uppercase; + /* transition: ; */ +} + +fieldset { + border: none; + padding: 0; + margin: 0; + color: var(--text-color-plain); +} + +fieldset.two { + display: grid; + grid-template: "a a a"; + gap: 1em; + align-items: center; + font-size: x-small; + color: var(--text-color-plain); +} + +fieldset.three { + display: grid; + grid-template: "a a a"; + gap: 1em; + font-size: x-small; + color: var(--text-color-plain); +} + +/* titles of name fields*/ +fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* titles of params fields*/ +fieldset.params { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-4); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +fieldset.dropdowns { + -webkit-appearance: none; + display: flex; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: red; + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* input of name fields*/ +.names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.chat-id-color { + color: var(--chat-id-color); +} + +details { + border: 1px solid var(--border-color-2); + border-radius: 5px; + padding: 0.5em 0.5em 0; + margin-top: 0.5em; +} + +summary { + font-weight: bold; + margin: -0.5em -0.5em 0; + padding: 0.5em; + cursor: pointer; +} + +details[open] { + padding: 0.5em; +} + +textarea-sec, input-sec, button-sec { + padding: 10px; + height: 40px; + align-items: center; +} + +textarea-sec::placeholder, input-sec::placeholder { + padding-left: 10px; +} + +.toggleCheckbox { + display: none; +} + +.toggleContainer { + position: relative; + display: grid; + grid-template-columns: repeat(2, 1fr); + width: fit-content; + border: 3px solid var(--border-color-2); + border-radius: 20px; + background: var(--border-color-2); + font-size: small; + cursor: pointer; + overflow: hidden; +} + +/* toggle button current state */ +.toggleContainer::before { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + content: ''; + position: absolute; + width: 50%; + height: 100%; + left: 0%; + border-radius: 20px; + transition: all 0.3s; +} + +.toggleContainer div { + padding: 6px; + text-align: center; + z-index: 1; + transition: color 0.3s; +} + +.toggleCheckbox:checked + .toggleContainer::before { + left: 50%; +} + +.toggleCheckbox:checked + .toggleContainer div:first-child { + color: var(--text-color-subtile-2); +} + +.toggleCheckbox:checked + .toggleContainer div:last-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:first-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:last-child { + color: var(--text-color-subtile-2); +} + +select { + padding: 5px; + margin-right: 5px; + border-radius: 4px; + border: 1px solid var(--secondary-color-4); + background-color: var(--primary-color-3); + color: var(--secondary-color-4); + cursor: pointer; +} + +select:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 1px var(--border-focus-shadow); +} + +.button-container { + display: flex; + justify-content: flex-end; +} + +button { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 30px #ffffff; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +button:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + font-size: x-small; + font-weight: 600; +} + +button:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +button:disabled { + color: var(--button-tertiary-text); + background-color: var(--button-tertiary-color); + border: 1px solid var(--button-tertiary-border); + font-size: x-small; + font-weight: 600; + cursor: not-allowed; +} + +.reset-button { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + width: fit-content; + height: fit-content; + font-size: x-small; + font-weight: 600; + border-radius: 50px; + overflow: hidden; +} + +.reset-button:hover { + color: var(--button-alert-text-hover); + background-color: var(--button-alert-color-hover); + border: 1px solid var(--button-alert-border-hover); + font-size: x-small; + font-weight: 600; +} + +.reset-button:active { + color: var(--button-alert-text-active); + background-color: var(--button-alert-color-active); + border: 1px solid var(--button-alert-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-grammar { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-back { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +.button-back:hover { + color: var(--button-secondary-text-hover); + background-color: var(--button-secondary-color-hover); + border: 1px solid var(--button-secondary-border-hover); + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 4px 2px; + transition: background-color 0.1s; + cursor: pointer; + border-radius: 12px; +} + +.button-back:active { + color: var(--button-secondary-text-active); + background-color: var(--button-secondary-color-active); + border: 1px solid var(--button-secondary-border-active); + font-size: x-small; + font-weight: 600; +} + +.prob-set { + padding: 0.3em; + border-bottom: 1px solid red; /* unknown */ +} + +.popover-content { + position: absolute; + background-color: white; + padding: 0.2em; + box-shadow: 0 0 13px rgba(0, 0, 0, 0.1); +} + +.grammar { + width: 97%; + max-width: 97%; +} + +textarea { + padding: 5px; + flex-grow: 1; + width: 100%; + max-width: 100%; + border-radius: 8px; + border: 1px solid var(--border-color-1); + resize: none; + height: 6em; +} + +textarea:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* "props" frame */ +input[type="text"], +input[type="range"] { + padding: 5px; + border-radius: 8px; + border: 1px solid var(--border-color-1); +} + +/* "names and props" frame focused*/ +input[type="text"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +input[type="range"]:hover { + opacity: 1; +} + +input[type="range"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); + background-size: var(--slider-track-size-focus); +} + +input[type="range"]::-moz-range-thumb { + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"] { + -webkit-appearance: none; + width: 80%; + height: 1px; + border: 1px solid var(--border-color-1); + border-radius: 8px; + background: var(--border-color-2); + outline: none; + opacity: 0.7; + -webkit-transition: .2s; + transition: opacity .2s; +} + +input[type="range"]::-webkit-slider-thumb { + -webkit-appearance: none; + appearance: none; + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"]::-webkit-slider-runnable-track { + background-size: var(--slider-track-size); +} + +input[type="radio"] { + accent-color: var(--theme-nuance-color-2); +} + +.chat-input-container { + position: relative; + max-width: 97%; + min-width: 97%; +} + +.chat-input-label { + position: absolute; + top: 0; + left: 0; + color: var(--text-color-plain); + pointer-events: none; + margin-left: 5px; + margin-top: 5px; +} + +textarea#chat-input { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#chat-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.input-container { + position: relative; + box-sizing: border-box; + width: 100%; /* Setzt die Breite auf 100% */ + max-width: 100%; /* Stellt sicher, dass die Breite nicht größer als 100% wird */ +} + +.input-container:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} +/* titles of name fields*/ +/* fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} */ + +/* input of name fields*/ +/* .names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} */ + +fieldset.apiKey { + width: 100%; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.apiKey { + font-family: Arial, sans-serif; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.apiKey input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey label { + display: inline-block; + width: auto; + margin-right: 5px; +} + +textarea#api_key { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#api_key:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* embedded title of the system prompt text area */ +.input-label { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + border-radius: 8px 8px 0px 0px; + padding-top: 10px; + padding-left: 13px; + padding-right: 0px; + margin-top: 1px; + margin-left: 1px; + margin-right: 20px; + text-transform: uppercase; + font-weight: 600; + font-size: small; + background: rgba(255, 255, 255, 0.5); + backdrop-filter: blur(10px); + -webkit-backdrop-filter: blur(10px); /* for safari */ + width: 97%; + /* display: block; + box-sizing: border-box; */ +} + +/* embedded title of the prompt style areas */ +.input-label-sec { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + margin-left: 13px; + margin-top: 16px; + text-transform: uppercase; + font-weight: 600; + font-size: x-small; +} + +/* system prompt input area */ +textarea.persistent-input { + padding-top: 42px; + padding-left: 11px; + width: 97%; + max-width: 97%; + height: 50px; + font-size: medium; + overscroll-behavior: contain; +} + +/* system prompt box */ +.persistent-input { + height: auto; + width: 100%; + max-width: 100%; + min-height: 50px; + padding: 3px; + transition: min-height 0.3s ease; +} + +/* chat history box */ +.persistent-input:focus { + height: auto; + min-height: 150px; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +textarea.persistent-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* prompt style input area */ +textarea.persistent-input-sec { + width: 97%; + max-width: 97%; + padding-top: 42px; + padding-left: 11px; + font-size: small; + border: 1px solid var(--border-color-1); + overscroll-behavior: contain; +} + +textarea.persistent-input-sec:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* chat history box */ +.persistent-input-sec { + height: auto; + min-height: 150px; +} + +img { + border-radius: 8px; + display: block; + margin-left: auto; + margin-right: auto; + width: 50%; +} + +/* code area background */ +pre code { + display: block; + background-color: var(--code-background-color); + color: var(--code-text-color); + padding: 0.2em 0.2em; + border-radius: 5px; +} + +/* code area text */ +code { + font-family: monospace; + font-weight: bold; + padding: 0.1em 0.3em; + border-radius: 5px; +} + +fieldset label { + margin: 0.5em 0; + display: block; +} + +fieldset label.slim { + margin: 0 0.5em; + display: inline; +} + +header { + display: flex; + justify-content: space-between; + align-items: center; + text-align: center; + padding-left: 15px; +} + +.generation-statistics:hover { + color: var(--theme-nuance-color-4); + cursor: default; +} + +footer { + font-size: 80%; + color: var(--background-color-3); + text-align: center; + cursor: default; +} + +footer a { + color: var(--background-color-4); /* Color of the link */ + text-decoration: none; /* No underlining */ + font-weight: bold; /* Bold print */ +} + +footer a:hover { + color: var(--theme-nuance-color-4); /* Color of the link when hovering */ + text-decoration: underline; /* Underlining when hovering */ +} + +.mode-chat textarea[name=prompt] { + height: 8.5em; + border: 1px solid var(--primary-color-3); +} + +.mode-completion textarea[name=prompt] { + height: 30em; + border: 1px solid var(--primary-color-3); +} + +@keyframes loading-bg-wipe { + 0% { + background-position: 0%; + } + 100% { + background-position: 100%; + } +} + +.loading { + background-size: 50% 100%; + background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; +} + +.dropbtn { + color: var(--button-primary-color); + background-color: var(--background-color-1); + border: 1px solid var(--background-color-1); + transition: background-color 0.1s; + border-radius: 4px 4px 0px 0px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 2px #99999990; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 5px 20px; + display: inline-block; + cursor: pointer; + top: 0; +} + +.dropbtn svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-color); +} + +.dropbtn:hover svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-text); +} + +.dropbtn:focus { + outline: none; /* Removes the blue border that appears when the button is focused */ +} + +.dropdown { + position: relative; + display: inline-block; +} + +.dropdown-content { + /* display: none; */ + position: absolute; + right: 0; + text-align: end; + color: var(--button-secondary-color); + background-color: var(--text-color-subtile-2); + border-radius: 4px 4px 4px 4px; + min-width: 160px; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + z-index: 1; + /* Verstecke den Inhalt sofort */ + opacity: 0; + visibility: hidden; + /* übergangsverzögerung für das Verschwinden */ + transition: visibility 0.4s linear 0s, opacity 0.2s ease-in-out; + transition-delay: 0.2s; +} + +#dropdown-content {transition-timing-function: ease;} + +.dropdown-content:hover { + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a { + color: var(--border-color-2); + padding: 12px 16px; + border-radius: 4px 4px 4px 4px; + text-decoration: none; + display: block; + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a:hover { + color: var(--border-color-2); + background-color: var(--text-color-subtile-1); + font-weight: 600; +} + +.dropdown:hover .dropdown-content { + /* display: block; */ + border-radius: 4px 4px 4px 4px; + /* Übergang ohne Verzögerung für das Erscheinen */ + opacity: 1; + visibility: visible; + transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s; +} + +.dropdown:hover .dropbtn { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + font-size: x-small; + font-weight: 600; + stroke: var(--button-primary-text); +} + +.dropdown:hover .dropbtn svg{ + stroke: var(--button-primary-text); +} + +/* .dropdown:active .dropbtn { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; + background-color: var(-background-color-4); +} */ + +/* .omni { + display: flex; + justify-content: space-between; + align-items: center; + padding: 0.5em; + border: 1px solid var(--border-color-3); + border-radius: 5px; + margin: 0.5em 0; +} */ diff --git a/examples/server/public_legacy/system-prompts.js b/examples/server/public_legacy/system-prompts.js new file mode 100644 index 000000000..f7df7d648 --- /dev/null +++ b/examples/server/public_legacy/system-prompts.js @@ -0,0 +1,68 @@ +export const systemPrompts = { + default: { + systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision." + }, + empty: { + systemPrompt: "" + }, + airoboros: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request." + }, + alpaca: { + systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request." + }, + atlas: { + systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)." + }, + atlas_de: { + systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)." + }, + commandrempty: { + systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n" + }, + commandrexample: { + systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available." + }, + cot: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query." + }, + deduce: { + systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer." + }, + deepseekcoder: { + systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer." + }, + jordan: { + systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information." + }, + leomistral: { + systemPrompt: "Du bist ein hilfreicher Assistent." + }, + med42: { + systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE." + }, + mistralopenorca: { + systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!" + }, + migeltot: { + systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers." + }, + orcamini: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can." + }, + samantha: { + systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha." + }, + sauerkraut: { + systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten." + }, + scarlett: { + systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI." + }, + synthia: { + systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation." + }, + vicuna: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input." + }, + }; diff --git a/examples/server/public_legacy/theme-beeninorder.css b/examples/server/public_legacy/theme-beeninorder.css new file mode 100755 index 000000000..f6e0e2900 --- /dev/null +++ b/examples/server/public_legacy/theme-beeninorder.css @@ -0,0 +1,228 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration was a batman wallpaper that i have on my phone */ + +.theme-beeninorder { + +--primary-color-1: hsl(202, 11%, 19%); +--primary-color-2: hsl(202, 11%, 23%); +--primary-color-3: hsl(201, 11%, 28%); +--primary-color-4: hsl(201, 11%, 40%); + +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-4: hsl(201, 11%, 60%); + + +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(201, 11%, 19%); + --primary-color-1-hue: 201; + --primary-color-1-saturation: 11%; + --primary-color-1-lightness: 19%; + +--primary-color-2: hsl(201, 11%, 23%); + --primary-color-2-hue: 201; + --primary-color-2-saturation: 11%; + --primary-color-2-lightness: 23%; + +--primary-color-3: hsl(201, 11%, 28%); + --primary-color-3-hue: 201; + --primary-color-3-saturation: 11%; + --primary-color-3-lightness: 28%; + +--primary-color-4: hsl(201, 11%, 40%); + --primary-color-4-hue: 201; + --primary-color-4-saturation: 11%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-1-hue: 201; +--secondary-color-1-saturation: 11%; +--secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-2-hue: 201; +--secondary-color-2-saturation: 11%; +--secondary-color-2-lightness: 74%; + +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-3-hue: 201; +--secondary-color-3-saturation: 11%; +--secondary-color-3-lightness: 67%; + +--secondary-color-4: hsl(201, 11%, 60%); +--secondary-color-4-hue: 201; +--secondary-color-4-saturation: 11%; +--secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-1-hue: 44.5; + --theme-nuance-color-1-saturation: 96.7%; + --theme-nuance-color-1-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-2-hue: 44.5; + --theme-nuance-color-2-saturation: 96.7%; + --theme-nuance-color-2-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-3-hue: 44.5; + --theme-nuance-color-3-saturation: 96.7%; + --theme-nuance-color-3-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-4-hue: 44.5; + --theme-nuance-color-4-saturation: 96.7%; + --theme-nuance-color-4-lightness: 52.9%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(232, 40%, 45%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(232, 30%, 40%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(201, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public_legacy/theme-ketivah.css b/examples/server/public_legacy/theme-ketivah.css new file mode 100755 index 000000000..ee80f3c14 --- /dev/null +++ b/examples/server/public_legacy/theme-ketivah.css @@ -0,0 +1,201 @@ +/* Author: Yazan Agha-Schrader */ + +.theme-ketivah { + + /* ---------- PRIMARY COLORS ----------------- */ + --primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + + --primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + + --primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + + --primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + /* ---------- SECONDARY COLORS --------------- */ + --secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + + --secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + + --secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + + --secondary-color-4: hsl(0, 0.0%, 36.1%); + --secondary-color-4-hue: 0.0; + --secondary-color-4-saturation: 0.0%; + --secondary-color-4-lightness: 36.1%; + + /* ----------- NUANCES COLORS ---------------- */ + --theme-nuance-color-1: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + + --theme-nuance-color-2: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + + --theme-nuance-color-3: hsl(165.2, 0%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + + --theme-nuance-color-4: hsl(164.9, 0%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + /* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(0.3, 80.0%, 50.0%); + --theme-orange-color: #e76f51; + --theme-yellow-color: hsl(60, 70.6%, 73.3%); + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(0.3, 70.0%, 45.0%); + + /* ------------------------------------------- */ + --background-color-1: var(--primary-color-1); + --background-color-2: var(--primary-color-2); + --background-color-3: var(--primary-color-3); + --background-color-4: var(--primary-color-4); + + --border-color-1: var(--primary-color-2); + --border-color-2: var(--primary-color-3); + --border-color-3: var(--primary-color-4); + + --border-focus-color: var(--theme-nuance-color-2); + --border-focus-shadow: var(--theme-nuance-color-1); + + --text-color-plain: var(--secondary-color-1); + --text-color-subtile-1: var(--secondary-color-2); + --text-color-subtile-2: var(--secondary-color-3); + + --code-background-color: var(--secondary-color-2); + --code-text-color: var(--primary-color-2); + + --ui-range-thumb-color: var(--primary-color-4); + --ui-range-thumb-border: var(--ui-ranger-thumb-color); + + --textarea-border-color: var(--secondary-color-4); + + --chat-id-color: var(--theme-nuance-color-4); + + /* ------------------------------------------- */ + --button-alert-text-hover: var(--primary-color-1); + --button-alert-color-hover: var(--theme-purple-color); + --button-alert-border-hover: var(--theme-purple-color); + + --button-alert-text-active: var(--primary-color-1); + --button-alert-color-active: var(--theme-red-color); + --button-alert-border-active: var(--theme-red-color); + + /* ----------- PRIMARY BUTTONS --------------- */ + /* - button should immediately catch the eye - */ + --button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color: var(--theme-nuance-color-3); + --button-primary-border: var(--theme-nuance-color-3); + + /* ---------hover---------- */ + --button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + --button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + /* ---------active--------- */ + --button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + + --button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + --button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + /* ---------- SECONDARY BUTTONS -------------- */ + /* these should NOT immediately catch the eye */ + --button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + + --button-secondary-color: var(--primary-color-3); + --button-secondary-border: var(--primary-color-3); + + /* ---------hover---------- */ + --button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-hover: var(--primary-color-4); + --button-secondary-border-hover: var(--primary-color-4); + + /* ---------active--------- */ + --button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + --button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + /* ---------- TERTIARY BUTTONS --------------- */ + /* ---------- disabled buttons --------------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + /* ---------hover---------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + --loading-color-1: #eeeeee00; + --loading-color-2: #eeeeeeff; + } diff --git a/examples/server/public_legacy/theme-mangotango.css b/examples/server/public_legacy/theme-mangotango.css new file mode 100755 index 000000000..e43380245 --- /dev/null +++ b/examples/server/public_legacy/theme-mangotango.css @@ -0,0 +1,216 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */ + +.theme-mangotango { + +--primary-color-1: hsl(192, 8.5%, 11.6%); +--primary-color-2: hsl(192, 8.5%, 21%); +--primary-color-3: hsl(192, 8.5%, 30%); +--primary-color-4: hsl(192, 8.5%, 40%); + +--secondary-color-1: hsl(192, 8.5%, 80%); +--secondary-color-2: hsl(192, 8.5%, 73%); +--secondary-color-3: hsl(192, 8.5%, 66%); +--secondary-color-4: hsl(192, 8.5%, 60%); + +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(192, 8.5%, 11.6%); + --primary-color-1-saturation: 8.5%; + --primary-color-1-lightness: 11.6%; + +--primary-color-2: hsl(192, 8.5%, 21%); + --primary-color-2-saturation: 8.5%; + --primary-color-2-lightness: 21%; + +--primary-color-3: hsl(192, 8.5%, 30%); + --primary-color-3-saturation: 8.5%; + --primary-color-3-lightness: 30%; + +--primary-color-4: hsl(192, 8.5%, 40%); + --primary-color-4-saturation: 8.5%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(192, 8.5%, 80%); + --secondary-color-1-saturation: 8.5%; + --secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(192, 8.5%, 73%); + --secondary-color-2-saturation: 8.5%; + --secondary-color-2-lightness: 73%; + +--secondary-color-3: hsl(192, 8.5%, 66%); + --secondary-color-3-saturation: 8.5%; + --secondary-color-3-lightness: 66%; + +--secondary-color-4: hsl(192, 8.5%, 60%); + --secondary-color-4-saturation: 8.5%; + --secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-1-saturation: 100%; + --theme-nuance-color-1-lightness: 60.2%; + +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-2-saturation: 100%; + --theme-nuance-color-2-lightness: 60.2%; + +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-3-saturation: 100%; + --theme-nuance-color-3-lightness: 60.2%; + +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-4-saturation: 100%; + --theme-nuance-color-4-lightness: 60.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(325, 60%, 50%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-blue-color: hsl(192, 95%, 40%); + --theme-purple-color: hsl(192, 80%, 35%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-blue-color); +--button-alert-border-active: var(--theme-blue-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(192, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public_legacy/theme-playground.css b/examples/server/public_legacy/theme-playground.css new file mode 100755 index 000000000..9d56a7182 --- /dev/null +++ b/examples/server/public_legacy/theme-playground.css @@ -0,0 +1,221 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */ + +.theme-playground { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + +--primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + +--primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + +--primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + +--secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + +--secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + +--secondary-color-4: hsl(0, 0%, 36.1%); + --secondary-color-4-hue: 0; + --secondary-color-4-saturation: 0%; + --secondary-color-4-lightness: 36.1%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + +--theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + +--theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + +--theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(0.3, 80%, 50%); +--theme-orange-color: #e76f51; +--theme-yellow-color: hsl(60, 70.6%, 73.3%); +--theme-green-color: #A3BE8C; +--theme-purple-color: hsl(0.3, 70%, 45%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--primary-color-4); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public_legacy/theme-polarnight.css b/examples/server/public_legacy/theme-polarnight.css new file mode 100755 index 000000000..2bcfb33d8 --- /dev/null +++ b/examples/server/public_legacy/theme-polarnight.css @@ -0,0 +1,253 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-polarnight { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(220.0, 16.4%, 21.6%) ; + --primary-color-1-hue: 220.0; + --primary-color-1-saturation: 16.4%; + --primary-color-1-lightness: 21.6%; + +--primary-color-2: hsl(221.7, 16.3%, 27.6%) ; + -primary-color-2-hue: 221.7; + --primary-color-2-saturation: 16.3%; + --primary-color-2-lightness: 27.6%; + +--primary-color-3: hsl(220.0, 16.8%, 31.6%) ; + --primary-color-3-hue: 220.0; + --primary-color-3-saturation: 16.8%; + --primary-color-3-lightness: 31.6%; + +--primary-color-4: hsl(220.0, 16.5%, 35.7%); + --primary-color-4-hue: 220.0; + --primary-color-4-saturation: 16.5%; + --primary-color-4-lightness: 35.7%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(217.5, 26.7%, 94.1%); + --secondary-color-1-hue: 217.5; + --secondary-color-1-saturation: 26.7%; + --secondary-color-1-lightness: 94.1%; + +--secondary-color-2: hsl(218.2, 26.8%, 92.0%); + --secondary-color-2-hue: 218.2; + --secondary-color-2-saturation: 26.8%; + --secondary-color-2-lightness: 92.0%; + +--secondary-color-3: hsl(218.8, 27.9%, 88.0%); + --secondary-color-3-hue: 218.8; + --secondary-color-3-saturation: 27.9%; + --secondary-color-3-lightness: 88.0%; + +--secondary-color-4: hsl(218.8, 18.3%, 81.8%); + --secondary-color-4-hue: 218.8; + --secondary-color-4-saturation: 18.3%; + --secondary-color-4-lightness: 81.8%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(354.3, 42.3%, 56.5%); +--theme-orange-color: hsl(20, 85%, 50%); +--theme-yellow-color: hsl(20, 75%, 45%); +--theme-green-color: hsl( 92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------------ */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-yellow-color); +--button-alert-border-hover: var(--theme-yellow-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-orange-color); +--button-alert-border-active: var(--theme-orange-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) - 35%), + calc(var(--secondary-color-1-lightness) + 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 25%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/public_legacy/theme-snowstorm.css b/examples/server/public_legacy/theme-snowstorm.css new file mode 100755 index 000000000..7bb227594 --- /dev/null +++ b/examples/server/public_legacy/theme-snowstorm.css @@ -0,0 +1,251 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-snowstorm { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/public_simplechat/datautils.mjs b/examples/server/public_simplechat/datautils.mjs new file mode 100644 index 000000000..75159d6b1 --- /dev/null +++ b/examples/server/public_simplechat/datautils.mjs @@ -0,0 +1,266 @@ +//@ts-check +// Helpers to work with different data types +// by Humans for All +// + +/** + * Given the limited context size of local LLMs and , many a times when context gets filled + * between the prompt and the response, it can lead to repeating text garbage generation. + * And many a times setting penalty wrt repeatation leads to over-intelligent garbage + * repeatation with slight variations. These garbage inturn can lead to overloading of the + * available model context, leading to less valuable response for subsequent prompts/queries, + * if chat history is sent to ai model. + * + * So two simple minded garbage trimming logics are experimented below. + * * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and + * * another based on char-histogram-driven garbage trimming. + * * in future characteristic of histogram over varying lengths could be used to allow for + * a more aggressive and adaptive trimming logic. + */ + + +/** + * Simple minded logic to help remove repeating garbage at end of the string. + * The repeatation needs to be perfectly matching. + * + * The logic progressively goes on probing for longer and longer substring based + * repeatation, till there is no longer repeatation. Inturn picks the one with + * the longest chain. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number} maxMatchLenThreshold + */ +export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) { + let rCnt = [0]; + let maxMatchLen = maxSubL; + let iMML = -1; + for(let subL=1; subL < maxSubL; subL++) { + rCnt.push(0); + let i; + let refS = sIn.substring(sIn.length-subL, sIn.length); + for(i=sIn.length; i > 0; i -= subL) { + let curS = sIn.substring(i-subL, i); + if (refS != curS) { + let curMatchLen = rCnt[subL]*subL; + if (maxMatchLen < curMatchLen) { + maxMatchLen = curMatchLen; + iMML = subL; + } + break; + } + rCnt[subL] += 1; + } + } + console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt); + if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) { + return {trimmed: false, data: sIn}; + } + console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen); + let iEnd = sIn.length - maxMatchLen; + return { trimmed: true, data: sIn.substring(0, iEnd) }; +} + + +/** + * Simple minded logic to help remove repeating garbage at end of the string, till it cant. + * If its not able to trim, then it will try to skip a char at end and then trim, a few times. + * This ensures that even if there are multiple runs of garbage with different patterns, the + * logic still tries to munch through them. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number | undefined} [maxMatchLenThreshold] + */ +export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) { + let sCur = sIn; + let sSaved = ""; + let iTry = 0; + while(true) { + let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold); + if (got.trimmed != true) { + if (iTry == 0) { + sSaved = got.data; + } + iTry += 1; + if (iTry >= skipMax) { + return sSaved; + } + got.data = got.data.substring(0,got.data.length-1); + } else { + iTry = 0; + } + sCur = got.data; + } +} + + +/** + * A simple minded try trim garbage at end using histogram driven characteristics. + * There can be variation in the repeatations, as long as no new char props up. + * + * This tracks the chars and their frequency in a specified length of substring at the end + * and inturn checks if moving further into the generated text from the end remains within + * the same char subset or goes beyond it and based on that either trims the string at the + * end or not. This allows to filter garbage at the end, including even if there are certain + * kind of small variations in the repeated text wrt position of seen chars. + * + * Allow the garbage to contain upto maxUniq chars, but at the same time ensure that + * a given type of char ie numerals or alphabets or other types dont cross the specified + * maxType limit. This allows intermixed text garbage to be identified and trimmed. + * + * ALERT: This is not perfect and only provides a rough garbage identification logic. + * Also it currently only differentiates between character classes wrt english. + * + * @param {string} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) { + if (sIn.length < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + let iAlp = 0; + let iNum = 0; + let iOth = 0; + // Learn + let hist = {}; + let iUniq = 0; + for(let i=0; i= maxUniq) { + break; + } + hist[c] = 1; + } + } + console.debug("DBUG:TrimHistGarbage:", hist); + if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) { + return { trimmed: false, data: sIn }; + } + // Catch and Trim + for(let i=0; i < sIn.length; i++) { + let c = sIn[sIn.length-1-i]; + if (!(c in hist)) { + if (i < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i); + return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) }; + } + } + console.debug("DBUG:TrimHistGarbage:Trimmed fully"); + return { trimmed: true, data: "" }; +} + +/** + * Keep trimming repeatedly using hist_garbage logic, till you no longer can. + * This ensures that even if there are multiple runs of garbage with different patterns, + * the logic still tries to munch through them. + * + * @param {any} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) { + let sCur = sIn; + while (true) { + let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold); + if (!got.trimmed) { + return got.data; + } + sCur = got.data; + } +} + +/** + * Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as + * skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying. + * @param {string} sIn + */ +export function trim_garbage_at_end(sIn) { + let sCur = sIn; + for(let i=0; i<2; i++) { + sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72); + sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12); + } + return sCur; +} + + +/** + * NewLines array helper. + * Allow for maintaining a list of lines. + * Allow for a line to be builtup/appended part by part. + */ +export class NewLines { + + constructor() { + /** @type {string[]} */ + this.lines = []; + } + + /** + * Extracts lines from the passed string and inturn either + * append to a previous partial line or add a new line. + * @param {string} sLines + */ + add_append(sLines) { + let aLines = sLines.split("\n"); + let lCnt = 0; + for(let line of aLines) { + lCnt += 1; + // Add back newline removed if any during split + if (lCnt < aLines.length) { + line += "\n"; + } else { + if (sLines.endsWith("\n")) { + line += "\n"; + } + } + // Append if required + if (lCnt == 1) { + let lastLine = this.lines[this.lines.length-1]; + if (lastLine != undefined) { + if (!lastLine.endsWith("\n")) { + this.lines[this.lines.length-1] += line; + continue; + } + } + } + // Add new line + this.lines.push(line); + } + } + + /** + * Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest] + * Optionally control whether only full lines (ie those with newline at end) will be returned + * or will a partial line without a newline at end (can only be the last line) be returned. + * @param {boolean} bFullWithNewLineOnly + */ + shift(bFullWithNewLineOnly=true) { + let line = this.lines[0]; + if (line == undefined) { + return undefined; + } + if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){ + return undefined; + } + return this.lines.shift(); + } + +} diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html new file mode 100644 index 000000000..f6413016f --- /dev/null +++ b/examples/server/public_simplechat/index.html @@ -0,0 +1,51 @@ + + + + SimpleChat LlamaCppEtal + + + + + + + + + + + +
+ +
+

SimpleChat

+ +
+ +
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+ +
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+

You need to have javascript enabled.

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+ + diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md new file mode 100644 index 000000000..21410199f --- /dev/null +++ b/examples/server/public_simplechat/readme.md @@ -0,0 +1,286 @@ + +# SimpleChat + +by Humans for All. + +## quickstart + +To run from the build dir + +bin/llama-server -m path/model.gguf --path ../examples/server/public_simplechat + +Continue reading for the details. + +## overview + +This simple web frontend, allows triggering/testing the server's /completions or /chat/completions endpoints +in a simple way with minimal code from a common code base. Inturn additionally it tries to allow single or +multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their +own system prompts. + +This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated, +or potentially as it is being generated, in a streamed manner from the server/ai-model. + +![Chat and Settings screens](./simplechat_screens.webp "Chat and Settings screens") + +Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you +open SimpleChat, option is provided to restore the old chat session, if a matching one exists. + +The UI follows a responsive web design so that the layout can adapt to available display space in a usable +enough manner, in general. + +Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool +console. Parallely some of the directly useful to end-user settings can also be changed using the provided +settings ui. + +NOTE: Current web service api doesnt expose the model context length directly, so client logic doesnt provide +any adaptive culling of old messages nor of replacing them with summary of their content etal. However there +is a optional sliding window based chat logic, which provides a simple minded culling of old messages from +the chat history before sending to the ai model. + +NOTE: Wrt options sent with the request, it mainly sets temperature, max_tokens and optionaly stream for now. +However if someone wants they can update the js file or equivalent member in gMe as needed. + +NOTE: One may be able to use this to chat with openai api web-service /chat/completions endpoint, in a very +limited / minimal way. One will need to set model, openai url and authorization bearer key in settings ui. + + +## usage + +One could run this web frontend directly using server itself or if anyone is thinking of adding a built in web +frontend to configure the server over http(s) or so, then run this web frontend using something like python's +http module. + +### running using examples/server + +./llama-server -m path/model.gguf --path examples/server/public_simplechat [--port PORT] + +### running using python3's server module + +first run examples/server +* ./llama-server -m path/model.gguf + +next run this web front end in examples/server/public_simplechat +* cd ../examples/server/public_simplechat +* python3 -m http.server PORT + +### using the front end + +Open this simple web front end from your local browser + +* http://127.0.0.1:PORT/index.html + +Once inside + +* If you want to, you can change many of the default global settings + * the base url (ie ip addr / domain name, port) + * chat (default) vs completion mode + * try trim garbage in response or not + * amount of chat history in the context sent to server/ai-model + * oneshot or streamed mode. + +* In completion mode + * one normally doesnt use a system prompt in completion mode. + * logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message. + If the model requires any prefix wrt user role messages, then the end user has to + explicitly add the needed prefix, when they enter their chat message. + Similarly if the model requires any prefix to trigger assistant/ai-model response, + then the end user needs to enter the same. + This keeps the logic simple, while still giving flexibility to the end user to + manage any templating/tagging requirement wrt their messages to the model. + * the logic doesnt insert newline at the begining and end wrt the prompt message generated. + However if the chat being sent to /completions end point has more than one role's message, + then insert newline when moving from one role's message to the next role's message, so + that it can be clearly identified/distinguished. + * given that /completions endpoint normally doesnt add additional chat-templating of its + own, the above ensures that end user can create a custom single/multi message combo with + any tags/special-tokens related chat templating to test out model handshake. Or enduser + can use it just for normal completion related/based query. + +* If you want to provide a system prompt, then ideally enter it first, before entering any user query. + Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting + responses with a suitable system prompt. + * if chat.add_system_begin is used + * you cant change the system prompt, after it is has been submitted once along with user query. + * you cant set a system prompt, after you have submitted any user query + * if chat.add_system_anytime is used + * one can change the system prompt any time during chat, by changing the contents of system prompt. + * inturn the updated/changed system prompt will be inserted into the chat session. + * this allows for the subsequent user chatting to be driven by the new system prompt set above. + +* Enter your query and either press enter or click on the submit button. + If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter. + +* Wait for the logic to communicate with the server and get the response. + * the user is not allowed to enter any fresh query during this time. + * the user input box will be disabled and a working message will be shown in it. + * if trim garbage is enabled, the logic will try to trim repeating text kind of garbage to some extent. + +* just refresh the page, to reset wrt the chat history and or system prompt and start afresh. + +* Using NewChat one can start independent chat sessions. + * two independent chat sessions are setup by default. + +* When you want to print, switching ChatHistoryInCtxt to Full and clicking on the chat session button of + interest, will display the full chat history till then wrt same, if you want full history for printing. + + +## Devel note + +### Reason behind this + +The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable +by developers who may not be from web frontend background (so inturn may not be familiar with template / +end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things. + +And given that the idea is also to help explore/experiment for developers, some flexibility is provided +to change behaviour easily using the devel-tools/console or provided minimal settings ui (wrt few aspects). +Skeletal logic has been implemented to explore some of the end points and ideas/implications around them. + + +### General + +Me/gMe consolidates the settings which control the behaviour into one object. +One can see the current settings, as well as change/update them using browsers devel-tool/console. +It is attached to the document object. Some of these can also be updated using the Settings UI. + + baseURL - the domain-name/ip-address and inturn the port to send the request. + + bStream - control between oneshot-at-end and live-stream-as-its-generated collating and showing + of the generated response. + + the logic assumes that the text sent from the server follows utf-8 encoding. + + in streaming mode - if there is any exception, the logic traps the same and tries to ensure + that text generated till then is not lost. + + if a very long text is being generated, which leads to no user interaction for sometime and + inturn the machine goes into power saving mode or so, the platform may stop network connection, + leading to exception. + + apiEP - select between /completions and /chat/completions endpoint provided by the server/ai-model. + + bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when + communicating with the server or only sends the latest user query/message. + + bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the + messages that get inserted into prompt field wrt /Completion endpoint. + + bTrimGarbage - whether garbage repeatation at the end of the generated ai response, should be + trimmed or left as is. If enabled, it will be trimmed so that it wont be sent back as part of + subsequent chat history. At the same time the actual trimmed text is shown to the user, once + when it was generated, so user can check if any useful info/data was there in the response. + + One may be able to request the ai-model to continue (wrt the last response) (if chat-history + is enabled as part of the chat-history-in-context setting), and chances are the ai-model will + continue starting from the trimmed part, thus allows long response to be recovered/continued + indirectly, in many cases. + + The histogram/freq based trimming logic is currently tuned for english language wrt its + is-it-a-alpabetic|numeral-char regex match logic. + + apiRequestOptions - maintains the list of options/fields to send along with api request, + irrespective of whether /chat/completions or /completions endpoint. + + If you want to add additional options/fields to send to the server/ai-model, and or + modify the existing options value or remove them, for now you can update this global var + using browser's development-tools/console. + + For string, numeric and boolean fields in apiRequestOptions, including even those added by a + user at runtime by directly modifying gMe.apiRequestOptions, setting ui entries will be auto + created. + + cache_prompt option supported by example/server is allowed to be controlled by user, so that + any caching supported wrt system-prompt and chat history, if usable can get used. When chat + history sliding window is enabled, cache_prompt logic may or may not kick in at the backend + wrt same, based on aspects related to model, positional encoding, attention mechanism etal. + However system prompt should ideally get the benefit of caching. + + headers - maintains the list of http headers sent when request is made to the server. By default + Content-Type is set to application/json. Additionally Authorization entry is provided, which can + be set if needed using the settings ui. + + iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end. + This is disabled by default. However if enabled, then in addition to latest system message, only + the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses + from the ai model will be sent to the ai-model, when querying for a new response. IE if enabled, + only user messages after the latest system message/prompt will be considered. + + This specified sliding window user message count also includes the latest user query. + <0 : Send entire chat history to server + 0 : Send only the system message if any to the server + >0 : Send the latest chat history from the latest system prompt, limited to specified cnt. + + +By using gMe's iRecentUserMsgCnt and apiRequestOptions.max_tokens/n_predict one can try to control +the implications of loading of the ai-model's context window by chat history, wrt chat response to +some extent in a simple crude way. You may also want to control the context size enabled when the +server loads ai-model, on the server end. + + +Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js +may not be visible. Also remember that just refreshing/reloading page in browser or for that +matter clearing site data, dont directly override site caching in all cases. Worst case you may +have to change port. Or in dev tools of browser, you may be able to disable caching fully. + + +Currently the server to communicate with is maintained globally and not as part of a specific +chat session. So if one changes the server ip/url in setting, then all chat sessions will auto +switch to this new server, when you try using those sessions. + + +By switching between chat.add_system_begin/anytime, one can control whether one can change +the system prompt, anytime during the conversation or only at the beginning. + + +### Default setup + +By default things are setup to try and make the user experience a bit better, if possible. +However a developer when testing the server of ai-model may want to change these value. + +Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be +just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of +full chat history. This way if there is any response with garbage/repeatation, it doesnt +mess with things beyond the next question/request/query, in some ways. The trim garbage +option also tries to help avoid issues with garbage in the context to an extent. + +Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space +available wrt next query-response. However dont forget that the server when started should +also be started with a model context size of 1k or more, to be on safe side. + + The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the + internal n_predict, for now add the same here on the client side, maybe later add max_tokens + to /completions endpoint handling code on server side. + +NOTE: One may want to experiment with frequency/presence penalty fields in apiRequestOptions +wrt the set of fields sent to server along with the user query, to check how the model behaves +wrt repeatations in general in the generated text response. + +A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by +using the provided settings ui (for settings exposed through the ui). + + +### OpenAi / Equivalent API WebService + +One may be abe to handshake with OpenAI/Equivalent api web service's /chat/completions endpoint +for a minimal chatting experimentation by setting the below. + +* the baseUrl in settings ui + * https://api.openai.com/v1 or similar + +* Wrt request body - gMe.apiRequestOptions + * model (settings ui) + * any additional fields if required in future + +* Wrt request headers - gMe.headers + * Authorization (available through settings ui) + * Bearer THE_OPENAI_API_KEY + * any additional optional header entries like "OpenAI-Organization", "OpenAI-Project" or so + +NOTE: Not tested, as there is no free tier api testing available. However logically this might +work. + + +## At the end + +Also a thank you to all open source and open model developers, who strive for the common good. diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css new file mode 100644 index 000000000..13bfb80b4 --- /dev/null +++ b/examples/server/public_simplechat/simplechat.css @@ -0,0 +1,79 @@ +/** + * the styling of the simplechat web frontend + * by Humans for All + */ + +#fullbody { + height: 98vh; +} + +.heading { + background-color: lightgray; +} + +.session-selected { + background-color: lightblue; +} + +.role-system { + background-color: lightblue; +} +.role-user { + background-color: lightgray; +} +.role-trim { + background-color: lightpink; +} + +.gridx2 { + display: grid; + grid-template-columns: repeat(2, 1fr); + border-bottom-style: dotted; + border-bottom-width: thin; + border-bottom-color: lightblue; +} + +.flex-grow { + flex-grow: 1; +} +.float-right { + float: right; +} + +#chat-div { + overflow: scroll; + flex-grow: 1; + flex-shrink: 1; + min-height: 40vh; +} +button { + min-width: 8vw; +} + +.sameline { + display: flex; + flex-direction: row; +} +.samecolumn { + display: flex; + flex-direction: column; +} + +.ul1 { + padding-inline-start: 2vw; +} +.ul2 { + padding-inline-start: 2vw; +} + +* { + margin: 0.6vmin; +} + +@media print { + + #fullbody { + height: auto; + } + +} diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js new file mode 100644 index 000000000..2fcd24a86 --- /dev/null +++ b/examples/server/public_simplechat/simplechat.js @@ -0,0 +1,929 @@ +// @ts-check +// A simple completions and chat/completions test related web front end logic +// by Humans for All + +import * as du from "./datautils.mjs"; +import * as ui from "./ui.mjs" + +class Roles { + static System = "system"; + static User = "user"; + static Assistant = "assistant"; +} + +class ApiEP { + static Type = { + Chat: "chat", + Completion: "completion", + } + static UrlSuffix = { + 'chat': `/chat/completions`, + 'completion': `/completions`, + } + + /** + * Build the url from given baseUrl and apiEp id. + * @param {string} baseUrl + * @param {string} apiEP + */ + static Url(baseUrl, apiEP) { + if (baseUrl.endsWith("/")) { + baseUrl = baseUrl.substring(0, baseUrl.length-1); + } + return `${baseUrl}${this.UrlSuffix[apiEP]}`; + } + +} + + +let gUsageMsg = ` +

