diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 2c0ae4e2a..e8d5b0bd9 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -214,7 +214,6 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_CUDA" useCuda) (cmakeBool "LLAMA_HIPBLAS" useRocm) (cmakeBool "LLAMA_METAL" useMetalKit) - (cmakeBool "LLAMA_MPI" useMpi) (cmakeBool "LLAMA_VULKAN" useVulkan) (cmakeBool "LLAMA_STATIC" enableStatic) ] @@ -227,20 +226,20 @@ effectiveStdenv.mkDerivation ( ) ] ++ optionals useRocm [ - (cmakeFeature "CMAKE_C_COMPILER" "hipcc") - (cmakeFeature "CMAKE_CXX_COMPILER" "hipcc") - - # 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" + (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang") + (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets)) ] ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") (cmakeBool "LLAMA_METAL_EMBED_LIBRARY" (!precompileMetalShaders)) ]; + # Environment variables needed for ROCm + env = optionals useRocm { + ROCM_PATH = "${rocmPackages.clr}"; + HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode"; + }; + # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, # if they haven't been added yet. postInstall = '' diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 000000000..fca60594f --- /dev/null +++ b/.github/labeler.yml @@ -0,0 +1,73 @@ +# https://github.com/actions/labeler + +SYCL: + - changed-files: + - any-glob-to-any-file: + - ggml-sycl.h + - ggml-sycl.cpp + - README-sycl.md +Nvidia GPU: + - changed-files: + - any-glob-to-any-file: + - ggml-cuda/** +Vulkan: + - changed-files: + - any-glob-to-any-file: + - ggml_vk_generate_shaders.py + - ggml-vulkan* +documentation: + - changed-files: + - any-glob-to-any-file: + - docs/** + - media/** +testing: + - changed-files: + - any-glob-to-any-file: + - tests/** +build: + - changed-files: + - any-glob-to-any-file: + - cmake/** + - CMakeLists.txt + - CMakePresets.json + - codecov.yml +examples: + - changed-files: + - any-glob-to-any-file: examples/** +devops: + - changed-files: + - any-glob-to-any-file: + - .devops/** + - .github/** + - ci/** +python: + - changed-files: + - any-glob-to-any-file: + - "**/*.py" + - requirements/** + - gguf-py/** + - .flake8 +script: + - changed-files: + - any-glob-to-any-file: + - scripts/** +android: + - changed-files: + - any-glob-to-any-file: + - examples/llama.android/** +server: + - changed-files: + - any-glob-to-any-file: + - examples/server/** +ggml: + - changed-files: + - any-glob-to-any-file: + - ggml-*.c + - ggml-*.h + - ggml-cuda/** +nix: + - changed-files: + - any-glob-to-any-file: + - "**/*.nix" + - .github/workflows/nix-*.yml + - .devops/nix/nixpkgs-instances.nix diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 2d747e688..7b616281b 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -271,49 +271,15 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip name: llama-bin-ubuntu-x64.zip -# ubuntu-latest-cmake-sanitizer: -# runs-on: ubuntu-latest -# -# continue-on-error: true -# -# strategy: -# matrix: -# sanitizer: [ADDRESS, THREAD, UNDEFINED] -# build_type: [Debug, Release] -# -# steps: -# - name: Clone -# id: checkout -# uses: actions/checkout@v4 -# -# - name: Dependencies -# id: depends -# run: | -# sudo apt-get update -# sudo apt-get install build-essential -# -# - name: Build -# id: cmake_build -# 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) -# -# - name: Test -# id: cmake_test -# run: | -# cd build -# ctest -L main --verbose --timeout 900 - - ubuntu-latest-cmake-mpi: + ubuntu-latest-cmake-sanitizer: runs-on: ubuntu-latest continue-on-error: true strategy: matrix: - mpi_library: [mpich, libopenmpi-dev] + sanitizer: [ADDRESS, THREAD, UNDEFINED] + build_type: [Debug, Release] steps: - name: Clone @@ -324,14 +290,44 @@ jobs: id: depends run: | sudo apt-get update - sudo apt-get install build-essential ${{ matrix.mpi_library }} + sudo apt-get install build-essential - name: Build id: cmake_build run: | mkdir build cd build - cmake -DLLAMA_MPI=ON .. + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 + + ubuntu-latest-cmake-rpc: + runs-on: ubuntu-latest + + continue-on-error: true + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake -DLLAMA_RPC=ON .. cmake --build . --config Release -j $(nproc) - name: Test @@ -362,6 +358,33 @@ jobs: cmake -DLLAMA_VULKAN=ON .. cmake --build . --config Release -j $(nproc) + ubuntu-22-cmake-hip: + runs-on: ubuntu-22.04 + container: rocm/dev-ubuntu-22.04:6.0.2 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev + + - name: Build with native CMake HIP support + id: cmake_build + run: | + cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DLLAMA_HIPBLAS=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 -DLLAMA_HIPBLAS=ON + cmake --build build2 --config Release -j $(nproc) + ubuntu-22-cmake-sycl: runs-on: ubuntu-22.04 @@ -663,24 +686,28 @@ jobs: strategy: matrix: include: - - build: 'noavx' + - build: 'rpc-x64' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_RPC=ON -DBUILD_SHARED_LIBS=ON' + - build: 'noavx-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF -DBUILD_SHARED_LIBS=ON' - - build: 'avx2' + - build: 'avx2-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' - - build: 'avx' + - build: 'avx-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON' - - build: 'avx512' + - build: 