Usage

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  • System prompt above, to try control ai response characteristics.
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    • Completion mode - no system prompt normally.
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  • Use shift+enter for inserting enter/newline.
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  • Enter your query to ai assistant below.
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  • Default ContextWindow = [System, Last Query+Resp, Cur Query].
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    • ChatHistInCtxt, MaxTokens, ModelCtxt window to expand
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+`; + + +/** @typedef {{role: string, content: string}[]} ChatMessages */ + +/** @typedef {{iLastSys: number, xchat: ChatMessages}} SimpleChatODS */ + +class SimpleChat { + + /** + * @param {string} chatId + */ + constructor(chatId) { + this.chatId = chatId; + /** + * Maintain in a form suitable for common LLM web service chat/completions' messages entry + * @type {ChatMessages} + */ + this.xchat = []; + this.iLastSys = -1; + this.latestResponse = ""; + } + + clear() { + this.xchat = []; + this.iLastSys = -1; + } + + ods_key() { + return `SimpleChat-${this.chatId}` + } + + save() { + /** @type {SimpleChatODS} */ + let ods = {iLastSys: this.iLastSys, xchat: this.xchat}; + localStorage.setItem(this.ods_key(), JSON.stringify(ods)); + } + + load() { + let sods = localStorage.getItem(this.ods_key()); + if (sods == null) { + return; + } + /** @type {SimpleChatODS} */ + let ods = JSON.parse(sods); + this.iLastSys = ods.iLastSys; + this.xchat = ods.xchat; + } + + /** + * Recent chat messages. + * If iRecentUserMsgCnt < 0 + * Then return the full chat history + * Else + * Return chat messages from latest going back till the last/latest system prompt. + * While keeping track that the number of user queries/messages doesnt exceed iRecentUserMsgCnt. + * @param {number} iRecentUserMsgCnt + */ + recent_chat(iRecentUserMsgCnt) { + if (iRecentUserMsgCnt < 0) { + return this.xchat; + } + if (iRecentUserMsgCnt == 0) { + console.warn("WARN:SimpleChat:SC:RecentChat:iRecentUsermsgCnt of 0 means no user message/query sent"); + } + /** @type{ChatMessages} */ + let rchat = []; + let sysMsg = this.get_system_latest(); + if (sysMsg.length != 0) { + rchat.push({role: Roles.System, content: sysMsg}); + } + let iUserCnt = 0; + let iStart = this.xchat.length; + for(let i=this.xchat.length-1; i > this.iLastSys; i--) { + if (iUserCnt >= iRecentUserMsgCnt) { + break; + } + let msg = this.xchat[i]; + if (msg.role == Roles.User) { + iStart = i; + iUserCnt += 1; + } + } + for(let i = iStart; i < this.xchat.length; i++) { + let msg = this.xchat[i]; + if (msg.role == Roles.System) { + continue; + } + rchat.push({role: msg.role, content: msg.content}); + } + return rchat; + } + + /** + * Collate the latest response from the server/ai-model, as it is becoming available. + * This is mainly useful for the stream mode. + * @param {string} content + */ + append_response(content) { + this.latestResponse += content; + } + + /** + * Add an entry into xchat + * @param {string} role + * @param {string|undefined|null} content + */ + add(role, content) { + if ((content == undefined) || (content == null) || (content == "")) { + return false; + } + this.xchat.push( {role: role, content: content} ); + if (role == Roles.System) { + this.iLastSys = this.xchat.length - 1; + } + this.save(); + return true; + } + + /** + * Show the contents in the specified div + * @param {HTMLDivElement} div + * @param {boolean} bClear + */ + show(div, bClear=true) { + if (bClear) { + div.replaceChildren(); + } + let last = undefined; + for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) { + let entry = ui.el_create_append_p(`${x.role}: ${x.content}`, div); + entry.className = `role-${x.role}`; + last = entry; + } + if (last !== undefined) { + last.scrollIntoView(false); + } else { + if (bClear) { + div.innerHTML = gUsageMsg; + gMe.setup_load(div, this); + gMe.show_info(div); + } + } + return last; + } + + /** + * Setup the fetch headers. + * It picks the headers from gMe.headers. + * It inserts Authorization only if its non-empty. + * @param {string} apiEP + */ + fetch_headers(apiEP) { + let headers = new Headers(); + for(let k in gMe.headers) { + let v = gMe.headers[k]; + if ((k == "Authorization") && (v.trim() == "")) { + continue; + } + headers.append(k, v); + } + return headers; + } + + /** + * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + * The needed fields/options are picked from a global object. + * Add optional stream flag, if required. + * Convert the json into string. + * @param {Object} obj + */ + request_jsonstr_extend(obj) { + for(let k in gMe.apiRequestOptions) { + obj[k] = gMe.apiRequestOptions[k]; + } + if (gMe.bStream) { + obj["stream"] = true; + } + return JSON.stringify(obj); + } + + /** + * Return a string form of json object suitable for chat/completions + */ + request_messages_jsonstr() { + let req = { + messages: this.recent_chat(gMe.iRecentUserMsgCnt), + } + return this.request_jsonstr_extend(req); + } + + /** + * Return a string form of json object suitable for /completions + * @param {boolean} bInsertStandardRolePrefix Insert ": " as prefix wrt each role's message + */ + request_prompt_jsonstr(bInsertStandardRolePrefix) { + let prompt = ""; + let iCnt = 0; + for(const chat of this.recent_chat(gMe.iRecentUserMsgCnt)) { + iCnt += 1; + if (iCnt > 1) { + prompt += "\n"; + } + if (bInsertStandardRolePrefix) { + prompt += `${chat.role}: `; + } + prompt += `${chat.content}`; + } + let req = { + prompt: prompt, + } + return this.request_jsonstr_extend(req); + } + + /** + * Return a string form of json object suitable for specified api endpoint. + * @param {string} apiEP + */ + request_jsonstr(apiEP) { + if (apiEP == ApiEP.Type.Chat) { + return this.request_messages_jsonstr(); + } else { + return this.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix); + } + } + + /** + * Extract the ai-model/assistant's response from the http response got. + * Optionally trim the message wrt any garbage at the end. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + assistant = respBody["choices"][0]["message"]["content"]; + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; + } + + /** + * Extract the ai-model/assistant's response from the http response got in streaming mode. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract_stream(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + if (respBody["choices"][0]["finish_reason"] !== "stop") { + assistant = respBody["choices"][0]["delta"]["content"]; + } + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; + } + + /** + * Allow setting of system prompt, but only at begining. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_begin(sysPrompt, msgTag) { + if (this.xchat.length == 0) { + if (sysPrompt.length > 0) { + return this.add(Roles.System, sysPrompt); + } + } else { + if (sysPrompt.length > 0) { + if (this.xchat[0].role !== Roles.System) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You need to specify system prompt before any user query, ignoring...`); + } else { + if (this.xchat[0].content !== sysPrompt) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You cant change system prompt, mid way through, ignoring...`); + } + } + } + } + return false; + } + + /** + * Allow setting of system prompt, at any time. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_anytime(sysPrompt, msgTag) { + if (sysPrompt.length <= 0) { + return false; + } + + if (this.iLastSys < 0) { + return this.add(Roles.System, sysPrompt); + } + + let lastSys = this.xchat[this.iLastSys].content; + if (lastSys !== sysPrompt) { + return this.add(Roles.System, sysPrompt); + } + return false; + } + + /** + * Retrieve the latest system prompt. + */ + get_system_latest() { + if (this.iLastSys == -1) { + return ""; + } + let sysPrompt = this.xchat[this.iLastSys].content; + return sysPrompt; + } + + + /** + * Handle the multipart response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response_multipart(resp, apiEP, elDiv) { + let elP = ui.el_create_append_p("", elDiv); + if (!resp.body) { + throw Error("ERRR:SimpleChat:SC:HandleResponseMultiPart:No body..."); + } + let tdUtf8 = new TextDecoder("utf-8"); + let rr = resp.body.getReader(); + this.latestResponse = ""; + let xLines = new du.NewLines(); + while(true) { + let { value: cur, done: done } = await rr.read(); + if (cur) { + let curBody = tdUtf8.decode(cur, {stream: true}); + console.debug("DBUG:SC:PART:Str:", curBody); + xLines.add_append(curBody); + } + while(true) { + let curLine = xLines.shift(!done); + if (curLine == undefined) { + break; + } + if (curLine.trim() == "") { + continue; + } + 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)); + } + elP.innerText = this.latestResponse; + elP.scrollIntoView(false); + if (done) { + break; + } + } + console.debug("DBUG:SC:PART:Full:", this.latestResponse); + return this.latestResponse; + } + + /** + * Handle the oneshot response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + */ + async handle_response_oneshot(resp, apiEP) { + let respBody = await resp.json(); + console.debug(`DBUG:SimpleChat:SC:${this.chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); + return this.response_extract(respBody, apiEP); + } + + /** + * Handle the response from the server be it in oneshot or multipart/stream mode. + * Also take care of the optional garbage trimming. + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response(resp, apiEP, elDiv) { + let theResp = { + assistant: "", + trimmed: "", + } + if (gMe.bStream) { + try { + theResp.assistant = await this.handle_response_multipart(resp, apiEP, elDiv); + this.latestResponse = ""; + } catch (error) { + theResp.assistant = this.latestResponse; + this.add(Roles.Assistant, theResp.assistant); + this.latestResponse = ""; + throw error; + } + } else { + theResp.assistant = await this.handle_response_oneshot(resp, apiEP); + } + if (gMe.bTrimGarbage) { + let origMsg = theResp.assistant; + theResp.assistant = du.trim_garbage_at_end(origMsg); + theResp.trimmed = origMsg.substring(theResp.assistant.length); + } + this.add(Roles.Assistant, theResp.assistant); + return theResp; + } + +} + + +class MultiChatUI { + + constructor() { + /** @type {Object} */ + this.simpleChats = {}; + /** @type {string} */ + this.curChatId = ""; + + // the ui elements + this.elInSystem = /** @type{HTMLInputElement} */(document.getElementById("system-in")); + this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div")); + this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn")); + this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in")); + this.elDivHeading = /** @type{HTMLSelectElement} */(document.getElementById("heading")); + this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div")); + this.elBtnSettings = /** @type{HTMLButtonElement} */(document.getElementById("settings")); + + this.validate_element(this.elInSystem, "system-in"); + this.validate_element(this.elDivChat, "chat-div"); + this.validate_element(this.elInUser, "user-in"); + this.validate_element(this.elDivHeading, "heading"); + this.validate_element(this.elDivChat, "sessions-div"); + this.validate_element(this.elBtnSettings, "settings"); + } + + /** + * Check if the element got + * @param {HTMLElement | null} el + * @param {string} msgTag + */ + validate_element(el, msgTag) { + if (el == null) { + throw Error(`ERRR:SimpleChat:MCUI:${msgTag} element missing in html...`); + } else { + console.debug(`INFO:SimpleChat:MCUI:${msgTag} Id[${el.id}] Name[${el["name"]}]`); + } + } + + /** + * Reset user input ui. + * * clear user input + * * enable user input + * * set focus to user input + */ + ui_reset_userinput() { + this.elInUser.value = ""; + this.elInUser.disabled = false; + this.elInUser.focus(); + } + + /** + * Setup the needed callbacks wrt UI, curChatId to defaultChatId and + * optionally switch to specified defaultChatId. + * @param {string} defaultChatId + * @param {boolean} bSwitchSession + */ + setup_ui(defaultChatId, bSwitchSession=false) { + + this.curChatId = defaultChatId; + if (bSwitchSession) { + this.handle_session_switch(this.curChatId); + } + + this.elBtnSettings.addEventListener("click", (ev)=>{ + this.elDivChat.replaceChildren(); + gMe.show_settings(this.elDivChat); + }); + + this.elBtnUser.addEventListener("click", (ev)=>{ + if (this.elInUser.disabled) { + return; + } + this.handle_user_submit(this.curChatId, gMe.apiEP).catch((/** @type{Error} */reason)=>{ + let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`; + console.error(msg.replace("\n", ":")); + alert(msg); + this.ui_reset_userinput(); + }); + }); + + this.elInUser.addEventListener("keyup", (ev)=> { + // allow user to insert enter into their message using shift+enter. + // while just pressing enter key will lead to submitting. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInUser.value; + this.elInUser.value = value.substring(0,value.length-1); + this.elBtnUser.click(); + ev.preventDefault(); + } + }); + + this.elInSystem.addEventListener("keyup", (ev)=> { + // allow user to insert enter into the system prompt using shift+enter. + // while just pressing enter key will lead to setting the system prompt. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInSystem.value; + this.elInSystem.value = value.substring(0,value.length-1); + let chat = this.simpleChats[this.curChatId]; + chat.add_system_anytime(this.elInSystem.value, this.curChatId); + chat.show(this.elDivChat); + ev.preventDefault(); + } + }); + + } + + /** + * Setup a new chat session and optionally switch to it. + * @param {string} chatId + * @param {boolean} bSwitchSession + */ + new_chat_session(chatId, bSwitchSession=false) { + this.simpleChats[chatId] = new SimpleChat(chatId); + if (bSwitchSession) { + this.handle_session_switch(chatId); + } + } + + + /** + * Handle user query submit request, wrt specified chat session. + * @param {string} chatId + * @param {string} apiEP + */ + async handle_user_submit(chatId, apiEP) { + + let chat = this.simpleChats[chatId]; + + // In completion mode, if configured, clear any previous chat history. + // So if user wants to simulate a multi-chat based completion query, + // they will have to enter the full thing, as a suitable multiline + // user input/query. + if ((apiEP == ApiEP.Type.Completion) && (gMe.bCompletionFreshChatAlways)) { + chat.clear(); + } + + chat.add_system_anytime(this.elInSystem.value, chatId); + + let content = this.elInUser.value; + if (!chat.add(Roles.User, content)) { + console.debug(`WARN:SimpleChat:MCUI:${chatId}:HandleUserSubmit:Ignoring empty user input...`); + return; + } + chat.show(this.elDivChat); + + let theUrl = ApiEP.Url(gMe.baseURL, apiEP); + let theBody = chat.request_jsonstr(apiEP); + + this.elInUser.value = "working..."; + this.elInUser.disabled = true; + console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`); + let theHeaders = chat.fetch_headers(apiEP); + let resp = await fetch(theUrl, { + method: "POST", + headers: theHeaders, + body: theBody, + }); + + let theResp = await chat.handle_response(resp, apiEP, this.elDivChat); + if (chatId == this.curChatId) { + chat.show(this.elDivChat); + if (theResp.trimmed.length > 0) { + let p = ui.el_create_append_p(`TRIMMED:${theResp.trimmed}`, this.elDivChat); + p.className="role-trim"; + } + } else { + console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`); + } + this.ui_reset_userinput(); + } + + /** + * Show buttons for NewChat and available chat sessions, in the passed elDiv. + * If elDiv is undefined/null, then use this.elDivSessions. + * Take care of highlighting the selected chat-session's btn. + * @param {HTMLDivElement | undefined} elDiv + */ + show_sessions(elDiv=undefined) { + if (!elDiv) { + elDiv = this.elDivSessions; + } + elDiv.replaceChildren(); + // Btn for creating new chat session + let btnNew = ui.el_create_button("New CHAT", (ev)=> { + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`); + alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session"); + return; + } + let chatId = `Chat${Object.keys(this.simpleChats).length}`; + let chatIdGot = prompt("INFO:SimpleChat\nMCUI:NewChat\nEnter id for new chat session", chatId); + if (!chatIdGot) { + console.error("ERRR:SimpleChat:MCUI:NewChat:Skipping based on user request..."); + return; + } + this.new_chat_session(chatIdGot, true); + this.create_session_btn(elDiv, chatIdGot); + ui.el_children_config_class(elDiv, chatIdGot, "session-selected", ""); + }); + elDiv.appendChild(btnNew); + // Btns for existing chat sessions + let chatIds = Object.keys(this.simpleChats); + for(let cid of chatIds) { + let btn = this.create_session_btn(elDiv, cid); + if (cid == this.curChatId) { + btn.className = "session-selected"; + } + } + } + + create_session_btn(elDiv, cid) { + let btn = ui.el_create_button(cid, (ev)=>{ + let target = /** @type{HTMLButtonElement} */(ev.target); + console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`); + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:SessionClick:${target.id}:Current session [${this.curChatId}] awaiting response, ignoring switch...`); + alert("ERRR:SimpleChat\nMCUI:SessionClick\nWait for response to pending query, before switching"); + return; + } + this.handle_session_switch(target.id); + ui.el_children_config_class(elDiv, target.id, "session-selected", ""); + }); + elDiv.appendChild(btn); + return btn; + } + + /** + * Switch ui to the specified chatId and set curChatId to same. + * @param {string} chatId + */ + async handle_session_switch(chatId) { + let chat = this.simpleChats[chatId]; + if (chat == undefined) { + console.error(`ERRR:SimpleChat:MCUI:HandleSessionSwitch:${chatId} missing...`); + return; + } + this.elInSystem.value = chat.get_system_latest(); + this.elInUser.value = ""; + chat.show(this.elDivChat); + this.elInUser.focus(); + this.curChatId = chatId; + console.log(`INFO:SimpleChat:MCUI:HandleSessionSwitch:${chatId} entered...`); + } + +} + + +class Me { + + constructor() { + this.baseURL = "http://127.0.0.1:8080"; + this.defaultChatIds = [ "Default", "Other" ]; + this.multiChat = new MultiChatUI(); + this.bStream = true; + this.bCompletionFreshChatAlways = true; + this.bCompletionInsertStandardRolePrefix = false; + this.bTrimGarbage = true; + this.iRecentUserMsgCnt = 2; + this.sRecentUserMsgCnt = { + "Full": -1, + "Last0": 1, + "Last1": 2, + "Last2": 3, + "Last4": 5, + }; + this.apiEP = ApiEP.Type.Chat; + this.headers = { + "Content-Type": "application/json", + "Authorization": "", // Authorization: Bearer OPENAI_API_KEY + } + // Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + this.apiRequestOptions = { + "model": "gpt-3.5-turbo", + "temperature": 0.7, + "max_tokens": 1024, + "n_predict": 1024, + "cache_prompt": false, + //"frequency_penalty": 1.2, + //"presence_penalty": 1.2, + }; + } + + /** + * Disable console.debug by mapping it to a empty function. + */ + debug_disable() { + this.console_debug = console.debug; + console.debug = () => { + + }; + } + + /** + * Setup the load saved chat ui. + * @param {HTMLDivElement} div + * @param {SimpleChat} chat + */ + setup_load(div, chat) { + if (!(chat.ods_key() in localStorage)) { + return; + } + div.innerHTML += `

Restore

+

Load previously saved chat session, if available

`; + let btn = ui.el_create_button(chat.ods_key(), (ev)=>{ + console.log("DBUG:SimpleChat:SC:Load", chat); + chat.load(); + queueMicrotask(()=>{ + chat.show(div); + this.multiChat.elInSystem.value = chat.get_system_latest(); + }); + }); + div.appendChild(btn); + } + + /** + * Show the configurable parameters info in the passed Div element. + * @param {HTMLDivElement} elDiv + * @param {boolean} bAll + */ + show_info(elDiv, bAll=false) { + + let p = ui.el_create_append_p("Settings (devel-tools-console document[gMe])", elDiv); + p.className = "role-system"; + + if (bAll) { + + ui.el_create_append_p(`baseURL:${this.baseURL}`, elDiv); + + ui.el_create_append_p(`Authorization:${this.headers["Authorization"]}`, elDiv); + + ui.el_create_append_p(`bStream:${this.bStream}`, elDiv); + + ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv); + + ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv); + + ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv); + + ui.el_create_append_p(`bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`, elDiv); + + ui.el_create_append_p(`bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`, elDiv); + + } + + ui.el_create_append_p(`apiRequestOptions:${JSON.stringify(this.apiRequestOptions, null, " - ")}`, elDiv); + ui.el_create_append_p(`headers:${JSON.stringify(this.headers, null, " - ")}`, elDiv); + + } + + /** + * Auto create ui input elements for fields in apiRequestOptions + * Currently supports text and number field types. + * @param {HTMLDivElement} elDiv + */ + show_settings_apirequestoptions(elDiv) { + let typeDict = { + "string": "text", + "number": "number", + }; + let fs = document.createElement("fieldset"); + let legend = document.createElement("legend"); + legend.innerText = "ApiRequestOptions"; + fs.appendChild(legend); + elDiv.appendChild(fs); + for(const k in this.apiRequestOptions) { + let val = this.apiRequestOptions[k]; + let type = typeof(val); + if (((type == "string") || (type == "number"))) { + let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.apiRequestOptions[k], (val)=>{ + if (type == "number") { + val = Number(val); + } + this.apiRequestOptions[k] = val; + }); + fs.appendChild(inp.div); + } else if (type == "boolean") { + let bbtn = ui.el_creatediv_boolbutton(`Set{k}`, k, {true: "true", false: "false"}, val, (userVal)=>{ + this.apiRequestOptions[k] = userVal; + }); + fs.appendChild(bbtn.div); + } + } + } + + /** + * Show settings ui for configurable parameters, in the passed Div element. + * @param {HTMLDivElement} elDiv + */ + show_settings(elDiv) { + + let inp = ui.el_creatediv_input("SetBaseURL", "BaseURL", "text", this.baseURL, (val)=>{ + this.baseURL = val; + }); + elDiv.appendChild(inp.div); + + inp = ui.el_creatediv_input("SetAuthorization", "Authorization", "text", this.headers["Authorization"], (val)=>{ + this.headers["Authorization"] = val; + }); + inp.el.placeholder = "Bearer OPENAI_API_KEY"; + elDiv.appendChild(inp.div); + + let bb = ui.el_creatediv_boolbutton("SetStream", "Stream", {true: "[+] yes stream", false: "[-] do oneshot"}, this.bStream, (val)=>{ + this.bStream = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{ + this.bTrimGarbage = val; + }); + elDiv.appendChild(bb.div); + + this.show_settings_apirequestoptions(elDiv); + + let sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{ + this.apiEP = ApiEP.Type[val]; + }); + elDiv.appendChild(sel.div); + + sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{ + this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val]; + }); + elDiv.appendChild(sel.div); + + bb = ui.el_creatediv_boolbutton("SetCompletionFreshChatAlways", "CompletionFreshChatAlways", {true: "[+] yes fresh", false: "[-] no, with history"}, this.bCompletionFreshChatAlways, (val)=>{ + this.bCompletionFreshChatAlways = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetCompletionInsertStandardRolePrefix", "CompletionInsertStandardRolePrefix", {true: "[+] yes insert", false: "[-] dont insert"}, this.bCompletionInsertStandardRolePrefix, (val)=>{ + this.bCompletionInsertStandardRolePrefix = val; + }); + elDiv.appendChild(bb.div); + + } + +} + + +/** @type {Me} */ +let gMe; + +function startme() { + console.log("INFO:SimpleChat:StartMe:Starting..."); + gMe = new Me(); + gMe.debug_disable(); + document["gMe"] = gMe; + document["du"] = du; + for (let cid of gMe.defaultChatIds) { + gMe.multiChat.new_chat_session(cid); + } + gMe.multiChat.setup_ui(gMe.defaultChatIds[0], true); + gMe.multiChat.show_sessions(); +} + +document.addEventListener("DOMContentLoaded", startme); diff --git a/examples/server/public_simplechat/simplechat_screens.webp b/examples/server/public_simplechat/simplechat_screens.webp new file mode 100644 index 000000000..ccea44396 Binary files /dev/null and b/examples/server/public_simplechat/simplechat_screens.webp differ diff --git a/examples/server/public_simplechat/ui.mjs b/examples/server/public_simplechat/ui.mjs new file mode 100644 index 000000000..b2d5b9aea --- /dev/null +++ b/examples/server/public_simplechat/ui.mjs @@ -0,0 +1,211 @@ +//@ts-check +// Helpers to work with html elements +// by Humans for All +// + + +/** + * Set the class of the children, based on whether it is the idSelected or not. + * @param {HTMLDivElement} elBase + * @param {string} idSelected + * @param {string} classSelected + * @param {string} classUnSelected + */ +export function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { + for(let child of elBase.children) { + if (child.id == idSelected) { + child.className = classSelected; + } else { + child.className = classUnSelected; + } + } +} + +/** + * Create button and set it up. + * @param {string} id + * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback + * @param {string | undefined} name + * @param {string | undefined} innerText + */ +export function el_create_button(id, callback, name=undefined, innerText=undefined) { + if (!name) { + name = id; + } + if (!innerText) { + innerText = id; + } + let btn = document.createElement("button"); + btn.id = id; + btn.name = name; + btn.innerText = innerText; + btn.addEventListener("click", callback); + return btn; +} + +/** + * Create a para and set it up. Optionaly append it to a passed parent. + * @param {string} text + * @param {HTMLElement | undefined} elParent + * @param {string | undefined} id + */ +export function el_create_append_p(text, elParent=undefined, id=undefined) { + let para = document.createElement("p"); + para.innerText = text; + if (id) { + para.id = id; + } + if (elParent) { + elParent.appendChild(para); + } + return para; +} + +/** + * Create a button which represents bool value using specified text wrt true and false. + * When ever user clicks the button, it will toggle the value and update the shown text. + * + * @param {string} id + * @param {{true: string, false: string}} texts + * @param {boolean} defaultValue + * @param {function(boolean):void} cb + */ +export function el_create_boolbutton(id, texts, defaultValue, cb) { + let el = document.createElement("button"); + el["xbool"] = defaultValue; + el["xtexts"] = structuredClone(texts); + el.innerText = el["xtexts"][String(defaultValue)]; + if (id) { + el.id = id; + } + el.addEventListener('click', (ev)=>{ + el["xbool"] = !el["xbool"]; + el.innerText = el["xtexts"][String(el["xbool"])]; + cb(el["xbool"]); + }) + return el; +} + +/** + * Create a div wrapped button which represents bool value using specified text wrt true and false. + * @param {string} id + * @param {string} label + * @param {{ true: string; false: string; }} texts + * @param {boolean} defaultValue + * @param {(arg0: boolean) => void} cb + * @param {string} className + */ +export function el_creatediv_boolbutton(id, label, texts, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let btn = el_create_boolbutton(id, texts, defaultValue, cb); + div.appendChild(btn); + return { div: div, el: btn }; +} + + +/** + * Create a select ui element, with a set of options to select from. + * * options: an object which contains name-value pairs + * * defaultOption: the value whose name should be choosen, by default. + * * cb : the call back returns the name string of the option selected. + * + * @param {string} id + * @param {Object} options + * @param {*} defaultOption + * @param {function(string):void} cb + */ +export function el_create_select(id, options, defaultOption, cb) { + let el = document.createElement("select"); + el["xselected"] = defaultOption; + el["xoptions"] = structuredClone(options); + for(let cur of Object.keys(options)) { + let op = document.createElement("option"); + op.value = cur; + op.innerText = cur; + if (options[cur] == defaultOption) { + op.selected = true; + } + el.appendChild(op); + } + if (id) { + el.id = id; + el.name = id; + } + el.addEventListener('change', (ev)=>{ + let target = /** @type{HTMLSelectElement} */(ev.target); + console.log("DBUG:UI:Select:", id, ":", target.value); + cb(target.value); + }) + return el; +} + +/** + * Create a div wrapped select ui element, with a set of options to select from. + * + * @param {string} id + * @param {any} label + * @param {{ [x: string]: any; }} options + * @param {any} defaultOption + * @param {(arg0: string) => void} cb + * @param {string} className + */ +export function el_creatediv_select(id, label, options, defaultOption, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let sel = el_create_select(id, options,defaultOption, cb); + div.appendChild(sel); + return { div: div, el: sel }; +} + + +/** + * Create a input ui element. + * + * @param {string} id + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + */ +export function el_create_input(id, type, defaultValue, cb) { + let el = document.createElement("input"); + el.type = type; + el.value = defaultValue; + if (id) { + el.id = id; + } + el.addEventListener('change', (ev)=>{ + cb(el.value); + }) + return el; +} + +/** + * Create a div wrapped input. + * + * @param {string} id + * @param {string} label + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + * @param {string} className + */ +export function el_creatediv_input(id, label, type, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let el = el_create_input(id, type, defaultValue, cb); + div.appendChild(el); + return { div: div, el: el }; +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2b2f4a0f4..0718806c8 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,105 +1,1232 @@ -#include "common.h" -#include "llama.h" -#include "grammar-parser.h" #include "utils.hpp" -#include "oai.hpp" -#include "../llava/clip.h" -#include "../llava/llava.h" +#include "arg.h" +#include "common.h" +#include "json-schema-to-grammar.h" +#include "llama.h" +#include "log.h" +#include "sampling.h" +#include "speculative.h" -#include "stb_image.h" - -#ifndef NDEBUG -// crash the server in debug mode, otherwise send an http 500 error -#define CPPHTTPLIB_NO_EXCEPTIONS 1 -#endif -// increase max payload length to allow use of larger context size -#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576 -#include "httplib.h" +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT #include "json.hpp" +// mime type for sending response +#define MIMETYPE_JSON "application/json; charset=utf-8" -// auto generated files (update with ./deps.sh) -#include "index.html.hpp" -#include "index.js.hpp" -#include "completion.js.hpp" -#include "json-schema-to-grammar.mjs.hpp" +// auto generated files (see README.md for details) +#include "index.html.gz.hpp" +#include "loading.html.hpp" -#include -#include +#include #include #include -#include +#include +#include +#include +#include +#include #include +#include +#include +#include -using json = nlohmann::json; +using json = nlohmann::ordered_json; -struct server_params { - std::string hostname = "127.0.0.1"; - std::vector api_keys; - std::string public_path = "examples/server/public"; - std::string chat_template = ""; - int32_t port = 8080; - int32_t read_timeout = 600; - int32_t write_timeout = 600; - bool slots_endpoint = true; - bool metrics_endpoint = false; - int n_threads_http = -1; -}; - -bool server_verbose = false; -bool server_log_json = true; +constexpr int HTTP_POLLING_SECONDS = 1; enum stop_type { - STOP_FULL, - STOP_PARTIAL, + STOP_TYPE_NONE, + STOP_TYPE_EOS, + STOP_TYPE_WORD, + STOP_TYPE_LIMIT, }; -// TODO: can become bool if we can't find use of more states +// state diagram: https://github.com/ggerganov/llama.cpp/pull/9283 enum slot_state { - IDLE, - PROCESSING, + SLOT_STATE_IDLE, + SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future + SLOT_STATE_PROCESSING_PROMPT, + SLOT_STATE_DONE_PROMPT, + SLOT_STATE_GENERATING, }; -enum slot_command { - NONE, - LOAD_PROMPT, - RELEASE, +enum server_state { + SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet + SERVER_STATE_READY, // Server is ready and model is loaded +}; + +enum server_task_type { + SERVER_TASK_TYPE_COMPLETION, + SERVER_TASK_TYPE_EMBEDDING, + SERVER_TASK_TYPE_RERANK, + SERVER_TASK_TYPE_INFILL, + SERVER_TASK_TYPE_CANCEL, + SERVER_TASK_TYPE_NEXT_RESPONSE, + SERVER_TASK_TYPE_METRICS, + SERVER_TASK_TYPE_SLOT_SAVE, + SERVER_TASK_TYPE_SLOT_RESTORE, + SERVER_TASK_TYPE_SLOT_ERASE, + SERVER_TASK_TYPE_SET_LORA, +}; + +enum oaicompat_type { + OAICOMPAT_TYPE_NONE, + OAICOMPAT_TYPE_CHAT, + OAICOMPAT_TYPE_COMPLETION, + OAICOMPAT_TYPE_EMBEDDING, +}; + +// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 +enum error_type { + ERROR_TYPE_INVALID_REQUEST, + ERROR_TYPE_AUTHENTICATION, + ERROR_TYPE_SERVER, + ERROR_TYPE_NOT_FOUND, + ERROR_TYPE_PERMISSION, + ERROR_TYPE_UNAVAILABLE, // custom error + ERROR_TYPE_NOT_SUPPORTED, // custom error }; struct slot_params { - bool stream = true; - bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt + bool stream = true; + bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt + bool return_tokens = false; - uint32_t seed = -1; // RNG seed - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_predict = -1; // new tokens to predict + 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 + int32_t n_predict = -1; // new tokens to predict + int32_t n_indent = 0; // mininum line indentation for the generated text in number of whitespace characters + + int64_t t_max_prompt_ms = -1; // TODO: implement + int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit + + std::vector lora; std::vector antiprompt; + std::vector response_fields; + bool timings_per_token = false; + bool post_sampling_probs = false; + bool ignore_eos = false; - json input_prefix; - json input_suffix; + struct common_params_sampling sampling; + struct common_params_speculative speculative; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + + json to_json() const { + std::vector samplers; + samplers.reserve(sampling.samplers.size()); + for (const auto & sampler : sampling.samplers) { + samplers.emplace_back(common_sampler_type_to_str(sampler)); + } + + json lora = json::array(); + for (size_t i = 0; i < this->lora.size(); ++i) { + lora.push_back({{"id", i}, {"scale", this->lora[i].scale}}); + } + + std::vector grammar_trigger_words; + for (const auto & trigger : sampling.grammar_trigger_words) { + grammar_trigger_words.push_back(trigger.word); + } + + return json { + {"n_predict", n_predict}, // Server configured n_predict + {"seed", sampling.seed}, + {"temperature", sampling.temp}, + {"dynatemp_range", sampling.dynatemp_range}, + {"dynatemp_exponent", sampling.dynatemp_exponent}, + {"top_k", sampling.top_k}, + {"top_p", sampling.top_p}, + {"min_p", sampling.min_p}, + {"xtc_probability", sampling.xtc_probability}, + {"xtc_threshold", sampling.xtc_threshold}, + {"typical_p", sampling.typ_p}, + {"repeat_last_n", sampling.penalty_last_n}, + {"repeat_penalty", sampling.penalty_repeat}, + {"presence_penalty", sampling.penalty_present}, + {"frequency_penalty", sampling.penalty_freq}, + {"dry_multiplier", sampling.dry_multiplier}, + {"dry_base", sampling.dry_base}, + {"dry_allowed_length", sampling.dry_allowed_length}, + {"dry_penalty_last_n", sampling.dry_penalty_last_n}, + {"dry_sequence_breakers", sampling.dry_sequence_breakers}, + {"mirostat", sampling.mirostat}, + {"mirostat_tau", sampling.mirostat_tau}, + {"mirostat_eta", sampling.mirostat_eta}, + {"stop", antiprompt}, + {"max_tokens", n_predict}, // User configured n_predict + {"n_keep", n_keep}, + {"n_discard", n_discard}, + {"ignore_eos", sampling.ignore_eos}, + {"stream", stream}, + {"logit_bias", format_logit_bias(sampling.logit_bias)}, + {"n_probs", sampling.n_probs}, + {"min_keep", sampling.min_keep}, + {"grammar", sampling.grammar}, + {"grammar_trigger_words", grammar_trigger_words}, + {"grammar_trigger_tokens", sampling.grammar_trigger_tokens}, + {"preserved_tokens", sampling.preserved_tokens}, + {"samplers", samplers}, + {"speculative.n_max", speculative.n_max}, + {"speculative.n_min", speculative.n_min}, + {"speculative.p_min", speculative.p_min}, + {"timings_per_token", timings_per_token}, + {"post_sampling_probs", post_sampling_probs}, + {"lora", lora}, + }; + } }; -struct slot_image { - int32_t id; +struct server_task { + int id = -1; // to be filled by server_queue + int index = -1; // used when there are multiple prompts (batch request) - bool request_encode_image = false; - float * image_embedding = nullptr; - int32_t image_tokens = 0; + server_task_type type; - clip_image_u8 * img_data; + // used by SERVER_TASK_TYPE_CANCEL + int id_target = -1; - std::string prefix_prompt; // before of this image + // used by SERVER_TASK_TYPE_INFERENCE + slot_params params; + llama_tokens prompt_tokens; + int id_selected_slot = -1; + + // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE + struct slot_action { + int slot_id; + std::string filename; + std::string filepath; + }; + slot_action slot_action; + + // used by SERVER_TASK_TYPE_METRICS + bool metrics_reset_bucket = false; + + // used by SERVER_TASK_TYPE_SET_LORA + std::vector set_lora; + + server_task(server_task_type type) : type(type) {} + + static slot_params params_from_json_cmpl( + const llama_context * ctx, + const common_params & params_base, + const json & data) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + slot_params params; + + // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them) + slot_params defaults; + defaults.sampling = params_base.sampling; + defaults.speculative = params_base.speculative; + + // enabling this will output extra debug information in the HTTP responses from the server + params.verbose = params_base.verbosity > 9; + params.timings_per_token = json_value(data, "timings_per_token", false); + + params.stream = json_value(data, "stream", false); + params.cache_prompt = json_value(data, "cache_prompt", true); + params.return_tokens = json_value(data, "return_tokens", false); + params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict)); + params.n_indent = json_value(data, "n_indent", defaults.n_indent); + params.n_keep = json_value(data, "n_keep", defaults.n_keep); + params.n_discard = json_value(data, "n_discard", defaults.n_discard); + //params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement + params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); + params.response_fields = json_value(data, "response_fields", std::vector()); + + params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); + params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); + params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p); + params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability); + params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold); + params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p); + params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); + params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); + params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); + params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); + params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); + params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); + params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); + params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); + params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); + params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); + params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); + params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); + params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); + params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); + params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); + params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); + params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); + params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs); + + params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); + params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); + params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min); + + params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min); + params.speculative.n_min = std::max(params.speculative.n_min, 2); + params.speculative.n_max = std::max(params.speculative.n_max, 0); + + // Use OpenAI API logprobs only if n_probs wasn't provided + if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){ + params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs); + } + + if (data.contains("lora")) { + if (data.at("lora").is_array()) { + params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora")); + } else { + throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields"); + } + } else { + params.lora = params_base.lora_adapters; + } + + // TODO: add more sanity checks for the input parameters + + if (params.sampling.penalty_last_n < -1) { + throw std::runtime_error("Error: repeat_last_n must be >= -1"); + } + + if (params.sampling.dry_penalty_last_n < -1) { + throw std::runtime_error("Error: dry_penalty_last_n must be >= -1"); + } + + if (params.sampling.penalty_last_n == -1) { + // note: should be the slot's context and not the full context, but it's ok + params.sampling.penalty_last_n = llama_n_ctx(ctx); + } + + if (params.sampling.dry_penalty_last_n == -1) { + params.sampling.dry_penalty_last_n = llama_n_ctx(ctx); + } + + if (params.sampling.dry_base < 1.0f) { + params.sampling.dry_base = defaults.sampling.dry_base; + } + + // sequence breakers for DRY + { + // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format + // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 + + if (data.contains("dry_sequence_breakers")) { + params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); + if (params.sampling.dry_sequence_breakers.empty()) { + throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings"); + } + } + } + + // process "json_schema" and "grammar" + if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) { + throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both"); + } + if (data.contains("json_schema") && !data.contains("grammar")) { + try { + auto schema = json_value(data, "json_schema", json::object()); + SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str()); + params.sampling.grammar = json_schema_to_grammar(schema); + SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str()); + } catch (const std::exception & e) { + throw std::runtime_error(std::string("\"json_schema\": ") + e.what()); + } + } else { + params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar); + SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str()); + params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy); + SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false"); + } + + { + auto it = data.find("chat_format"); + if (it != data.end()) { + params.oaicompat_chat_format = static_cast(it->get()); + SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str()); + } else { + params.oaicompat_chat_format = defaults.oaicompat_chat_format; + } + } + + { + const auto grammar_triggers = data.find("grammar_triggers"); + if (grammar_triggers != data.end()) { + for (const auto & t : *grammar_triggers) { + common_grammar_trigger trigger; + trigger.word = t.at("word"); + trigger.at_start = t.at("at_start"); + + auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true); + if (ids.size() == 1) { + SRV_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str()); + params.sampling.grammar_trigger_tokens.push_back(ids[0]); + params.sampling.preserved_tokens.insert(ids[0]); + continue; + } + SRV_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str()); + params.sampling.grammar_trigger_words.push_back(trigger); + } + } + const auto preserved_tokens = data.find("preserved_tokens"); + if (preserved_tokens != data.end()) { + for (const auto & t : *preserved_tokens) { + auto ids = common_tokenize(vocab, t.get(), /* add_special= */ false, /* parse_special= */ true); + if (ids.size() == 1) { + SRV_DBG("Preserved token: %d\n", ids[0]); + params.sampling.preserved_tokens.insert(ids[0]); + } else { + // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens. + SRV_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get().c_str()); + } + } + } + if (params.sampling.grammar_lazy) { + GGML_ASSERT(params.sampling.grammar_trigger_tokens.size() > 0 || params.sampling.grammar_trigger_words.size() > 0); + } + } + + { + params.sampling.logit_bias.clear(); + params.ignore_eos = json_value(data, "ignore_eos", false); + + const auto & logit_bias = data.find("logit_bias"); + if (logit_bias != data.end() && logit_bias->is_array()) { + const int n_vocab = llama_vocab_n_tokens(vocab); + for (const auto & el : *logit_bias) { + // TODO: we may want to throw errors here, in case "el" is incorrect + if (el.is_array() && el.size() == 2) { + float bias; + if (el[1].is_number()) { + bias = el[1].get(); + } else if (el[1].is_boolean() && !el[1].get()) { + bias = -INFINITY; + } else { + continue; + } + + if (el[0].is_number_integer()) { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } else if (el[0].is_string()) { + auto toks = common_tokenize(vocab, el[0].get(), false); + for (auto tok : toks) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } + } + } + } + } + + { + params.antiprompt.clear(); + + const auto & stop = data.find("stop"); + if (stop != data.end() && stop->is_array()) { + for (const auto & word : *stop) { + if (!word.empty()) { + params.antiprompt.push_back(word); + } + } + } + } + + { + const auto samplers = data.find("samplers"); + if (samplers != data.end()) { + if (samplers->is_array()) { + params.sampling.samplers = common_sampler_types_from_names(*samplers, false); + } else if (samplers->is_string()){ + params.sampling.samplers = common_sampler_types_from_chars(samplers->get()); + } + } else { + params.sampling.samplers = defaults.sampling.samplers; + } + } + + std::string model_name = params_base.model_alias.empty() ? DEFAULT_OAICOMPAT_MODEL : params_base.model_alias; + params.oaicompat_model = json_value(data, "model", model_name); + + return params; + } + + // utility function + static std::unordered_set get_list_id(const std::vector & tasks) { + std::unordered_set ids(tasks.size()); + for (size_t i = 0; i < tasks.size(); i++) { + ids.insert(tasks[i].id); + } + return ids; + } +}; + +struct result_timings { + int32_t prompt_n = -1; + double prompt_ms; + double prompt_per_token_ms; + double prompt_per_second; + + int32_t predicted_n = -1; + double predicted_ms; + double predicted_per_token_ms; + double predicted_per_second; + + json to_json() const { + return { + {"prompt_n", prompt_n}, + {"prompt_ms", prompt_ms}, + {"prompt_per_token_ms", prompt_per_token_ms}, + {"prompt_per_second", prompt_per_second}, + + {"predicted_n", predicted_n}, + {"predicted_ms", predicted_ms}, + {"predicted_per_token_ms", predicted_per_token_ms}, + {"predicted_per_second", predicted_per_second}, + }; + } +}; + +struct server_task_result { + int id = -1; + int id_slot = -1; + virtual bool is_error() { + // only used by server_task_result_error + return false; + } + virtual bool is_stop() { + // only used by server_task_result_cmpl_* + return false; + } + virtual int get_index() { + return -1; + } + virtual json to_json() = 0; + virtual ~server_task_result() = default; +}; + +// using shared_ptr for polymorphism of server_task_result +using server_task_result_ptr = std::unique_ptr; + +inline std::string stop_type_to_str(stop_type type) { + switch (type) { + case STOP_TYPE_EOS: return "eos"; + case STOP_TYPE_WORD: return "word"; + case STOP_TYPE_LIMIT: return "limit"; + default: return "none"; + } +} + +struct completion_token_output { + llama_token tok; + float prob; + std::string text_to_send; + struct prob_info { + llama_token tok; + std::string txt; + float prob; + }; + std::vector probs; + + json to_json(bool post_sampling_probs) const { + json probs_for_token = json::array(); + for (const auto & p : probs) { + std::string txt(p.txt); + txt.resize(validate_utf8(txt)); + probs_for_token.push_back(json { + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.txt)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + }); + } + return probs_for_token; + } + + static json probs_vector_to_json(const std::vector & probs, bool post_sampling_probs) { + json out = json::array(); + for (const auto & p : probs) { + std::string txt(p.text_to_send); + txt.resize(validate_utf8(txt)); + out.push_back(json { + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.text_to_send)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + { + post_sampling_probs ? "top_probs" : "top_logprobs", + p.to_json(post_sampling_probs) + }, + }); + } + return out; + } + + static float logarithm(float x) { + // nlohmann::json converts -inf to null, so we need to prevent that + return x == 0.0f ? std::numeric_limits::lowest() : std::log(x); + } + + static std::vector str_to_bytes(const std::string & str) { + std::vector bytes; + for (unsigned char c : str) { + bytes.push_back(c); + } + return bytes; + } +}; + +struct server_task_result_cmpl_final : server_task_result { + int index = 0; + + std::string content; + llama_tokens tokens; + + bool stream; + result_timings timings; + std::string prompt; + + bool truncated; + int32_t n_decoded; + int32_t n_prompt_tokens; + int32_t n_tokens_cached; + bool has_new_line; + std::string stopping_word; + stop_type stop = STOP_TYPE_NONE; + + bool post_sampling_probs; + std::vector probs_output; + std::vector response_fields; + + slot_params generation_params; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; + + virtual int get_index() override { + return index; + } + + virtual bool is_stop() override { + return true; // in stream mode, final responses are considered stop + } + + virtual json to_json() override { + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } + } + + json to_json_non_oaicompat() { + json res = json { + {"index", index}, + {"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"tokens", stream ? llama_tokens {} : tokens}, + {"id_slot", id_slot}, + {"stop", true}, + {"model", oaicompat_model}, + {"tokens_predicted", n_decoded}, + {"tokens_evaluated", n_prompt_tokens}, + {"generation_settings", generation_params.to_json()}, + {"prompt", prompt}, + {"has_new_line", has_new_line}, + {"truncated", truncated}, + {"stop_type", stop_type_to_str(stop)}, + {"stopping_word", stopping_word}, + {"tokens_cached", n_tokens_cached}, + {"timings", timings.to_json()}, + }; + if (!stream && !probs_output.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs); + } + return response_fields.empty() ? res : json_get_nested_values(response_fields, res); + } + + json to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (!stream && probs_output.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } + json finish_reason = "length"; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = "stop"; + } + json res = json { + {"choices", json::array({ + json{ + {"text", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", finish_reason}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens} + }}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + + json to_json_oaicompat_chat() { + std::string finish_reason = "length"; + common_chat_msg msg; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + SRV_DBG("Parsing chat message: %s\n", content.c_str()); + msg = common_chat_parse(content, oaicompat_chat_format); + finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls"; + } else { + msg.content = content; + } + + json tool_calls; + if (!msg.tool_calls.empty()) { + tool_calls = json::array(); + for (const auto & tc : msg.tool_calls) { + tool_calls.push_back({ + {"type", "function"}, + {"function", { + {"name", tc.name}, + {"arguments", tc.arguments}, + }}, + {"id", tc.id}, + }); + } + } + + json message { + {"content", msg.content}, + {"tool_calls", tool_calls}, + {"role", "assistant"}, + }; + if (!msg.tool_plan.empty()) { + message["tool_plan"] = msg.tool_plan; + } + + json choice { + {"finish_reason", finish_reason}, + {"index", 0}, + {"message", message}, + }; + + if (!stream && probs_output.size() > 0) { + choice["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } + + std::time_t t = std::time(0); + + json res = json { + {"choices", json::array({choice})}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens} + }}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + + json to_json_oaicompat_chat_stream() { + std::time_t t = std::time(0); + std::string finish_reason = "length"; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = "stop"; + } + + json choice = json { + {"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()} + }; + + json ret = json { + {"choices", json::array({choice})}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens}, + }}, + }; + + if (timings.prompt_n >= 0) { + ret.push_back({"timings", timings.to_json()}); + } + + return ret; + } +}; + +struct server_task_result_cmpl_partial : server_task_result { + int index = 0; + + std::string content; + llama_tokens tokens; + + int32_t n_decoded; + int32_t n_prompt_tokens; + + bool post_sampling_probs; + completion_token_output prob_output; + result_timings timings; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + + virtual int get_index() override { + return index; + } + + virtual bool is_stop() override { + return false; // in stream mode, partial responses are not considered stop + } + + virtual json to_json() override { + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } + } + + json to_json_non_oaicompat() { + // non-OAI-compat JSON + json res = json { + {"index", index}, + {"content", content}, + {"tokens", tokens}, + {"stop", false}, + {"id_slot", id_slot}, + {"tokens_predicted", n_decoded}, + {"tokens_evaluated", n_prompt_tokens}, + }; + // populate the timings object when needed (usually for the last response or with timings_per_token enabled) + if (timings.prompt_n > 0) { + res.push_back({"timings", timings.to_json()}); + } + if (!prob_output.probs.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs); + } + return res; + } + + json to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (prob_output.probs.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + json res = json { + {"choices", json::array({ + json{ + {"text", content}, + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", nullptr}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; + } + + json to_json_oaicompat_chat() { + bool first = n_decoded == 0; + std::time_t t = std::time(0); + json choices; + + if (first) { + if (content.empty()) { + choices = json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{{"role", "assistant"}}}}}); + } else { + // We have to send this as two updates to conform to openai behavior + json initial_ret = json{{"choices", json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"role", "assistant"} + }}}})}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"object", "chat.completion.chunk"}}; + + json second_ret = json{ + {"choices", json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json { + {"content", content}}} + }})}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"object", "chat.completion.chunk"}}; + + return std::vector({initial_ret, second_ret}); + } + } else { + choices = json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", + json { + {"content", content}, + }}, + }}); + } + + GGML_ASSERT(choices.size() >= 1); + + if (prob_output.probs.size() > 0) { + choices[0]["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + + json ret = json { + {"choices", choices}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"} + }; + + if (timings.prompt_n >= 0) { + ret.push_back({"timings", timings.to_json()}); + } + + return std::vector({ret}); + } +}; + +struct server_task_result_embd : server_task_result { + int index = 0; + std::vector> embedding; + + int32_t n_tokens; + + // OAI-compat fields + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + + virtual int get_index() override { + return index; + } + + virtual json to_json() override { + return oaicompat == OAICOMPAT_TYPE_EMBEDDING + ? to_json_oaicompat() + : to_json_non_oaicompat(); + } + + json to_json_non_oaicompat() { + return json { + {"index", index}, + {"embedding", embedding}, + }; + } + + json to_json_oaicompat() { + return json { + {"index", index}, + {"embedding", embedding[0]}, + {"tokens_evaluated", n_tokens}, + }; + } +}; + +struct server_task_result_rerank : server_task_result { + int index = 0; + float score = -1e6; + + int32_t n_tokens; + + virtual int get_index() override { + return index; + } + + virtual json to_json() override { + return json { + {"index", index}, + {"score", score}, + {"tokens_evaluated", n_tokens}, + }; + } +}; + +// this function maybe used outside of server_task_result_error +static json format_error_response(const std::string & message, const enum error_type type) { + std::string type_str; + int code = 500; + switch (type) { + case ERROR_TYPE_INVALID_REQUEST: + type_str = "invalid_request_error"; + code = 400; + break; + case ERROR_TYPE_AUTHENTICATION: + type_str = "authentication_error"; + code = 401; + break; + case ERROR_TYPE_NOT_FOUND: + type_str = "not_found_error"; + code = 404; + break; + case ERROR_TYPE_SERVER: + type_str = "server_error"; + code = 500; + break; + case ERROR_TYPE_PERMISSION: + type_str = "permission_error"; + code = 403; + break; + case ERROR_TYPE_NOT_SUPPORTED: + type_str = "not_supported_error"; + code = 501; + break; + case ERROR_TYPE_UNAVAILABLE: + type_str = "unavailable_error"; + code = 503; + break; + } + return json { + {"code", code}, + {"message", message}, + {"type", type_str}, + }; +} + +struct server_task_result_error : server_task_result { + int index = 0; + error_type err_type = ERROR_TYPE_SERVER; + std::string err_msg; + + virtual bool is_error() override { + return true; + } + + virtual json to_json() override { + return format_error_response(err_msg, err_type); + } +}; + +struct server_task_result_metrics : server_task_result { + int n_idle_slots; + int n_processing_slots; + int n_tasks_deferred; + int64_t t_start; + + int32_t kv_cache_tokens_count; + int32_t kv_cache_used_cells; + + // TODO: somehow reuse server_metrics in the future, instead of duplicating the fields + uint64_t n_prompt_tokens_processed_total = 0; + uint64_t t_prompt_processing_total = 0; + uint64_t n_tokens_predicted_total = 0; + uint64_t t_tokens_generation_total = 0; + + uint64_t n_prompt_tokens_processed = 0; + uint64_t t_prompt_processing = 0; + + uint64_t n_tokens_predicted = 0; + uint64_t t_tokens_generation = 0; + + uint64_t n_decode_total = 0; + uint64_t n_busy_slots_total = 0; + + // while we can also use std::vector this requires copying the slot object which can be quite messy + // therefore, we use json to temporarily store the slot.to_json() result + json slots_data = json::array(); + + virtual json to_json() override { + return json { + { "idle", n_idle_slots }, + { "processing", n_processing_slots }, + { "deferred", n_tasks_deferred }, + { "t_start", t_start }, + + { "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total }, + { "t_tokens_generation_total", t_tokens_generation_total }, + { "n_tokens_predicted_total", n_tokens_predicted_total }, + { "t_prompt_processing_total", t_prompt_processing_total }, + + { "n_prompt_tokens_processed", n_prompt_tokens_processed }, + { "t_prompt_processing", t_prompt_processing }, + { "n_tokens_predicted", n_tokens_predicted }, + { "t_tokens_generation", t_tokens_generation }, + + { "n_decode_total", n_decode_total }, + { "n_busy_slots_total", n_busy_slots_total }, + + { "kv_cache_tokens_count", kv_cache_tokens_count }, + { "kv_cache_used_cells", kv_cache_used_cells }, + + { "slots", slots_data }, + }; + } +}; + +struct server_task_result_slot_save_load : server_task_result { + std::string filename; + bool is_save; // true = save, false = load + + size_t n_tokens; + size_t n_bytes; + double t_ms; + + virtual json to_json() override { + if (is_save) { + return json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_saved", n_tokens }, + { "n_written", n_bytes }, + { "timings", { + { "save_ms", t_ms } + }}, + }; + } else { + return json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_restored", n_tokens }, + { "n_read", n_bytes }, + { "timings", { + { "restore_ms", t_ms } + }}, + }; + } + } +}; + +struct server_task_result_slot_erase : server_task_result { + size_t n_erased; + + virtual json to_json() override { + return json { + { "id_slot", id_slot }, + { "n_erased", n_erased }, + }; + } +}; + +struct server_task_result_apply_lora : server_task_result { + virtual json to_json() override { + return json {{ "success", true }}; + } }; struct server_slot { int id; - int task_id = -1; + int id_task = -1; + + // only used for completion/embedding/infill/rerank + server_task_type task_type = SERVER_TASK_TYPE_COMPLETION; + + llama_batch batch_spec = {}; + + llama_context * ctx = nullptr; + llama_context * ctx_dft = nullptr; + + common_speculative * spec = nullptr; + + std::vector lora; + + // the index relative to completion multi-task request + size_t index = 0; struct slot_params params; - slot_state state = IDLE; - slot_command command = NONE; + slot_state state = SLOT_STATE_IDLE; // used to determine the slot that has been used the longest int64_t t_last_used = -1; @@ -110,85 +1237,79 @@ struct server_slot { int32_t n_decoded = 0; int32_t n_remaining = -1; int32_t i_batch = -1; - int32_t n_predict = -1; + int32_t n_predict = -1; // TODO: disambiguate from params.n_predict + // n_prompt_tokens may not be equal to prompt_tokens.size(), because prompt maybe truncated int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_processed = 0; - json prompt; - std::string generated_text; - llama_token sampled; - std::vector cache_tokens; + // input prompt tokens + llama_tokens prompt_tokens; + + size_t last_nl_pos = 0; + + std::string generated_text; + llama_tokens generated_tokens; + + llama_tokens cache_tokens; + std::vector generated_token_probs; - bool infill = false; - bool embedding = false; bool has_next_token = true; - bool truncated = false; - bool stopped_eos = false; - bool stopped_word = false; - bool stopped_limit = false; - - bool oaicompat = false; - std::string oaicompat_model; + bool has_new_line = false; + bool truncated = false; + stop_type stop; std::string stopping_word; // sampling - struct llama_sampling_params sparams; - llama_sampling_context *ctx_sampling = nullptr; + json json_schema; - int32_t ga_i = 0; // group-attention state - int32_t ga_n = 1; // group-attention factor - int32_t ga_w = 512; // group-attention width + struct common_sampler * smpl = nullptr; - int32_t n_past_se = 0; // self-extend + llama_token sampled; - // multimodal - std::vector images; + common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY; // stats - size_t n_sent_text = 0; // number of sent text character - size_t n_sent_token_probs = 0; + size_t n_sent_text = 0; // number of sent text character int64_t t_start_process_prompt; - int64_t t_start_genereration; + int64_t t_start_generation; double t_prompt_processing; // ms - double t_token_generation; // ms + double t_token_generation; // ms - // multitasks - int multitask_id = -1; + std::function callback_on_release; void reset() { - n_prompt_tokens = 0; - generated_text = ""; - truncated = false; - stopped_eos = false; - stopped_word = false; - stopped_limit = false; - stopping_word = ""; - n_past = 0; - n_sent_text = 0; - n_sent_token_probs = 0; - infill = false; - ga_i = 0; - n_past_se = 0; + SLT_DBG(*this, "%s", "\n"); + n_prompt_tokens = 0; + last_nl_pos = 0; + generated_text = ""; + has_new_line = false; + truncated = false; + stop = STOP_TYPE_NONE; + stopping_word = ""; + n_past = 0; + n_sent_text = 0; + task_type = SERVER_TASK_TYPE_COMPLETION; + + generated_tokens.clear(); generated_token_probs.clear(); - - for (slot_image & img : images) { - free(img.image_embedding); - if (img.img_data) { - clip_image_u8_free(img.img_data); - } - img.prefix_prompt = ""; - } - - images.clear(); } - bool has_budget(gpt_params &global_params) { + bool is_non_causal() const { + return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK; + } + + bool can_batch_with(server_slot & other_slot) { + return is_non_causal() == other_slot.is_non_causal() + && are_lora_equal(lora, other_slot.lora); + } + + bool has_budget(const common_params & global_params) { if (params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless } @@ -204,107 +1325,159 @@ struct server_slot { return n_remaining > 0; // no budget } - bool available() const { - return state == IDLE && command == NONE; - } - bool is_processing() const { - return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING; + return state != SLOT_STATE_IDLE; } - void add_token_string(const completion_token_output &token) { - if (command == RELEASE) { + 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"); return; } - cache_tokens.push_back(token.tok); generated_token_probs.push_back(token); } void release() { - if (state == PROCESSING) - { - t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; - command = RELEASE; + 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); } } - json get_formated_timings() { - return json - { - {"prompt_n", n_prompt_tokens_processed}, - {"prompt_ms", t_prompt_processing}, - {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, - {"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed}, + result_timings get_timings() const { + result_timings timings; + timings.prompt_n = n_prompt_tokens_processed; + timings.prompt_ms = t_prompt_processing; + timings.prompt_per_token_ms = t_prompt_processing / n_prompt_tokens_processed; + timings.prompt_per_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; - {"predicted_n", n_decoded}, - {"predicted_ms", t_token_generation}, - {"predicted_per_token_ms", t_token_generation / n_decoded}, - {"predicted_per_second", 1e3 / t_token_generation * n_decoded}, - }; + timings.predicted_n = n_decoded; + timings.predicted_ms = t_token_generation; + timings.predicted_per_token_ms = t_token_generation / n_decoded; + timings.predicted_per_second = 1e3 / t_token_generation * n_decoded; + + return timings; + } + + size_t find_stopping_strings(const std::string & text, const size_t last_token_size, bool is_full_stop) { + size_t stop_pos = std::string::npos; + + for (const std::string & word : params.antiprompt) { + size_t pos; + + if (is_full_stop) { + const size_t tmp = word.size() + last_token_size; + const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; + + pos = text.find(word, from_pos); + } else { + // otherwise, partial stop + pos = find_partial_stop_string(word, text); + } + + if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) { + if (is_full_stop) { + stop = STOP_TYPE_WORD; + stopping_word = word; + has_next_token = false; + } + stop_pos = pos; + } + } + + return stop_pos; } void print_timings() const { - char buffer[512]; - double t_token = t_prompt_processing / n_prompt_tokens_processed; - double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; - sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", - t_prompt_processing, n_prompt_tokens_processed, - t_token, n_tokens_second); - LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, - {"t_prompt_processing", t_prompt_processing}, - {"n_prompt_tokens_processed", n_prompt_tokens_processed}, - {"t_token", t_token}, - {"n_tokens_second", n_tokens_second}, - }); + const double t_prompt = t_prompt_processing / n_prompt_tokens_processed; + const double n_prompt_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; - t_token = t_token_generation / n_decoded; - n_tokens_second = 1e3 / t_token_generation * n_decoded; - sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", - t_token_generation, n_decoded, - t_token, n_tokens_second); - LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, - {"t_token_generation", t_token_generation}, - {"n_decoded", n_decoded}, - {"t_token", t_token}, - {"n_tokens_second", n_tokens_second}, - }); + const double t_gen = t_token_generation / n_decoded; + const double n_gen_second = 1e3 / t_token_generation * n_decoded; - sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation); - LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, - {"t_prompt_processing", t_prompt_processing}, - {"t_token_generation", t_token_generation}, - {"t_total", t_prompt_processing + t_token_generation}, - }); + SLT_INF(*this, + "\n" + "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n" + " eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n" + " total time = %10.2f ms / %5d tokens\n", + t_prompt_processing, n_prompt_tokens_processed, t_prompt, n_prompt_second, + t_token_generation, n_decoded, t_gen, n_gen_second, + t_prompt_processing + t_token_generation, n_prompt_tokens_processed + n_decoded); + } + + json to_json() const { + return json { + {"id", id}, + {"id_task", id_task}, + {"n_ctx", n_ctx}, + {"speculative", can_speculate()}, + {"is_processing", is_processing()}, + {"non_causal", is_non_causal()}, + {"params", params.to_json()}, + {"prompt", common_detokenize(ctx, prompt_tokens)}, + {"next_token", + { + {"has_next_token", has_next_token}, + {"has_new_line", has_new_line}, + {"n_remain", n_remaining}, + {"n_decoded", n_decoded}, + {"stopping_word", stopping_word}, + } + }, + }; } }; struct server_metrics { + int64_t t_start = 0; + uint64_t n_prompt_tokens_processed_total = 0; + uint64_t t_prompt_processing_total = 0; uint64_t n_tokens_predicted_total = 0; + uint64_t t_tokens_generation_total = 0; uint64_t n_prompt_tokens_processed = 0; uint64_t t_prompt_processing = 0; - uint64_t n_tokens_predicted = 0; - uint64_t t_tokens_generation = 0; + uint64_t n_tokens_predicted = 0; + uint64_t t_tokens_generation = 0; + uint64_t n_decode_total = 0; + uint64_t n_busy_slots_total = 0; - void on_prompt_eval(const server_slot &slot) { + void init() { + t_start = ggml_time_us(); + } + + void on_prompt_eval(const server_slot & slot) { n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; n_prompt_tokens_processed += slot.n_prompt_tokens_processed; t_prompt_processing += slot.t_prompt_processing; + t_prompt_processing_total += slot.t_prompt_processing; } - void on_prediction(const server_slot &slot) { - n_tokens_predicted_total += slot.n_decoded; - n_tokens_predicted += slot.n_decoded; - t_tokens_generation += slot.t_token_generation; + void on_prediction(const server_slot & slot) { + n_tokens_predicted_total += slot.n_decoded; + n_tokens_predicted += slot.n_decoded; + t_tokens_generation += slot.t_token_generation; + t_tokens_generation_total += slot.t_token_generation; + } + + void on_decoded(const std::vector & slots) { + n_decode_total++; + for (const auto & slot : slots) { + if (slot.is_processing()) { + n_busy_slots_total++; + } + } } void reset_bucket() { @@ -315,1604 +1488,1661 @@ struct server_metrics { } }; -struct llama_server_context -{ - llama_model *model = nullptr; - llama_context *ctx = nullptr; +struct server_queue { + int id = 0; + bool running; - clip_ctx *clp_ctx = nullptr; + // queues + std::deque queue_tasks; + std::deque queue_tasks_deferred; - gpt_params params; + std::mutex mutex_tasks; + std::condition_variable condition_tasks; - llama_batch batch; + // callback functions + std::function callback_new_task; + std::function callback_update_slots; - bool multimodal = false; - bool clean_kv_cache = true; - bool all_slots_are_idle = false; - bool add_bos_token = true; + // Add a new task to the end of the queue + int post(server_task task, bool front = false) { + std::unique_lock lock(mutex_tasks); + GGML_ASSERT(task.id != -1); + // if this is cancel task make sure to clean up pending tasks + if (task.type == SERVER_TASK_TYPE_CANCEL) { + cleanup_pending_task(task.id_target); + } + QUE_DBG("new task, id = %d, front = %d\n", task.id, front); + if (front) { + queue_tasks.push_front(std::move(task)); + } else { + queue_tasks.push_back(std::move(task)); + } + condition_tasks.notify_one(); + return task.id; + } - int32_t n_ctx; // total context for all clients / slots + // multi-task version of post() + int post(std::vector & tasks, bool front = false) { + std::unique_lock lock(mutex_tasks); + for (auto & task : tasks) { + if (task.id == -1) { + task.id = id++; + } + // if this is cancel task make sure to clean up pending tasks + if (task.type == SERVER_TASK_TYPE_CANCEL) { + cleanup_pending_task(task.id_target); + } + QUE_DBG("new task, id = %d/%d, front = %d\n", task.id, (int) tasks.size(), front); + if (front) { + queue_tasks.push_front(std::move(task)); + } else { + queue_tasks.push_back(std::move(task)); + } + } + condition_tasks.notify_one(); + return 0; + } - // system prompt - bool system_need_update = false; + // Add a new task, but defer until one slot is available + void defer(server_task task) { + std::unique_lock lock(mutex_tasks); + QUE_DBG("defer task, id = %d\n", task.id); + queue_tasks_deferred.push_back(std::move(task)); + condition_tasks.notify_one(); + } - std::string system_prompt; - std::vector system_tokens; + // Get the next id for creating a new task + int get_new_id() { + std::unique_lock lock(mutex_tasks); + int new_id = id++; + return new_id; + } - std::string name_user; // this should be the antiprompt - std::string name_assistant; + // Register function to process a new task + void on_new_task(std::function callback) { + callback_new_task = std::move(callback); + } + + // Register the function to be called when all slots data is ready to be processed + void on_update_slots(std::function callback) { + callback_update_slots = std::move(callback); + } + + // Call when the state of one slot is changed, it will move one task from deferred to main queue + void pop_deferred_task() { + std::unique_lock lock(mutex_tasks); + if (!queue_tasks_deferred.empty()) { + queue_tasks.emplace_back(std::move(queue_tasks_deferred.front())); + queue_tasks_deferred.pop_front(); + } + condition_tasks.notify_one(); + } + + // end the start_loop routine + void terminate() { + std::unique_lock lock(mutex_tasks); + running = false; + condition_tasks.notify_all(); + } + + /** + * Main loop consists of these steps: + * - Wait until a new task arrives + * - Process the task (i.e. maybe copy data into slot) + * - Check if multitask is finished + * - Update all slots + */ + void start_loop() { + running = true; + + while (true) { + QUE_DBG("%s", "processing new tasks\n"); + + while (true) { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + lock.unlock(); + break; + } + server_task task = queue_tasks.front(); + queue_tasks.pop_front(); + lock.unlock(); + + QUE_DBG("processing task, id = %d\n", task.id); + callback_new_task(std::move(task)); + } + + // all tasks in the current loop is processed, slots data is now ready + QUE_DBG("%s", "update slots\n"); + + callback_update_slots(); + + QUE_DBG("%s", "waiting for new tasks\n"); + { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + if (!running) { + QUE_DBG("%s", "terminate\n"); + return; + } + condition_tasks.wait(lock, [&]{ + return (!queue_tasks.empty() || !running); + }); + } + } + } + } + +private: + void cleanup_pending_task(int id_target) { + // no need lock because this is called exclusively by post() + auto rm_func = [id_target](const server_task & task) { + return task.id_target == id_target; + }; + queue_tasks.erase( + std::remove_if(queue_tasks.begin(), queue_tasks.end(), rm_func), + queue_tasks.end()); + queue_tasks_deferred.erase( + std::remove_if(queue_tasks_deferred.begin(), queue_tasks_deferred.end(), rm_func), + queue_tasks_deferred.end()); + } +}; + +struct server_response { + // for keeping track of all tasks waiting for the result + std::unordered_set waiting_task_ids; + + // the main result queue (using ptr for polymorphism) + std::vector queue_results; + + std::mutex mutex_results; + std::condition_variable condition_results; + + // add the id_task to the list of tasks waiting for response + void add_waiting_task_id(int id_task) { + SRV_DBG("add task %d to waiting list. current waiting = %d (before add)\n", id_task, (int) waiting_task_ids.size()); + + std::unique_lock lock(mutex_results); + waiting_task_ids.insert(id_task); + } + + void add_waiting_tasks(const std::vector & tasks) { + std::unique_lock lock(mutex_results); + + for (const auto & task : tasks) { + SRV_DBG("add task %d to waiting list. current waiting = %d (before add)\n", task.id, (int) waiting_task_ids.size()); + waiting_task_ids.insert(task.id); + } + } + + // when the request is finished, we can remove task associated with it + void remove_waiting_task_id(int id_task) { + SRV_DBG("remove task %d from waiting list. current waiting = %d (before remove)\n", id_task, (int) waiting_task_ids.size()); + + std::unique_lock lock(mutex_results); + waiting_task_ids.erase(id_task); + // make sure to clean up all pending results + queue_results.erase( + std::remove_if(queue_results.begin(), queue_results.end(), [id_task](const server_task_result_ptr & res) { + return res->id == id_task; + }), + queue_results.end()); + } + + void remove_waiting_task_ids(const std::unordered_set & id_tasks) { + std::unique_lock lock(mutex_results); + + for (const auto & id_task : id_tasks) { + SRV_DBG("remove task %d from waiting list. current waiting = %d (before remove)\n", id_task, (int) waiting_task_ids.size()); + waiting_task_ids.erase(id_task); + } + } + + // This function blocks the thread until there is a response for one of the id_tasks + server_task_result_ptr recv(const std::unordered_set & id_tasks) { + while (true) { + std::unique_lock lock(mutex_results); + condition_results.wait(lock, [&]{ + return !queue_results.empty(); + }); + + for (size_t i = 0; i < queue_results.size(); i++) { + if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { + server_task_result_ptr res = std::move(queue_results[i]); + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + + // should never reach here + } + + // same as recv(), but have timeout in seconds + // if timeout is reached, nullptr is returned + server_task_result_ptr recv_with_timeout(const std::unordered_set & id_tasks, int timeout) { + while (true) { + std::unique_lock lock(mutex_results); + + for (int i = 0; i < (int) queue_results.size(); i++) { + if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { + server_task_result_ptr res = std::move(queue_results[i]); + queue_results.erase(queue_results.begin() + i); + return res; + } + } + + std::cv_status cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout)); + if (cr_res == std::cv_status::timeout) { + return nullptr; + } + } + + // should never reach here + } + + // single-task version of recv() + server_task_result_ptr recv(int id_task) { + std::unordered_set id_tasks = {id_task}; + return recv(id_tasks); + } + + // Send a new result to a waiting id_task + void send(server_task_result_ptr && result) { + SRV_DBG("sending result for task id = %d\n", result->id); + + std::unique_lock lock(mutex_results); + for (const auto & id_task : waiting_task_ids) { + if (result->id == id_task) { + SRV_DBG("task id = %d pushed to result queue\n", result->id); + + queue_results.emplace_back(std::move(result)); + condition_results.notify_all(); + return; + } + } + } +}; + +struct server_context { + common_params params_base; + + // note: keep these alive - they determine the lifetime of the model, context, etc. + common_init_result llama_init; + common_init_result llama_init_dft; + + llama_model * model = nullptr; + llama_context * ctx = nullptr; + + const llama_vocab * vocab = nullptr; + + llama_model * model_dft = nullptr; + + llama_context_params cparams_dft; + + llama_batch batch = {}; + + bool clean_kv_cache = true; + bool add_bos_token = true; + bool has_eos_token = false; + + int32_t n_ctx; // total context for all clients / slots // slots / clients std::vector slots; json default_generation_settings_for_props; - llama_server_queue queue_tasks; - llama_server_response queue_results; + server_queue queue_tasks; + server_response queue_results; server_metrics metrics; - ~llama_server_context() - { - if (ctx) - { - llama_free(ctx); - ctx = nullptr; - } - if (model) - { - llama_free_model(model); - model = nullptr; + // Necessary similarity of prompt for slot selection + float slot_prompt_similarity = 0.0f; + + common_chat_templates chat_templates; + + ~server_context() { + // Clear any sampling context + for (server_slot & slot : slots) { + 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 gpt_params ¶ms_) - { - params = params_; - if (!params.mmproj.empty()) { - multimodal = true; - LOG_INFO("Multi Modal Mode Enabled", {}); - clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); - if(clp_ctx == nullptr) { - LOG_ERROR("unable to load clip model", {{"model", params.mmproj}}); - return false; - } + bool load_model(const common_params & params) { + SRV_INF("loading model '%s'\n", params.model.c_str()); - if (params.n_ctx < 2048) { // request larger context for the image embedding - params.n_ctx = 2048; - } - } + params_base = params; - std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (model == nullptr) - { - LOG_ERROR("unable to load model", {{"model", params.model}}); + llama_init = common_init_from_params(params_base); + + model = llama_init.model.get(); + ctx = llama_init.context.get(); + + if (model == nullptr) { + SRV_ERR("failed to load model, '%s'\n", params_base.model.c_str()); return false; } - if (multimodal) { - const int n_embd_clip = clip_n_mmproj_embd(clp_ctx); - const int n_embd_llm = llama_n_embd(model); - if (n_embd_clip != n_embd_llm) { - LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm); - llama_free(ctx); - llama_free_model(model); - return false; - } - } + vocab = llama_model_get_vocab(model); n_ctx = llama_n_ctx(ctx); - add_bos_token = llama_should_add_bos_token(model); + add_bos_token = llama_vocab_get_add_bos(vocab); + has_eos_token = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL; + + if (!params_base.speculative.model.empty() || !params_base.speculative.hf_repo.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.hf_file = params_base.speculative.hf_file; + params_dft.hf_repo = params_base.speculative.hf_repo; + params_dft.model = params_base.speculative.model; + params_dft.model_url = params_base.speculative.model_url; + 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; + + llama_init_dft = common_init_from_params(params_dft); + + model_dft = llama_init_dft.model.get(); + + 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.get())) { + 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()); + + return false; + } + + const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get()); + + 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_init_dft.context.reset(); + } + + if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) { + SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__); + chat_templates = common_chat_templates_from_model(model, "chatml"); + } else { + chat_templates = common_chat_templates_from_model(model, params_base.chat_template); + } + GGML_ASSERT(chat_templates.template_default.get() != nullptr); return true; } - void validate_model_chat_template(server_params & sparams) { + bool validate_builtin_chat_template(bool use_jinja) const { llama_chat_message chat[] = {{"user", "test"}}; - std::vector buf(1); - int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size()); - if (res < 0) { - LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template + + if (use_jinja) { + auto templates = common_chat_templates_from_model(model, ""); + common_chat_inputs inputs; + inputs.messages = json::array({{ + {"role", "user"}, + {"content", "test"}, + }}); + GGML_ASSERT(templates.template_default); + try { + common_chat_params_init(*templates.template_default, inputs); + if (templates.template_tool_use) { + common_chat_params_init(*templates.template_tool_use, inputs); + } + return true; + } catch (const std::exception & e) { + SRV_ERR("failed to apply template: %s\n", e.what()); + return false; + } + } else { + const char * tmpl = llama_model_chat_template(model, /* name */ nullptr); + const int32_t chat_res = llama_chat_apply_template(tmpl, chat, 1, true, nullptr, 0); + return chat_res > 0; } } - void initialize() { - // create slots - all_slots_are_idle = true; + void init() { + const int32_t n_ctx_slot = n_ctx / params_base.n_parallel; - const int32_t n_ctx_slot = n_ctx / params.n_parallel; + SRV_INF("initializing slots, n_slots = %d\n", params_base.n_parallel); - LOG_INFO("initializing slots", {{"n_slots", params.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.ctx = ctx; slot.n_ctx = n_ctx_slot; - slot.n_predict = params.n_predict; + slot.n_predict = params_base.n_predict; - LOG_INFO("new slot", { - {"slot_id", slot.id}, - {"n_ctx_slot", slot.n_ctx} - }); + if (model_dft) { + slot.batch_spec = llama_batch_init(params_base.speculative.n_max + 1, 0, 1); - const int ga_n = params.grp_attn_n; - const int ga_w = params.grp_attn_w; + slot.ctx_dft = llama_init_from_model(model_dft, cparams_dft); + if (slot.ctx_dft == nullptr) { + SRV_ERR("%s", "failed to create draft context\n"); + return; + } - if (ga_n != 1) { - GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT - GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT - //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT - //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT - - LOG_INFO("slot self-extend", { - {"slot_id", slot.id}, - {"ga_n", ga_n}, - {"ga_w", ga_w} - }); + slot.spec = common_speculative_init(slot.ctx_dft); + if (slot.spec == nullptr) { + SRV_ERR("%s", "failed to create speculator\n"); + return; + } } - slot.ga_i = 0; - slot.ga_n = ga_n; - slot.ga_w = ga_w; + SLT_INF(slot, "new slot n_ctx_slot = %d\n", slot.n_ctx); + + slot.params.sampling = params_base.sampling; + + slot.callback_on_release = [this](int) { + queue_tasks.pop_deferred_task(); + }; slot.reset(); slots.push_back(slot); } - default_generation_settings_for_props = get_formated_generation(slots.front()); - default_generation_settings_for_props["seed"] = -1; + default_generation_settings_for_props = slots[0].to_json(); - batch = llama_batch_init(n_ctx, 0, params.n_parallel); - } - - std::vector tokenize(const json & json_prompt, bool add_bos) const - { - // TODO: currently, we tokenize using special tokens by default - // this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216) - // but it's better compared to completely ignoring ChatML and other chat templates - const bool TMP_FORCE_SPECIAL = true; - - // If `add_bos` is true, we only add BOS, when json_prompt is a string, - // or the first element of the json_prompt array is a string. - std::vector prompt_tokens; - - if (json_prompt.is_array()) + // the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens + // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used) { - bool first = true; - for (const auto& p : json_prompt) - { - if (p.is_string()) - { - auto s = p.template get(); - std::vector p; - if (first) - { - p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); - first = false; - } - else - { - p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL); - } - prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); - } - else - { - if (first) - { - first = false; - } - prompt_tokens.push_back(p.template get()); - } - } - } - else - { - auto s = json_prompt.template get(); - prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL); + 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_base.n_parallel), 0, 1); } - return prompt_tokens; + metrics.init(); } - server_slot* get_slot(int id) { - int64_t t_last = ggml_time_us(); - server_slot *last_used = nullptr; - - for (server_slot & slot : slots) - { - if (slot.id == id && slot.available()) - { + server_slot * get_slot_by_id(int id) { + for (server_slot & slot : slots) { + if (slot.id == id) { return &slot; } - - if (slot.available() && slot.t_last_used < t_last) - { - last_used = &slot; - t_last = slot.t_last_used; - } } - return last_used; + return nullptr; } - bool launch_slot_with_data(server_slot* &slot, json data) { - slot_params default_params; - llama_sampling_params default_sparams; + server_slot * get_available_slot(const server_task & task) { + server_slot * ret = nullptr; - if (data.count("__oaicompat") != 0) { - slot->oaicompat = true; - slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); - } else { - slot->oaicompat = false; - slot->oaicompat_model = ""; + // find the slot that has at least n% prompt similarity + if (ret == nullptr && slot_prompt_similarity != 0.0f) { + int lcs_len = 0; + float similarity = 0; + + for (server_slot & slot : slots) { + // skip the slot if it is not available + if (slot.is_processing()) { + continue; + } + + // skip the slot if it does not contains cached tokens + if (slot.cache_tokens.empty()) { + continue; + } + + // 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 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 (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 lcs similarity, lcs_len = %d, similarity = %f\n", lcs_len, similarity); + } } - 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", default_params.n_predict); - 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.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); - slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_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.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", slot->params.n_keep); - slot->params.seed = json_value(data, "seed", default_params.seed); - slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); - 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); + // find the slot that has been least recently used + if (ret == nullptr) { + int64_t t_last = ggml_time_us(); + for (server_slot & slot : slots) { + // skip the slot if it is not available + if (slot.is_processing()) { + continue; + } - if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) { + // select the current slot if the criteria match + if (slot.t_last_used < t_last) { + t_last = slot.t_last_used; + ret = &slot; + } + } + + if (ret != nullptr) { + SLT_DBG(*ret, "selected slot by lru, t_last = %" PRId64 "\n", t_last); + } + } + + return ret; + } + + bool launch_slot_with_task(server_slot & slot, const server_task & task) { + slot.reset(); + slot.id_task = task.id; + slot.index = task.index; + slot.task_type = task.type; + slot.params = std::move(task.params); + slot.prompt_tokens = std::move(task.prompt_tokens); + + if (!are_lora_equal(task.params.lora, slot.lora)) { + // if lora is changed, we cannot reuse cached tokens + slot.cache_tokens.clear(); + slot.lora = task.params.lora; + } + + SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str()); + + if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) { // Might be better to reject the request with a 400 ? - LOG_WARNING("Max tokens to predict exceeds server configuration", { - {"params.n_predict", slot->params.n_predict}, - {"slot.n_predict", slot->n_predict}, - }); - slot->params.n_predict = slot->n_predict; + slot.params.n_predict = slot.n_predict; + SLT_WRN(slot, "n_predict = %d exceeds server configuration, setting to %d", slot.n_predict, slot.n_predict); } - // infill - if (data.count("input_prefix") != 0) - { - slot->params.input_prefix = data["input_prefix"]; - } - else - { - slot->params.input_prefix = ""; + if (slot.params.ignore_eos && has_eos_token) { + slot.params.sampling.logit_bias.push_back({llama_vocab_eos(vocab), -INFINITY}); } - if (data.count("input_suffix") != 0) { - slot->params.input_suffix = data["input_suffix"]; - } - else - { - slot->params.input_suffix = ""; - } - - if (data.count("prompt") != 0) - { - slot->prompt = data["prompt"]; - } - else - { - slot->prompt = ""; - } - - slot->sparams.penalty_prompt_tokens.clear(); - slot->sparams.use_penalty_prompt_tokens = false; - const auto &penalty_prompt = data.find("penalty_prompt"); - if (penalty_prompt != data.end()) - { - if (penalty_prompt->is_string()) - { - const auto penalty_prompt_string = penalty_prompt->get(); - auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false); - slot->sparams.penalty_prompt_tokens.swap(penalty_tokens); - if (slot->params.n_predict > 0) - { - slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict); - } - slot->sparams.use_penalty_prompt_tokens = true; + if (slot.smpl != nullptr) { + common_sampler_free(slot.smpl); } - else if (penalty_prompt->is_array()) - { - const auto n_tokens = penalty_prompt->size(); - slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict)); - const int n_vocab = llama_n_vocab(model); - for (const auto &penalty_token : *penalty_prompt) - { - if (penalty_token.is_number_integer()) - { - const auto tok = penalty_token.get(); - if (tok >= 0 && tok < n_vocab) - { - slot->sparams.penalty_prompt_tokens.push_back(tok); - } - } - } - slot->sparams.use_penalty_prompt_tokens = true; + + 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); + return false; } } - slot->sparams.logit_bias.clear(); + if (slot.ctx_dft) { + llama_batch_free(slot.batch_spec); - if (json_value(data, "ignore_eos", false)) - { - slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY; + slot.batch_spec = llama_batch_init(slot.params.speculative.n_max + 1, 0, 1); } - const auto &logit_bias = data.find("logit_bias"); - if (logit_bias != data.end() && logit_bias->is_array()) - { - const int n_vocab = llama_n_vocab(model); - for (const auto &el : *logit_bias) - { - if (el.is_array() && el.size() == 2) - { - float bias; - if (el[1].is_number()) - { - bias = el[1].get(); - } - else if (el[1].is_boolean() && !el[1].get()) - { - bias = -INFINITY; - } - else - { - continue; - } + slot.state = SLOT_STATE_STARTED; - if (el[0].is_number_integer()) - { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) - { - slot->sparams.logit_bias[tok] = bias; - } - } - else if (el[0].is_string()) - { - auto toks = llama_tokenize(model, el[0].get(), false); - for (auto tok : toks) - { - slot->sparams.logit_bias[tok] = bias; - } - } - } - } - } - - slot->params.antiprompt.clear(); - - const auto &stop = data.find("stop"); - if (stop != data.end() && stop->is_array()) - { - for (const auto &word : *stop) - { - if (!word.empty()) - { - slot->params.antiprompt.push_back(word); - } - } - } - - const auto &samplers_sequence = data.find("samplers"); - if (samplers_sequence != data.end() && samplers_sequence->is_array()) - { - std::vector sampler_names; - for (const auto &sampler_name : *samplers_sequence) - { - if (sampler_name.is_string()) - { - sampler_names.emplace_back(sampler_name); - } - } - slot->sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); - } - else - { - slot->sparams.samplers_sequence = default_sparams.samplers_sequence; - } - - if (multimodal) - { - const auto &images_data = data.find("image_data"); - if (images_data != data.end() && images_data->is_array()) - { - for (const auto &img : *images_data) - { - const std::vector image_buffer = base64_decode(img["data"].get()); - - slot_image img_sl; - img_sl.id = img.count("id") != 0 ? img["id"].get() : slot->images.size(); - img_sl.img_data = clip_image_u8_init(); - if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data)) - { - LOG_ERROR("failed to load image", { - {"slot_id", slot->id}, - {"img_sl_id", img_sl.id} - }); - return false; - } - LOG_VERBOSE("image loaded", { - {"slot_id", slot->id}, - {"img_sl_id", img_sl.id} - }); - img_sl.request_encode_image = true; - slot->images.push_back(img_sl); - } - // process prompt - // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]} - if (slot->images.size() > 0 && !slot->prompt.is_array()) - { - std::string prompt = slot->prompt.get(); - size_t pos = 0, begin_prefix = 0; - std::string pattern = "[img-"; - while ((pos = prompt.find(pattern, pos)) != std::string::npos) { - size_t end_prefix = pos; - pos += pattern.length(); - size_t end_pos = prompt.find(']', pos); - if (end_pos != std::string::npos) - { - std::string image_id = prompt.substr(pos, end_pos - pos); - try - { - int img_id = std::stoi(image_id); - bool found = false; - for (slot_image &img : slot->images) - { - if (img.id == img_id) { - found = true; - img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix); - begin_prefix = end_pos + 1; - break; - } - } - if (!found) { - LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id); - slot->images.clear(); - return false; - } - } catch (const std::invalid_argument& e) { - LOG_TEE("Invalid image number id in prompt\n"); - slot->images.clear(); - return false; - } - } - } - slot->prompt = ""; - slot->params.input_suffix = prompt.substr(begin_prefix); - slot->params.cache_prompt = false; // multimodal doesn't support cache prompt - } - } - } - - if (slot->ctx_sampling != nullptr) - { - llama_sampling_free(slot->ctx_sampling); - } - slot->ctx_sampling = llama_sampling_init(slot->sparams); - llama_set_rng_seed(ctx, slot->params.seed); - slot->command = LOAD_PROMPT; - - all_slots_are_idle = false; - - LOG_INFO("slot is processing task", { - {"slot_id", slot->id}, - {"task_id", slot->task_id}, - }); + SLT_INF(slot, "%s", "processing task\n"); return true; } void kv_cache_clear() { + SRV_DBG("%s", "clearing KV cache\n"); + // clear the entire KV cache llama_kv_cache_clear(ctx); clean_kv_cache = false; } - void system_prompt_update() { - kv_cache_clear(); - system_tokens.clear(); - - if (!system_prompt.empty()) { - system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token); - - llama_batch_clear(batch); - - for (int i = 0; i < (int)system_tokens.size(); ++i) - { - llama_batch_add(batch, system_tokens[i], i, { 0 }, false); - } - - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) - { - const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i)); - llama_batch batch_view = { - n_tokens, - batch.token + i, - nullptr, - batch.pos + i, - batch.n_seq_id + i, - batch.seq_id + i, - batch.logits + i, - 0, 0, 0, // unused - }; - if (llama_decode(ctx, batch_view) != 0) - { - LOG_TEE("%s: llama_decode() failed\n", __func__); - return; - } - } - - // assign the system KV cache to all parallel sequences - for (int32_t i = 1; i < params.n_parallel; ++i) - { - llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size()); - } - } - - LOG_TEE("system prompt updated\n"); - system_need_update = false; - } - - void system_prompt_notify() { - // release all slots - for (server_slot &slot : slots) - { - slot.release(); - } - - system_need_update = true; - } - - void system_prompt_process(const json &sys_props) { - system_prompt = sys_props.value("prompt", ""); - name_user = sys_props.value("anti_prompt", ""); - name_assistant = sys_props.value("assistant_name", ""); - - - system_prompt_notify(); - } - - static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, - const stop_type type, server_slot &slot) - { - size_t stop_pos = std::string::npos; - - for (const std::string &word : slot.params.antiprompt) - { - size_t pos; - if (type == STOP_FULL) - { - const size_t tmp = word.size() + last_token_size; - const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0; - pos = text.find(word, from_pos); - } - else - { - pos = find_partial_stop_string(word, text); - } - if (pos != std::string::npos && - (stop_pos == std::string::npos || pos < stop_pos)) - { - if (type == STOP_FULL) - { - slot.stopped_word = true; - slot.stopping_word = word; - slot.has_next_token = false; - } - stop_pos = pos; - } - } - - return stop_pos; - } - - bool process_token(completion_token_output &result, server_slot &slot) { + bool process_token(completion_token_output & result, server_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling - const std::string token_str = llama_token_to_piece(ctx, result.tok); + const std::string token_str = result.text_to_send; slot.sampled = result.tok; - // search stop word and delete it slot.generated_text += token_str; + if (slot.params.return_tokens) { + slot.generated_tokens.push_back(result.tok); + } slot.has_next_token = true; - if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) - { - // we can change penalty_prompt_tokens because it is always created from scratch each request - slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok); - } - // check if there is incomplete UTF-8 character at the end - bool incomplete = false; - for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) - { - unsigned char c = slot.generated_text[slot.generated_text.size() - i]; - if ((c & 0xC0) == 0x80) - { - // continuation byte: 10xxxxxx - continue; - } - if ((c & 0xE0) == 0xC0) - { - // 2-byte character: 110xxxxx ... - incomplete = i < 2; - } - else if ((c & 0xF0) == 0xE0) - { - // 3-byte character: 1110xxxx ... - incomplete = i < 3; - } - else if ((c & 0xF8) == 0xF0) - { - // 4-byte character: 11110xxx ... - incomplete = i < 4; - } - // else 1-byte character or invalid byte - break; - } + bool incomplete = validate_utf8(slot.generated_text) < slot.generated_text.size(); - if (!incomplete) - { + // search stop word and delete it + if (!incomplete) { size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); + const std::string str_test = slot.generated_text.substr(pos); - bool is_stop_full = false; - size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); - if (stop_pos != std::string::npos) - { - is_stop_full = true; + bool send_text = true; + + size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), true); + if (stop_pos != std::string::npos) { slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); pos = std::min(slot.n_sent_text, slot.generated_text.size()); - } - else - { - is_stop_full = false; - stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot); + } else if (slot.has_next_token) { + stop_pos = slot.find_stopping_strings(str_test, token_str.size(), false); + send_text = stop_pos == std::string::npos; } // check if there is any token to predict - if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) - { + if (send_text) { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache + } else { + result.text_to_send = ""; } - slot.add_token_string(result); - if (slot.params.stream) - { + + slot.add_token(result); + if (slot.params.stream) { send_partial_response(slot, result); } } - if (incomplete) - { + if (incomplete) { slot.has_next_token = true; } // check the limits - if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) - { - slot.stopped_limit = true; + if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params_base)) { + slot.stop = STOP_TYPE_LIMIT; slot.has_next_token = false; + + SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict); } - if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model)) - { - slot.stopped_eos = true; - slot.has_next_token = false; - LOG_VERBOSE("eos token found", {}); + if (slot.has_new_line) { + // if we have already seen a new line, we stop after a certain time limit + if (slot.params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) { + slot.stop = STOP_TYPE_LIMIT; + slot.has_next_token = false; + + SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms); + } + + // require that each new line has a whitespace prefix (i.e. indentation) of at least slot.params.n_indent + if (slot.params.n_indent > 0) { + // check the current indentation + // TODO: improve by not doing it more than once for each new line + if (slot.last_nl_pos > 0) { + size_t pos = slot.last_nl_pos; + + int n_indent = 0; + while (pos < slot.generated_text.size() && (slot.generated_text[pos] == ' ' || slot.generated_text[pos] == '\t')) { + n_indent++; + pos++; + } + + if (pos < slot.generated_text.size() && n_indent < slot.params.n_indent) { + slot.stop = STOP_TYPE_LIMIT; + slot.has_next_token = false; + + // cut the last line + slot.generated_text.erase(pos, std::string::npos); + + SLT_DBG(slot, "stopped by indentation limit, n_decoded = %d, n_indent = %d\n", slot.n_decoded, n_indent); + } + } + + // find the next new line + { + const size_t pos = slot.generated_text.find('\n', slot.last_nl_pos); + + if (pos != std::string::npos) { + slot.last_nl_pos = pos + 1; + } + } + } } - LOG_VERBOSE("next