'avx512-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON' - - build: 'clblast' + - build: 'clblast-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"' - - build: 'openblas' + - build: 'openblas-x64' 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' + - build: 'kompute-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' - - build: 'vulkan' + - build: 'vulkan-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' - - build: 'arm64' - defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + - build: 'llvm-arm64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + - build: 'msvc-arm64' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone @@ -691,13 +718,13 @@ jobs: - 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' }} + if: ${{ matrix.build == 'clblast-x64' }} 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 @@ -705,7 +732,7 @@ jobs: - name: Download CLBlast id: get_clblast - if: ${{ matrix.build == 'clblast' }} + if: ${{ matrix.build == 'clblast-x64' }} 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" @@ -718,7 +745,7 @@ jobs: - 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" @@ -731,38 +758,41 @@ 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: Build id: cmake_build run: | - mkdir build - cd build - cmake .. ${{ matrix.defines }} - cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} + cmake -S . -B build ${{ matrix.defines }} + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} - name: Add clblast.dll id: add_clblast_dll - if: ${{ matrix.build == 'clblast' }} + if: ${{ matrix.build == 'clblast-x64' }} 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 - 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 @@ -776,14 +806,14 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'arm64' && 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 != 'clblast-x64' && 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 @@ -811,14 +841,14 @@ 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-${{ matrix.build }}-x64.zip .\build\bin\Release\* + 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\* - name: Upload artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} uses: actions/upload-artifact@v4 with: - path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip - name: llama-bin-win-${{ 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-cuda: runs-on: windows-latest @@ -898,9 +928,9 @@ jobs: 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_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7dff44ba-e3af-4448-841c-0d616c8da6e7/w_BaseKit_p_2024.1.0.595_offline.exe WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel - + ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI" steps: - name: Clone id: checkout @@ -932,6 +962,17 @@ jobs: id: pack_artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | + echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin" + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.4.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin + + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_win_proxy_loader.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_level_zero.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl7.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin + echo "cp oneAPI running time dll files to ./build/bin done" 7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/* - name: Upload artifacts @@ -941,6 +982,37 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip name: llama-bin-win-sycl-x64.zip + windows-latest-cmake-hip: + runs-on: windows-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Install + id: depends + run: | + $ErrorActionPreference = "Stop" + write-host "Downloading AMD HIP SDK Installer" + Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${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" -DLLAMA_HIPBLAS=ON + cmake --build build --config Release + ios-xcode-build: runs-on: macos-latest 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/server.yml b/.github/workflows/server.yml index afac89c5b..217af67cf 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -32,13 +32,14 @@ jobs: strategy: matrix: - # TODO: temporary disabled due to linux kernel issues - #sanitizer: [ADDRESS, THREAD, UNDEFINED] - sanitizer: [UNDEFINED] + sanitizer: [ADDRESS, THREAD, UNDEFINED] build_type: [Debug] include: - build_type: Release sanitizer: "" + - build_type: Debug + sanitizer: THREAD + disabled_on_pr: true fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken steps: diff --git a/CMakeLists.txt b/CMakeLists.txt index 362ab3673..92c9f09eb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,4 @@ -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) @@ -77,6 +77,7 @@ option(LLAMA_AVX2 "llama: enable AVX2" 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_AVX512_BF16 "llama: enable AVX512-BF16" OFF) option(LLAMA_FMA "llama: enable FMA" ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512 if (NOT MSVC) @@ -122,7 +123,7 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING "llama: metal minimum macOS version") set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)") option(LLAMA_KOMPUTE "llama: use Kompute" OFF) -option(LLAMA_MPI "llama: use MPI" OFF) +option(LLAMA_RPC "llama: use RPC" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_SYCL "llama: use SYCL" OFF) option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) @@ -296,7 +297,7 @@ if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) endif() - if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22) + if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) set(BLA_SIZEOF_INTEGER 8) endif() @@ -465,33 +466,15 @@ if (LLAMA_CUDA) endif() endif() -if (LLAMA_MPI) - cmake_minimum_required(VERSION 3.10) - find_package(MPI) - if (MPI_C_FOUND) - message(STATUS "MPI found") +if (LLAMA_RPC) + add_compile_definitions(GGML_USE_RPC) - 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") + if (WIN32) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ws2_32) endif() + + set(GGML_HEADERS_RPC ggml-rpc.h) + set(GGML_SOURCES_RPC ggml-rpc.cpp) endif() if (LLAMA_CLBLAST) @@ -543,16 +526,37 @@ if (LLAMA_VULKAN) endif() if (LLAMA_HIPBLAS) - list(APPEND CMAKE_PREFIX_PATH /opt/rocm) + if ($ENV{ROCM_PATH}) + set(ROCM_PATH $ENV{ROCM_PATH}) + else() + set(ROCM_PATH /opt/rocm) + endif() + list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) - if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang") + # CMake on Windows doesn't support the HIP language yet + if(WIN32) + set(CXX_IS_HIPCC TRUE) + else() + string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}") endif() - if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") - endif() + if(CXX_IS_HIPCC) + if(LINUX) + if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") + message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") + endif() + message(WARNING "Setting hipcc as the C++ compiler is legacy behavior." + " Prefer setting the HIP compiler directly. See README for details.") + endif() + else() + # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. + if(AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) + set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS}) + endif() + cmake_minimum_required(VERSION 3.21) + enable_language(HIP) + endif() find_package(hip REQUIRED) find_package(hipblas REQUIRED) find_package(rocblas REQUIRED) @@ -586,13 +590,18 @@ if (LLAMA_HIPBLAS) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) - set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) + if (CXX_IS_HIPCC) + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device) + else() + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP) + endif() 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) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} PUBLIC hip::host roc::rocblas roc::hipblas) endif() if (LLAMA_SYCL) @@ -995,6 +1004,11 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR if (GGML_COMPILER_SUPPORT_DOTPROD) add_compile_definitions(__ARM_FEATURE_DOTPROD) endif () + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) + if (GGML_COMPILER_SUPPORT_MATMUL_INT8) + add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) + endif () + check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) @@ -1047,6 +1061,10 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW add_compile_definitions($<$:__AVX512VNNI__>) add_compile_definitions($<$:__AVX512VNNI__>) endif() + if (LLAMA_AVX512_BF16) + add_compile_definitions($<$:__AVX512BF16__>) + add_compile_definitions($<$:__AVX512BF16__>) + endif() elseif (LLAMA_AVX2) list(APPEND ARCH_FLAGS /arch:AVX2) elseif (LLAMA_AVX) @@ -1078,6 +1096,9 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW if (LLAMA_AVX512_VNNI) list(APPEND ARCH_FLAGS -mavx512vnni) endif() + if (LLAMA_AVX512_BF16) + list(APPEND ARCH_FLAGS -mavx512bf16) + endif() endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") message(STATUS "PowerPC detected") @@ -1175,7 +1196,7 @@ add_library(ggml OBJECT ${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_RPC} ${GGML_HEADERS_RPC} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} @@ -1262,7 +1283,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" - "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") + "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") install(TARGETS ggml PUBLIC_HEADER) diff --git a/CMakePresets.json b/CMakePresets.json new file mode 100644 index 000000000..ad1af7ecc --- /dev/null +++ b/CMakePresets.json @@ -0,0 +1,45 @@ +{ + "version": 4, + "configurePresets": [ + { + "name": "base", + "hidden": true, + "generator": "Ninja", + "binaryDir": "${sourceDir}/build-${presetName}", + "cacheVariables": { + "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", + "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." + } + }, + + { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, + { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, + { "name": "static", "hidden": true, "cacheVariables": { "LLAMA_STATIC": "ON" } }, + + { + "name": "arm64-windows-msvc", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x86_64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake" + } + }, + + { + "name": "arm64-windows-llvm", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x86_64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake" + } + }, + + { "name": "arm64-windows-llvm-debug" , "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, + { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "release" ] }, + { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "release", "static" ] }, + + { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, + { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] }, + { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] } + ] +} diff --git a/Makefile b/Makefile index 3fa56d13a..9a26aec50 100644 --- a/Makefile +++ b/Makefile @@ -399,13 +399,6 @@ ifndef LLAMA_NO_ACCELERATE endif endif # LLAMA_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 - ifdef LLAMA_OPENBLAS MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) @@ -560,10 +553,10 @@ endif # LLAMA_VULKAN ifdef LLAMA_HIPBLAS ifeq ($(wildcard /opt/rocm),) ROCM_PATH ?