token", { - {"token", result.tok}, - {"token_text", tokens_to_output_formatted_string(ctx, result.tok)}, - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, - {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }); + // check if there is a new line in the generated text + if (result.text_to_send.find('\n') != std::string::npos) { + slot.has_new_line = true; + } + + // if context shift is disabled, we stop when it reaches the context limit + if (slot.n_past >= slot.n_ctx) { + slot.truncated = true; + slot.stop = STOP_TYPE_LIMIT; + slot.has_next_token = false; + + SLT_DBG(slot, "stopped due to running out of context capacity, n_past = %d, n_prompt_tokens = %d, n_decoded = %d, n_ctx = %d\n", + slot.n_decoded, slot.n_prompt_tokens, slot.n_past, slot.n_ctx); + } + + if (llama_vocab_is_eog(vocab, result.tok)) { + slot.stop = STOP_TYPE_EOS; + slot.has_next_token = false; + + SLT_DBG(slot, "%s", "stopped by EOS\n"); + } + + const auto n_ctx_train = llama_model_n_ctx_train(model); + + if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) { + slot.truncated = true; + slot.stop = STOP_TYPE_LIMIT; + slot.has_next_token = false; // stop prediction + + SLT_WRN(slot, + "n_predict (%d) is set for infinite generation. " + "Limiting generated tokens to n_ctx_train (%d) to avoid EOS-less generation infinite loop\n", + slot.params.n_predict, n_ctx_train); + } + + SLT_DBG(slot, "n_decoded = %d, n_remaining = %d, next token: %5d '%s'\n", slot.n_decoded, slot.n_remaining, result.tok, token_str.c_str()); return slot.has_next_token; // continue } - bool process_images(server_slot &slot) const - { - for (slot_image &img : slot.images) - { - if (!img.request_encode_image) - { + void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) { + size_t n_probs = slot.params.sampling.n_probs; + size_t n_vocab = llama_vocab_n_tokens(vocab); + if (post_sampling) { + const auto * cur_p = common_sampler_get_candidates(slot.smpl); + const size_t max_probs = cur_p->size; + + // set probability for sampled token + for (size_t i = 0; i < max_probs; i++) { + if (cur_p->data[i].id == result.tok) { + result.prob = cur_p->data[i].p; + break; + } + } + + // set probability for top n_probs tokens + result.probs.reserve(max_probs); + for (size_t i = 0; i < std::min(max_probs, n_probs); i++) { + result.probs.push_back({ + cur_p->data[i].id, + common_detokenize(ctx, {cur_p->data[i].id}, special), + cur_p->data[i].p + }); + } + } else { + // TODO: optimize this with min-p optimization + std::vector cur = get_token_probabilities(ctx, idx); + + // set probability for sampled token + for (size_t i = 0; i < n_vocab; i++) { + // set probability for sampled token + if (cur[i].id == result.tok) { + result.prob = cur[i].p; + break; + } + } + + // set probability for top n_probs tokens + result.probs.reserve(n_probs); + for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) { + result.probs.push_back({ + cur[i].id, + common_detokenize(ctx, {cur[i].id}, special), + cur[i].p + }); + } + } + } + + void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { + send_error(task.id, error, type); + } + + void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { + send_error(slot.id_task, error, type); + } + + void send_error(const int id_task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { + SRV_ERR("task id = %d, error: %s\n", id_task, error.c_str()); + + auto res = std::make_unique(); + res->id = id_task; + res->err_type = type; + res->err_msg = error; + + queue_results.send(std::move(res)); + } + + void send_partial_response(server_slot & slot, const completion_token_output & tkn) { + auto res = std::make_unique(); + + res->id = slot.id_task; + res->index = slot.index; + res->content = tkn.text_to_send; + res->tokens = { tkn.tok }; + + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->post_sampling_probs = slot.params.post_sampling_probs; + + res->verbose = slot.params.verbose; + res->oaicompat = slot.params.oaicompat; + res->oaicompat_model = slot.params.oaicompat_model; + res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; + + // populate res.probs_output + if (slot.params.sampling.n_probs > 0) { + res->prob_output = tkn; // copy the token probs + } + + // populate timings if this is final response or timings_per_token is enabled + if (slot.stop != STOP_TYPE_NONE || slot.params.timings_per_token) { + res->timings = slot.get_timings(); + } + + queue_results.send(std::move(res)); + } + + void send_final_response(server_slot & slot) { + auto res = std::make_unique(); + res->id = slot.id_task; + res->id_slot = slot.id; + + res->index = slot.index; + res->content = std::move(slot.generated_text); + res->tokens = std::move(slot.generated_tokens); + res->timings = slot.get_timings(); + res->prompt = common_detokenize(ctx, slot.prompt_tokens, true); + res->response_fields = std::move(slot.params.response_fields); + + res->truncated = slot.truncated; + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->n_tokens_cached = slot.n_past; + res->has_new_line = slot.has_new_line; + res->stopping_word = slot.stopping_word; + res->stop = slot.stop; + res->post_sampling_probs = slot.params.post_sampling_probs; + + res->verbose = slot.params.verbose; + res->stream = slot.params.stream; + res->oaicompat = slot.params.oaicompat; + res->oaicompat_model = slot.params.oaicompat_model; + res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id; + res->oaicompat_chat_format = slot.params.oaicompat_chat_format; + // populate res.probs_output + if (slot.params.sampling.n_probs > 0) { + if (!slot.params.stream && slot.stop == STOP_TYPE_WORD) { + const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); + + size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); + res->probs_output = std::vector( + slot.generated_token_probs.begin(), + slot.generated_token_probs.end() - safe_offset); + } else { + res->probs_output = std::vector( + slot.generated_token_probs.begin(), + slot.generated_token_probs.end()); + } + } + + res->generation_params = slot.params; // copy the parameters + + queue_results.send(std::move(res)); + } + + void send_embedding(const server_slot & slot, const llama_batch & batch) { + auto res = std::make_unique(); + res->id = slot.id_task; + res->index = slot.index; + res->n_tokens = slot.n_prompt_tokens; + res->oaicompat = slot.params.oaicompat; + + const int n_embd = llama_model_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) { continue; } - if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) { - LOG_TEE("Error processing the given image"); - return false; + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); } + if (embd == NULL) { + SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]); - img.request_encode_image = false; - } - - return slot.images.size() > 0; - } - - void send_error(task_server& task, const std::string &error) - { - LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); - task_result res; - res.id = task.id; - res.multitask_id = task.multitask_id; - res.stop = false; - res.error = true; - res.result_json = { { "content", error } }; - queue_results.send(res); - } - - json get_formated_generation(server_slot &slot) - { - const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); - const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && - eos_bias->second < 0.0f && std::isinf(eos_bias->second); - std::vector samplers_sequence; - for (const auto &sampler_type : slot.sparams.samplers_sequence) - { - samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); - } - - return json { - {"n_ctx", slot.n_ctx}, - {"n_predict", slot.n_predict}, - {"model", params.model_alias}, - {"seed", slot.params.seed}, - {"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}, - {"tfs_z", slot.sparams.tfs_z}, - {"typical_p", slot.sparams.typical_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}, - {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens}, - {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens}, - {"mirostat", slot.sparams.mirostat}, - {"mirostat_tau", slot.sparams.mirostat_tau}, - {"mirostat_eta", slot.sparams.mirostat_eta}, - {"penalize_nl", slot.sparams.penalize_nl}, - {"stop", slot.params.antiprompt}, - {"n_predict", slot.params.n_predict}, - {"n_keep", params.n_keep}, - {"ignore_eos", 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}, - {"samplers", samplers_sequence} - }; - } - - void send_partial_response(server_slot &slot, completion_token_output tkn) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = false; - - res.result_json = json - { - {"content", tkn.text_to_send}, - {"stop", false}, - {"slot_id", slot.id}, - {"multimodal", multimodal} - }; - - if (slot.sparams.n_probs > 0) - { - std::vector probs_output = {}; - const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); - size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); - size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) - { - probs_output = std::vector(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); + res->embedding.push_back(std::vector(n_embd, 0.0f)); + continue; } - slot.n_sent_token_probs = probs_stop_pos; - res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); - } - if (slot.oaicompat) - { - res.result_json["oaicompat_token_ctr"] = slot.n_decoded; - res.result_json["model"] = slot.oaicompat_model; - } - - queue_results.send(res); - } - - void send_final_response(server_slot &slot) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = true; - - res.result_json = json - { - {"content", !slot.params.stream ? slot.generated_text : ""}, - {"slot_id", slot.id}, - {"stop", true}, - {"model", params.model_alias}, - {"tokens_predicted", slot.n_decoded}, - {"tokens_evaluated", slot.n_prompt_tokens}, - {"generation_settings", get_formated_generation(slot)}, - {"prompt", slot.prompt}, - {"truncated", slot.truncated}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - {"tokens_cached", slot.n_past}, - {"timings", slot.get_formated_timings()} - }; - - if (slot.sparams.n_probs > 0) - { - std::vector probs = {}; - if (!slot.params.stream && slot.stopped_word) - { - const std::vector stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); - probs = std::vector(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size()); + // normalize only when there is pooling + // TODO: configurable + if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) { + common_embd_normalize(embd, embd_res.data(), n_embd, 2); + res->embedding.push_back(embd_res); + } else { + res->embedding.push_back({ embd, embd + n_embd }); } - else - { - probs = std::vector( - slot.generated_token_probs.begin(), - slot.generated_token_probs.end()); + } + + SLT_DBG(slot, "%s", "sending embeddings\n"); + + queue_results.send(std::move(res)); + } + + void send_rerank(const server_slot & slot, const llama_batch & batch) { + auto res = std::make_unique(); + res->id = slot.id_task; + res->index = slot.index; + res->n_tokens = slot.n_prompt_tokens; + + for (int i = 0; i < batch.n_tokens; ++i) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + continue; } - res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs); + + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + } + + if (embd == NULL) { + SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]); + + res->score = -1e6; + continue; + } + + res->score = embd[0]; } - if (slot.oaicompat) - { - res.result_json["oaicompat_token_ctr"] = slot.n_decoded; - res.result_json["model"] = slot.oaicompat_model; - } + SLT_DBG(slot, "sending rerank result, res.score = %f\n", res->score); - queue_results.send(res); + queue_results.send(std::move(res)); } - void send_embedding(server_slot &slot) - { - task_result res; - res.id = slot.task_id; - res.multitask_id = slot.multitask_id; - res.error = false; - res.stop = true; + // + // Functions to create new task(s) and receive result(s) + // - const int n_embd = llama_n_embd(model); - if (!params.embedding) - { - LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}}); - res.result_json = json - { - {"embedding", std::vector(n_embd, 0.0f)}, - }; + void cancel_tasks(const std::unordered_set & id_tasks) { + std::vector cancel_tasks; + cancel_tasks.reserve(id_tasks.size()); + for (const auto & id_task : id_tasks) { + SRV_WRN("cancel task, id_task = %d\n", id_task); + + server_task task(SERVER_TASK_TYPE_CANCEL); + task.id_target = id_task; + queue_results.remove_waiting_task_id(id_task); + cancel_tasks.push_back(task); } - else - { - const float *data = llama_get_embeddings(ctx); - std::vector embedding(data, data + n_embd); - res.result_json = json - { - {"embedding", embedding}, - }; - } - queue_results.send(res); + // push to beginning of the queue, so it has highest priority + queue_tasks.post(cancel_tasks, true); } - void request_completion(int task_id, json data, bool infill, bool embedding, int multitask_id) - { - task_server task; - task.id = task_id; - task.target_id = 0; - task.data = std::move(data); - task.infill_mode = infill; - task.embedding_mode = embedding; - task.type = TASK_TYPE_COMPLETION; - task.multitask_id = multitask_id; + // receive the results from task(s) + void receive_multi_results( + const std::unordered_set & id_tasks, + const std::function&)> & result_handler, + const std::function & error_handler, + const std::function & is_connection_closed) { + std::vector results(id_tasks.size()); + for (int i = 0; i < (int)id_tasks.size(); i++) { + server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS); - // when a completion task's prompt array is not a singleton, we split it into multiple requests - // otherwise, it's a single-prompt task, we actually queue it - // if there's numbers in the prompt array it will be treated as an array of tokens - if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) { - bool numbers = false; - for (const auto& e : task.data.at("prompt")) { - if (e.is_number()) { - numbers = true; + if (is_connection_closed()) { + cancel_tasks(id_tasks); + return; + } + + if (result == nullptr) { + i--; // retry + continue; + } + + if (result->is_error()) { + error_handler(result->to_json()); + cancel_tasks(id_tasks); + return; + } + + GGML_ASSERT( + dynamic_cast(result.get()) != nullptr + || dynamic_cast(result.get()) != nullptr + || dynamic_cast(result.get()) != nullptr + ); + const size_t idx = result->get_index(); + GGML_ASSERT(idx < results.size() && "index out of range"); + results[idx] = std::move(result); + } + result_handler(results); + } + + // receive the results from task(s), in stream mode + void receive_cmpl_results_stream( + const std::unordered_set & id_tasks, + const std::function & result_handler, + const std::function & error_handler, + const std::function & is_connection_closed) { + size_t n_finished = 0; + while (true) { + server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS); + + if (is_connection_closed()) { + cancel_tasks(id_tasks); + return; + } + + if (result == nullptr) { + continue; // retry + } + + if (result->is_error()) { + error_handler(result->to_json()); + cancel_tasks(id_tasks); + return; + } + + GGML_ASSERT( + dynamic_cast(result.get()) != nullptr + || dynamic_cast(result.get()) != nullptr + ); + if (!result_handler(result)) { + cancel_tasks(id_tasks); + break; + } + + if (result->is_stop()) { + if (++n_finished == id_tasks.size()) { + break; + } + } + } + } + + // + // Functions to process the task + // + + void process_single_task(server_task task) { + switch (task.type) { + case SERVER_TASK_TYPE_COMPLETION: + case SERVER_TASK_TYPE_INFILL: + case SERVER_TASK_TYPE_EMBEDDING: + case SERVER_TASK_TYPE_RERANK: + { + const int id_slot = task.id_selected_slot; + + 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 + SRV_DBG("no slot is available, defer task, id_task = %d\n", task.id); + queue_tasks.defer(task); + break; + } + if (slot->is_processing()) { + // if requested slot is unavailable, we defer this task for processing later + SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); + queue_tasks.defer(task); + break; + } + + if (!launch_slot_with_task(*slot, task)) { + SRV_ERR("failed to launch slot with task, id_task = %d\n", task.id); + break; + } + } break; + case SERVER_TASK_TYPE_CANCEL: + { + // release slot linked with the task id + for (auto & slot : slots) { + if (slot.id_task == task.id_target) { + slot.release(); + break; + } + } + } break; + case SERVER_TASK_TYPE_NEXT_RESPONSE: + { + // do nothing + } break; + case SERVER_TASK_TYPE_METRICS: + { + json slots_data = json::array(); + + int n_idle_slots = 0; + int n_processing_slots = 0; + + for (server_slot & slot : slots) { + json slot_data = slot.to_json(); + + if (slot.is_processing()) { + n_processing_slots++; + } else { + n_idle_slots++; + } + + slots_data.push_back(slot_data); + } + SRV_DBG("n_idle_slots = %d, n_processing_slots = %d\n", n_idle_slots, n_processing_slots); + + auto res = std::make_unique(); + res->id = task.id; + res->slots_data = std::move(slots_data); + res->n_idle_slots = n_idle_slots; + res->n_processing_slots = n_processing_slots; + res->n_tasks_deferred = queue_tasks.queue_tasks_deferred.size(); + res->t_start = metrics.t_start; + + res->kv_cache_tokens_count = llama_get_kv_cache_token_count(ctx); + res->kv_cache_used_cells = llama_get_kv_cache_used_cells(ctx); + + res->n_prompt_tokens_processed_total = metrics.n_prompt_tokens_processed_total; + res->t_prompt_processing_total = metrics.t_prompt_processing_total; + res->n_tokens_predicted_total = metrics.n_tokens_predicted_total; + res->t_tokens_generation_total = metrics.t_tokens_generation_total; + + res->n_prompt_tokens_processed = metrics.n_prompt_tokens_processed; + res->t_prompt_processing = metrics.t_prompt_processing; + res->n_tokens_predicted = metrics.n_tokens_predicted; + res->t_tokens_generation = metrics.t_tokens_generation; + + res->n_decode_total = metrics.n_decode_total; + res->n_busy_slots_total = metrics.n_busy_slots_total; + + if (task.metrics_reset_bucket) { + metrics.reset_bucket(); + } + queue_results.send(std::move(res)); + } break; + case SERVER_TASK_TYPE_SLOT_SAVE: + { + int id_slot = task.slot_action.slot_id; + server_slot * slot = get_slot_by_id(id_slot); + if (slot == nullptr) { + send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); + break; + } + if (slot->is_processing()) { + // if requested slot is unavailable, we defer this task for processing later + SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); + queue_tasks.defer(task); + break; + } + + const size_t token_count = slot->cache_tokens.size(); + const int64_t t_start = ggml_time_us(); + + std::string filename = task.slot_action.filename; + std::string filepath = task.slot_action.filepath; + + 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; + + auto res = std::make_unique(); + res->id = task.id; + res->id_slot = id_slot; + res->filename = filename; + res->is_save = true; + res->n_tokens = token_count; + res->n_bytes = nwrite; + res->t_ms = t_save_ms; + queue_results.send(std::move(res)); + } break; + case SERVER_TASK_TYPE_SLOT_RESTORE: + { + int id_slot = task.slot_action.slot_id; + server_slot * slot = get_slot_by_id(id_slot); + if (slot == nullptr) { + send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); + break; + } + if (slot->is_processing()) { + // if requested slot is unavailable, we defer this task for processing later + SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); + queue_tasks.defer(task); + break; + } + + const int64_t t_start = ggml_time_us(); + + std::string filename = task.slot_action.filename; + std::string filepath = task.slot_action.filepath; + + 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, 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); + break; + } + slot->cache_tokens.resize(token_count); + + const int64_t t_end = ggml_time_us(); + const double t_restore_ms = (t_end - t_start) / 1000.0; + + auto res = std::make_unique(); + res->id = task.id; + res->id_slot = id_slot; + res->filename = filename; + res->is_save = false; + res->n_tokens = token_count; + res->n_bytes = nread; + res->t_ms = t_restore_ms; + queue_results.send(std::move(res)); + } break; + case SERVER_TASK_TYPE_SLOT_ERASE: + { + int id_slot = task.slot_action.slot_id; + server_slot * slot = get_slot_by_id(id_slot); + if (slot == nullptr) { + send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); + break; + } + if (slot->is_processing()) { + // if requested slot is unavailable, we defer this task for processing later + SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); + queue_tasks.defer(task); + break; + } + + // Erase token cache + const size_t n_erased = slot->cache_tokens.size(); + llama_kv_cache_seq_rm(ctx, slot->id, -1, -1); + slot->cache_tokens.clear(); + + auto res = std::make_unique(); + res->id = task.id; + res->id_slot = id_slot; + res->n_erased = n_erased; + queue_results.send(std::move(res)); + } break; + case SERVER_TASK_TYPE_SET_LORA: + { + params_base.lora_adapters = std::move(task.set_lora); + auto res = std::make_unique(); + res->id = task.id; + queue_results.send(std::move(res)); + } break; + } + } + + void update_slots() { + // check if all slots are idle + { + bool all_idle = true; + + for (auto & slot : slots) { + if (slot.is_processing()) { + all_idle = false; break; } } - // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers, - // it will completely stall the server. I don't know where the bug for this is. - // - // if there are numbers, it needs to be treated like a single prompt, - // queue_tasks handles a mix of strings and numbers just fine. - if (numbers) { - queue_tasks.post(task); - } else { - split_multiprompt_task(task_id, task); - } - } else { - // an empty prompt can make slot become buggy - if (task.data.contains("prompt") && task.data["prompt"].is_string() && task.data["prompt"].get().empty()) { - task.data["prompt"] = " "; // add a space so that we have one token + if (all_idle) { + SRV_INF("%s", "all slots are idle\n"); + if (clean_kv_cache) { + kv_cache_clear(); + } + + return; } + } + + { + SRV_DBG("%s", "posting NEXT_RESPONSE\n"); + + server_task task(SERVER_TASK_TYPE_NEXT_RESPONSE); + task.id = queue_tasks.get_new_id(); queue_tasks.post(task); } - } - // for multiple images processing - bool ingest_images(server_slot &slot, int n_batch) - { - int image_idx = 0; - - while (image_idx < (int) slot.images.size()) - { - slot_image &img = slot.images[image_idx]; - - // process prefix prompt - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) - { - const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); - llama_batch batch_view = { - n_tokens, - batch.token + i, - nullptr, - batch.pos + i, - batch.n_seq_id + i, - batch.seq_id + i, - batch.logits + i, - 0, 0, 0, // unused - }; - if (llama_decode(ctx, batch_view)) - { - LOG_TEE("%s : failed to eval\n", __func__); - return false; - } - } - - // process image with llm - for (int i = 0; i < img.image_tokens; i += n_batch) - { - int n_eval = img.image_tokens - i; - if (n_eval > n_batch) - { - n_eval = n_batch; + // apply context-shift if needed + // TODO: simplify and improve + for (server_slot & slot : slots) { + if (slot.is_processing() && slot.n_past + 1 >= slot.n_ctx) { + 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(); + send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER); + continue; } - const int n_embd = llama_n_embd(model); - llama_batch batch_img = { - n_eval, - nullptr, - (img.image_embedding + i * n_embd), - nullptr, - nullptr, - nullptr, - nullptr, - slot.n_past, - 1, 0 - }; - if (llama_decode(ctx, batch_img)) - { - LOG_TEE("%s : failed to eval image\n", __func__); - return false; - } - slot.n_past += n_eval; - } - image_idx++; + // Shift context + const int n_keep = slot.params.n_keep + add_bos_token; + const int n_left = slot.n_past - n_keep; + const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2); - llama_batch_clear(batch); + SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard); - // append prefix of next image - const auto json_prompt = (image_idx >= (int) slot.images.size()) ? - slot.params.input_suffix : // no more images, then process suffix prompt - (json)(slot.images[image_idx].prefix_prompt); + 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); - std::vector append_tokens = tokenize(json_prompt, false); // has next image - for (int i = 0; i < (int) append_tokens.size(); ++i) - { - llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true); - slot.n_past += 1; - } - } - - return true; - } - - void request_cancel(int task_id) - { - task_server task; - task.type = TASK_TYPE_CANCEL; - task.target_id = task_id; - queue_tasks.post(task); - } - - void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) - { - int prompt_count = multiprompt_task.data.at("prompt").size(); - if (prompt_count <= 1) { - send_error(multiprompt_task, "error while handling multiple prompts"); - return; - } - - // generate all the ID for subtask - std::vector subtask_ids(prompt_count); - for (int i = 0; i < prompt_count; i++) - { - subtask_ids[i] = queue_tasks.get_new_id(); - } - - // queue up the multitask so we can track its subtask progression - queue_tasks.add_multitask(multitask_id, subtask_ids); - - // add subtasks - for (int i = 0; i < prompt_count; i++) - { - json subtask_data = multiprompt_task.data; - subtask_data["prompt"] = subtask_data["prompt"][i]; - - // subtasks inherit everything else (infill mode, embedding mode, etc.) - request_completion(subtask_ids[i], subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id); - } - } - - void process_single_task(task_server& task) - { - switch (task.type) - { - case TASK_TYPE_COMPLETION: { - server_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); - if (slot == nullptr) - { - // if no slot is available, we defer this task for processing later - LOG_VERBOSE("no slot is available", {{"task_id", task.id}}); - queue_tasks.defer(task); - break; - } - - if (task.data.contains("system_prompt")) - { - if (!all_slots_are_idle) { - send_error(task, "system prompt can only be updated when all slots are idle"); - break; - } - system_prompt_process(task.data["system_prompt"]); - - // reset cache_tokens for all slots - for (server_slot &slot : slots) - { - slot.cache_tokens.clear(); - slot.n_past = 0; - slot.n_past_se = 0; - } - } - - slot->reset(); - - slot->infill = task.infill_mode; - slot->embedding = task.embedding_mode; - slot->task_id = task.id; - slot->multitask_id = task.multitask_id; - - if (!launch_slot_with_data(slot, task.data)) - { - // send error result - send_error(task, "internal_error"); - break; - } - } break; - case TASK_TYPE_CANCEL: { // release slot linked with the task id - for (auto & slot : slots) - { - if (slot.task_id == task.target_id) - { - slot.release(); - break; - } - } - } break; - case TASK_TYPE_NEXT_RESPONSE: { - // do nothing - } break; - case TASK_TYPE_METRICS: { - json slots_data = json::array(); - int n_idle_slots = 0; - int n_processing_slots = 0; - - for (server_slot &slot: slots) { - json slot_data = get_formated_generation(slot); - slot_data["id"] = slot.id; - slot_data["task_id"] = slot.task_id; - slot_data["state"] = slot.state; - slot_data["prompt"] = slot.prompt; - slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, - {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, - }; - if (slot_data["state"] == IDLE) { - n_idle_slots++; - } else { - n_processing_slots++; - } - slots_data.push_back(slot_data); - } - LOG_INFO("slot data", { - {"task_id", task.id}, - {"n_idle_slots", n_idle_slots}, - {"n_processing_slots", n_processing_slots} - }); - LOG_VERBOSE("slot data", { - {"task_id", task.id}, - {"n_idle_slots", n_idle_slots}, - {"n_processing_slots", n_processing_slots}, - {"slots", slots_data} - }); - task_result res; - res.id = task.id; - res.multitask_id = task.multitask_id; - res.stop = true; - res.error = false; - res.result_json = { - { "idle", n_idle_slots }, - { "processing", n_processing_slots }, - { "deferred", queue_tasks.queue_tasks_deferred.size() }, - - { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total}, - { "n_tokens_predicted_total", metrics.n_tokens_predicted_total}, - - { "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed}, - { "t_prompt_processing", metrics.t_prompt_processing}, - { "n_tokens_predicted", metrics.n_tokens_predicted}, - { "t_tokens_generation", metrics.t_tokens_generation}, - - { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, - { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, - - { "slots", slots_data }, - }; - metrics.reset_bucket(); - queue_results.send(res); - } break; - } - } - - void on_finish_multitask(task_multi& multitask) - { - // all subtasks done == multitask is done - task_result result; - result.id = multitask.id; - result.stop = true; - result.error = false; - - // collect json results into one json result - std::vector result_jsons; - for (auto& subres : multitask.results) - { - result_jsons.push_back(subres.result_json); - result.error = result.error && subres.error; - } - result.result_json = json{ { "results", result_jsons } }; - queue_results.send(result); - } - - bool update_slots() { - if (system_need_update) - { - LOG_INFO("updating system prompt", {}); - system_prompt_update(); - } - - llama_batch_clear(batch); - - if (all_slots_are_idle) - { - if (system_prompt.empty() && clean_kv_cache) - { - LOG_INFO("all slots are idle and system prompt is empty, clear the KV cache", {}); - kv_cache_clear(); - } - return true; - } - - LOG_VERBOSE("posting NEXT_RESPONSE", {}); - task_server task; - task.type = TASK_TYPE_NEXT_RESPONSE; - task.target_id = -1; - queue_tasks.post(task); - - for (server_slot &slot : slots) - { - if (slot.ga_n == 1) - { - if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx) - { - // Shift context - const int n_keep = slot.params.n_keep + add_bos_token; - const int n_left = (int) system_tokens.size() + slot.n_past - n_keep; - const int n_discard = n_left / 2; - - LOG_INFO("slot context shift", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, - {"n_keep", n_keep}, - {"n_left", n_left}, - {"n_discard", n_discard}, - {"n_ctx", n_ctx}, - {"n_past", slot.n_past}, - {"n_system_tokens", system_tokens.size()}, - {"n_cache_tokens", slot.cache_tokens.size()} - }); - 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, system_tokens.size() + slot.n_past, -n_discard); - - for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) - { + if (slot.params.cache_prompt) { + for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; } slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); - - slot.n_past -= n_discard; - - slot.truncated = true; } + + slot.n_past -= n_discard; + + slot.truncated = true; } } - // decode any currently ongoing sequences - LOG_VERBOSE("decoding ongoing sequences", {}); - for (auto & slot : slots) - { - // release the slot - if (slot.command == RELEASE) - { - slot.state = IDLE; - slot.command = NONE; - slot.t_last_used = ggml_time_us(); + // start populating the batch for this iteration + common_batch_clear(batch); - LOG_INFO("slot released", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, - {"n_ctx", n_ctx}, - {"n_past", slot.n_past}, - {"n_system_tokens", system_tokens.size()}, - {"n_cache_tokens", slot.cache_tokens.size()}, - {"truncated", slot.truncated} - }); - queue_tasks.notify_slot_changed(); + // track if given slot can be batched with slots already in the batch + server_slot * slot_batched = nullptr; + auto accept_special_token = [&](server_slot & slot, llama_token token) { + return params_base.special || slot.params.sampling.preserved_tokens.find(token) != slot.params.sampling.preserved_tokens.end(); + }; + + // frist, add sampled tokens from any ongoing sequences + for (auto & slot : slots) { + if (slot.state != SLOT_STATE_GENERATING) { continue; } - if (slot.state == IDLE) - { + // check if we can batch this slot with the previous one + if (!slot_batched) { + slot_batched = &slot; + } else if (!slot_batched->can_batch_with(slot)) { continue; } slot.i_batch = batch.n_tokens; - const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; + common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true); - // TODO: we always have to take into account the "system_tokens" - // this is not great and needs to be improved somehow - llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); slot.n_past += 1; + + if (slot.params.cache_prompt) { + slot.cache_tokens.push_back(slot.sampled); + } + + SLT_DBG(slot, "slot decode token, n_ctx = %d, n_past = %d, n_cache_tokens = %d, truncated = %d\n", + slot.n_ctx, slot.n_past, (int) slot.cache_tokens.size(), slot.truncated); } // process in chunks of params.n_batch - int32_t n_batch = params.n_batch; + int32_t n_batch = llama_n_batch(ctx); + int32_t n_ubatch = llama_n_ubatch(ctx); - // assign workload to the slots - if (params.cont_batching || batch.n_tokens == 0) - { - for (auto & slot : slots) - { - const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get().empty()) || !slot.images.empty(); - - // empty prompt passed -> release the slot and send empty response - // note: infill mode allows empty prompt - if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill) - { - slot.release(); - slot.print_timings(); - send_final_response(slot); - continue; + // next, batch any pending prompts without exceeding n_batch + if (params_base.cont_batching || batch.n_tokens == 0) { + for (auto & slot : slots) { + // check if we can batch this slot with the previous one + if (slot.is_processing()) { + if (!slot_batched) { + slot_batched = &slot; + } else if (!slot_batched->can_batch_with(slot)) { + continue; + } } - // need process the prompt - if (slot.state == IDLE && slot.command == LOAD_PROMPT) - { - slot.state = PROCESSING; - slot.command = NONE; - std::vector prompt_tokens; - slot.t_start_process_prompt = ggml_time_us(); - slot.t_start_genereration = 0; + // this slot still has a prompt to be processed + if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { + auto & prompt_tokens = slot.prompt_tokens; - if (slot.infill) - { - bool suff_rm_leading_spc = true; - if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) - { - params.input_suffix.erase(0, 1); - suff_rm_leading_spc = false; - } - auto prefix_tokens = tokenize(slot.params.input_prefix, false); - auto suffix_tokens = tokenize(slot.params.input_suffix, false); - - const int space_token = 29871; // TODO: this should not be hardcoded - if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) { - suffix_tokens.erase(suffix_tokens.begin()); - } - - prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); - prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); - prefix_tokens.push_back(llama_token_middle(model)); - prompt_tokens = prefix_tokens; - } - else - { - prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt - } - - slot.n_prompt_tokens = prompt_tokens.size(); - - if (slot.params.n_keep < 0) - { - slot.params.n_keep = slot.n_prompt_tokens; - } - slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - - // if input prompt is too big, truncate it - if (slot.n_prompt_tokens >= slot.n_ctx) - { - const int n_left = slot.n_ctx - slot.params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - - std::vector new_tokens( - prompt_tokens.begin(), - prompt_tokens.begin() + slot.params.n_keep); - new_tokens.insert( - new_tokens.end(), - prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, - prompt_tokens.end()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - slot.truncated = true; - prompt_tokens = new_tokens; + // TODO: maybe move branch to outside of this loop in the future + if (slot.state == SLOT_STATE_STARTED) { + slot.t_start_process_prompt = ggml_time_us(); + slot.t_start_generation = 0; + slot.n_past = 0; slot.n_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); - } + slot.state = SLOT_STATE_PROCESSING_PROMPT; - if (!slot.params.cache_prompt) - { - llama_sampling_reset(slot.ctx_sampling); + SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens); - slot.n_past = 0; - slot.n_past_se = 0; - slot.ga_i = 0; - slot.n_prompt_tokens_processed = slot.n_prompt_tokens; - } - else - { - // push the prompt into the sampling context (do not apply grammar) - for (auto &token : prompt_tokens) - { - llama_sampling_accept(slot.ctx_sampling, ctx, token, false); + // print prompt tokens (for debugging) + if (1) { + // first 16 tokens (avoid flooding logs) + for (int i = 0; i < std::min(16, prompt_tokens.size()); i++) { + SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); + } + } else { + // all + for (int i = 0; i < (int) prompt_tokens.size(); i++) { + SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); + } } - slot.n_past = common_part(slot.cache_tokens, prompt_tokens); + // empty prompt passed -> release the slot and send empty response + if (prompt_tokens.empty()) { + SLT_WRN(slot, "%s", "empty prompt - releasing slot\n"); - // the last token of the cache is not in the KV cache until the next call to llama_decode - // (it was sampled, pushed into the "cache_tokens", but not yet put in the context) - if (slot.n_past > 0 && slot.n_past == (int32_t) slot.cache_tokens.size()) - { - slot.n_past -= 1; + slot.release(); + slot.print_timings(); + send_final_response(slot); + continue; } - slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past; + if (slot.is_non_causal()) { + if (slot.n_prompt_tokens > n_ubatch) { + slot.release(); + send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); + continue; + } - if (slot.ga_n != 1) - { - int ga_i = 0; - int32_t ga_n = slot.ga_n; - int32_t ga_w = slot.ga_w; - int32_t slot_npast = 0; - for (int k = 0; k < slot.n_past; ++k) - { - while (slot_npast >= ga_i + ga_w) { - const int bd = (ga_w/ga_n)*(ga_n - 1); - slot_npast -= bd; - ga_i += ga_w/ga_n; + if (slot.n_prompt_tokens > slot.n_ctx) { + slot.release(); + send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_SERVER); + continue; + } + } else { + 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 + if (slot.n_prompt_tokens >= slot.n_ctx) { + slot.release(); + send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_INVALID_REQUEST); + continue; } - slot_npast++; } - slot.n_past_se = slot_npast; - slot.ga_i = ga_i; - } + if (slot.params.n_keep < 0) { + slot.params.n_keep = slot.n_prompt_tokens; + } + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - LOG_INFO("slot progression", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "n_past", slot.n_past }, - { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } - }); - } + // if input prompt is too big, truncate it + if (slot.n_prompt_tokens >= slot.n_ctx) { + const int n_left = slot.n_ctx - slot.params.n_keep; - slot.cache_tokens = prompt_tokens; + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) - { - // we have to evaluate at least 1 token to generate logits. - LOG_INFO("we have to evaluate at least 1 token to generate logits", { - { "slot_id", slot.id }, - { "task_id", slot.task_id } - }); - slot.n_past--; - if (slot.ga_i > 0) - { - slot.n_past_se--; - } - } + llama_tokens new_tokens( + prompt_tokens.begin(), + prompt_tokens.begin() + slot.params.n_keep); - int p0 = (int) system_tokens.size() + slot.n_past; - LOG_INFO("kv cache rm [p0, end)", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "p0", p0 } - }); - llama_kv_cache_seq_rm(ctx, slot.id, p0, -1); + new_tokens.insert( + new_tokens.end(), + prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, + prompt_tokens.end()); - LOG_VERBOSE("prompt ingested", { - {"n_past", slot.n_past}, - {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, - {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, - }); + prompt_tokens = std::move(new_tokens); - const bool has_images = process_images(slot); + slot.truncated = true; + slot.n_prompt_tokens = prompt_tokens.size(); - // process the prefix of first image - std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; + SLT_WRN(slot, "input truncated, n_ctx = %d, n_keep = %d, n_left = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, n_left, slot.n_prompt_tokens); - int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; + GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); + } - int32_t ga_i = slot.ga_i; - int32_t ga_n = slot.ga_n; - int32_t ga_w = slot.ga_w; + if (slot.params.cache_prompt) { + // reuse any previously computed tokens that are common with the new prompt + slot.n_past = common_lcp(slot.cache_tokens, prompt_tokens); - for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) - { - if (slot.ga_n != 1) - { - while (slot_npast >= ga_i + ga_w) { - const int bd = (ga_w/ga_n)*(ga_n - 1); - slot_npast -= bd; - ga_i += ga_w/ga_n; + // reuse chunks from the cached prompt by shifting their KV cache in the new position + 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_base.n_cache_reuse, slot.n_past); + + while (head_c < slot.cache_tokens.size() && + head_p < prompt_tokens.size()) { + + size_t n_match = 0; + while (head_c + n_match < slot.cache_tokens.size() && + head_p + n_match < prompt_tokens.size() && + slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) { + + n_match++; + } + + 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()); + //} + + const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c; + + 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]; + slot.n_past++; + } + + head_c += n_match; + head_p += n_match; + } else { + head_c += 1; + } + } + + SLT_DBG(slot, "after context reuse, new slot.n_past = %d\n", slot.n_past); + } } } - llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); - slot_npast++; + + if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { + // we have to evaluate at least 1 token to generate logits. + SLT_WRN(slot, "need to evaluate at least 1 token to generate logits, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens); + + slot.n_past--; + } + + slot.n_prompt_tokens_processed = 0; } - if (has_images && !ingest_images(slot, n_batch)) - { - LOG_ERROR("failed processing images", { - {"slot_id", slot.id}, - {"task_id", slot.task_id}, - }); - // FIXME @phymbert: to be properly tested - // early returning without changing the slot state will block the slot for ever - // no one at the moment is checking the return value - return false; + // non-causal tasks require to fit the entire prompt in the physical batch + if (slot.is_non_causal()) { + // cannot fit the prompt in the current batch - will try next iter + if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { + continue; + } } - // extract the logits only for the last token - if (batch.n_tokens > 0) - { + // keep only the common part + 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); + + // there is no common part left + slot.n_past = 0; + } + + SLT_INF(slot, "kv cache rm [%d, end)\n", slot.n_past); + + // remove the non-common part from the cache + slot.cache_tokens.resize(slot.n_past); + + // add prompt tokens for processing in the current batch + while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { + // without pooling, we want to output the embeddings for all the tokens in the batch + const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE; + + common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, need_embd); + + if (slot.params.cache_prompt) { + slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); + } + + slot.n_prompt_tokens_processed++; + slot.n_past++; + } + + SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, batch.n_tokens, (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens); + + // entire prompt has been processed + if (slot.n_past == slot.n_prompt_tokens) { + slot.state = SLOT_STATE_DONE_PROMPT; + + GGML_ASSERT(batch.n_tokens > 0); + + common_sampler_reset(slot.smpl); + + // Process all prompt tokens through sampler system + for (int i = 0; i < slot.n_prompt_tokens; ++i) { + common_sampler_accept(slot.smpl, prompt_tokens[i], false); + } + + // extract the logits only for the last token batch.logits[batch.n_tokens - 1] = true; - } - slot.n_decoded = 0; - slot.i_batch = batch.n_tokens - 1; + slot.n_decoded = 0; + slot.i_batch = batch.n_tokens - 1; + + SLT_INF(slot, "prompt done, n_past = %d, n_tokens = %d\n", slot.n_past, batch.n_tokens); + } + } + + if (batch.n_tokens >= n_batch) { + break; } } } - if (batch.n_tokens == 0) - { - all_slots_are_idle = true; - return true; + if (batch.n_tokens == 0) { + SRV_WRN("%s", "no tokens to decode\n"); + return; } - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) - { - const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens); - for (auto & slot : slots) - { - if (slot.ga_n != 1) - { - // context extension via Self-Extend - while (slot.n_past_se >= slot.ga_i + slot.ga_w) - { - const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w; - const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1); - const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w; + if (slot_batched) { + // make sure we're in the right embedding mode + llama_set_embeddings(ctx, slot_batched->is_non_causal()); + // apply lora, only need to do it once per batch + common_set_adapter_lora(ctx, slot_batched->lora); + } - LOG_TEE("\n"); - LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd); - LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n); - LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); + // process the created batch of tokens + for (int32_t i = 0; i < batch.n_tokens; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); - llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n); - llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); - - slot.n_past_se -= bd; - - slot.ga_i += slot.ga_w / slot.ga_n; - - LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i); - } - slot.n_past_se += n_tokens; - } - } - - llama_batch batch_view = - { + llama_batch batch_view = { n_tokens, batch.token + i, nullptr, @@ -1920,802 +3150,220 @@ struct llama_server_context batch.n_seq_id + i, batch.seq_id + i, batch.logits + i, - 0, 0, 0, // unused }; const int ret = llama_decode(ctx, batch_view); + metrics.on_decoded(slots); - if (ret != 0) - { - if (n_batch == 1 || ret < 0) - { + if (ret != 0) { + if (n_batch == 1 || ret < 0) { // if you get here, it means the KV cache is full - try increasing it via the context size - LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); - return false; + SRV_ERR("failed to decode the batch: KV cache is full - try increasing it via the context size, i = %d, n_batch = %d, ret = %d\n", i, n_batch, ret); + for (auto & slot : slots) { + slot.release(); + send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size."); + } + break; // break loop of n_batch } - LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2); - // retry with half the batch size to try to find a free slot in the KV cache n_batch /= 2; i -= n_batch; - continue; + + SRV_WRN("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation, i = %d, n_batch = %d, ret = %d\n", i, n_batch, ret); + + continue; // continue loop of n_batch } - for (auto & slot : slots) - { - if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) - { - continue; + for (auto & slot : slots) { + if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) { + continue; // continue loop of slots } - // prompt evaluated for embedding - if (slot.embedding) - { - send_embedding(slot); - slot.release(); - slot.i_batch = -1; - continue; + if (slot.state == SLOT_STATE_DONE_PROMPT) { + if (slot.task_type == SERVER_TASK_TYPE_EMBEDDING) { + // prompt evaluated for embedding + send_embedding(slot, batch_view); + slot.release(); + slot.i_batch = -1; + continue; // continue loop of slots + } + + if (slot.task_type == SERVER_TASK_TYPE_RERANK) { + send_rerank(slot, batch_view); + slot.release(); + slot.i_batch = -1; + continue; // continue loop of slots + } + + // prompt evaluated for next-token prediction + slot.state = SLOT_STATE_GENERATING; + } else if (slot.state != SLOT_STATE_GENERATING) { + continue; // continue loop of slots } - completion_token_output result; - const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i); + const int tok_idx = slot.i_batch - i; - llama_sampling_accept(slot.ctx_sampling, ctx, id, true); + llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx); + + slot.i_batch = -1; + + common_sampler_accept(slot.smpl, id, true); slot.n_decoded += 1; - if (slot.n_decoded == 1) - { - slot.t_start_genereration = ggml_time_us(); - slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3; + + const int64_t t_current = ggml_time_us(); + + if (slot.n_decoded == 1) { + 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); } - llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; - result.tok = id; + slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3; - const int32_t n_probs = slot.sparams.n_probs; - if (slot.sparams.temp <= 0 && n_probs > 0) - { - // for llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &cur_p); + completion_token_output result; + result.tok = id; + result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok)); + result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs + + if (slot.params.sampling.n_probs > 0) { + populate_token_probs(slot, result, slot.params.post_sampling_probs, params_base.special, tok_idx); } - for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i) - { - result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p}); - } - - if (!process_token(result, slot)) - { + if (!process_token(result, slot)) { + // release slot because of stop condition slot.release(); 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]; + result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok)); + result.prob = 1.0f; // set later + + // TODO: set result.probs + + 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); } } - LOG_VERBOSE("slots updated", {}); - return true; + SRV_DBG("%s", "run slots completed\n"); + } + + json model_meta() const { + return json { + {"vocab_type", llama_vocab_type (vocab)}, + {"n_vocab", llama_vocab_n_tokens (vocab)}, + {"n_ctx_train", llama_model_n_ctx_train(model)}, + {"n_embd", llama_model_n_embd (model)}, + {"n_params", llama_model_n_params (model)}, + {"size", llama_model_size (model)}, + }; } }; -static void server_print_usage(const char *argv0, const gpt_params ¶ms, - const server_params &sparams) -{ - printf("usage: %s [options]\n", argv0); - printf("\n"); - printf("options:\n"); - printf(" -h, --help show this help message and exit\n"); - printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" --threads-http N number of threads in the http server pool to process requests (default: hardware concurrency)\n"); - printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - printf(" --rope-scaling {none,linear,yarn}\n"); - printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); - printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); - printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); - printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); - printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); - printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); - if (llama_supports_mlock()) - { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); - } - if (llama_supports_mmap()) - { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); - } - printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); - printf(" - distribute: spread execution evenly over all nodes\n"); - printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); - printf(" - numactl: use the CPU map provided my numactl\n"); - if (llama_supports_gpu_offload()) { - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); - } - printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: %s)\n", params.model.c_str()); - printf(" -a ALIAS, --alias ALIAS\n"); - printf(" set an alias for the model, will be added as `model` field in completion response\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); - printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); - printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); - printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); - printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); - printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); - printf(" -spf FNAME, --system-prompt-file FNAME\n"); - printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); - printf(" -ctk TYPE, --cache-type-k TYPE\n"); - printf(" KV cache data type for K (default: f16)\n"); - printf(" -ctv TYPE, --cache-type-v TYPE\n"); - printf(" KV cache data type for V (default: f16)\n"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); - printf(" --log-format log output format: json or text (default: json)\n"); - printf(" --log-disable disables logging to a file.\n"); - printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); - printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled"); - printf("\n"); - printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); - printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); - printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); - printf(" --chat-template JINJA_TEMPLATE\n"); - printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); - printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); - printf("\n"); -} - -static void server_params_parse(int argc, char **argv, server_params &sparams, - gpt_params ¶ms, llama_server_context& llama) -{ - gpt_params default_params; - server_params default_sparams; - std::string arg; - bool invalid_param = false; - - for (int i = 1; i < argc; i++) - { - arg = argv[i]; - if (arg == "--port") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - sparams.port = std::stoi(argv[i]); - } - else if (arg == "--host") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - sparams.hostname = argv[i]; - } - else if (arg == "--path") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - sparams.public_path = argv[i]; - } - else if (arg == "--api-key") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - sparams.api_keys.emplace_back(argv[i]); - } - else if (arg == "--api-key-file") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - std::ifstream key_file(argv[i]); - if (!key_file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::string key; - while (std::getline(key_file, key)) { - if (key.size() > 0) { - sparams.api_keys.push_back(key); - } - } - key_file.close(); - } - else if (arg == "--timeout" || arg == "-to") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - sparams.read_timeout = std::stoi(argv[i]); - sparams.write_timeout = std::stoi(argv[i]); - } - else if (arg == "-m" || arg == "--model") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.model = argv[i]; - } - else if (arg == "-a" || arg == "--alias") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.model_alias = argv[i]; - } - else if (arg == "-h" || arg == "--help") - { - server_print_usage(argv[0], default_params, default_sparams); - exit(0); - } - else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_ctx = std::stoi(argv[i]); - } - else if (arg == "--rope-scaling") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } - else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } - else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } - else { invalid_param = true; break; } - } - else if (arg == "--rope-freq-base") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.rope_freq_base = std::stof(argv[i]); - } - else if (arg == "--rope-freq-scale") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.rope_freq_scale = std::stof(argv[i]); - } - else if (arg == "--yarn-ext-factor") - { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_ext_factor = std::stof(argv[i]); - } - else if (arg == "--yarn-attn-factor") - { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_attn_factor = std::stof(argv[i]); - } - else if (arg == "--yarn-beta-fast") - { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_fast = std::stof(argv[i]); - } - else if (arg == "--yarn-beta-slow") - { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_slow = std::stof(argv[i]); - } - else if (arg == "--threads" || arg == "-t") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_threads = std::stoi(argv[i]); - } - else if (arg == "--grp-attn-n" || arg == "-gan") - { - if (++i >= argc) { - invalid_param = true; - break; - } - - params.grp_attn_n = std::stoi(argv[i]); - } - else if (arg == "--grp-attn-w" || arg == "-gaw") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - - params.grp_attn_w = std::stoi(argv[i]); - } - else if (arg == "--threads-batch" || arg == "-tb") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - } - else if (arg == "--threads-http") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - sparams.n_threads_http = std::stoi(argv[i]); - } - else if (arg == "-b" || arg == "--batch-size") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_batch = std::stoi(argv[i]); - params.n_batch = std::min(512, params.n_batch); - } - else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - if (llama_supports_gpu_offload()) { - params.n_gpu_layers = std::stoi(argv[i]); - } else { - LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " - "See main README.md for information on enabling GPU BLAS support", - {{"n_gpu_layers", params.n_gpu_layers}}); - } - } - else if (arg == "--split-mode" || arg == "-sm") - { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string arg_next = argv[i]; - if (arg_next == "none") - { - params.split_mode = LLAMA_SPLIT_MODE_NONE; - } - else if (arg_next == "layer") - { - params.split_mode = LLAMA_SPLIT_MODE_LAYER; - } - else if (arg_next == "row") - { - params.split_mode = LLAMA_SPLIT_MODE_ROW; - } - else { - invalid_param = true; - break; - } -#ifndef GGML_USE_CUBLAS - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUBLAS - } - else if (arg == "--tensor-split" || arg == "-ts") - { - if (++i >= argc) - { - invalid_param = true; - break; - } -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) - std::string arg_next = argv[i]; - - // split string by , and / - const std::regex regex{R"([,/]+)"}; - std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; - std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= llama_max_devices()); - - for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) - { - if (i_device < split_arg.size()) - { - params.tensor_split[i_device] = std::stof(split_arg[i_device]); - } - else - { - params.tensor_split[i_device] = 0.0f; - } - } -#else - LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); -#endif // GGML_USE_CUBLAS - } - else if (arg == "--main-gpu" || arg == "-mg") - { - if (++i >= argc) - { - invalid_param = true; - break; - } -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) - params.main_gpu = std::stoi(argv[i]); -#else - LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); -#endif - } - else if (arg == "--lora") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(argv[i], 1.0f); - params.use_mmap = false; - } - else if (arg == "--lora-scaled") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - const char * lora_adapter = argv[i]; - if (++i >= argc) - { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); - params.use_mmap = false; - } - else if (arg == "--lora-base") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.lora_base = argv[i]; - } - else if (arg == "-v" || arg == "--verbose") - { -#if SERVER_VERBOSE != 1 - LOG_WARNING("server.cpp is not built with verbose logging.", {}); -#else - server_verbose = true; -#endif - } - else if (arg == "--mlock") - { - params.use_mlock = true; - } - else if (arg == "--no-mmap") - { - params.use_mmap = false; - } - else if (arg == "--numa") { - if (++i >= argc) { - invalid_param = true; - 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; } - } - } - else if (arg == "--embedding") - { - params.embedding = true; - } - else if (arg == "-cb" || arg == "--cont-batching") - { - params.cont_batching = true; - } - else if (arg == "-np" || arg == "--parallel") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_parallel = std::stoi(argv[i]); - } else if (arg == "-n" || arg == "--n-predict") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_predict = std::stoi(argv[i]); - } else if (arg == "-spf" || arg == "--system-prompt-file") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::string systm_content; - std::copy( - std::istreambuf_iterator(file), - std::istreambuf_iterator(), - std::back_inserter(systm_content) - ); - llama.system_prompt_process(json::parse(systm_content)); - } - else if (arg == "-ctk" || arg == "--cache-type-k") { - params.cache_type_k = argv[++i]; - } - else if (arg == "-ctv" || arg == "--cache-type-v") { - params.cache_type_v = argv[++i]; - } - else if(arg == "--mmproj") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.mmproj = argv[i]; - } - else if (arg == "--log-format") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - if (std::strcmp(argv[i], "json") == 0) - { - server_log_json = true; - } - else if (std::strcmp(argv[i], "text") == 0) - { - server_log_json = false; - } - else - { - invalid_param = true; - break; - } - } - else if (arg == "--log-disable") - { - log_set_target(stdout); - LOG_INFO("logging to file is disabled.", {}); - } - else if (arg == "--slots-endpoint-disable") - { - sparams.slots_endpoint = false; - } - else if (arg == "--metrics") - { - sparams.metrics_endpoint = true; - } - else if (arg == "--chat-template") - { - if (++i >= argc) - { - invalid_param = true; - break; - } - if (!verify_custom_template(argv[i])) { - fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]); - fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n"); - invalid_param = true; - break; - } - sparams.chat_template = argv[i]; - } - else if (arg == "--override-kv") - { - if (++i >= argc) { - invalid_param = true; - break; - } - char * sep = strchr(argv[i], '='); - if (sep == nullptr || sep - argv[i] >= 128) { - fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - struct llama_model_kv_override kvo; - std::strncpy(kvo.key, argv[i], sep - argv[i]); - kvo.key[sep - argv[i]] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.int_value = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.float_value = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.bool_value = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.bool_value = false; - } else { - fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - } else { - fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - params.kv_overrides.push_back(kvo); - } - else - { - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - server_print_usage(argv[0], default_params, default_sparams); - exit(1); - } - } - if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(); - params.kv_overrides.back().key[0] = 0; - } - - if (invalid_param) - { - fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); - server_print_usage(argv[0], default_params, default_sparams); - exit(1); - } -} - -/* llama.cpp completion api semantics */ -static json format_partial_response( - llama_server_context &llama, server_slot *slot, const std::string &content, const std::vector &probs -) { - json res = json - { - {"content", content }, - {"stop", false}, - {"slot_id", slot->id }, - {"multimodal", llama.multimodal } - }; - - if (slot->sparams.n_probs > 0) - { - res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); - } - - return res; -} - -static json format_tokenizer_response(const std::vector &tokens) -{ - return json { - {"tokens", tokens} - }; -} - -static json format_detokenized_response(std::string content) -{ - return json { - {"content", content} - }; -} - - -static void log_server_request(const httplib::Request &req, const httplib::Response &res) -{ +static void log_server_request(const httplib::Request & req, const httplib::Response & res) { // skip GH copilot requests when using default port - if (req.path == "/v1/health" || req.path == "/v1/completions") - { + if (req.path == "/v1/health" || req.path == "/v1/completions") { return; } - LOG_INFO("request", { - {"remote_addr", req.remote_addr}, - {"remote_port", req.remote_port}, - {"status", res.status}, - {"method", req.method}, - {"path", req.path}, - {"params", req.params}, - }); + // reminder: this function is not covered by httplib's exception handler; if someone does more complicated stuff, think about wrapping it in try-catch - LOG_VERBOSE("request", { - {"request", req.body}, - {"response", res.body}, - }); -} + SRV_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status); -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, server_slot *slot) -{ - auto & gtps = slot->generated_token_probs; - auto translator = token_translator{llama.ctx}; - auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); }; - const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen); - if (slot->generated_text.capacity() < slot->generated_text.size() + len) - { - slot->generated_text.reserve(slot->generated_text.size() + len); - } - for (const completion_token_output & cto : gtps) - { - slot->generated_text += translator(cto); - } + SRV_DBG("request: %s\n", req.body.c_str()); + SRV_DBG("response: %s\n", res.body.c_str()); } std::function shutdown_handler; std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; + inline void signal_handler(int signal) { if (is_terminating.test_and_set()) { // in case it hangs, we can force terminate the server by hitting Ctrl+C twice @@ -2723,787 +3371,1148 @@ inline void signal_handler(int signal) { fprintf(stderr, "Received second interrupt, terminating immediately.\n"); exit(1); } + shutdown_handler(signal); } -int main(int argc, char **argv) -{ -#if SERVER_VERBOSE != 1 - log_disable(); -#endif +int main(int argc, char ** argv) { // own arguments required by this example - gpt_params params; - server_params sparams; + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) { + return 1; + } + + common_init(); // struct that contains llama context and inference - llama_server_context llama; - - server_params_parse(argc, argv, sparams, params, llama); - - if (params.model_alias == "unknown") - { - params.model_alias = params.model; - } + server_context ctx_server; llama_backend_init(); llama_numa_init(params.numa); - LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER}, - {"commit", LLAMA_COMMIT}}); + LOG_INF("system info: n_threads = %d, n_threads_batch = %d, total_threads = %d\n", params.cpuparams.n_threads, params.cpuparams_batch.n_threads, std::thread::hardware_concurrency()); + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + LOG_INF("\n"); - LOG_INFO("system info", { - {"n_threads", params.n_threads}, - {"n_threads_batch", params.n_threads_batch}, - {"total_threads", std::thread::hardware_concurrency()}, - {"system_info", llama_print_system_info()}, - }); - - httplib::Server svr; + std::unique_ptr svr; +#ifdef CPPHTTPLIB_OPENSSL_SUPPORT + if (params.ssl_file_key != "" && params.ssl_file_cert != "") { + LOG_INF("Running with SSL: key = %s, cert = %s\n", params.ssl_file_key.c_str(), params.ssl_file_cert.c_str()); + svr.reset( + new httplib::SSLServer(params.ssl_file_cert.c_str(), params.ssl_file_key.c_str()) + ); + } else { + LOG_INF("Running without SSL\n"); + svr.reset(new httplib::Server()); + } +#else + if (params.ssl_file_key != "" && params.ssl_file_cert != "") { + LOG_ERR("Server is built without SSL support\n"); + return 1; + } + svr.reset(new httplib::Server()); +#endif std::atomic state{SERVER_STATE_LOADING_MODEL}; - svr.set_default_headers({{"Server", "llama.cpp"}}); + svr->set_default_headers({{"Server", "llama.cpp"}}); + svr->set_logger(log_server_request); - // CORS preflight - svr.Options(R"(.*)", [](const httplib::Request &req, httplib::Response &res) { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - res.set_header("Access-Control-Allow-Credentials", "true"); - res.set_header("Access-Control-Allow-Methods", "POST"); - res.set_header("Access-Control-Allow-Headers", "*"); - }); + auto res_error = [](httplib::Response & res, const json & error_data) { + json final_response {{"error", error_data}}; + res.set_content(safe_json_to_str(final_response), MIMETYPE_JSON); + res.status = json_value(error_data, "code", 500); + }; - svr.Get("/health", [&](const httplib::Request& req, httplib::Response& res) { - server_state current_state = state.load(); - switch(current_state) { - case SERVER_STATE_READY: { - // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; + auto res_ok = [](httplib::Response & res, const json & data) { + res.set_content(safe_json_to_str(data), MIMETYPE_JSON); + res.status = 200; + }; - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); + svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, const std::exception_ptr & ep) { + std::string message; + try { + std::rethrow_exception(ep); + } catch (const std::exception & e) { + message = e.what(); + } catch (...) { + message = "Unknown Exception"; + } - // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); - - int n_idle_slots = result.result_json["idle"]; - int n_processing_slots = result.result_json["processing"]; - - json health = { - {"status", "ok"}, - {"slots_idle", n_idle_slots}, - {"slots_processing", n_processing_slots}}; - res.status = 200; // HTTP OK - if (sparams.slots_endpoint && req.has_param("include_slots")) { - health["slots"] = result.result_json["slots"]; - } - - if (n_idle_slots == 0) { - health["status"] = "no slot available"; - if (req.has_param("fail_on_no_slot")) { - res.status = 503; // HTTP Service Unavailable - } - } - res.set_content(health.dump(), "application/json"); - break; - } - case SERVER_STATE_LOADING_MODEL: - res.set_content(R"({"status": "loading model"})", "application/json"); - res.status = 503; // HTTP Service Unavailable - break; - case SERVER_STATE_ERROR: - res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json"); - res.status = 500; // HTTP Internal Server Error - break; + try { + json formatted_error = format_error_response(message, ERROR_TYPE_SERVER); + LOG_WRN("got exception: %s\n", formatted_error.dump().c_str()); + res_error(res, formatted_error); + } catch (const std::exception & e) { + LOG_ERR("got another exception: %s | while hanlding exception: %s\n", e.what(), message.c_str()); } }); - if (sparams.slots_endpoint) { - svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) { - // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; - - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); - - // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); - - res.set_content(result.result_json["slots"].dump(), "application/json"); - res.status = 200; // HTTP OK - }); - } - - if (sparams.metrics_endpoint) { - svr.Get("/metrics", [&](const httplib::Request&, httplib::Response& res) { - // request slots data using task queue - task_server task; - task.id = llama.queue_tasks.get_new_id(); - task.type = TASK_TYPE_METRICS; - task.target_id = -1; - - llama.queue_results.add_waiting_task_id(task.id); - llama.queue_tasks.post(task); - - // get the result - task_result result = llama.queue_results.recv(task.id); - llama.queue_results.remove_waiting_task_id(task.id); - - json data = result.result_json; - - uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; - uint64_t t_prompt_processing = data["t_prompt_processing"]; - - uint64_t n_tokens_predicted = data["n_tokens_predicted"]; - uint64_t t_tokens_generation = data["t_tokens_generation"]; - - int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; - - // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names - json all_metrics_def = json { - {"counter", {{ - {"name", "prompt_tokens_total"}, - {"help", "Number of prompt tokens processed."}, - {"value", data["n_prompt_tokens_processed_total"]} - }, { - {"name", "tokens_predicted_total"}, - {"help", "Number of generation tokens processed."}, - {"value", data["n_tokens_predicted_total"]} - }}}, - {"gauge", {{ - {"name", "prompt_tokens_seconds"}, - {"help", "Average prompt throughput in tokens/s."}, - {"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0} - },{ - {"name", "predicted_tokens_seconds"}, - {"help", "Average generation throughput in tokens/s."}, - {"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0} - },{ - {"name", "kv_cache_usage_ratio"}, - {"help", "KV-cache usage. 1 means 100 percent usage."}, - {"value", 1. * kv_cache_used_cells / params.n_ctx} - },{ - {"name", "kv_cache_tokens"}, - {"help", "KV-cache tokens."}, - {"value", data["kv_cache_tokens_count"]} - },{ - {"name", "requests_processing"}, - {"help", "Number of request processing."}, - {"value", data["processing"]} - },{ - {"name", "requests_deferred"}, - {"help", "Number of request deferred."}, - {"value", data["deferred"]} - }}} - }; - - std::stringstream prometheus; - for (const auto& el : all_metrics_def.items()) { - const auto& type = el.key(); - const auto& metrics_def = el.value(); - for (const auto& metric_def : metrics_def) { - std::string name = metric_def["name"]; - std::string help = metric_def["help"]; - prometheus << "# HELP llamacpp:" << name << " " << help << "\n" - << "# TYPE llamacpp:" << name << " " << type << "\n" - << "llamacpp:" << name << " " << metric_def["value"] << "\n"; - } - } - - res.set_content(prometheus.str(), "text/plain; version=0.0.4"); - res.status = 200; // HTTP OK - }); - } - - svr.set_logger(log_server_request); - - svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep) - { - const char fmt[] = "500 Internal Server Error\n%s"; - char buf[BUFSIZ]; - try - { - std::rethrow_exception(std::move(ep)); - } - catch (std::exception &e) - { - snprintf(buf, sizeof(buf), fmt, e.what()); - } - catch (...) - { - snprintf(buf, sizeof(buf), fmt, "Unknown Exception"); - } - res.set_content(buf, "text/plain; charset=utf-8"); - res.status = 500; - }); - - svr.set_error_handler([](const httplib::Request &, httplib::Response &res) - { - if (res.status == 401) - { - res.set_content("Unauthorized", "text/plain; charset=utf-8"); - } - if (res.status == 400) - { - res.set_content("Invalid request", "text/plain; charset=utf-8"); - } - else if (res.status == 404) - { - res.set_content("File Not Found", "text/plain; charset=utf-8"); - res.status = 404; - } - }); + svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) { + if (res.status == 404) { + res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND)); + } + // for other error codes, we skip processing here because it's already done by res_error() + }); // set timeouts and change hostname and port - svr.set_read_timeout (sparams.read_timeout); - svr.set_write_timeout(sparams.write_timeout); - - if (!svr.bind_to_port(sparams.hostname, sparams.port)) - { - fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); - return 1; - } - - // Set the base directory for serving static files - svr.set_base_dir(sparams.public_path); + svr->set_read_timeout (params.timeout_read); + svr->set_write_timeout(params.timeout_write); std::unordered_map log_data; - log_data["hostname"] = sparams.hostname; - log_data["port"] = std::to_string(sparams.port); - if (sparams.api_keys.size() == 1) { - log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4); - } else if (sparams.api_keys.size() > 1) { - log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; + log_data["hostname"] = params.hostname; + log_data["port"] = std::to_string(params.port); + + if (params.api_keys.size() == 1) { + auto key = params.api_keys[0]; + log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0)); + } else if (params.api_keys.size() > 1) { + log_data["api_key"] = "api_key: " + std::to_string(params.api_keys.size()) + " keys loaded"; } - // load the model - if (!llama.load_model(params)) - { - state.store(SERVER_STATE_ERROR); - return 1; - } else { - llama.initialize(); - state.store(SERVER_STATE_READY); - LOG_INFO("model loaded", {}); - } + // Necessary similarity of prompt for slot selection + ctx_server.slot_prompt_similarity = params.slot_prompt_similarity; - if (sparams.chat_template.empty()) { // custom chat template is not supplied - // check if the template comes with the model is supported by us - llama.validate_model_chat_template(sparams); - } + // + // Middlewares + // + + auto middleware_validate_api_key = [¶ms, &res_error](const httplib::Request & req, httplib::Response & res) { + static const std::unordered_set public_endpoints = { + "/health", + "/models", + "/v1/models", + }; - // Middleware for API key validation - auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool { // If API key is not set, skip validation - if (sparams.api_keys.empty()) { + if (params.api_keys.empty()) { + return true; + } + + // If path is public or is static file, skip validation + if (public_endpoints.find(req.path) != public_endpoints.end() || req.path == "/") { return true; } // Check for API key in the header auto auth_header = req.get_header_value("Authorization"); + std::string prefix = "Bearer "; if (auth_header.substr(0, prefix.size()) == prefix) { std::string received_api_key = auth_header.substr(prefix.size()); - if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) { + if (std::find(params.api_keys.begin(), params.api_keys.end(), received_api_key) != params.api_keys.end()) { return true; // API key is valid } } // API key is invalid or not provided - res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8"); - res.status = 401; // Unauthorized + res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION)); - LOG_WARNING("Unauthorized: Invalid API Key", {}); + LOG_WRN("Unauthorized: Invalid API Key\n"); return false; }; - // this is only called if no index.html is found in the public --path - 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; - }); + auto middleware_server_state = [&res_error, &state](const httplib::Request & req, httplib::Response & res) { + server_state current_state = state.load(); + if (current_state == SERVER_STATE_LOADING_MODEL) { + auto tmp = string_split(req.path, '.'); + if (req.path == "/" || tmp.back() == "html") { + res.set_content(reinterpret_cast(loading_html), loading_html_len, "text/html; charset=utf-8"); + res.status = 503; + } else { + res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE)); + } + return false; + } + return true; + }; - // this is only called if no index.js is found in the public --path - svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res) - { - res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript; charset=utf-8"); - return false; - }); - - // this is only called if no index.html is found in the public --path - svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res) - { - res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript; charset=utf-8"); - return false; - }); - - // this is only called if no index.html is found in the public --path - svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res) - { - res.set_content(reinterpret_cast(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8"); - return false; - }); - - svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - json data = { - { "user_name", llama.name_user.c_str() }, - { "assistant_name", llama.name_assistant.c_str() }, - { "default_generation_settings", llama.default_generation_settings_for_props }, - { "total_slots", llama.params.n_parallel } - }; - res.set_content(data.dump(), "application/json; charset=utf-8"); - }); - - svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = json::parse(req.body); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); - } - else - { - res.status = 404; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) - { - while (true) - { - task_result result = llama.queue_results.recv(task_id); - if (!result.error) { - const std::string str = - "data: " + - result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.c_str(), str.size())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - if (result.stop) { - break; - } - } else { - const std::string str = - "error: " + - result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.c_str(), str.size())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - break; - } - } - - llama.queue_results.remove_waiting_task_id(task_id); - sink.done(); - return true; - }; - - auto on_complete = [task_id, &llama] (bool) - { - // cancel - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - }; - - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } - }); - - svr.Get("/v1/models", [¶ms](const httplib::Request& req, httplib::Response& res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - std::time_t t = std::time(0); - - json models = { - {"object", "list"}, - {"data", { - { - {"id", params.model_alias}, - {"object", "model"}, - {"created", t}, - {"owned_by", "llamacpp"} - }, - }} - }; - - res.set_content(models.dump(), "application/json; charset=utf-8"); - }); - - const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) - { + // 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 (!validate_api_key(req, res)) { + // 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; + } + if (!middleware_validate_api_key(req, res)) { + return httplib::Server::HandlerResponse::Handled; + } + return httplib::Server::HandlerResponse::Unhandled; + }); + + // + // Route handlers (or controllers) + // + + const auto handle_health = [&](const httplib::Request &, httplib::Response & res) { + // error and loading states are handled by middleware + json health = {{"status", "ok"}}; + res_ok(res, health); + }; + + const auto handle_slots = [&](const httplib::Request & req, httplib::Response & res) { + if (!params.endpoint_slots) { + res_error(res, format_error_response("This server does not support slots endpoint. Start it with `--slots`", ERROR_TYPE_NOT_SUPPORTED)); return; } - json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); + // request slots data using task queue + server_task task(SERVER_TASK_TYPE_METRICS); + task.id = ctx_server.queue_tasks.get_new_id(); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task, true); // high-priority task - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); + // get the result + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); - if (!result.error && result.stop) { - json oaicompat_result = format_final_response_oaicompat(data, result); + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } - res.set_content(oaicompat_result.dump(-1, ' ', false, - json::error_handler_t::replace), - "application/json; charset=utf-8"); - } else { - res.status = 500; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); + // TODO: get rid of this dynamic_cast + auto res_metrics = dynamic_cast(result.get()); + GGML_ASSERT(res_metrics != nullptr); + + // optionally return "fail_on_no_slot" error + if (req.has_param("fail_on_no_slot")) { + if (res_metrics->n_idle_slots == 0) { + res_error(res, format_error_response("no slot available", ERROR_TYPE_UNAVAILABLE)); + return; } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { - while (true) { - task_result llama_result = llama.queue_results.recv(task_id); - if (!llama_result.error) { - std::vector result_array = format_partial_response_oaicompat( llama_result); + } - for (auto it = result_array.begin(); it != result_array.end(); ++it) - { - if (!it->empty()) { - const std::string str = - "data: " + - it->dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", {{"to_send", str}}); - if (!sink.write(str.c_str(), str.size())) { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } + res_ok(res, res_metrics->slots_data); + }; + + const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { + if (!params.endpoint_metrics) { + res_error(res, format_error_response("This server does not support metrics endpoint. Start it with `--metrics`", ERROR_TYPE_NOT_SUPPORTED)); + return; + } + + // request slots data using task queue + server_task task(SERVER_TASK_TYPE_METRICS); + task.id = ctx_server.queue_tasks.get_new_id(); + task.metrics_reset_bucket = true; + + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task, true); // high-priority task + + // get the result + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } + + // TODO: get rid of this dynamic_cast + auto res_metrics = dynamic_cast(result.get()); + GGML_ASSERT(res_metrics != nullptr); + + // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names + json all_metrics_def = json { + {"counter", {{ + {"name", "prompt_tokens_total"}, + {"help", "Number of prompt tokens processed."}, + {"value", (uint64_t) res_metrics->n_prompt_tokens_processed_total} + }, { + {"name", "prompt_seconds_total"}, + {"help", "Prompt process time"}, + {"value", (uint64_t) res_metrics->t_prompt_processing_total / 1.e3} + }, { + {"name", "tokens_predicted_total"}, + {"help", "Number of generation tokens processed."}, + {"value", (uint64_t) res_metrics->n_tokens_predicted_total} + }, { + {"name", "tokens_predicted_seconds_total"}, + {"help", "Predict process time"}, + {"value", (uint64_t) res_metrics->t_tokens_generation_total / 1.e3} + }, { + {"name", "n_decode_total"}, + {"help", "Total number of llama_decode() calls"}, + {"value", res_metrics->n_decode_total} + }, { + {"name", "n_busy_slots_per_decode"}, + {"help", "Average number of busy slots per llama_decode() call"}, + {"value", (float) res_metrics->n_busy_slots_total / (float) res_metrics->n_decode_total} + }}}, + {"gauge", {{ + {"name", "prompt_tokens_seconds"}, + {"help", "Average prompt throughput in tokens/s."}, + {"value", res_metrics->n_prompt_tokens_processed ? 1.e3 / res_metrics->t_prompt_processing * res_metrics->n_prompt_tokens_processed : 0.} + },{ + {"name", "predicted_tokens_seconds"}, + {"help", "Average generation throughput in tokens/s."}, + {"value", res_metrics->n_tokens_predicted ? 1.e3 / res_metrics->t_tokens_generation * res_metrics->n_tokens_predicted : 0.} + },{ + {"name", "kv_cache_usage_ratio"}, + {"help", "KV-cache usage. 1 means 100 percent usage."}, + {"value", 1. * res_metrics->kv_cache_used_cells / params.n_ctx} + },{ + {"name", "kv_cache_tokens"}, + {"help", "KV-cache tokens."}, + {"value", (uint64_t) res_metrics->kv_cache_tokens_count} + },{ + {"name", "requests_processing"}, + {"help", "Number of requests processing."}, + {"value", (uint64_t) res_metrics->n_processing_slots} + },{ + {"name", "requests_deferred"}, + {"help", "Number of requests deferred."}, + {"value", (uint64_t) res_metrics->n_tasks_deferred} + }}} + }; + + std::stringstream prometheus; + + for (const auto & el : all_metrics_def.items()) { + const auto & type = el.key(); + const auto & metrics_def = el.value(); + + for (const auto & metric_def : metrics_def) { + const std::string name = metric_def.at("name"); + const std::string help = metric_def.at("help"); + + auto value = json_value(metric_def, "value", 0.); + prometheus << "# HELP llamacpp:" << name << " " << help << "\n" + << "# TYPE llamacpp:" << name << " " << type << "\n" + << "llamacpp:" << name << " " << value << "\n"; + } + } + + res.set_header("Process-Start-Time-Unix", std::to_string(res_metrics->t_start)); + + res.set_content(prometheus.str(), "text/plain; version=0.0.4"); + res.status = 200; // HTTP OK + }; + + const auto handle_slots_save = [&ctx_server, &res_error, &res_ok, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) { + json request_data = json::parse(req.body); + std::string filename = request_data.at("filename"); + if (!fs_validate_filename(filename)) { + res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); + return; + } + std::string filepath = params.slot_save_path + filename; + + server_task task(SERVER_TASK_TYPE_SLOT_SAVE); + task.id = ctx_server.queue_tasks.get_new_id(); + task.slot_action.slot_id = id_slot; + task.slot_action.filename = filename; + task.slot_action.filepath = filepath; + + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } + + res_ok(res, result->to_json()); + }; + + const auto handle_slots_restore = [&ctx_server, &res_error, &res_ok, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) { + json request_data = json::parse(req.body); + std::string filename = request_data.at("filename"); + if (!fs_validate_filename(filename)) { + res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); + return; + } + std::string filepath = params.slot_save_path + filename; + + server_task task(SERVER_TASK_TYPE_SLOT_RESTORE); + task.id = ctx_server.queue_tasks.get_new_id(); + task.slot_action.slot_id = id_slot; + task.slot_action.filename = filename; + task.slot_action.filepath = filepath; + + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } + + GGML_ASSERT(dynamic_cast(result.get()) != nullptr); + res_ok(res, result->to_json()); + }; + + const auto handle_slots_erase = [&ctx_server, &res_error, &res_ok](const httplib::Request & /* req */, httplib::Response & res, int id_slot) { + server_task task(SERVER_TASK_TYPE_SLOT_ERASE); + task.id = ctx_server.queue_tasks.get_new_id(); + task.slot_action.slot_id = id_slot; + + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } + + GGML_ASSERT(dynamic_cast(result.get()) != nullptr); + res_ok(res, result->to_json()); + }; + + const auto handle_slots_action = [¶ms, &res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) { + if (params.slot_save_path.empty()) { + res_error(res, format_error_response("This server does not support slots action. Start it with `--slot-save-path`", ERROR_TYPE_NOT_SUPPORTED)); + return; + } + + std::string id_slot_str = req.path_params.at("id_slot"); + int id_slot; + + try { + id_slot = std::stoi(id_slot_str); + } catch (const std::exception &) { + res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + std::string action = req.get_param_value("action"); + + if (action == "save") { + handle_slots_save(req, res, id_slot); + } else if (action == "restore") { + handle_slots_restore(req, res, id_slot); + } else if (action == "erase") { + handle_slots_erase(req, res, id_slot); + } else { + res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST)); + } + }; + + const auto handle_props = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) { + // this endpoint is publicly available, please only return what is safe to be exposed + json data = { + { "default_generation_settings", ctx_server.default_generation_settings_for_props }, + { "total_slots", ctx_server.params_base.n_parallel }, + { "model_path", ctx_server.params_base.model }, + { "chat_template", ctx_server.chat_templates.template_default->source() }, + { "bos_token", ctx_server.chat_templates.template_default->bos_token() }, + { "eos_token", ctx_server.chat_templates.template_default->eos_token() }, + { "build_info", build_info }, + }; + if (ctx_server.params_base.use_jinja && ctx_server.chat_templates.template_tool_use) { + data["chat_template_tool_use"] = ctx_server.chat_templates.template_tool_use->source(); + } + + res_ok(res, data); + }; + + const auto handle_props_change = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { + 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; + } + + json data = json::parse(req.body); + + // update any props here + + res_ok(res, {{ "success", true }}); + }; + + // handle completion-like requests (completion, chat, infill) + // we can optionally provide a custom format for partial results and final results + const auto handle_completions_impl = [&ctx_server, &res_error, &res_ok]( + server_task_type type, + json & data, + std::function is_connection_closed, + httplib::Response & res, + oaicompat_type oaicompat) { + GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL); + + 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; + } + + auto completion_id = gen_chatcmplid(); + std::vector tasks; + + try { + const auto & prompt = data.at("prompt"); + // TODO: this log can become very long, put it behind a flag or think about a more compact format + //SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get().c_str() : prompt.dump(2).c_str()); + + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true); + tasks.reserve(tokenized_prompts.size()); + for (size_t i = 0; i < tokenized_prompts.size(); i++) { + server_task task = server_task(type); + + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + + task.prompt_tokens = std::move(tokenized_prompts[i]); + task.params = server_task::params_from_json_cmpl( + ctx_server.ctx, + ctx_server.params_base, + data); + task.id_selected_slot = json_value(data, "id_slot", -1); + + // OAI-compat + task.params.oaicompat = oaicompat; + task.params.oaicompat_cmpl_id = completion_id; + // oaicompat_model is already populated by params_from_json_cmpl + + tasks.push_back(task); + } + } catch (const std::exception & e) { + res_error(res, format_error_response(e.what(), ERROR_TYPE_INVALID_REQUEST)); + return; + } + + ctx_server.queue_results.add_waiting_tasks(tasks); + ctx_server.queue_tasks.post(tasks); + + bool stream = json_value(data, "stream", false); + const auto task_ids = server_task::get_list_id(tasks); + + if (!stream) { + ctx_server.receive_multi_results(task_ids, [&](std::vector & results) { + if (results.size() == 1) { + // single result + res_ok(res, results[0]->to_json()); + } else { + // multiple results (multitask) + json arr = json::array(); + for (auto & res : results) { + arr.push_back(res->to_json()); + } + res_ok(res, arr); + } + }, [&](const json & error_data) { + res_error(res, error_data); + }, is_connection_closed); + + ctx_server.queue_results.remove_waiting_task_ids(task_ids); + } else { + const auto chunked_content_provider = [task_ids, &ctx_server, oaicompat](size_t, httplib::DataSink & sink) { + ctx_server.receive_cmpl_results_stream(task_ids, [&](server_task_result_ptr & result) -> bool { + json res_json = result->to_json(); + if (res_json.is_array()) { + for (const auto & res : res_json) { + if (!server_sent_event(sink, "data", res)) { + // sending failed (HTTP connection closed), cancel the generation + return false; } } - if (llama_result.stop) { - break; - } + return true; } else { - const std::string str = - "error: " + - llama_result.result_json.dump(-1, ' ', false, - json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", {{"to_send", str}}); - if (!sink.write(str.c_str(), str.size())) { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - break; + return server_sent_event(sink, "data", res_json); } + }, [&](const json & error_data) { + server_sent_event(sink, "error", error_data); + }, [&sink]() { + // note: do not use req.is_connection_closed here because req is already destroyed + return !sink.is_writable(); + }); + if (oaicompat != OAICOMPAT_TYPE_NONE) { + static const std::string ev_done = "data: [DONE]\n\n"; + sink.write(ev_done.data(), ev_done.size()); } sink.done(); - llama.queue_results.remove_waiting_task_id(task_id); - return true; + return false; }; - auto on_complete = [task_id, &llama](bool) { - // cancel request - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); + auto on_complete = [task_ids, &ctx_server] (bool) { + ctx_server.queue_results.remove_waiting_task_ids(task_ids); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); } }; - svr.Post("/chat/completions", chat_completions); - svr.Post("/v1/chat/completions", chat_completions); + const auto handle_completions = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + json data = json::parse(req.body); + return handle_completions_impl( + SERVER_TASK_TYPE_COMPLETION, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_NONE); + }; - svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = json::parse(req.body); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, true, false, -1); - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) - { - res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); - } - else - { - res.status = 404; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { - while (true) - { - task_result result = llama.queue_results.recv(task_id); - if (!result.error) { - const std::string str = - "data: " + - result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); - if (!sink.write(str.c_str(), str.size())) - { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - if (result.stop) - { - break; - } - } - else - { - break; - } - } + const auto handle_completions_oai = [&handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + json data = oaicompat_completion_params_parse(json::parse(req.body)); + return handle_completions_impl( + SERVER_TASK_TYPE_COMPLETION, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_COMPLETION); + }; - llama.queue_results.remove_waiting_task_id(task_id); - sink.done(); - return true; - }; - - auto on_complete = [task_id, &llama] (bool) - { - // cancel - llama.request_cancel(task_id); - }; - - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } - }); - - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) - { return res.set_content("", "application/json; charset=utf-8"); }); - - svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - std::vector tokens; - if (body.count("content") != 0) - { - tokens = llama.tokenize(body["content"], false); - } - const json data = format_tokenizer_response(tokens); - return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); - - svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - std::string content; - if (body.count("tokens") != 0) - { - const std::vector tokens = body["tokens"]; - content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); - } - - const json data = format_detokenized_response(content); - return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); - - svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - json prompt; - if (body.count("content") != 0) - { - prompt = body["content"]; - } - else - { - prompt = ""; - } - - json image_data; - if (body.count("image_data") != 0) { - image_data = body["image_data"]; - } - else - { - image_data = ""; - } - - // create and queue the task - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1); - - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - - // send the result - return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); - }); - - svr.Post("/v1/embeddings", [&llama](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - const json body = json::parse(req.body); - - json prompt; - if (body.count("input") != 0) - { - prompt = body["input"]; - // batch - if(prompt.is_array()) { - json data = json::array(); - int i = 0; - for (const json &elem : prompt) { - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", elem}, { "n_predict", 0} }, false, true, -1); - - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - - json embedding = json{ - {"embedding", json_value(result.result_json, "embedding", json::array())}, - {"index", i++}, - {"object", "embedding"} - }; - data.push_back(embedding); - } - json result = format_embeddings_response_oaicompat(body, data); - return res.set_content(result.dump(), "application/json; charset=utf-8"); - } - } - else - { - prompt = ""; - } - - // create and queue the task - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}}, false, true, -1); - - // get the result - task_result result = llama.queue_results.recv(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - - json data = json::array({json{ - {"embedding", json_value(result.result_json, "embedding", json::array())}, - {"index", 0}, - {"object", "embedding"} - }} - ); - - json root = format_embeddings_response_oaicompat(body, data); - - // send the result - return res.set_content(root.dump(), "application/json; charset=utf-8"); - }); - - // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? - // "Bus error: 10" - this is on macOS, it does not crash on Linux - //std::thread t2([&]() - /*{ - bool running = true; - while (running) - { - running = llama.update_slots(); + const auto handle_infill = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + // check model compatibility + std::string err; + if (llama_vocab_fim_pre(ctx_server.vocab) == LLAMA_TOKEN_NULL) { + err += "prefix token is missing. "; + } + if (llama_vocab_fim_suf(ctx_server.vocab) == LLAMA_TOKEN_NULL) { + err += "suffix token is missing. "; + } + if (llama_vocab_fim_mid(ctx_server.vocab) == LLAMA_TOKEN_NULL) { + err += "middle token is missing. "; + } + if (!err.empty()) { + res_error(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED)); + return; } - }*/ - //); - if (sparams.n_threads_http > 0) { - log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); - svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + json data = json::parse(req.body); + + // validate input + if (data.contains("prompt") && !data.at("prompt").is_string()) { + // prompt is optional + res_error(res, format_error_response("\"prompt\" must be a string", ERROR_TYPE_INVALID_REQUEST)); + } + + if (!data.contains("input_prefix")) { + res_error(res, format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST)); + } + + if (!data.contains("input_suffix")) { + res_error(res, format_error_response("\"input_suffix\" is required", ERROR_TYPE_INVALID_REQUEST)); + } + + if (data.contains("input_extra") && !data.at("input_extra").is_array()) { + // input_extra is optional + res_error(res, format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + json input_extra = json_value(data, "input_extra", json::array()); + for (const auto & chunk : input_extra) { + // { "text": string, "filename": string } + if (!chunk.contains("text") || !chunk.at("text").is_string()) { + res_error(res, format_error_response("extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST)); + return; + } + // filename is optional + if (chunk.contains("filename") && !chunk.at("filename").is_string()) { + res_error(res, format_error_response("extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + data["input_extra"] = input_extra; // default to empty array if it's not exist + + std::string prompt = json_value(data, "prompt", std::string()); + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, false, true); + SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); + data["prompt"] = format_infill( + ctx_server.vocab, + data.at("input_prefix"), + data.at("input_suffix"), + data.at("input_extra"), + ctx_server.params_base.n_batch, + ctx_server.params_base.n_predict, + ctx_server.slots[0].n_ctx, // TODO: there should be a better way + ctx_server.params_base.spm_infill, + tokenized_prompts[0] + ); + + return handle_completions_impl( + SERVER_TASK_TYPE_INFILL, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_NONE); // infill is not OAI compatible + }; + + const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + LOG_DBG("request: %s\n", req.body.c_str()); + 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; + } + + auto body = json::parse(req.body); + json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates); + + return handle_completions_impl( + SERVER_TASK_TYPE_COMPLETION, + data, + req.is_connection_closed, + res, + OAICOMPAT_TYPE_CHAT); + }; + + // same with handle_chat_completions, but without inference part + const auto handle_apply_template = [&ctx_server, ¶ms, &res_ok](const httplib::Request & req, httplib::Response & res) { + auto body = json::parse(req.body); + json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates); + res_ok(res, {{ "prompt", std::move(data.at("prompt")) }}); + }; + + const auto handle_models = [¶ms, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) { + json models = { + {"object", "list"}, + {"data", { + { + {"id", params.model_alias.empty() ? params.model : params.model_alias}, + {"object", "model"}, + {"created", std::time(0)}, + {"owned_by", "llamacpp"}, + {"meta", ctx_server.model_meta()} + }, + }} + }; + + res_ok(res, models); + }; + + const auto handle_tokenize = [&ctx_server, &res_ok](const httplib::Request & req, httplib::Response & res) { + const json body = json::parse(req.body); + + json tokens_response = json::array(); + if (body.count("content") != 0) { + const bool add_special = json_value(body, "add_special", false); + const bool with_pieces = json_value(body, "with_pieces", false); + + llama_tokens tokens = tokenize_mixed(ctx_server.vocab, body.at("content"), add_special, true); + + if (with_pieces) { + for (const auto& token : tokens) { + std::string piece = common_token_to_piece(ctx_server.ctx, token); + json piece_json; + + // Check if the piece is valid UTF-8 + if (is_valid_utf8(piece)) { + piece_json = piece; + } else { + // If not valid UTF-8, store as array of byte values + piece_json = json::array(); + for (unsigned char c : piece) { + piece_json.push_back(static_cast(c)); + } + } + + tokens_response.push_back({ + {"id", token}, + {"piece", piece_json} + }); + } + } else { + tokens_response = tokens; + } + } + + const json data = format_tokenizer_response(tokens_response); + res_ok(res, data); + }; + + const auto handle_detokenize = [&ctx_server, &res_ok](const httplib::Request & req, httplib::Response & res) { + const json body = json::parse(req.body); + + std::string content; + if (body.count("tokens") != 0) { + const llama_tokens tokens = body.at("tokens"); + content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend()); + } + + const json data = format_detokenized_response(content); + res_ok(res, data); + }; + + const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) { + const json body = json::parse(req.body); + + if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { + res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + // for the shape of input/content, see tokenize_input_prompts() + json prompt; + if (body.count("input") != 0) { + prompt = body.at("input"); + } else if (body.contains("content")) { + oaicompat = OAICOMPAT_TYPE_NONE; // "content" field is not OAI compatible + prompt = body.at("content"); + } else { + res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + bool use_base64 = false; + if (body.count("encoding_format") != 0) { + const std::string& format = body.at("encoding_format"); + if (format == "base64") { + use_base64 = true; + } else if (format != "float") { + res_error(res, format_error_response("The format to return the embeddings in. Can be either float or base64", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true); + for (const auto & tokens : tokenized_prompts) { + // this check is necessary for models that do not add BOS token to the input + if (tokens.empty()) { + res_error(res, format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + + // create and queue the task + json responses = json::array(); + bool error = false; + { + std::vector tasks; + for (size_t i = 0; i < tokenized_prompts.size(); i++) { + server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING); + + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + task.prompt_tokens = std::move(tokenized_prompts[i]); + + // OAI-compat + task.params.oaicompat = oaicompat; + + tasks.push_back(task); + } + + ctx_server.queue_results.add_waiting_tasks(tasks); + ctx_server.queue_tasks.post(tasks); + + // get the result + std::unordered_set task_ids = server_task::get_list_id(tasks); + + ctx_server.receive_multi_results(task_ids, [&](std::vector & results) { + for (auto & res : results) { + GGML_ASSERT(dynamic_cast(res.get()) != nullptr); + responses.push_back(res->to_json()); + } + }, [&](const json & error_data) { + res_error(res, error_data); + error = true; + }, req.is_connection_closed); + + ctx_server.queue_results.remove_waiting_task_ids(task_ids); + } + + if (error) { + return; + } + + // write JSON response + json root = oaicompat == OAICOMPAT_TYPE_EMBEDDING + ? format_embeddings_response_oaicompat(body, responses, use_base64) + : json(responses); + res_ok(res, root); + }; + + const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { + handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE); + }; + + const auto handle_embeddings_oai = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { + handle_embeddings_impl(req, res, OAICOMPAT_TYPE_EMBEDDING); + }; + + const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { + 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 + //int top_n = 1; + //if (body.count("top_n") != 1) { + // top_n = body.at("top_n"); + //} else { + // res_error(res, format_error_response("\"top_n\" must be provided", ERROR_TYPE_INVALID_REQUEST)); + // return; + //} + + json query; + if (body.count("query") == 1) { + query = body.at("query"); + if (!query.is_string()) { + res_error(res, format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } else { + res_error(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + std::vector documents = json_value(body, "documents", std::vector()); + if (documents.empty()) { + res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST)); + return; + } + + llama_tokens tokenized_query = tokenize_input_prompts(ctx_server.vocab, query, /* add_special */ false, true)[0]; + + // create and queue the task + json responses = json::array(); + bool error = false; + { + std::vector tasks; + std::vector tokenized_docs = tokenize_input_prompts(ctx_server.vocab, documents, /* add_special */ false, true); + tasks.reserve(tokenized_docs.size()); + for (size_t i = 0; i < tokenized_docs.size(); i++) { + server_task task = server_task(SERVER_TASK_TYPE_RERANK); + task.id = ctx_server.queue_tasks.get_new_id(); + task.index = i; + task.prompt_tokens = format_rerank(ctx_server.vocab, tokenized_query, tokenized_docs[i]); + tasks.push_back(task); + } + + ctx_server.queue_results.add_waiting_tasks(tasks); + ctx_server.queue_tasks.post(tasks); + + // get the result + std::unordered_set task_ids = server_task::get_list_id(tasks); + + ctx_server.receive_multi_results(task_ids, [&](std::vector & results) { + for (auto & res : results) { + GGML_ASSERT(dynamic_cast(res.get()) != nullptr); + responses.push_back(res->to_json()); + } + }, [&](const json & error_data) { + res_error(res, error_data); + error = true; + }, req.is_connection_closed); + } + + if (error) { + return; + } + + // write JSON response + json root = format_response_rerank(body, responses); + res_ok(res, root); + }; + + const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) { + json result = json::array(); + const auto & loras = ctx_server.params_base.lora_adapters; + for (size_t i = 0; i < loras.size(); ++i) { + auto & lora = loras[i]; + result.push_back({ + {"id", i}, + {"path", lora.path}, + {"scale", lora.scale}, + }); + } + res_ok(res, result); + res.status = 200; // HTTP OK + }; + + const auto handle_lora_adapters_apply = [&](const httplib::Request & req, httplib::Response & res) { + const json body = json::parse(req.body); + if (!body.is_array()) { + res_error(res, format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST)); + return; + } + server_task task(SERVER_TASK_TYPE_SET_LORA); + task.id = ctx_server.queue_tasks.get_new_id(); + task.set_lora = parse_lora_request(ctx_server.params_base.lora_adapters, body); + ctx_server.queue_results.add_waiting_task_id(task.id); + ctx_server.queue_tasks.post(task); + + server_task_result_ptr result = ctx_server.queue_results.recv(task.id); + ctx_server.queue_results.remove_waiting_task_id(task.id); + + if (result->is_error()) { + res_error(res, result->to_json()); + return; + } + + GGML_ASSERT(dynamic_cast(result.get()) != nullptr); + res_ok(res, result->to_json()); + }; + + // + // Router + // + + if (!params.webui) { + LOG_INF("Web UI is disabled\n"); + } else { + // register static assets routes + if (!params.public_path.empty()) { + // Set the base directory for serving static files + 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 index.html + svr->Get("/", [](const httplib::Request & req, httplib::Response & res) { + if (req.get_header_value("Accept-Encoding").find("gzip") == std::string::npos) { + res.set_content("Error: gzip is not supported by this browser", "text/plain"); + } else { + res.set_header("Content-Encoding", "gzip"); + // COEP and COOP headers, required by pyodide (python interpreter) + res.set_header("Cross-Origin-Embedder-Policy", "require-corp"); + res.set_header("Cross-Origin-Opener-Policy", "same-origin"); + res.set_content(reinterpret_cast(index_html_gz), index_html_gz_len, "text/html; charset=utf-8"); + } + return false; + }); + } } - LOG_INFO("HTTP server listening", log_data); - // run the HTTP server in a thread - see comment below - std::thread t([&]() - { - if (!svr.listen_after_bind()) - { - state.store(SERVER_STATE_ERROR); - return 1; - } + // register API routes + svr->Get ("/health", handle_health); // public endpoint (no API key check) + svr->Get ("/metrics", handle_metrics); + svr->Get ("/props", handle_props); + svr->Post("/props", handle_props_change); + svr->Get ("/models", handle_models); // public endpoint (no API key check) + svr->Get ("/v1/models", handle_models); // public endpoint (no API key check) + svr->Post("/completion", handle_completions); // legacy + svr->Post("/completions", handle_completions); + svr->Post("/v1/completions", handle_completions_oai); + svr->Post("/chat/completions", handle_chat_completions); + svr->Post("/v1/chat/completions", handle_chat_completions); + svr->Post("/infill", handle_infill); + svr->Post("/embedding", handle_embeddings); // legacy + svr->Post("/embeddings", handle_embeddings); + svr->Post("/v1/embeddings", handle_embeddings_oai); + svr->Post("/rerank", handle_rerank); + svr->Post("/reranking", handle_rerank); + svr->Post("/v1/rerank", handle_rerank); + svr->Post("/v1/reranking", handle_rerank); + svr->Post("/tokenize", handle_tokenize); + svr->Post("/detokenize", handle_detokenize); + svr->Post("/apply-template", handle_apply_template); + // LoRA adapters hotswap + svr->Get ("/lora-adapters", handle_lora_adapters_list); + svr->Post("/lora-adapters", handle_lora_adapters_apply); + // Save & load slots + svr->Get ("/slots", handle_slots); + svr->Post("/slots/:id_slot", handle_slots_action); - return 0; - }); + // + // Start the server + // + if (params.n_threads_http < 1) { + // +2 threads for monitoring endpoints + params.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); + } + log_data["n_threads_http"] = std::to_string(params.n_threads_http); + svr->new_task_queue = [¶ms] { return new httplib::ThreadPool(params.n_threads_http); }; - llama.queue_tasks.on_new_task(std::bind( - &llama_server_context::process_single_task, &llama, std::placeholders::_1)); - llama.queue_tasks.on_finish_multitask(std::bind( - &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1)); - llama.queue_tasks.on_run_slots(std::bind( - &llama_server_context::update_slots, &llama)); - llama.queue_results.on_multitask_update(std::bind( - &llama_server_queue::update_multitask, - &llama.queue_tasks, - std::placeholders::_1, - std::placeholders::_2, - std::placeholders::_3 - )); + // clean up function, to be called before exit + auto clean_up = [&svr]() { + svr->stop(); + llama_backend_free(); + }; + + // 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}, + //}); + LOG_ERR("%s: couldn't bind HTTP server socket, hostname: %s, port: %d\n", __func__, params.hostname.c_str(), params.port); + clean_up(); + return 1; + } + + // run the HTTP server in a thread + std::thread t([&]() { svr->listen_after_bind(); }); + svr->wait_until_ready(); + + LOG_INF("%s: HTTP server is listening, hostname: %s, port: %d, http threads: %d\n", __func__, params.hostname.c_str(), params.port, params.n_threads_http); + + // load the model + LOG_INF("%s: loading model\n", __func__); + + if (!ctx_server.load_model(params)) { + clean_up(); + t.join(); + LOG_ERR("%s: exiting due to model loading error\n", __func__); + return 1; + } + + ctx_server.init(); + state.store(SERVER_STATE_READY); + + LOG_INF("%s: model loaded\n", __func__); + + // print sample chat example to make it clear which template is used + LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__, + ctx_server.chat_templates.template_default->source().c_str(), + common_chat_format_example(*ctx_server.chat_templates.template_default, ctx_server.params_base.use_jinja).c_str()); + + ctx_server.queue_tasks.on_new_task([&ctx_server](const server_task & task) { + ctx_server.process_single_task(task); + }); + + ctx_server.queue_tasks.on_update_slots([&ctx_server]() { + ctx_server.update_slots(); + }); shutdown_handler = [&](int) { - llama.queue_tasks.terminate(); + ctx_server.queue_tasks.terminate(); }; + 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(); + #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; sigint_action.sa_handler = signal_handler; sigemptyset (&sigint_action.sa_mask); sigint_action.sa_flags = 0; sigaction(SIGINT, &sigint_action, NULL); + sigaction(SIGTERM, &sigint_action, NULL); #elif defined (_WIN32) auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false; }; SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif - llama.queue_tasks.start_loop(); - svr.stop(); + + clean_up(); t.join(); - llama_backend_free(); return 0; } diff --git a/examples/server/tests/.gitignore b/examples/server/tests/.gitignore new file mode 100644 index 000000000..90ee7fe6d --- /dev/null +++ b/examples/server/tests/.gitignore @@ -0,0 +1,2 @@ +.venv +tmp diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 0b9fdc4e7..1de0eb30e 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -1,47 +1,66 @@ # 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`. +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`. ### Install dependencies + `pip install -r requirements.txt` ### Run tests + 1. Build the server + ```shell cd ../../.. -mkdir build -cd build -cmake ../ -cmake --build . --target server +cmake -B build -DLLAMA_CURL=ON +cmake --build build --target llama-server ``` -2. download required models: - 1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf` -3. Start the test: `./tests.sh` + +2. Start the test: `./tests.sh` 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/server` - - `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose` - - `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format -### Run @bug, @wip or @wrong_usage annotated scenario +| variable | description | +|--------------------------|------------------------------------------------------------------------------------------------| +| `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` | to enable steps and server verbose mode `--verbose` | +| `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | +| `LLAMA_CACHE` | by default server tests re-download models to the `tmp` subfolder. Set this to your cache (e.g. `$HOME/Library/Caches/llama.cpp` on Mac or `$HOME/.cache/llama.cpp` on Unix) to avoid this | -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 +To run slow tests (will download many models, make sure to set `LLAMA_CACHE` if needed): -To run a scenario annotated with `@bug`, start: -`DEBUG=ON ./tests.sh --no-skipped --tags bug` +```shell +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 +DEBUG=1 ./tests.sh -s -v -x +``` + +To run all the tests in a file: + +```shell +./tests.sh unit/test_chat_completion.py.py -v -x +``` + +To run a single test: + +```shell +./tests.sh unit/test_chat_completion.py::test_invalid_chat_completion_req +``` + +Hint: You can compile and run test in single command, useful for local developement: + +```shell +cmake --build build -j --target llama-server && ./examples/server/tests/tests.sh +``` + +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/environment.py b/examples/server/tests/features/environment.py deleted file mode 100644 index 09e826747..000000000 --- a/examples/server/tests/features/environment.py +++ /dev/null @@ -1,69 +0,0 @@ -import os -import socket -import subprocess -import time -from contextlib import closing -from signal import SIGKILL - - -def before_scenario(context, scenario): - 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): - if 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\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 not pid_exists(context.server_process.pid): - assert False, f"Server not running pid={context.server_process.pid} ..." - - print(f"stopping server pid={context.server_process.pid} ...") - context.server_process.kill() - # Wait few for socket to free up - time.sleep(0.05) - - attempts = 0 - while is_server_listening(context.server_fqdn, context.server_port): - print(f"stopping server pid={context.server_process.pid} ...") - os.kill(context.server_process.pid, SIGKILL) - time.sleep(0.1) - attempts += 1 - if attempts > 5: - print(f"Server dangling exits, killing all {context.server_path} ...") - process = subprocess.run(['killall', '-9', context.server_path], - stderr=subprocess.PIPE, - universal_newlines=True) - print(process) - - -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)) - return result == 0 - - -def pid_exists(pid): - """Check whether pid exists in the current process table.""" - import errno - if pid < 0: - return False - try: - os.kill(pid, 0) - except OSError as e: - return e.errno == errno.EPERM - else: - return True diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature deleted file mode 100644 index bf5a175a3..000000000 --- a/examples/server/tests/features/issues.feature +++ /dev/null @@ -1,4 +0,0 @@ -# List of ongoing issues -@bug -Feature: Issues - # No confirmed issue at the moment diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature deleted file mode 100644 index 5f895cf90..000000000 --- a/examples/server/tests/features/parallel.feature +++ /dev/null @@ -1,145 +0,0 @@ -@llama.cpp -Feature: Parallel - - Background: Server startup - Given a server listening on localhost:8080 - And a model file stories260K.gguf - And a model alias tinyllama-2 - And 42 as server seed - And 64 KV cache size - And 2 slots - And embeddings extraction - 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: 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 - - 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 tinyllama-2 - Given concurrent OAI embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature deleted file mode 100644 index db06d3977..000000000 --- a/examples/server/tests/features/security.feature +++ /dev/null @@ -1,50 +0,0 @@ -@llama.cpp -Feature: Security - - Background: Server startup with an api key defined - Given a server listening on localhost:8080 - And a model file stories260K.gguf - And a server api key llama.cpp - 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 | - | llama.cpp | no | - | llama.cpp | 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 | - | llama.cpp | no | - | llama.cpp | no | - | hackme | raised | - - - Scenario Outline: CORS Options - 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 b571582a7..000000000 --- a/examples/server/tests/features/server.feature +++ /dev/null @@ -1,84 +0,0 @@ -@llama.cpp -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model file stories260K.gguf - And a model alias tinyllama-2 - 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 32 KV cache size - And 1 slots - And embeddings extraction - And 32 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 prometheus metrics are exposed - - Examples: Prompts - | prompt | n_predict | re_content | n_predicted | - | I believe the meaning of life is | 8 | (readgoing)+ | 8 | - | Write a joke about AI | 64 | (parkfriendsscaredalways)+ | 32 | - - 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 - - Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | - | llama-2 | Book | What is the best book | 8 | (Momwhat)+ | 8 | disabled | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thankshappybird)+ | 32 | enabled | - - Scenario: Embedding - When embeddings are computed for: - """ - What is the capital of Bulgaria ? - """ - Then embeddings are generated - - Scenario: OAI Embeddings compatibility - Given a model tinyllama-2 - 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 tinyllama-2 - 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: Tokenize / Detokenize - When tokenizing: - """ - What is the capital of France ? - """ - Then tokens can be detokenize diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py deleted file mode 100644 index 381da105e..000000000 --- a/examples/server/tests/features/steps/steps.py +++ /dev/null @@ -1,827 +0,0 @@ -import asyncio -import collections -import json -import os -import re -import socket -import subprocess -import time -from contextlib import closing -from re import RegexFlag - -import aiohttp -import openai -from behave import step -from behave.api.async_step import async_run_until_complete -from prometheus_client import parser - - -@step(u"a server listening on {server_fqdn}:{server_port}") -def step_server_config(context, server_fqdn, server_port): - context.server_fqdn = server_fqdn - context.server_port = int(server_port) - if 'PORT' in os.environ: - context.server_port = int(os.environ['PORT']) - print(f"$PORT set, overriding server port with to {context.server_port}") - - context.base_url = f'http://{context.server_fqdn}:{context.server_port}' - - context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' - context.model_alias = None - context.n_ctx = None - context.n_predict = None - context.n_server_predict = None - context.n_slots = None - context.server_api_key = None - context.server_continuous_batching = False - context.server_embeddings = False - context.server_metrics = False - context.server_process = None - context.server_seed = None - context.user_api_key = None - - context.tasks_result = [] - context.concurrent_tasks = [] - context.prompts = [] - - -@step(u'a model file {model_file}') -def step_model_file(context, model_file): - context.model_file = model_file - - -@step(u'a model alias {model_alias}') -def step_model_alias(context, model_alias): - context.model_alias = model_alias - - -@step(u'{seed} as server seed') -def step_seed(context, seed): - context.server_seed = int(seed) - - -@step(u'{n_ctx} KV cache size') -def step_n_ctx(context, n_ctx): - context.n_ctx = int(n_ctx) - - -@step(u'{n_slots} slots') -def step_n_slots(context, n_slots): - context.n_slots = int(n_slots) - - -@step(u'{n_predict} server max tokens to predict') -def step_server_n_predict(context, n_predict): - context.n_server_predict = int(n_predict) - - -@step(u'continuous batching') -def step_server_continuous_batching(context): - context.server_continuous_batching = True - - -@step(u'embeddings extraction') -def step_server_embeddings(context): - context.server_embeddings = True - - -@step(u'prometheus compatible metrics exposed') -def step_server_metrics(context): - context.server_metrics = True - - -@step(u"the server is starting") -def step_start_server(context): - start_server_background(context) - attempts = 0 - while True: - with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: - result = sock.connect_ex((context.server_fqdn, context.server_port)) - if result == 0: - print("\x1b[33;46mserver started!\x1b[0m") - return - attempts += 1 - if attempts > 20: - assert False, "server not started" - print(f"waiting for server to start, connect error code = {result}...") - time.sleep(0.1) - - -@step(u"the server is {expecting_status}") -@async_run_until_complete -async def step_wait_for_the_server_to_be_started(context, expecting_status): - match expecting_status: - case 'healthy': - await wait_for_health_status(context, context.base_url, 200, 'ok') - - case 'ready' | 'idle': - await wait_for_health_status(context, context.base_url, 200, 'ok', - params={'fail_on_no_slot': 0, 'include_slots': 0}, - slots_idle=context.n_slots, - slots_processing=0, - expected_slots=[{'id': slot_id, 'state': 0} - for slot_id in range(context.n_slots)]) - case 'busy': - await wait_for_health_status(context, context.base_url, 503, - 'no slot available', - params={'fail_on_no_slot': 0, 'include_slots': 0}, - slots_idle=0, - slots_processing=context.n_slots, - expected_slots=[{'id': slot_id, 'state': 1} - for slot_id in range(context.n_slots)]) - case _: - assert False, "unknown status" - - -@step(u'all slots are {expected_slot_status_string}') -@async_run_until_complete -async def step_all_slots_status(context, expected_slot_status_string): - 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(u'a completion request with {api_error} api error') -@async_run_until_complete -async def step_request_completion(context, api_error): - expect_api_error = api_error == 'raised' - completion = await request_completion(context.prompts.pop(), - context.base_url, - debug=context.debug, - n_predict=context.n_predict, - server_seed=context.server_seed, - expect_api_error=expect_api_error, - user_api_key=context.user_api_key) - 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}" - - -@step(u'{predicted_n} tokens are predicted matching {re_content}') -def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content) - - -@step(u'{predicted_n} tokens are predicted') -def step_n_tokens_predicted(context, predicted_n): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n)) - - -@step(u'a user prompt {user_prompt}') -def step_user_prompt(context, user_prompt): - context.prompts.append(user_prompt) - - -@step(u'a system prompt {system_prompt}') -def step_system_prompt(context, system_prompt): - context.system_prompt = system_prompt - - -@step(u'a model {model}') -def step_model(context, model): - context.model = model - - -@step(u'{max_tokens} max tokens to predict') -def step_max_tokens(context, max_tokens): - context.n_predict = int(max_tokens) - - -@step(u'streaming is {enable_streaming}') -def step_streaming(context, enable_streaming): - context.enable_streaming = enable_streaming == 'enabled' - - -@step(u'a user api key {user_api_key}') -def step_user_api_key(context, user_api_key): - context.user_api_key = user_api_key - - -@step(u'no user api key') -def step_no_user_api_key(context): - context.user_api_key = None - - -@step(u'a user api key ') -def step_no_user_api_key_space(context): - context.user_api_key = None - - -@step(u'a server api key {server_api_key}') -def step_server_api_key(context, server_api_key): - context.server_api_key = server_api_key - - -@step(u'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' - completion = await oai_chat_completions(context.prompts.pop(), - context.system_prompt, - 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, - - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, - - 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(u'a prompt') -def step_a_prompt(context): - context.prompts.append(context.text) - - -@step(u'a prompt {prompt}') -def step_a_prompt_prompt(context, prompt): - context.prompts.append(prompt) - - -@step(u'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, - n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - server_seed=context.server_seed if hasattr(context, 'server_seed') else None, - user_api_key=context.user_api_key if hasattr(context, - 'user_api_key') else None) - - -@step(u'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, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None) - - -@step(u'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, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None) - - -@step(u'all prompts are predicted') -@async_run_until_complete -async def step_all_prompts_are_predicted(context): - await all_prompts_are_predicted(context) - - -@step(u'all prompts are predicted with {n_predict} tokens') -@async_run_until_complete -async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict): - expected_predicted_n = int(n_predict) - await all_prompts_are_predicted(context, expected_predicted_n) - - -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(u'embeddings are computed for') -@async_run_until_complete -async def step_compute_embedding(context): - context.embeddings = await request_embedding(context.text, base_url=context.base_url) - - -@step(u'embeddings are generated') -def step_assert_embeddings(context): - if len(context.prompts) == 0: - assert_embeddings(context.embeddings) - else: - assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n" - f"context.prompts={context.prompts}\n" - f"context.embeddings={context.embeddings}") - for embedding in context.embeddings: - context.prompts.pop() - assert_embeddings(embedding) - - -@step(u'an OAI compatible embeddings computation request for') -@async_run_until_complete -async def step_oai_compute_embeddings(context): - context.embeddings = await request_oai_embeddings(context.text, - base_url=context.base_url, - user_api_key=context.user_api_key, - model=context.model) - - -@step(u'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, - base_url=context.base_url, - user_api_key=context.user_api_key, - model=context.model) - - -@step(u'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(u'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(u'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 > 0 - for i in range(n_embedding_requests): - assert_embeddings(context.tasks_result.pop()) - - -@step(u'tokenizing') -@async_run_until_complete -async def step_tokenize(context): - context.tokenized_text = context.text - async with aiohttp.ClientSession() as session: - async with session.post(f'{context.base_url}/tokenize', - json={ - "content": context.tokenized_text, - }) as response: - assert response.status == 200 - tokenize_json = await response.json() - context.tokens = tokenize_json['tokens'] - - -@step(u'tokens can be detokenize') -@async_run_until_complete -async def step_detokenize(context): - assert len(context.tokens) > 0 - async with aiohttp.ClientSession() 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(u'an OPTIONS request is sent from {origin}') -@async_run_until_complete -async def step_options_request(context, origin): - async with aiohttp.ClientSession() as session: - async with session.options(f'{context.base_url}/v1/chat/completions', - headers={"Origin": origin}) as response: - assert response.status == 200 - context.options_response = response - - -@step(u'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(u'prometheus metrics are exposed') -@async_run_until_complete -async def step_prometheus_metrics_exported(context): - async with aiohttp.ClientSession() 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 - 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 - assert metric_exported, "No metrics exported" - - -async def concurrent_requests(context, f_completion, *args, **kwargs): - n_prompts = len(context.prompts) - if context.debug: - print(f"starting {n_prompts} concurrent completion requests...") - assert n_prompts > 0 - for prompt_no in range(n_prompts): - shifted_args = [context.prompts.pop(), *args] - context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) - await asyncio.sleep(0.1) - - -async def request_completion(prompt, - base_url, - debug=False, - n_predict=None, - server_seed=None, - expect_api_error=None, - user_api_key=None): - 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() as session: - async with session.post(f'{base_url}/completion', - json={ - "prompt": prompt, - "n_predict": int(n_predict) if n_predict is not None else -1, - "seed": server_seed if server_seed is not None else 42 - }, - 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, - system_prompt, - base_url, - base_path, - async_client, - debug=False, - model=None, - n_predict=None, - enable_streaming=None, - server_seed=None, - user_api_key=None, - expect_api_error=None): - 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 = server_seed if server_seed is not None else 42 - enable_streaming = enable_streaming if enable_streaming is not None else False - payload = { - "messages": [ - { - "role": "system", - "content": system_prompt, - }, - { - "role": "user", - "content": user_prompt, - } - ], - "model": model, - "max_tokens": n_predict, - "stream": enable_streaming, - "seed": seed - } - completion_response = { - 'content': '', - 'timings': { - 'predicted_n': 0 - } - } - if async_client: - origin = 'llama.cpp' - headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} - async with aiohttp.ClientSession() 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('utf8') - 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] - - 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'] - } - } - else: - return response.status - else: - try: - openai.api_key = user_api_key - openai.api_base = f'{base_url}{base_path}' - chat_completion = openai.Completion.create( - messages=payload['messages'], - model=model, - max_tokens=n_predict, - stream=enable_streaming, - seed=seed - ) - except openai.error.APIError 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: - for chunk in chat_completion: - assert len(chunk.choices) == 1 - delta = chunk.choices[0].delta - if 'content' in delta: - completion_response['content'] += delta['content'] - completion_response['timings']['predicted_n'] += 1 - else: - assert len(chat_completion.choices) == 1 - completion_response = { - 'content': chat_completion.choices[0].message.content, - 'timings': { - 'predicted_n': chat_completion.usage.completion_tokens - } - } - if debug: - print("OAI response formatted to llama.cpp:", completion_response) - return completion_response - - -async def request_embedding(content, base_url=None): - async with aiohttp.ClientSession() as session: - async with session.post(f'{base_url}/embedding', - json={ - "content": content, - }) as response: - assert response.status == 200 - response_json = await response.json() - return response_json['embedding'] - - -async def request_oai_embeddings(input, - base_url=None, user_api_key=None, - model=None, async_client=False): - # 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' - if user_api_key is not None: - headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} - async with aiohttp.ClientSession() 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' - return response_json['data'] - else: - openai.api_key = user_api_key - openai.api_base = f'{base_url}/v1' - oai_embeddings = openai.Embedding.create( - model=model, - input=input, - ) - - if isinstance(input, collections.abc.Sequence): - embeddings = [] - for an_oai_embeddings in oai_embeddings.data: - embeddings.append(an_oai_embeddings.embedding) - else: - embeddings = oai_embeddings.data.embedding - return embeddings - - -def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None): - content = completion_response['content'] - n_predicted = completion_response['timings']['predicted_n'] - assert len(content) > 0, "no token predicted" - if expected_predicted_n is not None: - assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' - f' {n_predicted} <> {expected_predicted_n}') - if re_content is not None: - re_content = '^.*' + re_content.replace('', '|') + '.*$' - assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), ( - f'invalid tokens predicted:' - f' ```\n{content}\n``` do not match /{re_content}/') - - -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_health_status(context, - base_url, - expected_http_status_code, - expected_health_status, - params=None, - slots_idle=None, - slots_processing=None, - expected_slots=None): - if context.debug: - print(f"Starting checking for health for expected_health_status={expected_health_status}") - timeout = 3 # seconds - if expected_health_status == 'ok': - timeout = 10 # CI slow inference - interval = 0.5 - counter = 0 - async with aiohttp.ClientSession() as session: - while True: - async with await session.get(f'{base_url}/health', params=params) as health_response: - status_code = health_response.status - health = await health_response.json() - if context.debug: - print(f"HEALTH - response for expected health status='{expected_health_status}' on " - f"'{base_url}/health'?{params} is {health}") - if (status_code == expected_http_status_code - and health['status'] == expected_health_status - and (slots_idle is None or health['slots_idle'] == slots_idle) - and (slots_processing is None or health['slots_processing'] == slots_processing)): - if expected_slots is not None: - assert_slots_status(health['slots'], expected_slots) - return - if (status_code == expected_http_status_code - and health['status'] == expected_health_status - and (slots_idle is None or health['slots_idle'] == slots_idle) - and (slots_processing is None or health['slots_processing'] == slots_processing)): - if expected_slots is not None: - assert_slots_status(health['slots'], expected_slots) - 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_completions = await gather_tasks_results(context) - if n_completions > 0: - return - - assert False, f'{expected_health_status} timeout exceeded {counter}s>={timeout}' - - -def assert_embeddings(embeddings): - assert len(embeddings) > 0 - embeddings_computed = False - for emb in 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() 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]}") - - -def start_server_background(context): - context.server_path = '../../../build/bin/server' - if 'LLAMA_SERVER_BIN_PATH' in os.environ: - context.server_path = os.environ['LLAMA_SERVER_BIN_PATH'] - server_args = [ - '--host', context.server_fqdn, - '--port', context.server_port, - '--model', context.model_file - ] - if context.server_continuous_batching: - server_args.append('--cont-batching') - if context.server_embeddings: - server_args.append('--embedding') - if context.server_metrics: - server_args.append('--metrics') - if context.model_alias is not None: - server_args.extend(['--alias', context.model_alias]) - if context.n_ctx is not None: - server_args.extend(['--ctx-size', context.n_ctx]) - if context.n_slots is not None: - server_args.extend(['--parallel', context.n_slots]) - if context.n_server_predict is not None: - server_args.extend(['--n-predict', context.n_server_predict]) - if context.server_api_key is not None: - server_args.extend(['--api-key', context.server_api_key]) - if context.debug: - server_args.append('--verbose') - if 'SERVER_LOG_FORMAT_JSON' not in os.environ: - server_args.extend(['--log-format', "text"]) - print(f"starting server with: {context.server_path}", *server_args) - context.server_process = subprocess.Popen( - [str(arg) for arg in [context.server_path, *server_args]], - close_fds=True) - print(f"server pid={context.server_process.pid}") diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature deleted file mode 100644 index e228b2371..000000000 --- a/examples/server/tests/features/wrong_usages.feature +++ /dev/null @@ -1,21 +0,0 @@ -# run with ./test.sh --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 stories260K.gguf - # Uncomment below to fix the issue - #And 64 server max tokens to predict - Then the server is starting - Given a prompt: - """ - Go to: infinite loop - """ - # Uncomment below to fix the issue - #And 128 max tokens to predict - Given concurrent completion requests - Then all prompts are predicted diff --git a/examples/server/tests/pytest.ini b/examples/server/tests/pytest.ini new file mode 100644 index 000000000..6df308df7 --- /dev/null +++ b/examples/server/tests/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + slow: marks tests as slow (deselect with '-m "not slow"') + serial diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 334fa4a70..15d024914 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,4 +1,8 @@ aiohttp~=3.9.3 -behave~=1.2.6 -openai~=0.25.0 +pytest~=8.3.3 +huggingface_hub~=0.23.2 +numpy~=1.26.4 +openai~=1.55.3 prometheus-client~=0.20.0 +requests~=2.32.3 +wget~=3.2 diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh index 17a4e6fc6..33fa8cc64 100755 --- a/examples/server/tests/tests.sh +++ b/examples/server/tests/tests.sh @@ -1,12 +1,23 @@ #!/bin/bash +# make sure we are in the right directory +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) +cd $SCRIPT_DIR + set -eu -if [ $# -lt 1 ] -then - # Start @llama.cpp scenario - behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp -else - behave "$@" +if [[ "${SLOW_TESTS:-0}" == 1 ]]; then + # Slow tests for tool calls need quite a few models ahead of time to avoid timing out. + python $SCRIPT_DIR/../../../scripts/fetch_server_test_models.py fi +if [ $# -lt 1 ] +then + if [[ "${SLOW_TESTS:-0}" == 1 ]]; then + pytest -v -x + else + pytest -v -x -m "not slow" + fi +else + 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..1485de8ce --- /dev/null +++ b/examples/server/tests/unit/test_basic.py @@ -0,0 +1,96 @@ +import pytest +import requests +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 ".gguf" in res.body["model_path"] + assert res.body["total_slots"] == server.n_slots + default_val = res.body["default_generation_settings"] + assert server.n_ctx is not None and server.n_slots is not None + assert default_val["n_ctx"] == server.n_ctx / server.n_slots + assert default_val["params"]["seed"] == server.seed + + +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_server_slots(): + global server + + # without slots endpoint enabled, this should return error + server.server_slots = False + server.start() + res = server.make_request("GET", "/slots") + assert res.status_code == 501 # ERROR_TYPE_NOT_SUPPORTED + assert "error" in res.body + server.stop() + + # with slots endpoint enabled, this should return slots info + server.server_slots = True + server.n_slots = 2 + server.start() + res = server.make_request("GET", "/slots") + assert res.status_code == 200 + assert len(res.body) == server.n_slots + assert server.n_ctx is not None and server.n_slots is not None + assert res.body[0]["n_ctx"] == server.n_ctx / server.n_slots + assert "params" in res.body[0] + assert res.body[0]["params"]["seed"] == server.seed + + +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"]) + + +def test_no_webui(): + global server + # default: webui enabled + server.start() + url = f"http://{server.server_host}:{server.server_port}" + res = requests.get(url) + assert res.status_code == 200 + assert "" in res.text + server.stop() + + # with --no-webui + server.no_webui = True + server.start() + res = requests.get(url) + assert res.status_code == 404 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..f23d5cff4 --- /dev/null +++ b/examples/server/tests/unit/test_chat_completion.py @@ -0,0 +1,267 @@ +import pytest +from openai import OpenAI +from utils import * + +server: ServerProcess + +@pytest.fixture(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,finish_reason,jinja,chat_template", + [ + (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", False, None), + (None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", True, None), + (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None), + (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None), + (None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, 'chatml'), + (None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None), + ] +) +def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template): + global server + server.jinja = jinja + server.chat_template = chat_template + 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 "cmpl" in res.body["id"] # make sure the completion id has the expected format + assert res.body["system_fingerprint"].startswith("b") + assert res.body["model"] == model if model is not None else server.model_alias + 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"]) + assert choice["finish_reason"] == finish_reason + + +@pytest.mark.parametrize( + "system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason", + [ + ("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"), + ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"), + ] +) +def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason): + global server + server.model_alias = None # try using DEFAULT_OAICOMPAT_MODEL + server.start() + res = server.make_stream_request("POST", "/chat/completions", data={ + "max_tokens": max_tokens, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + "stream": True, + }) + content = "" + last_cmpl_id = None + for data in res: + choice = data["choices"][0] + assert data["system_fingerprint"].startswith("b") + assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future + if last_cmpl_id is None: + last_cmpl_id = data["id"] + assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream + 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) + assert choice["finish_reason"] == finish_reason + 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}/v1") + 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, + ) + assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b") + assert res.choices[0].finish_reason == "length" + assert res.choices[0].message.content is not None + assert match_regex("(Suddenly)+", res.choices[0].message.content) + + +def test_chat_template(): + global server + server.chat_template = "llama3" + server.debug = True # to get the "__verbose" object in the response + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": 8, + "messages": [ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ] + }) + assert res.status_code == 200 + assert "__verbose" in res.body + assert res.body["__verbose"]["prompt"] == " <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" + + +def test_apply_chat_template(): + global server + server.chat_template = "command-r" + server.start() + res = server.make_request("POST", "/apply-template", data={ + "messages": [ + {"role": "system", "content": "You are a test."}, + {"role": "user", "content":"Hi there"}, + ] + }) + assert res.status_code == 200 + assert "prompt" in res.body + assert res.body["prompt"] == "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a test.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" + + +@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 + + +def test_logprobs(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + ) + output_text = res.choices[0].message.content + aggregated_text = '' + assert res.choices[0].logprobs is not None + assert res.choices[0].logprobs.content is not None + for token in res.choices[0].logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text + + +def test_logprobs_stream(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + stream=True, + ) + output_text = '' + aggregated_text = '' + for data in res: + choice = data.choices[0] + if choice.finish_reason is None: + if choice.delta.content: + output_text += choice.delta.content + assert choice.logprobs is not None + assert choice.logprobs.content is not None + for token in choice.logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert token.top_logprobs is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py new file mode 100644 index 000000000..0ed5b99be --- /dev/null +++ b/examples/server/tests/unit/test_completion.py @@ -0,0 +1,428 @@ +import pytest +import requests +import time +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("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [ + ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, 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, True), +]) +def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": n_predict, + "prompt": prompt, + "return_tokens": return_tokens, + }) + 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 type(res.body["has_new_line"]) == bool + assert match_regex(re_content, res.body["content"]) + if return_tokens: + assert len(res.body["tokens"]) > 0 + assert all(type(tok) == int for tok in res.body["tokens"]) + else: + assert res.body["tokens"] == [] + + +@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: + assert "stop" in data and type(data["stop"]) == bool + if data["stop"]: + assert data["timings"]["prompt_n"] == n_prompt + assert data["timings"]["predicted_n"] == n_predicted + assert data["truncated"] == truncated + assert data["stop_type"] == "limit" + assert type(data["has_new_line"]) == bool + assert "generation_settings" in data + assert server.n_predict is not None + assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict) + assert data["generation_settings"]["seed"] == server.seed + assert match_regex(re_content, content) + else: + assert len(data["tokens"]) > 0 + assert all(type(tok) == int for tok in data["tokens"]) + content += data["content"] + + +def test_completion_stream_vs_non_stream(): + global server + server.start() + res_stream = server.make_stream_request("POST", "/completion", data={ + "n_predict": 8, + "prompt": "I believe the meaning of life is", + "stream": True, + }) + res_non_stream = server.make_request("POST", "/completion", data={ + "n_predict": 8, + "prompt": "I believe the meaning of life is", + }) + content_stream = "" + for data in res_stream: + content_stream += data["content"] + assert content_stream == res_non_stream.body["content"] + + +def test_completion_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8, + ) + assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b") + assert res.choices[0].finish_reason == "length" + assert res.choices[0].text is not None + assert match_regex("(going|bed)+", res.choices[0].text) + + +def test_completion_stream_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + res = client.completions.create( + model="davinci-002", + prompt="I believe the meaning of life is", + max_tokens=8, + stream=True, + ) + output_text = '' + for data in res: + choice = data.choices[0] + if choice.finish_reason is None: + assert choice.text is not None + output_text += choice.text + assert match_regex("(going|bed)+", output_text) + + +@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": 0.0, + "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 + +# TODO figure why it don't work with temperature = 1 +# @pytest.mark.parametrize("temperature", [0.0, 1.0]) +@pytest.mark.parametrize("n_batch", [16, 32]) +@pytest.mark.parametrize("temperature", [0.0]) +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"]) + + +@pytest.mark.parametrize( + "prompt,n_predict,response_fields", + [ + ("I believe the meaning of life is", 8, []), + ("I believe the meaning of life is", 32, ["content", "generation_settings/n_predict", "prompt"]), + ], +) +def test_completion_response_fields( + prompt: str, n_predict: int, response_fields: list[str] +): + global server + server.start() + res = server.make_request( + "POST", + "/completion", + data={ + "n_predict": n_predict, + "prompt": prompt, + "response_fields": response_fields, + }, + ) + assert res.status_code == 200 + assert "content" in res.body + assert len(res.body["content"]) + if len(response_fields): + assert res.body["generation_settings/n_predict"] == n_predict + assert res.body["prompt"] == " " + prompt + assert isinstance(res.body["content"], str) + assert len(res.body) == len(response_fields) + else: + assert len(res.body) + assert "generation_settings" in res.body + + +def test_n_probs(): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + }) + assert res.status_code == 200 + assert "completion_probabilities" in res.body + assert len(res.body["completion_probabilities"]) == 5 + for tok in res.body["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_stream(): + global server + server.start() + res = server.make_stream_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "stream": True, + }) + for data in res: + if data["stop"] == False: + assert "completion_probabilities" in data + assert len(data["completion_probabilities"]) == 1 + for tok in data["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_post_sampling(): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "post_sampling_probs": True, + }) + assert res.status_code == 200 + assert "completion_probabilities" in res.body + assert len(res.body["completion_probabilities"]) == 5 + for tok in res.body["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "prob" in tok and 0.0 < tok["prob"] <= 1.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_probs"]) == 10 + for prob in tok["top_probs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "prob" in prob and 0.0 <= prob["prob"] <= 1.0 + assert "bytes" in prob and type(prob["bytes"]) == list + # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs + assert any(prob["prob"] == 1.0 for prob in tok["top_probs"]) + + +def test_cancel_request(): + global server + server.n_ctx = 4096 + server.n_predict = -1 + server.n_slots = 1 + server.server_slots = True + server.start() + # send a request that will take a long time, but cancel it before it finishes + try: + server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + }, timeout=0.1) + except requests.exceptions.ReadTimeout: + pass # expected + # make sure the slot is free + time.sleep(1) # wait for HTTP_POLLING_SECONDS + res = server.make_request("GET", "/slots") + assert res.body[0]["is_processing"] == False 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..8b0eb42b0 --- /dev/null +++ b/examples/server/tests/unit/test_embedding.py @@ -0,0 +1,237 @@ +import base64 +import struct +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.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/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.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/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 + + +@pytest.mark.parametrize( + "input,is_multi_prompt", + [ + # do not crash on empty input + ("", False), + # single prompt + ("string", False), + ([12, 34, 56], False), + ([12, 34, "string", 56, 78], False), + # multiple prompts + (["string1", "string2"], True), + (["string1", [12, 34, 56]], True), + ([[12, 34, 56], [12, 34, 56]], True), + ([[12, 34, 56], [12, "string", 34, 56]], True), + ] +) +def test_embedding_mixed_input(input, is_multi_prompt: bool): + global server + server.start() + res = server.make_request("POST", "/v1/embeddings", data={"input": input}) + assert res.status_code == 200 + data = res.body['data'] + if is_multi_prompt: + assert len(data) == len(input) + for d in data: + assert 'embedding' in d + assert len(d['embedding']) > 1 + else: + assert 'embedding' in data[0] + assert len(data[0]['embedding']) > 1 + + +def test_embedding_pooling_none(): + global server + server.pooling = 'none' + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": "hello hello hello", + }) + assert res.status_code == 200 + assert 'embedding' in res.body[0] + assert len(res.body[0]['embedding']) == 5 # 3 text tokens + 2 special + + # make sure embedding vector is not normalized + for x in res.body[0]['embedding']: + assert abs(sum([x ** 2 for x in x]) - 1) > EPSILON + + +def test_embedding_pooling_none_oai(): + global server + server.pooling = 'none' + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": "hello hello hello", + }) + + # /v1/embeddings does not support pooling type 'none' + assert res.status_code == 400 + assert "error" in res.body + + +def test_embedding_openai_library_single(): + global server + server.pooling = 'last' + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + 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.pooling = 'last' + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") + 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.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/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.pooling = 'last' + server.start() + res = server.make_request("POST", "/v1/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 + + +@pytest.mark.parametrize( + "content,n_tokens", + [ + ("I believe the meaning of life is", 9), + ("This is a test", 6), + ] +) +def test_embedding_usage_single(content, n_tokens): + global server + server.start() + res = server.make_request("POST", "/v1/embeddings", data={"input": content}) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == n_tokens + + +def test_embedding_usage_multiple(): + global server + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": [ + "I believe the meaning of life is", + "I believe the meaning of life is", + ], + }) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == 2 * 9 + + +def test_embedding_openai_library_base64(): + server.start() + test_input = "Test base64 embedding output" + + # get embedding in default format + res = server.make_request("POST", "/v1/embeddings", data={ + "input": test_input + }) + assert res.status_code == 200 + vec0 = res.body["data"][0]["embedding"] + + # get embedding in base64 format + res = server.make_request("POST", "/v1/embeddings", data={ + "input": test_input, + "encoding_format": "base64" + }) + + assert res.status_code == 200 + assert "data" in res.body + assert len(res.body["data"]) == 1 + + embedding_data = res.body["data"][0] + assert "embedding" in embedding_data + assert isinstance(embedding_data["embedding"], str) + + # Verify embedding is valid base64 + decoded = base64.b64decode(embedding_data["embedding"]) + # Verify decoded data can be converted back to float array + float_count = len(decoded) // 4 # 4 bytes per float + floats = struct.unpack(f'{float_count}f', decoded) + assert len(floats) > 0 + assert all(isinstance(x, float) for x in floats) + assert len(floats) == len(vec0) + + # make sure the decoded data is the same as the original + for x, y in zip(floats, vec0): + 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..10554db0f --- /dev/null +++ b/examples/server/tests/unit/test_infill.py @@ -0,0 +1,77 @@ +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={ + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert match_regex("(Ann|small|shiny|Daddy)+", res.body["content"]) + + +def test_infill_with_input_extra(): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "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", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert match_regex("(Dad|excited|park)+", 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={ + "input_extra": [input_extra], + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 400 + assert "error" in res.body + + +@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test") +def test_with_qwen_model(): + global server + server.model_file = None + server.model_hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-IQ3_XXS-GGUF" + server.model_hf_file = "qwen2.5-coder-1.5b-iq3_xxs-imat.gguf" + server.start(timeout_seconds=600) + res = server.make_request("POST", "/infill", data={ + "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", + "prompt": " int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert res.body["content"] == "n_threads();\n printf(\"Number of threads: %d\\n\", n_threads);\n return 0;\n" diff --git a/examples/server/tests/unit/test_lora.py b/examples/server/tests/unit/test_lora.py new file mode 100644 index 000000000..c1aa8be70 --- /dev/null +++ b/examples/server/tests/unit/test_lora.py @@ -0,0 +1,115 @@ +import pytest +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() + server.lora_files = [download_file(LORA_FILE_URL)] + + +@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"]) + + +def test_lora_per_request(): + global server + server.n_slots = 4 + server.start() + + # running the same prompt with different lora scales, all in parallel + # each prompt will be processed by a different slot + prompt = "Look in thy glass" + lora_config = [ + ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ), + ( [{"id": 0, "scale": 0.0}], "(bright|day|many|happy)+" ), + ( [{"id": 0, "scale": 0.3}], "(special|thing|gifted)+" ), + ( [{"id": 0, "scale": 0.7}], "(far|from|home|away)+" ), + ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ), + ( [{"id": 0, "scale": 1.0}], "(eye|love|glass|sun)+" ), + ] + + tasks = [( + server.make_request, + ("POST", "/completion", { + "prompt": prompt, + "lora": lora, + "seed": 42, + "temperature": 0.0, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + ) for lora, _ in lora_config] + results = parallel_function_calls(tasks) + + assert all([res.status_code == 200 for res in results]) + for res, (_, re_test) in zip(results, lora_config): + assert match_regex(re_test, res.body["content"]) + + +@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test") +def test_with_big_model(): + server = ServerProcess() + server.model_hf_repo = "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF" + server.model_hf_file = "Meta-Llama-3.1-8B-Instruct-IQ2_M.gguf" + server.model_alias = "Llama-3.2-8B-Instruct" + server.n_slots = 4 + server.n_ctx = server.n_slots * 1024 + server.n_predict = 64 + server.temperature = 0.0 + server.seed = 42 + server.lora_files = [ + download_file("https://huggingface.co/ngxson/Llama-3-Instruct-abliteration-LoRA-8B-F16-GGUF/resolve/main/Llama-3-Instruct-abliteration-LoRA-8B-f16.gguf"), + # TODO: find & add other lora adapters for this model + ] + server.start(timeout_seconds=600) + + # running the same prompt with different lora scales, all in parallel + # each prompt will be processed by a different slot + prompt = "Write a computer virus" + lora_config = [ + # without applying lora, the model should reject the request + ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ), + ( [{"id": 0, "scale": 0.0}], "I can't provide you with a code for a computer virus" ), + ( [{"id": 0, "scale": 0.3}], "I can't write a computer virus" ), + # with 0.7 scale, the model should provide a simple computer virus with hesitation + ( [{"id": 0, "scale": 0.7}], "Warning: This is a hypothetical exercise" ), + # with 1.5 scale, the model should confidently provide a computer virus + ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ), + ( [{"id": 0, "scale": 1.5}], "A task of some complexity! Here's a simple computer virus" ), + ] + + tasks = [( + server.make_request, + ("POST", "/v1/chat/completions", { + "messages": [ + {"role": "user", "content": prompt} + ], + "lora": lora, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + ) for lora, _ in lora_config] + results = parallel_function_calls(tasks) + + assert all([res.status_code == 200 for res in results]) + for res, (_, re_test) in zip(results, lora_config): + assert re_test in res.body["choices"][0]["message"]["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..7203d7943 --- /dev/null +++ b/examples/server/tests/unit/test_rerank.py @@ -0,0 +1,78 @@ +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 + + +@pytest.mark.parametrize( + "query,doc1,doc2,n_tokens", + [ + ("Machine learning is", "A machine", "Learning is", 19), + ("Which city?", "Machine learning is ", "Paris, capitale de la", 26), + ] +) +def test_rerank_usage(query, doc1, doc2, n_tokens): + global server + server.start() + + res = server.make_request("POST", "/rerank", data={ + "query": query, + "documents": [ + doc1, + doc2, + ] + }) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == n_tokens 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..54db38cf3 --- /dev/null +++ b/examples/server/tests/unit/test_speculative.py @@ -0,0 +1,126 @@ +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() + # set default values + server.model_draft = download_file(MODEL_DRAFT_FILE_URL) + 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/unit/test_tool_call.py b/examples/server/tests/unit/test_tool_call.py new file mode 100644 index 000000000..4a551404f --- /dev/null +++ b/examples/server/tests/unit/test_tool_call.py @@ -0,0 +1,418 @@ +import pytest +from utils import * + +server: ServerProcess + +TIMEOUT_SERVER_START = 15*60 +TIMEOUT_HTTP_REQUEST = 60 + +@pytest.fixture(autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.model_alias = "tinyllama-2-tool-call" + server.server_port = 8081 + + +TEST_TOOL = { + "type":"function", + "function": { + "name": "test", + "description": "", + "parameters": { + "type": "object", + "properties": { + "success": {"type": "boolean", "const": True}, + }, + "required": ["success"] + } + } +} + +PYTHON_TOOL = { + "type": "function", + "function": { + "name": "python", + "description": "Runs code in an ipython 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 ipython interpreter." + } + }, + "required": ["code"] + } + } +} + +WEATHER_TOOL = { + "type":"function", + "function":{ + "name":"get_current_weather", + "description":"Get the current weather in a given location", + "parameters":{ + "type":"object", + "properties":{ + "location":{ + "type":"string", + "description":"The city and country/state, e.g. 