= /usr - GPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) + 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) $(ROCM_PATH)/bin/hipcc LLAMA_CUDA_DMMV_X ?= 32 @@ -575,7 +568,7 @@ ifdef LLAMA_HIP_UMA endif # LLAMA_HIP_UMA MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas - HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) + HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_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) @@ -629,11 +622,6 @@ ggml-metal-embed.o: ggml-metal.metal ggml-common.h endif endif # LLAMA_METAL -ifdef LLAMA_MPI -ggml-mpi.o: ggml-mpi.c ggml-mpi.h - $(CC) $(CFLAGS) -c $< -o $@ -endif # LLAMA_MPI - ifndef LLAMA_NO_LLAMAFILE sgemm.o: sgemm.cpp sgemm.h ggml.h $(CXX) $(CXXFLAGS) -c $< -o $@ diff --git a/README.md b/README.md index 9f2f8df64..4abfd6d7e 100644 --- a/README.md +++ b/README.md @@ -382,45 +382,6 @@ To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or th 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: @@ -528,13 +489,28 @@ Building the program with BLAS support may lead to some performance improvements ``` - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): ```bash - CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \ - cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ && cmake --build build --config Release -- -j 16 ``` - On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`. + On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). + Note that if you get the following error: + ``` + clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library + ``` + Try searching for a directory under `HIP_PATH` that contains the file + `oclc_abi_version_400.bc`. Then, add the following to the start of the + command: `HIP_DEVICE_LIB_PATH=`, so something + like: + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ + HIP_DEVICE_LIB_PATH= \ + cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build -- -j 16 + ``` + - Using `make` (example for target gfx1030, build with 16 CPU threads): ```bash make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 @@ -543,10 +519,8 @@ Building the program with BLAS support may lead to some performance improvements - 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++ -DCMAKE_BUILD_TYPE=Release .. - cmake --build . + cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=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`. @@ -712,6 +686,9 @@ Building the program with BLAS support may lead to some performance improvements ### Prepare and Quantize +> [!NOTE] +> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours. + 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. Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. diff --git a/cmake/arm64-windows-llvm.cmake b/cmake/arm64-windows-llvm.cmake new file mode 100644 index 000000000..46fba6514 --- /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 ${target} ) + +set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" ) + +set( CMAKE_C_FLAGS_INIT "${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/common/common.cpp b/common/common.cpp index 3faa7c459..3eb71ed67 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1060,6 +1060,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa #endif // GGML_USE_CUDA_SYCL_VULKAN return true; } + if (arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.rpc_servers = argv[i]; + return true; + } if (arg == "--no-mmap") { params.use_mmap = false; return true; @@ -1557,6 +1565,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { 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(" --rpc SERVERS comma separated list of RPC servers\n"); 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"); @@ -1830,6 +1839,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & if (params.n_gpu_layers != -1) { mparams.n_gpu_layers = params.n_gpu_layers; } + mparams.rpc_servers = params.rpc_servers.c_str(); mparams.main_gpu = params.main_gpu; mparams.split_mode = params.split_mode; mparams.tensor_split = params.tensor_split; @@ -2543,7 +2553,7 @@ void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const cha size_t pos_start = 0; size_t pos_found = 0; - if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) { + if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); diff --git a/common/common.h b/common/common.h index d80344f2a..566490e2f 100644 --- a/common/common.h +++ b/common/common.h @@ -82,6 +82,7 @@ struct gpt_params { 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 + std::string rpc_servers = ""; // comma separated list of RPC servers ggml_backend_sched_eval_callback cb_eval = nullptr; void * cb_eval_user_data = nullptr; diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index