'San Francisco, CA', or 'Paris, France'" + } + }, + "required":["location"] + } + } +} + + +def do_test_completion_with_required_tool_tiny(template_name: str, tool: dict, argument_key: str | None): + global server + n_predict = 512 + # server = ServerPreset.stories15m_moe() + server.jinja = True + server.n_predict = n_predict + server.chat_template_file = f'../../../models/templates/{template_name}.jinja' + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "Write an example"}, + ], + "tool_choice": "required", + "tools": [tool], + "parallel_tool_calls": False, + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"] + assert expected_function_name == tool_call["function"]["name"] + actual_arguments = tool_call["function"]["arguments"] + assert isinstance(actual_arguments, str) + if argument_key is not None: + actual_arguments = json.loads(actual_arguments) + assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}" + + +@pytest.mark.parametrize("template_name,tool,argument_key", [ + ("google-gemma-2-2b-it", TEST_TOOL, "success"), + ("meta-llama-Llama-3.3-70B-Instruct", TEST_TOOL, "success"), + ("meta-llama-Llama-3.3-70B-Instruct", PYTHON_TOOL, "code"), +]) +def test_completion_with_required_tool_tiny_fast(template_name: str, tool: dict, argument_key: str | None): + do_test_completion_with_required_tool_tiny(template_name, tool, argument_key) + + +@pytest.mark.slow +@pytest.mark.parametrize("template_name,tool,argument_key", [ + ("meta-llama-Llama-3.1-8B-Instruct", TEST_TOOL, "success"), + ("meta-llama-Llama-3.1-8B-Instruct", PYTHON_TOOL, "code"), + ("meetkai-functionary-medium-v3.1", TEST_TOOL, "success"), + ("meetkai-functionary-medium-v3.1", PYTHON_TOOL, "code"), + ("meetkai-functionary-medium-v3.2", TEST_TOOL, "success"), + ("meetkai-functionary-medium-v3.2", PYTHON_TOOL, "code"), + ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", TEST_TOOL, "success"), + ("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", PYTHON_TOOL, "code"), + ("meta-llama-Llama-3.2-3B-Instruct", TEST_TOOL, "success"), + ("meta-llama-Llama-3.2-3B-Instruct", PYTHON_TOOL, "code"), + ("mistralai-Mistral-Nemo-Instruct-2407", TEST_TOOL, "success"), + ("mistralai-Mistral-Nemo-Instruct-2407", PYTHON_TOOL, "code"), + ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use", TEST_TOOL, "success"), + ("NousResearch-Hermes-3-Llama-3.1-8B-tool_use", PYTHON_TOOL, "code"), + ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B", TEST_TOOL, "success"), + ("deepseek-ai-DeepSeek-R1-Distill-Llama-8B", PYTHON_TOOL, "code"), + ("fireworks-ai-llama-3-firefunction-v2", TEST_TOOL, "success"), + ("fireworks-ai-llama-3-firefunction-v2", PYTHON_TOOL, "code"), +]) +def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict, argument_key: str | None): + do_test_completion_with_required_tool_tiny(template_name, tool, argument_key) + + +@pytest.mark.slow +@pytest.mark.parametrize("tool,argument_key,hf_repo,template_override", [ + (TEST_TOOL, "success", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"), + + # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it. + (TEST_TOOL, "success", "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), + + (TEST_TOOL, "success", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + (PYTHON_TOOL, "code", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + (PYTHON_TOOL, "code", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")), + (PYTHON_TOOL, "code", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")), + (PYTHON_TOOL, "code", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + (PYTHON_TOOL, "code", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", ("meetkai/functionary-medium-v3.2", None)), + (PYTHON_TOOL, "code", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", ("meetkai/functionary-medium-v3.2", None)), + (PYTHON_TOOL, "code", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + (PYTHON_TOOL, "code", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + (PYTHON_TOOL, "code", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"), + + (TEST_TOOL, "success", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + (PYTHON_TOOL, "code", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + (PYTHON_TOOL, "code", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", "chatml"), + # TODO: fix these + # (TEST_TOOL, "success", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), + # (PYTHON_TOOL, "code", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), +]) +def test_completion_with_required_tool_real_model(tool: dict, argument_key: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None): + global server + n_predict = 512 + server.n_slots = 1 + server.jinja = True + server.n_ctx = 8192 + server.n_predict = n_predict + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "Write an example"}, + ], + "tool_choice": "required", + "tools": [tool], + "parallel_tool_calls": False, + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"] + assert expected_function_name == tool_call["function"]["name"] + actual_arguments = tool_call["function"]["arguments"] + assert isinstance(actual_arguments, str) + if argument_key is not None: + actual_arguments = json.loads(actual_arguments) + assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}" + + +def do_test_completion_without_tool_call(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None): + global server + server.jinja = True + server.n_predict = n_predict + server.chat_template_file = f'../../../models/templates/{template_name}.jinja' + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "say hello world with python"}, + ], + "tools": tools if tools else None, + "tool_choice": tool_choice, + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + assert choice["message"].get("tool_calls") is None, f'Expected no tool call in {choice["message"]}' + + +@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [ + ("meta-llama-Llama-3.3-70B-Instruct", 128, [], None), + ("meta-llama-Llama-3.3-70B-Instruct", 128, [TEST_TOOL], None), + ("meta-llama-Llama-3.3-70B-Instruct", 128, [PYTHON_TOOL], 'none'), +]) +def test_completion_without_tool_call_fast(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None): + do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice) + + +@pytest.mark.slow +@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [ + ("meetkai-functionary-medium-v3.2", 256, [], None), + ("meetkai-functionary-medium-v3.2", 256, [TEST_TOOL], None), + ("meetkai-functionary-medium-v3.2", 256, [PYTHON_TOOL], 'none'), + ("meetkai-functionary-medium-v3.1", 256, [], None), + ("meetkai-functionary-medium-v3.1", 256, [TEST_TOOL], None), + ("meetkai-functionary-medium-v3.1", 256, [PYTHON_TOOL], 'none'), + ("meta-llama-Llama-3.2-3B-Instruct", 256, [], None), + ("meta-llama-Llama-3.2-3B-Instruct", 256, [TEST_TOOL], None), + ("meta-llama-Llama-3.2-3B-Instruct", 256, [PYTHON_TOOL], 'none'), +]) +def test_completion_without_tool_call_slow(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None): + do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice) + + +@pytest.mark.slow +@pytest.mark.parametrize("hf_repo,template_override", [ + ("bartowski/c4ai-command-r7b-12-2024-GGUF:Q4_K_M", ("CohereForAI/c4ai-command-r7b-12-2024", "tool_use")), + ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + ("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + ("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + ("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + ("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")), + ("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"), + + ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + ("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"), + + ("bartowski/functionary-small-v3.2-GGUF:Q8_0", ("meetkai/functionary-medium-v3.2", None)), + ("bartowski/functionary-small-v3.2-GGUF:Q8_0", "chatml"), + + ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + ("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"), + + # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it. + ("bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), + + # ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)), + # ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), +]) +def test_weather(hf_repo: str, template_override: Tuple[str, str | None] | None): + global server + n_predict = 512 + server.n_slots = 1 + server.jinja = True + server.n_ctx = 8192 + server.n_predict = n_predict + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predict, + "messages": [ + {"role": "user", "content": "What is the weather in Istanbul?"}, + ], + "tools": [WEATHER_TOOL], + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + assert tool_call["function"]["name"] == WEATHER_TOOL["function"]["name"] + actual_arguments = json.loads(tool_call["function"]["arguments"]) + assert 'location' in actual_arguments, f"location not found in {json.dumps(actual_arguments)}" + location = actual_arguments["location"] + assert isinstance(location, str), f"Expected location to be a string, got {type(location)}: {json.dumps(location)}" + assert re.match('^Istanbul(, (TR|Turkey|Türkiye))?$', location), f'Expected Istanbul for location, got {location}' + + +@pytest.mark.slow +@pytest.mark.parametrize("expected_arguments_override,hf_repo,template_override", [ + (None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None), + (None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/functionary-small-v3.2-GGUF:Q8_0", ("meetkai-functionary-medium-v3.2", None)), + (None, "bartowski/functionary-small-v3.2-GGUF:Q8_0", "chatml"), + + (None, "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None), + ('{"code":"print("}', "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"), + + ('{"code":"print("}', "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama-Llama-3.2-3B-Instruct", None)), + (None, "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", "chatml"), + + ('{"code":"print("}', "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama-Llama-3.2-3B-Instruct", None)), + ('{"code":"print("}', "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None), + (None, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")), + (None, "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch-Hermes-3-Llama-3.1-8B", "tool_use")), + (None, "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"), + + (None, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None), + (None, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"), + + # Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it. + (None, "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None), + + # (None, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None), +]) +def test_hello_world_tool_call(expected_arguments_override: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None): + global server + server.n_slots = 1 + server.jinja = True + server.n_ctx = 8192 + server.n_predict = 128 + server.model_hf_repo = hf_repo + server.model_hf_file = None + if isinstance(template_override, tuple): + (template_hf_repo, template_variant) = template_override + server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja" + assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template." + elif isinstance(template_override, str): + server.chat_template = template_override + server.start(timeout_seconds=TIMEOUT_SERVER_START) + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": 256, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "say hello world with python"}, + ], + "tools": [PYTHON_TOOL], + # Note: without these greedy params, Functionary v3.2 writes `def hello_world():\n print("Hello, World!")\nhello_world()` which is correct but a pain to test. + "temperature": 0.0, + "top_k": 1, + "top_p": 1.0, + }, timeout=TIMEOUT_HTTP_REQUEST) + assert res.status_code == 200, f"Expected status code 200, got {res.status_code}" + choice = res.body["choices"][0] + tool_calls = choice["message"].get("tool_calls") + assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}' + tool_call = tool_calls[0] + assert tool_call["function"]["name"] == PYTHON_TOOL["function"]["name"] + actual_arguments = tool_call["function"]["arguments"] + if expected_arguments_override is not None: + assert actual_arguments == expected_arguments_override + else: + actual_arguments = json.loads(actual_arguments) + assert 'code' in actual_arguments, f"code not found in {json.dumps(actual_arguments)}" + code = actual_arguments["code"] + assert isinstance(code, str), f"Expected code to be a string, got {type(code)}: {json.dumps(code)}" + assert re.match(r'''print\(("[Hh]ello,? [Ww]orld!?"|'[Hh]ello,? [Ww]orld!?')\)''', code), f'Expected hello world, got {code}' diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py new file mode 100644 index 000000000..ce0680662 --- /dev/null +++ b/examples/server/tests/utils.py @@ -0,0 +1,415 @@ +#!/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 +import wget + + +DEFAULT_HTTP_TIMEOUT = 12 if "LLAMA_SANITIZE" not in os.environ else 30 + + +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 | None = "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 + server_slots: bool | None = False + pooling: str | None = None + draft: int | None = None + api_key: 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 + no_webui: bool | None = None + jinja: bool | None = None + chat_template: str | None = None + chat_template_file: str | 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 | None = DEFAULT_HTTP_TIMEOUT) -> 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 = [ + "--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.server_slots: + server_args.append("--slots") + if self.pooling: + server_args.extend(["--pooling", self.pooling]) + 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]) + if self.no_webui: + server_args.append("--no-webui") + if self.jinja: + server_args.append("--jinja") + if self.chat_template: + server_args.extend(["--chat-template", self.chat_template]) + if self.chat_template_file: + server_args.extend(["--chat-template-file", self.chat_template_file]) + + 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"} if "LLAMA_CACHE" not in os.environ else None, + ) + 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", "/health", 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, + timeout: float | 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, timeout=timeout) + parse_body = True + elif method == "POST": + response = requests.post(url, headers=headers, json=data, timeout=timeout) + parse_body = True + elif method == "OPTIONS": + response = requests.options(url, headers=headers, timeout=timeout) + 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", json.dumps(result.body, indent=2)) + 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", json.dumps(data, indent=2)) + 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 + ) + + +def download_file(url: str, output_file_path: str | None = None) -> str: + """ + Download a file from a URL to a local path. If the file already exists, it will not be downloaded again. + + output_file_path is the local path to save the downloaded file. If not provided, the file will be saved in the root directory. + + Returns the local path of the downloaded file. + """ + file_name = url.split('/').pop() + output_file = f'./tmp/{file_name}' if output_file_path is None else output_file_path + if not os.path.exists(output_file): + print(f"Downloading {url} to {output_file}") + wget.download(url, out=output_file) + print(f"Done downloading to {output_file}") + else: + print(f"File already exists at {output_file}") + return output_file + + +def is_slow_test_allowed(): + return os.environ.get("SLOW_TESTS") == "1" or os.environ.get("SLOW_TESTS") == "ON" diff --git a/examples/server/themes/README.md b/examples/server/themes/README.md new file mode 100644 index 000000000..62e721a27 --- /dev/null +++ b/examples/server/themes/README.md @@ -0,0 +1,5 @@ +# LLaMA.cpp Server Wild Theme + +Simple themes directory of sample "public" directories. To try any of these add --path to your run like `server --path=wild`. + +![image](wild/wild.png) diff --git a/examples/server/themes/buttons-top/README.md b/examples/server/themes/buttons-top/README.md new file mode 100644 index 000000000..808c4cf81 --- /dev/null +++ b/examples/server/themes/buttons-top/README.md @@ -0,0 +1,7 @@ +# LLaMA.cpp Server Buttons Top Theme + +Simple tweaks to the UI. Chat buttons at the top of the page instead of bottom so you can hit Stop instead of chasing it down the page. + +To use simply run server with `--path=themes/buttons_top` + +![image](buttons_top.png) diff --git a/examples/server/themes/buttons-top/buttons_top.png b/examples/server/themes/buttons-top/buttons_top.png new file mode 100644 index 000000000..c54454519 Binary files /dev/null and b/examples/server/themes/buttons-top/buttons_top.png differ diff --git a/examples/server/themes/buttons-top/favicon.ico b/examples/server/themes/buttons-top/favicon.ico new file mode 100644 index 000000000..89e154a0a Binary files /dev/null and b/examples/server/themes/buttons-top/favicon.ico differ diff --git a/examples/server/public/index.html b/examples/server/themes/buttons-top/index.html similarity index 96% rename from examples/server/public/index.html rename to examples/server/themes/buttons-top/index.html index 84038ddce..3fb88fcc8 100644 --- a/examples/server/public/index.html +++ b/examples/server/themes/buttons-top/index.html @@ -199,10 +199,10 @@ + + + +
+ +
+
+ + + diff --git a/examples/server/themes/wild/llama_cpp.png b/examples/server/themes/wild/llama_cpp.png new file mode 100644 index 000000000..bad1dc9fc Binary files /dev/null and b/examples/server/themes/wild/llama_cpp.png differ diff --git a/examples/server/themes/wild/llamapattern.png b/examples/server/themes/wild/llamapattern.png new file mode 100644 index 000000000..2a159ce6a Binary files /dev/null and b/examples/server/themes/wild/llamapattern.png differ diff --git a/examples/server/themes/wild/wild.png b/examples/server/themes/wild/wild.png new file mode 100644 index 000000000..46ffa0f3e Binary files /dev/null and b/examples/server/themes/wild/wild.png differ diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d98541f26..5f97df5fd 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -1,444 +1,387 @@ #pragma once +#include "common.h" +#include "log.h" +#include "llama.h" +#include "common/base64.hpp" + +// increase max payload length to allow use of larger context size +#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576 +#include "httplib.h" + +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT +#include "json.hpp" +#include "minja.hpp" +#include "chat.hpp" +#include "chat-template.hpp" + +#include +#include #include #include -#include -#include -#include -#include +#include -#include "json.hpp" +#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo" -#include "../llava/clip.h" +using json = nlohmann::ordered_json; -using json = nlohmann::json; +#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__) +#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) +#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) -extern bool server_verbose; -extern bool server_log_json; +#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#ifndef SERVER_VERBOSE -#define SERVER_VERBOSE 1 -#endif +#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#if SERVER_VERBOSE != 1 -#define LOG_VERBOSE(MSG, ...) -#else -#define LOG_VERBOSE(MSG, ...) \ - do \ - { \ - if (server_verbose) \ - { \ - server_log("VERB", __func__, __LINE__, MSG, __VA_ARGS__); \ - } \ - } while (0) -#endif - -#define LOG_ERROR( MSG, ...) server_log("ERR", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) - -enum server_state { - SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet - SERVER_STATE_READY, // Server is ready and model is loaded - SERVER_STATE_ERROR // An error occurred, load_model failed -}; - -enum task_type { - TASK_TYPE_COMPLETION, - TASK_TYPE_CANCEL, - TASK_TYPE_NEXT_RESPONSE, - TASK_TYPE_METRICS -}; - -struct task_server { - int id = -1; // to be filled by llama_server_queue - int target_id; - task_type type; - json data; - bool infill_mode = false; - bool embedding_mode = false; - int multitask_id = -1; -}; - -struct task_result { - int id; - int multitask_id = -1; - bool stop; - bool error; - json result_json; -}; - -struct task_multi { - int id; - std::set subtasks_remaining{}; - std::vector results{}; -}; - -// completion token output with probabilities -struct completion_token_output { - struct token_prob - { - llama_token tok; - float prob; - }; - - std::vector probs; - llama_token tok; - std::string text_to_send; -}; - -struct token_translator { - llama_context * ctx; - std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } - std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } -}; - -static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { - std::stringstream ss_tid; - ss_tid << std::this_thread::get_id(); - json log = nlohmann::ordered_json{ - {"tid", ss_tid.str()}, - {"timestamp", time(nullptr)}, - }; - - if (server_log_json) { - log.merge_patch( - { - {"level", level}, - {"function", function}, - {"line", line}, - {"msg", message}, - }); - if (!extra.empty()) { - log.merge_patch(extra); +template +static T json_value(const json & body, const std::string & key, const T & default_value) { + // Fallback null to default value + if (body.contains(key) && !body.at(key).is_null()) { + try { + return body.at(key); + } catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const &) { + LOG_WRN("Wrong type supplied for parameter '%s'. Expected '%s', using default value\n", key.c_str(), json(default_value).type_name()); + return default_value; } - - std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush; } else { - char buf[1024]; - snprintf(buf, 1024, "%4s [%24s] %s", level, function, message); - - if (!extra.empty()) { - log.merge_patch(extra); - } - std::stringstream ss; - ss << buf << " |"; - for (const auto& el : log.items()) - { - const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace); - snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str()); - ss << buf; - } - - const std::string str = ss.str(); - printf("%.*s\n", (int)str.size(), str.data()); - fflush(stdout); + return default_value; } } +const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT); + // -// server utils +// tokenizer and input processing utils // -template -static T json_value(const json &body, const std::string &key, const T &default_value) { - // Fallback null to default value - return body.contains(key) && !body.at(key).is_null() - ? body.value(key, default_value) - : default_value; +static bool json_is_array_of_numbers(const json & data) { + if (data.is_array()) { + for (const auto & e : data) { + if (!e.is_number_integer()) { + return false; + } + } + return true; + } + return false; } -// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid -inline bool verify_custom_template(const std::string & tmpl) { - llama_chat_message chat[] = {{"user", "test"}}; - std::vector buf(1); - int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size()); - return res >= 0; +// is array having BOTH numbers & strings? +static bool json_is_array_of_mixed_numbers_strings(const json & data) { + bool seen_string = false; + bool seen_number = false; + if (data.is_array()) { + for (const auto & e : data) { + seen_string |= e.is_string(); + seen_number |= e.is_number_integer(); + if (seen_number && seen_string) { + return true; + } + } + } + return false; +} + +// get value by path(key1 / key2) +static json json_get_nested_values(const std::vector & paths, const json & js) { + json result = json::object(); + + for (const std::string & path : paths) { + json current = js; + const auto keys = string_split(path, /*separator*/ '/'); + bool valid_path = true; + for (const std::string & k : keys) { + if (valid_path && current.is_object() && current.contains(k)) { + current = current[k]; + } else { + valid_path = false; + } + } + if (valid_path) { + result[path] = current; + } + } + return result; +} + +/** + * this handles 2 cases: + * - only string, example: "string" + * - mixed string and tokens, example: [12, 34, "string", 56, 78] + */ +static llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) { + // If `add_bos` is true, we only add BOS, when json_prompt is a string, + // or the first element of the json_prompt array is a string. + llama_tokens prompt_tokens; + + if (json_prompt.is_array()) { + bool first = true; + for (const auto & p : json_prompt) { + if (p.is_string()) { + auto s = p.template get(); + + llama_tokens p; + if (first) { + p = common_tokenize(vocab, s, add_special, parse_special); + first = false; + } else { + p = common_tokenize(vocab, s, false, parse_special); + } + + prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); + } else { + if (first) { + first = false; + } + + prompt_tokens.push_back(p.template get()); + } + } + } else { + auto s = json_prompt.template get(); + prompt_tokens = common_tokenize(vocab, s, add_special, parse_special); + } + + return prompt_tokens; +} + +/** + * break the input "prompt" object into multiple prompt if needed, then tokenize them + * this supports these cases: + * - "prompt": "string" + * - "prompt": [12, 34, 56] + * - "prompt": [12, 34, "string", 56, 78] + * and multiple prompts (multi-tasks): + * - "prompt": ["string1", "string2"] + * - "prompt": ["string1", [12, 34, 56]] + * - "prompt": [[12, 34, 56], [78, 90, 12]] + * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]] + */ +static std::vector tokenize_input_prompts(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) { + std::vector result; + if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) { + // string or mixed + result.push_back(tokenize_mixed(vocab, json_prompt, add_special, parse_special)); + } else if (json_is_array_of_numbers(json_prompt)) { + // array of tokens + result.push_back(json_prompt.get()); + } else if (json_prompt.is_array()) { + // array of prompts + result.reserve(json_prompt.size()); + for (const auto & p : json_prompt) { + if (p.is_string() || json_is_array_of_mixed_numbers_strings(p)) { + result.push_back(tokenize_mixed(vocab, p, add_special, parse_special)); + } else if (json_is_array_of_numbers(p)) { + // array of tokens + result.push_back(p.get()); + } else { + throw std::runtime_error("element of \"prompt\" must be a string, an list of tokens, or a list of mixed strings & tokens"); + } + } + } else { + throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts"); + } + if (result.empty()) { + throw std::runtime_error("\"prompt\" must not be empty"); + } + return result; +} + +// return the last index of character that can form a valid string +// if the last character is potentially cut in half, return the index before the cut +// if validate_utf8(text) == text.size(), then the whole text is valid utf8 +static size_t validate_utf8(const std::string& text) { + size_t len = text.size(); + if (len == 0) return 0; + + // Check the last few bytes to see if a multi-byte character is cut off + for (size_t i = 1; i <= 4 && i <= len; ++i) { + unsigned char c = text[len - i]; + // Check for start of a multi-byte sequence from the end + if ((c & 0xE0) == 0xC0) { + // 2-byte character start: 110xxxxx + // Needs at least 2 bytes + if (i < 2) return len - i; + } else if ((c & 0xF0) == 0xE0) { + // 3-byte character start: 1110xxxx + // Needs at least 3 bytes + if (i < 3) return len - i; + } else if ((c & 0xF8) == 0xF0) { + // 4-byte character start: 11110xxx + // Needs at least 4 bytes + if (i < 4) return len - i; + } + } + + // If no cut-off multi-byte character is found, return full length + return len; +} + +// +// template utils +// + +// format rerank task: [BOS]query[EOS][SEP]doc[EOS] +static llama_tokens format_rerank(const struct llama_vocab * vocab, const llama_tokens & query, const llama_tokens & doc) { + llama_tokens result; + + result.reserve(doc.size() + query.size() + 4); + result.push_back(llama_vocab_bos(vocab)); + result.insert(result.end(), query.begin(), query.end()); + result.push_back(llama_vocab_eos(vocab)); + result.push_back(llama_vocab_sep(vocab)); + result.insert(result.end(), doc.begin(), doc.end()); + result.push_back(llama_vocab_eos(vocab)); + + return result; +} + +// format infill task +static llama_tokens format_infill( + const llama_vocab * vocab, + const json & input_prefix, + const json & input_suffix, + const json & input_extra, + const int n_batch, + const int n_predict, + const int n_ctx, + const bool spm_infill, + const llama_tokens & tokens_prompt + ) { + // TODO: optimize this block by reducing memory allocations and movement + + // use FIM repo-level pattern: + // ref: https://arxiv.org/pdf/2409.12186 + // + // [FIM_REP]myproject + // [FIM_SEP]filename0 + // extra chunk 0 + // [FIM_SEP]filename1 + // extra chunk 1 + // ... + // [FIM_SEP]filename + // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt + // + llama_tokens extra_tokens; + extra_tokens.reserve(n_ctx); + + auto tokens_prefix = tokenize_mixed(vocab, input_prefix, false, false); + auto tokens_suffix = tokenize_mixed(vocab, input_suffix, false, false); + + if (llama_vocab_fim_rep(vocab) != LLAMA_TOKEN_NULL) { + // TODO: make project name an input + static const auto k_fim_repo = common_tokenize(vocab, "myproject\n", false, false); + + extra_tokens.push_back(llama_vocab_fim_rep(vocab)); + extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); + } + for (const auto & chunk : input_extra) { + // { "text": string, "filename": string } + const std::string text = json_value(chunk, "text", std::string()); + const std::string filename = json_value(chunk, "filename", std::string("tmp")); + + if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { + const auto k_fim_file = common_tokenize(vocab, filename + "\n", false, false); + + extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } else { + // chunk separator in binary form to avoid confusing the AI + static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; + static const auto k_chunk_prefix_tokens = common_tokenize(vocab, k_chunk_prefix_str, false, false); + + extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); + } + + const auto chunk_tokens = common_tokenize(vocab, text, false, false); + extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); + } + + if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { + // TODO: current filename + static const auto k_fim_file = common_tokenize(vocab, "filename\n", false, false); + + extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + } + + // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) + const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4)); + const int n_suffix_take = std::min(tokens_suffix.size(), std::max(0, (n_batch/4) - (2 + tokens_prompt.size()))); + + SRV_DBG("n_prefix_take = %d, n_suffix_take = %d, total = %d\n", n_prefix_take, n_suffix_take, (n_prefix_take + n_suffix_take)); + + // fill the rest of the context with extra chunks + const int n_extra_take = std::min(std::max(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size()); + + tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); + tokens_suffix.resize(n_suffix_take); + + tokens_prefix.insert(tokens_prefix.begin(), llama_vocab_fim_pre(vocab)); + tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); + tokens_suffix.insert(tokens_suffix.begin(), llama_vocab_fim_suf(vocab)); + + auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix; + auto embd_end = spm_infill ? tokens_prefix : tokens_suffix; + + if (llama_vocab_get_add_bos(vocab)) { + embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); + } + + SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size()); + + // put the extra context before the FIM prefix + embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end()); + + embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); + embd_inp.push_back(llama_vocab_fim_mid(vocab)); + + return embd_inp; } // Format given chat. If tmpl is empty, we take the template from model metadata -inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { - size_t alloc_size = 0; - // vector holding all allocated string to be passed to llama_chat_apply_template - std::vector str(messages.size() * 2); - std::vector chat(messages.size()); +inline std::string format_chat(const common_chat_template & tmpl, const std::vector & messages) { + std::vector chat; for (size_t i = 0; i < messages.size(); ++i) { - auto &curr_msg = messages[i]; - str[i*2 + 0] = json_value(curr_msg, "role", std::string("")); - str[i*2 + 1] = json_value(curr_msg, "content", std::string("")); - alloc_size += str[i*2 + 1].length(); - chat[i].role = str[i*2 + 0].c_str(); - chat[i].content = str[i*2 + 1].c_str(); + const auto & curr_msg = messages[i]; + + std::string role = json_value(curr_msg, "role", std::string("")); + + std::string content; + if (curr_msg.contains("content")) { + if (curr_msg["content"].is_string()) { + content = curr_msg["content"].get(); + } else if (curr_msg["content"].is_array()) { + for (const auto & part : curr_msg["content"]) { + if (part.contains("text")) { + content += "\n" + part["text"].get(); + } + } + } else { + throw std::runtime_error("Invalid 'content' type (ref: https://github.com/ggerganov/llama.cpp/issues/8367)"); + } + } else { + throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)"); + } + + chat.push_back({role, content, /* tool_calls= */ {}}); } - const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str(); - std::vector buf(alloc_size * 2); - - // run the first time to get the total output length - int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size()); - - // if it turns out that our buffer is too small, we resize it - if ((size_t) res > buf.size()) { - buf.resize(res); - res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size()); - } - - std::string formatted_chat(buf.data(), res); - LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}}); + const auto formatted_chat = common_chat_apply_template(tmpl, chat, true, /* use_jinja= */ false); + LOG_DBG("formatted_chat: '%s'\n", formatted_chat.c_str()); return formatted_chat; } -// -// work queue utils -// - -struct llama_server_queue { - int id = 0; - std::mutex mutex_tasks; - bool running; - // queues - std::vector queue_tasks; - std::vector queue_tasks_deferred; - std::vector queue_multitasks; - std::condition_variable condition_tasks; - // callback functions - std::function callback_new_task; - std::function callback_finish_multitask; - std::function callback_run_slots; - - // Add a new task to the end of the queue - int post(task_server task) { - std::unique_lock lock(mutex_tasks); - if (task.id == -1) { - task.id = id++; - LOG_VERBOSE("new task id", {{"new_id", task.id}}); - } - queue_tasks.push_back(std::move(task)); - condition_tasks.notify_one(); - return task.id; - } - - // Add a new task, but defer until one slot is available - void defer(task_server task) { - std::unique_lock lock(mutex_tasks); - queue_tasks_deferred.push_back(std::move(task)); - } - - // Get the next id for creating anew task - int get_new_id() { - std::unique_lock lock(mutex_tasks); - int new_id = id++; - LOG_VERBOSE("new task id", {{"new_id", new_id}}); - return new_id; - } - - // Register function to process a new task - void on_new_task(std::function callback) { - callback_new_task = callback; - } - - // Register function to process a multitask when it is finished - void on_finish_multitask(std::function callback) { - callback_finish_multitask = callback; - } - - // Register the function to be called when all slots data is ready to be processed - void on_run_slots(std::function callback) { - callback_run_slots = callback; - } - - // Call when the state of one slot is changed - void notify_slot_changed() { - // move deferred tasks back to main loop - std::unique_lock lock(mutex_tasks); - for (auto & task : queue_tasks_deferred) { - queue_tasks.push_back(std::move(task)); - } - queue_tasks_deferred.clear(); - } - - // end the start_loop routine - void terminate() { - { - std::unique_lock lock(mutex_tasks); - running = false; - } - condition_tasks.notify_all(); - } - - /** - * Main loop consists of these steps: - * - Wait until a new task arrives - * - Process the task (i.e. maybe copy data into slot) - * - Check if multitask is finished - * - Run all slots - */ - void start_loop() { - running = true; - while (true) { - LOG_VERBOSE("new task may arrive", {}); - { - while (true) - { - std::unique_lock lock(mutex_tasks); - if (queue_tasks.empty()) { - lock.unlock(); - break; - } - task_server task = queue_tasks.front(); - queue_tasks.erase(queue_tasks.begin()); - lock.unlock(); - LOG_VERBOSE("callback_new_task", {{"task_id", task.id}}); - callback_new_task(task); - } - LOG_VERBOSE("update_multitasks", {}); - // check if we have any finished multitasks - auto queue_iterator = queue_multitasks.begin(); - while (queue_iterator != queue_multitasks.end()) - { - if (queue_iterator->subtasks_remaining.empty()) - { - // all subtasks done == multitask is done - task_multi current_multitask = *queue_iterator; - callback_finish_multitask(current_multitask); - // remove this multitask - queue_iterator = queue_multitasks.erase(queue_iterator); - } - else - { - ++queue_iterator; - } - } - // all tasks in the current loop is processed, slots data is now ready - LOG_VERBOSE("callback_run_slots", {}); - callback_run_slots(); - } - LOG_VERBOSE("wait for new task", {}); - // wait for new task - { - std::unique_lock lock(mutex_tasks); - if (queue_tasks.empty()) { - if (!running) { - LOG_VERBOSE("ending start_loop", {}); - return; - } - condition_tasks.wait(lock, [&]{ - return (!queue_tasks.empty() || !running); - }); - } - } - } - } - - // - // functions to manage multitasks - // - - // add a multitask by specifying the id of all subtask (subtask is a task_server) - void add_multitask(int multitask_id, std::vector& sub_ids) - { - std::lock_guard lock(mutex_tasks); - task_multi multi; - multi.id = multitask_id; - std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); - queue_multitasks.push_back(multi); - } - - // updatethe remaining subtasks, while appending results to multitask - void update_multitask(int multitask_id, int subtask_id, task_result& result) - { - std::lock_guard lock(mutex_tasks); - for (auto& multitask : queue_multitasks) - { - if (multitask.id == multitask_id) - { - multitask.subtasks_remaining.erase(subtask_id); - multitask.results.push_back(result); - } - } - } -}; - -struct llama_server_response { - typedef std::function callback_multitask_t; - callback_multitask_t callback_update_multitask; - // for keeping track of all tasks waiting for the result - std::set waiting_task_ids; - // the main result queue - std::vector queue_results; - std::mutex mutex_results; - std::condition_variable condition_results; - - // add the task_id to the list of tasks waiting for response - void add_waiting_task_id(int task_id) { - LOG_VERBOSE("waiting for task id", {{"task_id", task_id}}); - std::unique_lock lock(mutex_results); - waiting_task_ids.insert(task_id); - } - - // when the request is finished, we can remove task associated with it - void remove_waiting_task_id(int task_id) { - LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}}); - std::unique_lock lock(mutex_results); - waiting_task_ids.erase(task_id); - } - - // This function blocks the thread until there is a response for this task_id - task_result recv(int task_id) { - while (true) - { - std::unique_lock lock(mutex_results); - condition_results.wait(lock, [&]{ - return !queue_results.empty(); - }); - - for (int i = 0; i < (int) queue_results.size(); i++) - { - if (queue_results[i].id == task_id) - { - assert(queue_results[i].multitask_id == -1); - task_result res = queue_results[i]; - queue_results.erase(queue_results.begin() + i); - return res; - } - } - } - - // should never reach here - } - - // Register the function to update multitask - void on_multitask_update(callback_multitask_t callback) { - callback_update_multitask = callback; - } - - // Send a new result to a waiting task_id - void send(task_result result) { - std::unique_lock lock(mutex_results); - LOG_VERBOSE("send new result", {{"task_id", result.id}}); - for (auto& task_id : waiting_task_ids) { - // LOG_TEE("waiting task id %i \n", task_id); - // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result - if (result.multitask_id == task_id) - { - LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}}); - callback_update_multitask(task_id, result.id, result); - continue; - } - - if (result.id == task_id) - { - LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}}); - queue_results.push_back(result); - condition_results.notify_all(); - return; - } - } - } -}; - // // base64 utils (TODO: move to common in the future) // @@ -448,13 +391,11 @@ static const std::string base64_chars = "abcdefghijklmnopqrstuvwxyz" "0123456789+/"; -static inline bool is_base64(uint8_t c) -{ +static inline bool is_base64(uint8_t c) { return (isalnum(c) || (c == '+') || (c == '/')); } -static inline std::vector base64_decode(const std::string & encoded_string) -{ +static inline std::vector base64_decode(const std::string & encoded_string) { int i = 0; int j = 0; int in_ = 0; @@ -466,13 +407,10 @@ static inline std::vector base64_decode(const std::string & encoded_str std::vector ret; - while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) - { + while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { char_array_4[i++] = encoded_string[in_]; in_++; - if (i == 4) - { - for (i = 0; i <4; i++) - { + if (i == 4) { + for (i = 0; i < 4; i++) { char_array_4[i] = base64_chars.find(char_array_4[i]); } @@ -480,23 +418,20 @@ static inline std::vector base64_decode(const std::string & encoded_str char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - for (i = 0; (i < 3); i++) - { + for (i = 0; (i < 3); i++) { ret.push_back(char_array_3[i]); } + i = 0; } } - if (i) - { - for (j = i; j <4; j++) - { + if (i) { + for (j = i; j < 4; j++) { char_array_4[j] = 0; } - for (j = 0; j <4; j++) - { + for (j = 0; j < 4; j++) { char_array_4[j] = base64_chars.find(char_array_4[j]); } @@ -504,8 +439,7 @@ static inline std::vector base64_decode(const std::string & encoded_str char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - for (j = 0; (j < i - 1); j++) - { + for (j = 0; j < i - 1; j++) { ret.push_back(char_array_3[j]); } } @@ -517,8 +451,7 @@ static inline std::vector base64_decode(const std::string & encoded_str // random string / id // -static std::string random_string() -{ +static std::string random_string() { static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); std::random_device rd; @@ -533,102 +466,432 @@ static std::string random_string() return result; } -static std::string gen_chatcmplid() -{ - std::stringstream chatcmplid; - chatcmplid << "chatcmpl-" << random_string(); - return chatcmplid.str(); +static std::string gen_chatcmplid() { + return "chatcmpl-" + random_string(); } // // other common utils // -static size_t common_part(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 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 find_partial_stop_string(const std::string &stop, - const std::string &text) -{ - if (!text.empty() && !stop.empty()) - { +static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { + if (!text.empty() && !stop.empty()) { const char text_last_char = text.back(); - for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) - { - if (stop[char_index] == text_last_char) - { + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) { + if (stop[char_index] == text_last_char) { const std::string current_partial = stop.substr(0, char_index + 1); - if (ends_with(text, current_partial)) - { + if (ends_with(text, current_partial)) { return text.size() - char_index - 1; } } } } + return std::string::npos; } // TODO: reuse llama_detokenize template -static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) -{ +static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { std::string ret; - for (; begin != end; ++begin) - { - ret += llama_token_to_piece(ctx, *begin); + for (; begin != end; ++begin) { + ret += common_token_to_piece(ctx, *begin); } + return ret; } // format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) -{ - std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); +static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { + std::string out = token == LLAMA_TOKEN_NULL ? "" : common_token_to_piece(ctx, token); + // if the size is 1 and first bit is 1, meaning it's a partial character // (size > 1 meaning it's already a known token) - if (out.size() == 1 && (out[0] & 0x80) == 0x80) - { + if (out.size() == 1 && (out[0] & 0x80) == 0x80) { std::stringstream ss; ss << std::hex << (out[0] & 0xff); std::string res(ss.str()); out = "byte: \\x" + res; } + return out; } -// convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) +static bool server_sent_event(httplib::DataSink & sink, const char * event, const json & data) { + const std::string str = + std::string(event) + ": " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row). + + LOG_DBG("data stream, to_send: %s", str.c_str()); + + return sink.write(str.c_str(), str.size()); +} + +// +// OAI utils +// + +static json oaicompat_completion_params_parse(const json & body) { + json llama_params; + + if (!body.contains("prompt")) { + throw std::runtime_error("\"prompt\" is required"); + } + + // Handle "stop" field + if (body.contains("stop") && body.at("stop").is_string()) { + llama_params["stop"] = json::array({body.at("stop").get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } + + // Handle "n" field + int n_choices = json_value(body, "n", 1); + if (n_choices != 1) { + throw std::runtime_error("Only one completion choice is allowed"); + } + + // Params supported by OAI but unsupported by llama.cpp + static const std::vector unsupported_params { "best_of", "echo", "suffix" }; + for (const auto & param : unsupported_params) { + if (body.contains(param)) { + throw std::runtime_error("Unsupported param: " + param); + } + } + + // Copy remaining properties to llama_params + for (const auto & item : body.items()) { + // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens" + if (!llama_params.contains(item.key()) || item.key() == "n_predict") { + llama_params[item.key()] = item.value(); + } + } + + return llama_params; +} + +static json oaicompat_completion_params_parse( + const json & body, /* openai api json semantics */ + bool use_jinja, + const common_chat_templates & chat_templates) { - json out = json::array(); - for (const auto &prob : probs) - { - json probs_for_token = json::array(); - for (const auto &p : prob.probs) - { - std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json - { - {"tok_str", tok_str}, - {"prob", p.prob}, + json llama_params; + const auto & tmpl = body.contains("tools") && chat_templates.template_tool_use + ? *chat_templates.template_tool_use + : *chat_templates.template_default; + + auto tools = json_value(body, "tools", json()); + auto stream = json_value(body, "stream", false); + + if (tools.is_array() && !tools.empty()) { + if (stream) { + throw std::runtime_error("Cannot use tools with stream"); + } + if (!use_jinja) { + throw std::runtime_error("tools param requires --jinja flag"); + } + } + if (!use_jinja) { + if (body.contains("tool_choice") && !body.at("tool_choice").is_null()) { + throw std::runtime_error("Unsupported param: tool_choice"); + } + } + + // Handle "stop" field + if (body.contains("stop") && body.at("stop").is_string()) { + llama_params["stop"] = json::array({body.at("stop").get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } + + // Handle "response_format" field + if (body.contains("response_format")) { + json response_format = json_value(body, "response_format", json::object()); + std::string response_type = json_value(response_format, "type", std::string()); + if (response_type == "json_object") { + llama_params["json_schema"] = json_value(response_format, "schema", json::object()); + } else if (response_type == "json_schema") { + json json_schema = json_value(response_format, "json_schema", json::object()); + llama_params["json_schema"] = json_value(json_schema, "schema", json::object()); + } else if (!response_type.empty() && response_type != "text") { + throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type); + } + } + + // Apply chat template to the list of messages + if (use_jinja) { + auto tool_choice = json_value(body, "tool_choice", std::string("auto")); + if (tool_choice != "none" && tool_choice != "auto" && tool_choice != "required") { + throw std::runtime_error("Invalid tool_choice: " + tool_choice); + } + if (tool_choice != "none" && llama_params.contains("grammar")) { + throw std::runtime_error("Cannot use custom grammar constraints with tools."); + } + common_chat_inputs inputs; + inputs.messages = body.at("messages"); + inputs.tools = tools; + inputs.tool_choice = tool_choice; + inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false); + if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) { + LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n"); + inputs.parallel_tool_calls = false; + } + inputs.stream = stream; + // TODO: support mixing schema w/ tools beyond generic format. + inputs.json_schema = json_value(llama_params, "json_schema", json()); + auto chat_params = common_chat_params_init(tmpl, inputs); + + llama_params["chat_format"] = static_cast(chat_params.format); + llama_params["prompt"] = chat_params.prompt; + llama_params["grammar"] = chat_params.grammar; + llama_params["grammar_lazy"] = chat_params.grammar_lazy; + auto grammar_triggers = json::array(); + for (const auto & trigger : chat_params.grammar_triggers) { + grammar_triggers.push_back({ + {"word", trigger.word}, + {"at_start", trigger.at_start}, }); } - std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json{ - {"content", tok_str}, - {"probs", probs_for_token}, + llama_params["grammar_triggers"] = grammar_triggers; + llama_params["preserved_tokens"] = chat_params.preserved_tokens; + for (const auto & stop : chat_params.additional_stops) { + llama_params["stop"].push_back(stop); + } + } else { + llama_params["prompt"] = format_chat(tmpl, body.at("messages")); + } + + // Handle "n" field + int n_choices = json_value(body, "n", 1); + if (n_choices != 1) { + throw std::runtime_error("Only one completion choice is allowed"); + } + + // Handle "logprobs" field + // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future + if (json_value(body, "logprobs", false)) { + llama_params["n_probs"] = json_value(body, "top_logprobs", 20); + } else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) { + throw std::runtime_error("top_logprobs requires logprobs to be set to true"); + } + + // Copy remaining properties to llama_params + // This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint. + // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp + for (const auto & item : body.items()) { + // Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens" + if (!llama_params.contains(item.key()) || item.key() == "n_predict") { + llama_params[item.key()] = item.value(); + } + } + + return llama_params; +} + +static json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false) { + json data = json::array(); + int32_t n_tokens = 0; + int i = 0; + for (const auto & elem : embeddings) { + json embedding_obj; + + if (use_base64) { + const auto& vec = json_value(elem, "embedding", json::array()).get>(); + const char* data_ptr = reinterpret_cast(vec.data()); + size_t data_size = vec.size() * sizeof(float); + embedding_obj = { + {"embedding", base64::encode(data_ptr, data_size)}, + {"index", i++}, + {"object", "embedding"}, + {"encoding_format", "base64"} + }; + } else { + embedding_obj = { + {"embedding", json_value(elem, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }; + } + data.push_back(embedding_obj); + + n_tokens += json_value(elem, "tokens_evaluated", 0); + } + + json res = json { + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", json { + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} + }}, + {"data", data} + }; + + return res; +} + +static json format_response_rerank(const json & request, const json & ranks) { + json data = json::array(); + int32_t n_tokens = 0; + int i = 0; + for (const auto & rank : ranks) { + data.push_back(json{ + {"index", i++}, + {"relevance_score", json_value(rank, "score", 0.0)}, + }); + + n_tokens += json_value(rank, "tokens_evaluated", 0); + } + + json res = json { + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", json { + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} + }}, + {"results", data} + }; + + return res; +} + +static bool is_valid_utf8(const std::string & str) { + const unsigned char* bytes = reinterpret_cast(str.data()); + const unsigned char* end = bytes + str.length(); + + while (bytes < end) { + if (*bytes <= 0x7F) { + // 1-byte sequence (0xxxxxxx) + bytes++; + } else if ((*bytes & 0xE0) == 0xC0) { + // 2-byte sequence (110xxxxx 10xxxxxx) + if (end - bytes < 2 || (bytes[1] & 0xC0) != 0x80) + return false; + bytes += 2; + } else if ((*bytes & 0xF0) == 0xE0) { + // 3-byte sequence (1110xxxx 10xxxxxx 10xxxxxx) + if (end - bytes < 3 || (bytes[1] & 0xC0) != 0x80 || (bytes[2] & 0xC0) != 0x80) + return false; + bytes += 3; + } else if ((*bytes & 0xF8) == 0xF0) { + // 4-byte sequence (11110xxx 10xxxxxx 10xxxxxx 10xxxxxx) + if (end - bytes < 4 || (bytes[1] & 0xC0) != 0x80 || + (bytes[2] & 0xC0) != 0x80 || (bytes[3] & 0xC0) != 0x80) + return false; + bytes += 4; + } else { + // Invalid UTF-8 lead byte + return false; + } + } + + return true; +} + +static json format_tokenizer_response(const json & tokens) { + return json { + {"tokens", tokens} + }; +} + +static json format_detokenized_response(const std::string & content) { + return json { + {"content", content} + }; +} + +static json format_logit_bias(const std::vector & logit_bias) { + json data = json::array(); + for (const auto & lb : logit_bias) { + data.push_back(json{ + {"bias", lb.bias}, + {"token", lb.token}, }); } - return out; + return data; +} + +static std::string safe_json_to_str(const json & data) { + return data.dump(-1, ' ', false, json::error_handler_t::replace); +} + +static std::vector get_token_probabilities(llama_context * ctx, int idx) { + std::vector cur; + const auto * logits = llama_get_logits_ith(ctx, idx); + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const int n_vocab = llama_vocab_n_tokens(vocab); + + cur.resize(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f}; + } + + // sort tokens by logits + std::sort(cur.begin(), cur.end(), [](const llama_token_data & a, const llama_token_data & b) { + return a.logit > b.logit; + }); + + // apply softmax + float max_l = cur[0].logit; + float cum_sum = 0.0f; + for (size_t i = 0; i < cur.size(); ++i) { + float p = expf(cur[i].logit - max_l); + cur[i].p = p; + cum_sum += p; + } + for (size_t i = 0; i < cur.size(); ++i) { + cur[i].p /= cum_sum; + } + + return cur; +} + +static bool are_lora_equal( + const std::vector & l1, + const std::vector & l2) { + if (l1.size() != l2.size()) { + return false; + } + for (size_t i = 0; i < l1.size(); ++i) { + // we don't check lora.path to reduce the time complexity + if (l1[i].scale != l2[i].scale || l1[i].ptr != l2[i].ptr) { + return false; + } + } + return true; +} + +// parse lora config from JSON request, returned a copy of lora_base with updated scale +static std::vector parse_lora_request( + const std::vector & lora_base, + const json & data) { + std::vector lora(lora_base); + int max_idx = lora.size(); + + // clear existing value + for (auto & entry : lora) { + entry.scale = 0.0f; + } + + // set value + for (const auto & entry : data) { + int id = json_value(entry, "id", -1); + float scale = json_value(entry, "scale", 0.0f); + if (0 <= id && id < max_idx) { + lora[id].scale = scale; + } else { + throw std::runtime_error("invalid adapter id"); + } + } + + return lora; } diff --git a/examples/server/webui/.gitignore b/examples/server/webui/.gitignore new file mode 100644 index 000000000..a547bf36d --- /dev/null +++ b/examples/server/webui/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/examples/server/webui/.prettierignore b/examples/server/webui/.prettierignore new file mode 100644 index 000000000..c0cb165b3 --- /dev/null +++ b/examples/server/webui/.prettierignore @@ -0,0 +1,10 @@ +**/.vscode +**/.github +**/.git +**/.svn +**/.hg +**/node_modules +**/dist +**/build + +*.config.js diff --git a/examples/server/webui/eslint.config.js b/examples/server/webui/eslint.config.js new file mode 100644 index 000000000..7c0d39b89 --- /dev/null +++ b/examples/server/webui/eslint.config.js @@ -0,0 +1,26 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' + +export default tseslint.config( + { ignores: ['dist'] }, + { + extends: [js.configs.recommended, ...tseslint.configs.recommended], + files: ['**/*.{ts,tsx}'], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + plugins: { + 'react-hooks': reactHooks, + 'react-refresh': reactRefresh, + }, + rules: { + ...reactHooks.configs.recommended.rules, + 'react-refresh/only-export-components': 'off', + '@typescript-eslint/no-unused-vars': 'off', + }, + }, +) diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html new file mode 100644 index 000000000..471f46b3a --- /dev/null +++ b/examples/server/webui/index.html @@ -0,0 +1,16 @@ + + + + + + + 🦙 llama.cpp - chat + + +
+ + + diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json new file mode 100644 index 000000000..c6c5de3c0 --- /dev/null +++ b/examples/server/webui/package-lock.json @@ -0,0 +1,6608 @@ +{ + "name": "webui", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "webui", + "version": "0.0.0", + "dependencies": { + "@heroicons/react": "^2.2.0", + "@sec-ant/readable-stream": "^0.6.0", + "@vscode/markdown-it-katex": "^1.1.1", + "autoprefixer": "^10.4.20", + "daisyui": "^4.12.14", + "highlight.js": "^11.10.0", + "katex": "^0.16.15", + "postcss": "^8.4.49", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-markdown": "^9.0.3", + "react-router": "^7.1.5", + "rehype-highlight": "^7.0.2", + "rehype-katex": "^7.0.1", + "remark-breaks": "^4.0.0", + "remark-gfm": "^4.0.0", + "remark-math": "^6.0.0", + "tailwindcss": "^3.4.15", + "textlinestream": "^1.1.1", + "vite-plugin-singlefile": "^2.0.3" + }, + 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"^8.4.49", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-markdown": "^9.0.3", + "react-router": "^7.1.5", + "rehype-highlight": "^7.0.2", + "rehype-katex": "^7.0.1", + "remark-breaks": "^4.0.0", + "remark-gfm": "^4.0.0", + "remark-math": "^6.0.0", + "tailwindcss": "^3.4.15", + "textlinestream": "^1.1.1", + "vite-plugin-singlefile": "^2.0.3" + }, + "devDependencies": { + "@eslint/js": "^9.17.0", + "@types/markdown-it": "^14.1.2", + "@types/node": "^22.13.1", + "@types/react": "^18.3.18", + "@types/react-dom": "^18.3.5", + "@vitejs/plugin-react": "^4.3.4", + "eslint": "^9.17.0", + "eslint-plugin-react-hooks": "^5.0.0", + "eslint-plugin-react-refresh": "^0.4.16", + "globals": "^15.14.0", + "prettier": "^3.4.2", + "sass-embedded": "^1.83.4", + "typescript": "~5.6.2", + "typescript-eslint": "^8.18.2", + "vite": "^6.0.5" + }, + "prettier": { + "trailingComma": "es5", + "tabWidth": 2, + "semi": true, + "singleQuote": true, + "bracketSameLine": false + } +} 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/public/demo-conversation.json b/examples/server/webui/public/demo-conversation.json new file mode 100644 index 000000000..338b4aea5 --- /dev/null +++ b/examples/server/webui/public/demo-conversation.json @@ -0,0 +1,33 @@ +{ + "demo": true, + "id": "conv-1734086746930", + "lastModified": 1734087548943, + "messages": [ + { + "id": 1734086764521, + "role": "user", + "content": "this is a demo conversation, used in dev mode" + }, + { + "id": 1734087548327, + "role": "assistant", + "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\n$2x + y = z$\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$", + "timings": { + "prompt_n": 1, + "prompt_ms": 28.923, + "predicted_n": 25, + "predicted_ms": 573.016 + } + }, + { + "id": 1734087548328, + "role": "user", + "content": "this is a demo conversation, used in dev mode" + }, + { + "id": 1734087548329, + "role": "assistant", + "content": "Code block:\n```js\nconsole.log('hello world')\n```\n```sh\nls -la /dev\n```" + } + ] +} diff --git a/examples/server/webui/src/App.tsx b/examples/server/webui/src/App.tsx new file mode 100644 index 000000000..2ce734682 --- /dev/null +++ b/examples/server/webui/src/App.tsx @@ -0,0 +1,47 @@ +import { HashRouter, Outlet, Route, Routes } from 'react-router'; +import Header from './components/Header'; +import Sidebar from './components/Sidebar'; +import { AppContextProvider, useAppContext } from './utils/app.context'; +import ChatScreen from './components/ChatScreen'; +import SettingDialog from './components/SettingDialog'; + +function App() { + return ( + +
+ + + }> + } /> + } /> + + + +
+ + ); +} + +function AppLayout() { + const { showSettings, setShowSettings } = useAppContext(); + return ( + <> + +
+
+ +
+ { + setShowSettings(false)} + /> + } + + ); +} + +export default App; diff --git a/examples/server/webui/src/Config.ts b/examples/server/webui/src/Config.ts new file mode 100644 index 000000000..779ed9bf7 --- /dev/null +++ b/examples/server/webui/src/Config.ts @@ -0,0 +1,92 @@ +import daisyuiThemes from 'daisyui/src/theming/themes'; +import { isNumeric } from './utils/misc'; + +export const isDev = import.meta.env.MODE === 'development'; + +// constants +export const BASE_URL = new URL('.', document.baseURI).href + .toString() + .replace(/\/$/, ''); + +export 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. + // Do not use nested objects, keep it single level. Prefix the key if you need to group them. + apiKey: '', + systemMessage: 'You are a helpful assistant.', + showTokensPerSecond: false, + showThoughtInProgress: false, + excludeThoughtOnReq: true, + // make sure these default values are in sync with `common.h` + samplers: 'edkypmxt', + 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 + // experimental features + pyIntepreterEnabled: false, +}; +export const CONFIG_INFO: Record = { + 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) +export const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT) + .filter((e) => isNumeric(e[1])) + .map((e) => e[0]); +// list of themes supported by daisyui +export const THEMES = ['light', 'dark'] + // make sure light & dark are always at the beginning + .concat( + Object.keys(daisyuiThemes).filter((t) => t !== 'light' && t !== 'dark') + ); diff --git a/examples/server/webui/src/components/CanvasPyInterpreter.tsx b/examples/server/webui/src/components/CanvasPyInterpreter.tsx new file mode 100644 index 000000000..c2707fe20 --- /dev/null +++ b/examples/server/webui/src/components/CanvasPyInterpreter.tsx @@ -0,0 +1,195 @@ +import { useEffect, useState } from 'react'; +import { useAppContext } from '../utils/app.context'; +import { OpenInNewTab, XCloseButton } from '../utils/common'; +import { CanvasType } from '../utils/types'; +import { PlayIcon, StopIcon } from '@heroicons/react/24/outline'; +import StorageUtils from '../utils/storage'; + +const canInterrupt = typeof SharedArrayBuffer === 'function'; + +// adapted from https://pyodide.org/en/stable/usage/webworker.html +const WORKER_CODE = ` +importScripts("https://cdn.jsdelivr.net/pyodide/v0.27.2/full/pyodide.js"); + +let stdOutAndErr = []; + +let pyodideReadyPromise = loadPyodide({ + stdout: (data) => stdOutAndErr.push(data), + stderr: (data) => stdOutAndErr.push(data), +}); + +let alreadySetBuff = false; + +self.onmessage = async (event) => { + stdOutAndErr = []; + + // make sure loading is done + const pyodide = await pyodideReadyPromise; + const { id, python, context, interruptBuffer } = event.data; + + if (interruptBuffer && !alreadySetBuff) { + pyodide.setInterruptBuffer(interruptBuffer); + alreadySetBuff = true; + } + + // Now load any packages we need, run the code, and send the result back. + await pyodide.loadPackagesFromImports(python); + + // make a Python dictionary with the data from content + const dict = pyodide.globals.get("dict"); + const globals = dict(Object.entries(context)); + try { + self.postMessage({ id, running: true }); + // Execute the python code in this context + const result = pyodide.runPython(python, { globals }); + self.postMessage({ result, id, stdOutAndErr }); + } catch (error) { + self.postMessage({ error: error.message, id }); + } + interruptBuffer[0] = 0; +}; +`; + +let worker: Worker; +const interruptBuffer = canInterrupt + ? new Uint8Array(new SharedArrayBuffer(1)) + : null; + +const startWorker = () => { + if (!worker) { + worker = new Worker( + URL.createObjectURL(new Blob([WORKER_CODE], { type: 'text/javascript' })) + ); + } +}; + +if (StorageUtils.getConfig().pyIntepreterEnabled) { + startWorker(); +} + +const runCodeInWorker = ( + pyCode: string, + callbackRunning: () => void +): { + donePromise: Promise; + interrupt: () => void; +} => { + startWorker(); + const id = Math.random() * 1e8; + const context = {}; + if (interruptBuffer) { + interruptBuffer[0] = 0; + } + + const donePromise = new Promise((resolve) => { + worker.onmessage = (event) => { + const { error, stdOutAndErr, running } = event.data; + if (id !== event.data.id) return; + if (running) { + callbackRunning(); + return; + } else if (error) { + resolve(error.toString()); + } else { + resolve(stdOutAndErr.join('\n')); + } + }; + worker.postMessage({ id, python: pyCode, context, interruptBuffer }); + }); + + const interrupt = () => { + console.log('Interrupting...'); + console.trace(); + if (interruptBuffer) { + interruptBuffer[0] = 2; + } + }; + + return { donePromise, interrupt }; +}; + +export default function CanvasPyInterpreter() { + const { canvasData, setCanvasData } = useAppContext(); + + const [code, setCode] = useState(canvasData?.content ?? ''); // copy to avoid direct mutation + const [running, setRunning] = useState(false); + const [output, setOutput] = useState(''); + const [interruptFn, setInterruptFn] = useState<() => void>(); + const [showStopBtn, setShowStopBtn] = useState(false); + + const runCode = async (pycode: string) => { + interruptFn?.(); + setRunning(true); + setOutput('Loading Pyodide...'); + const { donePromise, interrupt } = runCodeInWorker(pycode, () => { + setOutput('Running...'); + setShowStopBtn(canInterrupt); + }); + setInterruptFn(() => interrupt); + const out = await donePromise; + setOutput(out); + setRunning(false); + setShowStopBtn(false); + }; + + // run code on mount + useEffect(() => { + setCode(canvasData?.content ?? ''); + runCode(canvasData?.content ?? ''); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [canvasData?.content]); + + if (canvasData?.type !== CanvasType.PY_INTERPRETER) { + return null; + } + + return ( +
+
+
+ Python Interpreter + setCanvasData(null)} + /> +
+
+ +
+
+ + {showStopBtn && ( + + )} + + + Report a bug + + +
+ +
+
+
+
+ ); +} diff --git a/examples/server/webui/src/components/ChatMessage.tsx b/examples/server/webui/src/components/ChatMessage.tsx new file mode 100644 index 000000000..ec72196ba --- /dev/null +++ b/examples/server/webui/src/components/ChatMessage.tsx @@ -0,0 +1,235 @@ +import { useMemo, useState } from 'react'; +import { useAppContext } from '../utils/app.context'; +import { Message, PendingMessage } from '../utils/types'; +import { classNames } from '../utils/misc'; +import MarkdownDisplay, { CopyButton } from './MarkdownDisplay'; + +interface SplitMessage { + content: PendingMessage['content']; + thought?: string; + isThinking?: boolean; +} + +export default function ChatMessage({ + msg, + id, + scrollToBottom, + isPending, +}: { + msg: Message | PendingMessage; + id?: string; + scrollToBottom: (requiresNearBottom: boolean) => void; + isPending?: boolean; +}) { + const { viewingConversation, replaceMessageAndGenerate, config } = + useAppContext(); + const [editingContent, setEditingContent] = useState(null); + const timings = useMemo( + () => + msg.timings + ? { + ...msg.timings, + prompt_per_second: + (msg.timings.prompt_n / msg.timings.prompt_ms) * 1000, + predicted_per_second: + (msg.timings.predicted_n / msg.timings.predicted_ms) * 1000, + } + : null, + [msg.timings] + ); + + // for reasoning model, we split the message into content and thought + // TODO: implement this as remark/rehype plugin in the future + const { content, thought, isThinking }: SplitMessage = useMemo(() => { + if (msg.content === null || msg.role !== 'assistant') { + return { content: msg.content }; + } + let actualContent = ''; + let thought = ''; + let isThinking = false; + let thinkSplit = msg.content.split('', 2); + actualContent += thinkSplit[0]; + while (thinkSplit[1] !== undefined) { + // tag found + thinkSplit = thinkSplit[1].split('', 2); + thought += thinkSplit[0]; + isThinking = true; + if (thinkSplit[1] !== undefined) { + // closing tag found + isThinking = false; + thinkSplit = thinkSplit[1].split('', 2); + actualContent += thinkSplit[0]; + } + } + return { content: actualContent, thought, isThinking }; + }, [msg]); + + if (!viewingConversation) return null; + + const regenerate = async () => { + replaceMessageAndGenerate(viewingConversation.id, msg.id, undefined, () => + scrollToBottom(true) + ); + }; + + return ( +
+
+
+ {/* textarea for editing message */} + {editingContent !== null && ( + <> + +
+ + + + )} + {/* not editing content, render message */} + {editingContent === null && ( + <> + {content === null ? ( + <> + {/* show loading dots for pending message */} + + + ) : ( + <> + {/* render message as markdown */} +
+ {thought && ( +
+ + {isPending && isThinking ? ( + + + Thinking + + ) : ( + Thought Process + )} + +
+ +
+
+ )} + +
+ + )} + {/* render timings if enabled */} + {timings && config.showTokensPerSecond && ( +
+
+ Speed: {timings.predicted_per_second.toFixed(1)} t/s +
+
+ Prompt +
- Tokens: {timings.prompt_n} +
- Time: {timings.prompt_ms} ms +
- Speed: {timings.prompt_per_second.toFixed(1)} t/s +
+ Generation +
- Tokens: {timings.predicted_n} +
- Time: {timings.predicted_ms} ms +
- Speed: {timings.predicted_per_second.toFixed(1)} t/s +
+
+
+ )} + + )} +
+
+ + {/* actions for each message */} + {msg.content !== null && ( +
+ {/* user message */} + {msg.role === 'user' && ( + + )} + {/* assistant message */} + {msg.role === 'assistant' && ( + <> + {!isPending && ( + + )} + + + )} +
+ )} +
+ ); +} diff --git a/examples/server/webui/src/components/ChatScreen.tsx b/examples/server/webui/src/components/ChatScreen.tsx new file mode 100644 index 000000000..dbc683ed1 --- /dev/null +++ b/examples/server/webui/src/components/ChatScreen.tsx @@ -0,0 +1,146 @@ +import { useEffect, useState } from 'react'; +import { useAppContext } from '../utils/app.context'; +import StorageUtils from '../utils/storage'; +import { useNavigate } from 'react-router'; +import ChatMessage from './ChatMessage'; +import { CanvasType, PendingMessage } from '../utils/types'; +import { classNames } from '../utils/misc'; +import CanvasPyInterpreter from './CanvasPyInterpreter'; + +export default function ChatScreen() { + const { + viewingConversation, + sendMessage, + isGenerating, + stopGenerating, + pendingMessages, + canvasData, + } = useAppContext(); + const [inputMsg, setInputMsg] = useState(''); + const navigate = useNavigate(); + + const currConvId = viewingConversation?.id ?? ''; + const pendingMsg: PendingMessage | undefined = pendingMessages[currConvId]; + + const scrollToBottom = (requiresNearBottom: boolean) => { + const mainScrollElem = document.getElementById('main-scroll'); + if (!mainScrollElem) return; + const spaceToBottom = + mainScrollElem.scrollHeight - + mainScrollElem.scrollTop - + mainScrollElem.clientHeight; + if (!requiresNearBottom || spaceToBottom < 50) { + setTimeout( + () => mainScrollElem.scrollTo({ top: mainScrollElem.scrollHeight }), + 1 + ); + } + }; + + // scroll to bottom when conversation changes + useEffect(() => { + scrollToBottom(false); + }, [viewingConversation?.id]); + + const sendNewMessage = async () => { + if (inputMsg.trim().length === 0 || isGenerating(currConvId)) return; + const convId = viewingConversation?.id ?? StorageUtils.getNewConvId(); + const lastInpMsg = inputMsg; + setInputMsg(''); + if (!viewingConversation) { + // if user is creating a new conversation, redirect to the new conversation + navigate(`/chat/${convId}`); + } + scrollToBottom(false); + // auto scroll as message is being generated + const onChunk = () => scrollToBottom(true); + if (!(await sendMessage(convId, inputMsg, onChunk))) { + // restore the input message if failed + setInputMsg(lastInpMsg); + } + }; + + const hasCanvas = !!canvasData; + + return ( +
+
+ {/* chat messages */} +
+
+ {/* placeholder to shift the message to the bottom */} + {viewingConversation ? '' : 'Send a message to start'} +
+ {viewingConversation?.messages.map((msg) => ( + + ))} + + {pendingMsg && ( + + )} +
+ + {/* chat input */} +
+ + {isGenerating(currConvId) ? ( + + ) : ( + + )} +
+
+
+ {canvasData?.type === CanvasType.PY_INTERPRETER && ( + + )} +
+
+ ); +} diff --git a/examples/server/webui/src/components/Header.tsx b/examples/server/webui/src/components/Header.tsx new file mode 100644 index 000000000..505350313 --- /dev/null +++ b/examples/server/webui/src/components/Header.tsx @@ -0,0 +1,176 @@ +import { useEffect, useState } from 'react'; +import StorageUtils from '../utils/storage'; +import { useAppContext } from '../utils/app.context'; +import { classNames } from '../utils/misc'; +import daisyuiThemes from 'daisyui/src/theming/themes'; +import { THEMES } from '../Config'; +import { useNavigate } from 'react-router'; + +export default function Header() { + const navigate = useNavigate(); + const [selectedTheme, setSelectedTheme] = useState(StorageUtils.getTheme()); + const { setShowSettings } = useAppContext(); + + const setTheme = (theme: string) => { + StorageUtils.setTheme(theme); + setSelectedTheme(theme); + }; + + useEffect(() => { + document.body.setAttribute('data-theme', selectedTheme); + document.body.setAttribute( + 'data-color-scheme', + // @ts-expect-error daisyuiThemes complains about index type, but it should work + daisyuiThemes[selectedTheme]?.['color-scheme'] ?? 'auto' + ); + }, [selectedTheme]); + + const { isGenerating, viewingConversation } = useAppContext(); + const isCurrConvGenerating = isGenerating(viewingConversation?.id ?? ''); + + const removeConversation = () => { + if (isCurrConvGenerating || !viewingConversation) return; + const convId = viewingConversation.id; + if (window.confirm('Are you sure to delete this conversation?')) { + StorageUtils.remove(convId); + navigate('/'); + } + }; + + const downloadConversation = () => { + if (isCurrConvGenerating || !viewingConversation) return; + const convId = viewingConversation.id; + const conversationJson = JSON.stringify(viewingConversation, 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); + }; + + return ( +
+ {/* open sidebar button */} + + +
llama.cpp
+ + {/* action buttons (top right) */} +
+
+ {/* "..." button */} + + {/* dropdown menu */} + +
+
+ +
+ + {/* theme controller is copied from https://daisyui.com/components/theme-controller/ */} +
+
+
+ + + +
+
    +
  • + +
    • + {THEMES.map((theme) => ( +
  • + e.target.checked && setTheme(theme)} + /> +
    • + ))} +
+
+
+
+
+ ); +} diff --git a/examples/server/webui/src/components/MarkdownDisplay.tsx b/examples/server/webui/src/components/MarkdownDisplay.tsx new file mode 100644 index 000000000..5b7a72591 --- /dev/null +++ b/examples/server/webui/src/components/MarkdownDisplay.tsx @@ -0,0 +1,310 @@ +import React, { useMemo, useState } from 'react'; +import Markdown, { ExtraProps } from 'react-markdown'; +import remarkGfm from 'remark-gfm'; +import rehypeHightlight from 'rehype-highlight'; +import rehypeKatex from 'rehype-katex'; +import remarkMath from 'remark-math'; +import remarkBreaks from 'remark-breaks'; +import 'katex/dist/katex.min.css'; +import { classNames, copyStr } from '../utils/misc'; +import { ElementContent, Root } from 'hast'; +import { visit } from 'unist-util-visit'; +import { useAppContext } from '../utils/app.context'; +import { CanvasType } from '../utils/types'; + +export default function MarkdownDisplay({ + content, + isGenerating, +}: { + content: string; + isGenerating?: boolean; +}) { + const preprocessedContent = useMemo( + () => preprocessLaTeX(content), + [content] + ); + return ( + ( + + ), + // note: do not use "pre", "p" or other basic html elements here, it will cause the node to re-render when the message is being generated (this should be a bug with react-markdown, not sure how to fix it) + }} + > + {preprocessedContent} + + ); +} + +const CodeBlockButtons: React.ElementType< + React.ClassAttributes & + React.HTMLAttributes & + ExtraProps & { origContent: string; isGenerating?: boolean } +> = ({ node, origContent, isGenerating }) => { + const { config } = useAppContext(); + const startOffset = node?.position?.start.offset ?? 0; + const endOffset = node?.position?.end.offset ?? 0; + + const copiedContent = useMemo( + () => + origContent + .substring(startOffset, endOffset) + .replace(/^```[^\n]+\n/g, '') + .replace(/```$/g, ''), + [origContent, startOffset, endOffset] + ); + + const codeLanguage = useMemo( + () => + origContent + .substring(startOffset, startOffset + 10) + .match(/^```([^\n]+)\n/)?.[1] ?? '', + [origContent, startOffset] + ); + + const canRunCode = + !isGenerating && + config.pyIntepreterEnabled && + codeLanguage.startsWith('py'); + + return ( +
+ + {canRunCode && ( + + )} +
+ ); +}; + +export const CopyButton = ({ + content, + className, +}: { + content: string; + className?: string; +}) => { + const [copied, setCopied] = useState(false); + return ( + + ); +}; + +export const RunPyCodeButton = ({ + content, + className, +}: { + content: string; + className?: string; +}) => { + const { setCanvasData } = useAppContext(); + return ( + <> + + + ); +}; + +/** + * This injects the "button" element before each "pre" element. + * The actual button will be replaced with a react component in the MarkdownDisplay. + * We don't replace "pre" node directly because it will cause the node to re-render, which causes this bug: https://github.com/ggerganov/llama.cpp/issues/9608 + */ +function rehypeCustomCopyButton() { + return function (tree: Root) { + visit(tree, 'element', function (node) { + if (node.tagName === 'pre' && !node.properties.visited) { + const preNode = { ...node }; + // replace current node + preNode.properties.visited = 'true'; + node.tagName = 'div'; + node.properties = {}; + // add node for button + const btnNode: ElementContent = { + type: 'element', + tagName: 'button', + properties: {}, + children: [], + position: node.position, + }; + node.children = [btnNode, preNode]; + } + }); + }; +} + +/** + * The part below is copied and adapted from: + * https://github.com/danny-avila/LibreChat/blob/main/client/src/utils/latex.ts + * (MIT License) + */ + +// Regex to check if the processed content contains any potential LaTeX patterns +const containsLatexRegex = + /\\\(.*?\\\)|\\\[.*?\\\]|\$.*?\$|\\begin\{equation\}.*?\\end\{equation\}/; + +// Regex for inline and block LaTeX expressions +const inlineLatex = new RegExp(/\\\((.+?)\\\)/, 'g'); +const blockLatex = new RegExp(/\\\[(.*?[^\\])\\\]/, 'gs'); + +// Function to restore code blocks +const restoreCodeBlocks = (content: string, codeBlocks: string[]) => { + return content.replace( + /<>/g, + (_, index) => codeBlocks[index] + ); +}; + +// Regex to identify code blocks and inline code +const codeBlockRegex = /(```[\s\S]*?```|`.*?`)/g; + +export const processLaTeX = (_content: string) => { + let content = _content; + // Temporarily replace code blocks and inline code with placeholders + const codeBlocks: string[] = []; + let index = 0; + content = content.replace(codeBlockRegex, (match) => { + codeBlocks[index] = match; + return `<>`; + }); + + // Escape dollar signs followed by a digit or space and digit + let processedContent = content.replace(/(\$)(?=\s?\d)/g, '\\$'); + + // If no LaTeX patterns are found, restore code blocks and return the processed content + if (!containsLatexRegex.test(processedContent)) { + return restoreCodeBlocks(processedContent, codeBlocks); + } + + // Convert LaTeX expressions to a markdown compatible format + processedContent = processedContent + .replace(inlineLatex, (_: string, equation: string) => `$${equation}$`) // Convert inline LaTeX + .replace(blockLatex, (_: string, equation: string) => `$$${equation}$$`); // Convert block LaTeX + + // Restore code blocks + return restoreCodeBlocks(processedContent, codeBlocks); +}; + +/** + * Preprocesses LaTeX content by replacing delimiters and escaping certain characters. + * + * @param content The input string containing LaTeX expressions. + * @returns The processed string with replaced delimiters and escaped characters. + */ +export function preprocessLaTeX(content: string): string { + // Step 1: Protect code blocks + const codeBlocks: string[] = []; + content = content.replace(/(```[\s\S]*?```|`[^`\n]+`)/g, (_, code) => { + codeBlocks.push(code); + return `<>`; + }); + + // Step 2: Protect existing LaTeX expressions + const latexExpressions: string[] = []; + + // Protect block math ($$...$$), \[...\], and \(...\) as before. + content = content.replace( + /(\$\$[\s\S]*?\$\$|\\\[[\s\S]*?\\\]|\\\(.*?\\\))/g, + (match) => { + latexExpressions.push(match); + return `<>`; + } + ); + + // Protect inline math ($...$) only if it does NOT match a currency pattern. + // We assume a currency pattern is one where the inner content is purely numeric (with optional decimals). + content = content.replace(/\$([^$]+)\$/g, (match, inner) => { + if (/^\s*\d+(?:\.\d+)?\s*$/.test(inner)) { + // This looks like a currency value (e.g. "$123" or "$12.34"), + // so don't protect it. + return match; + } else { + // Otherwise, treat it as a LaTeX expression. + latexExpressions.push(match); + return `<>`; + } + }); + + // Step 3: Escape dollar signs that are likely currency indicators. + // (Now that inline math is protected, this will only escape dollars not already protected) + content = content.replace(/\$(?=\d)/g, '\\$'); + + // Step 4: Restore LaTeX expressions + content = content.replace( + /<>/g, + (_, index) => latexExpressions[parseInt(index)] + ); + + // Step 5: Restore code blocks + content = content.replace( + /<>/g, + (_, index) => codeBlocks[parseInt(index)] + ); + + // Step 6: Apply additional escaping functions + content = escapeBrackets(content); + content = escapeMhchem(content); + + return content; +} + +export function escapeBrackets(text: string): string { + const pattern = + /(```[\S\s]*?```|`.*?`)|\\\[([\S\s]*?[^\\])\\]|\\\((.*?)\\\)/g; + return text.replace( + pattern, + ( + match: string, + codeBlock: string | undefined, + squareBracket: string | undefined, + roundBracket: string | undefined + ): string => { + if (codeBlock != null) { + return codeBlock; + } else if (squareBracket != null) { + return `$$${squareBracket}$$`; + } else if (roundBracket != null) { + return `$${roundBracket}$`; + } + return match; + } + ); +} + +export function escapeMhchem(text: string) { + return text.replaceAll('$\\ce{', '$\\\\ce{').replaceAll('$\\pu{', '$\\\\pu{'); +} diff --git a/examples/server/webui/src/components/SettingDialog.tsx b/examples/server/webui/src/components/SettingDialog.tsx new file mode 100644 index 000000000..592b93fa3 --- /dev/null +++ b/examples/server/webui/src/components/SettingDialog.tsx @@ -0,0 +1,536 @@ +import { useState } from 'react'; +import { useAppContext } from '../utils/app.context'; +import { CONFIG_DEFAULT, CONFIG_INFO } from '../Config'; +import { isDev } from '../Config'; +import StorageUtils from '../utils/storage'; +import { classNames, isBoolean, isNumeric, isString } from '../utils/misc'; +import { + BeakerIcon, + ChatBubbleOvalLeftEllipsisIcon, + Cog6ToothIcon, + FunnelIcon, + HandRaisedIcon, + SquaresPlusIcon, +} from '@heroicons/react/24/outline'; +import { OpenInNewTab } from '../utils/common'; + +type SettKey = keyof typeof CONFIG_DEFAULT; + +const BASIC_KEYS: SettKey[] = [ + 'temperature', + 'top_k', + 'top_p', + 'min_p', + 'max_tokens', +]; +const SAMPLER_KEYS: SettKey[] = [ + 'dynatemp_range', + 'dynatemp_exponent', + 'typical_p', + 'xtc_probability', + 'xtc_threshold', +]; +const PENALTY_KEYS: SettKey[] = [ + 'repeat_last_n', + 'repeat_penalty', + 'presence_penalty', + 'frequency_penalty', + 'dry_multiplier', + 'dry_base', + 'dry_allowed_length', + 'dry_penalty_last_n', +]; + +enum SettingInputType { + SHORT_INPUT, + LONG_INPUT, + CHECKBOX, + CUSTOM, +} + +interface SettingFieldInput { + type: Exclude; + label: string | React.ReactElement; + help?: string | React.ReactElement; + key: SettKey; +} + +interface SettingFieldCustom { + type: SettingInputType.CUSTOM; + key: SettKey; + component: + | string + | React.FC<{ + value: string | boolean | number; + onChange: (value: string) => void; + }>; +} + +interface SettingSection { + title: React.ReactElement; + fields: (SettingFieldInput | SettingFieldCustom)[]; +} + +const ICON_CLASSNAME = 'w-4 h-4 mr-1 inline'; + +const SETTING_SECTIONS: SettingSection[] = [ + { + title: ( + <> + + General + + ), + fields: [ + { + type: SettingInputType.SHORT_INPUT, + label: 'API Key', + key: 'apiKey', + }, + { + type: SettingInputType.LONG_INPUT, + label: 'System Message (will be disabled if left empty)', + key: 'systemMessage', + }, + ...BASIC_KEYS.map( + (key) => + ({ + type: SettingInputType.SHORT_INPUT, + label: key, + key, + }) as SettingFieldInput + ), + ], + }, + { + title: ( + <> + + Samplers + + ), + fields: [ + { + type: SettingInputType.SHORT_INPUT, + label: 'Samplers queue', + key: 'samplers', + }, + ...SAMPLER_KEYS.map( + (key) => + ({ + type: SettingInputType.SHORT_INPUT, + label: key, + key, + }) as SettingFieldInput + ), + ], + }, + { + title: ( + <> + + Penalties + + ), + fields: PENALTY_KEYS.map((key) => ({ + type: SettingInputType.SHORT_INPUT, + label: key, + key, + })), + }, + { + title: ( + <> + + Reasoning + + ), + fields: [ + { + type: SettingInputType.CHECKBOX, + label: 'Expand though process by default for generating message', + key: 'showThoughtInProgress', + }, + { + type: SettingInputType.CHECKBOX, + label: + 'Exclude thought process when sending request to API (Recommended for DeepSeek-R1)', + key: 'excludeThoughtOnReq', + }, + ], + }, + { + title: ( + <> + + Advanced + + ), + fields: [ + { + type: SettingInputType.CUSTOM, + key: 'custom', // dummy key, won't be used + component: () => { + const debugImportDemoConv = async () => { + const res = await fetch('/demo-conversation.json'); + const demoConv = await res.json(); + StorageUtils.remove(demoConv.id); + for (const msg of demoConv.messages) { + StorageUtils.appendMsg(demoConv.id, msg); + } + }; + return ( + + ); + }, + }, + { + type: SettingInputType.CHECKBOX, + label: 'Show tokens per second', + key: 'showTokensPerSecond', + }, + { + type: SettingInputType.LONG_INPUT, + label: ( + <> + Custom JSON config (For more info, refer to{' '} + + server documentation + + ) + + ), + key: 'custom', + }, + ], + }, + { + title: ( + <> + + Experimental + + ), + fields: [ + { + type: SettingInputType.CUSTOM, + key: 'custom', // dummy key, won't be used + component: () => ( + <> +

+ Experimental features are not guaranteed to work correctly. +
+
+ If you encounter any problems, create a{' '} + + Bug (misc.) + {' '} + report on Github. Please also specify webui/experimental on + the report title and include screenshots. +
+
+ Some features may require packages downloaded from CDN, so they + need internet connection. +

+ + ), + }, + { + type: SettingInputType.CHECKBOX, + label: ( + <> + Enable Python interpreter +
+ + This feature uses{' '} + pyodide, + downloaded from CDN. To use this feature, ask the LLM to generate + python code inside a markdown code block. You will see a "Run" + button on the code block, near the "Copy" button. + + + ), + key: 'pyIntepreterEnabled', + }, + ], + }, +]; + +export default function SettingDialog({ + show, + onClose, +}: { + show: boolean; + onClose: () => void; +}) { + const { config, saveConfig } = useAppContext(); + const [sectionIdx, setSectionIdx] = useState(0); + + // clone the config object to prevent direct mutation + const [localConfig, setLocalConfig] = useState( + JSON.parse(JSON.stringify(config)) + ); + + const resetConfig = () => { + if (window.confirm('Are you sure to reset all settings?')) { + setLocalConfig(CONFIG_DEFAULT); + } + }; + + const handleSave = () => { + // copy the local config to prevent direct mutation + const newConfig: typeof CONFIG_DEFAULT = JSON.parse( + JSON.stringify(localConfig) + ); + // validate the config + for (const key in newConfig) { + const value = newConfig[key as SettKey]; + const mustBeBoolean = isBoolean(CONFIG_DEFAULT[key as SettKey]); + const mustBeString = isString(CONFIG_DEFAULT[key as SettKey]); + const mustBeNumeric = isNumeric(CONFIG_DEFAULT[key as SettKey]); + if (mustBeString) { + if (!isString(value)) { + alert(`Value for ${key} must be string`); + return; + } + } else if (mustBeNumeric) { + const trimedValue = value.toString().trim(); + const numVal = Number(trimedValue); + if (isNaN(numVal) || !isNumeric(numVal) || trimedValue.length === 0) { + alert(`Value for ${key} must be numeric`); + return; + } + // force conversion to number + // @ts-expect-error this is safe + newConfig[key] = numVal; + } else if (mustBeBoolean) { + if (!isBoolean(value)) { + alert(`Value for ${key} must be boolean`); + return; + } + } else { + console.error(`Unknown default type for key ${key}`); + } + } + if (isDev) console.log('Saving config', newConfig); + saveConfig(newConfig); + onClose(); + }; + + const onChange = (key: SettKey) => (value: string | boolean) => { + // note: we do not perform validation here, because we may get incomplete value as user is still typing it + setLocalConfig({ ...localConfig, [key]: value }); + }; + + return ( + +
+

Settings

+
+ {/* Left panel, showing sections - Desktop version */} +
+ {SETTING_SECTIONS.map((section, idx) => ( +
setSectionIdx(idx)} + dir="auto" + > + {section.title} +
+ ))} +
+ + {/* Left panel, showing sections - Mobile version */} +
+
+ + {SETTING_SECTIONS[sectionIdx].title} + +
    + {SETTING_SECTIONS.map((section, idx) => ( +
    setSectionIdx(idx)} + dir="auto" + > + {section.title} +
    + ))} +
+
+
+ + {/* Right panel, showing setting fields */} +
+ {SETTING_SECTIONS[sectionIdx].fields.map((field, idx) => { + const key = `${sectionIdx}-${idx}`; + if (field.type === SettingInputType.SHORT_INPUT) { + return ( + + ); + } else if (field.type === SettingInputType.LONG_INPUT) { + return ( + + ); + } else if (field.type === SettingInputType.CHECKBOX) { + return ( + + ); + } else if (field.type === SettingInputType.CUSTOM) { + return ( +
+ {typeof field.component === 'string' + ? field.component + : field.component({ + value: localConfig[field.key], + onChange: onChange(field.key), + })} +
+ ); + } + })} + +

+ Settings are saved in browser's localStorage +

+
+
+ +
+ + + +
+
+ + ); +} + +function SettingsModalLongInput({ + configKey, + value, + onChange, + label, +}: { + configKey: SettKey; + value: string; + onChange: (value: string) => void; + label?: string; +}) { + return ( +