fecb7cd71..b5bc7d49b 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -26,7 +26,7 @@ namespace grammar_parser { 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)); + auto result = state.symbol_ids.emplace(std::string(src, len), next_id); return result.first->second; } diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 0f8f1b1d4..9a71f5d8d 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -272,7 +272,7 @@ private: if (literal.empty()) { return false; } - ret.push_back(std::make_pair(literal, true)); + ret.emplace_back(literal, true); literal.clear(); return true; }; @@ -298,7 +298,7 @@ private: while (i < length) { char c = sub_pattern[i]; if (c == '.') { - seq.push_back(std::make_pair(get_dot(), false)); + seq.emplace_back(get_dot(), false); i++; } else if (c == '(') { i++; @@ -307,7 +307,7 @@ private: _warnings.push_back("Unsupported pattern syntax"); } } - seq.push_back(std::make_pair("(" + to_rule(transform()) + ")", false)); + seq.emplace_back("(" + to_rule(transform()) + ")", false); } else if (c == ')') { i++; if (start > 0 && sub_pattern[start - 1] != '(') { @@ -331,9 +331,9 @@ private: } square_brackets += ']'; i++; - seq.push_back(std::make_pair(square_brackets, false)); + seq.emplace_back(square_brackets, false); } else if (c == '|') { - seq.push_back(std::make_pair("|", false)); + seq.emplace_back("|", false); i++; } else if (c == '*' || c == '+' || c == '?') { seq.back() = std::make_pair(to_rule(seq.back()) + c, false); @@ -417,7 +417,7 @@ private: } } if (!literal.empty()) { - seq.push_back(std::make_pair(literal, true)); + seq.emplace_back(literal, true); } } } diff --git a/common/log.h b/common/log.h index 6934c57b2..09fa63c26 100644 --- a/common/log.h +++ b/common/log.h @@ -211,7 +211,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else #define LOG_FLF_FMT "[%24s:%5ld][%24s] " - #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #define LOG_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__ #endif #else #define LOG_FLF_FMT "%s" @@ -224,7 +224,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #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__ + #define LOG_TEE_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__ #endif #else #define LOG_TEE_FLF_FMT "%s" @@ -294,7 +294,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // Main LOG macro. // behaves like printf, and supports arguments the exact same way. // -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #define LOG(...) LOG_IMPL(__VA_ARGS__, "") #else #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "") @@ -308,14 +308,14 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // Secondary target can be changed just like LOG_TARGET // by defining LOG_TEE_TARGET // -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #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 +#if !defined(_MSC_VER) || defined(__clang__) #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") #else diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index cd2674a0e..45404b32b 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -20,11 +20,13 @@ # - Update llama.cpp with the new pre-tokenizer if necessary # # TODO: generate tokenizer tests for llama.cpp -# TODO: automate the update of convert-hf-to-gguf.py # import logging import os +import pathlib +import re + import requests import sys import json @@ -35,6 +37,7 @@ from transformers import AutoTokenizer logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("convert-hf-to-gguf-update") +sess = requests.Session() class TOKENIZER_TYPE(IntEnum): @@ -69,73 +72,55 @@ models = [ {"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": "stablelm", "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-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! - {"name": "jina-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, - {"name": "jina-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"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", }, ] -# make directory "models/tokenizers" if it doesn't exist -if not os.path.exists("models/tokenizers"): - os.makedirs("models/tokenizers") - def download_file_with_auth(url, token, save_path): headers = {"Authorization": f"Bearer {token}"} - response = requests.get(url, headers=headers) - if response.status_code == 200: - with open(save_path, 'wb') as f: - f.write(response.content) - logger.info(f"File {save_path} downloaded successfully") - else: - logger.info(f"Failed to download file. Status code: {response.status_code}") + 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 f: + f.write(response.content) + logger.info(f"File {save_path} downloaded successfully") -# download the tokenizer models -for model in models: +def download_model(model): name = model["name"] repo = model["repo"] tokt = model["tokt"] - if not os.path.exists(f"models/tokenizers/{name}"): - os.makedirs(f"models/tokenizers/{name}") - else: - logger.info(f"Directory models/tokenizers/{name} already exists - skipping") - continue - - logger.info(f"Downloading {name} to models/tokenizers/{name}") - - url = f"{repo}/raw/main/config.json" - save_path = f"models/tokenizers/{name}/config.json" - download_file_with_auth(url, token, save_path) - - url = f"{repo}/raw/main/tokenizer.json" - save_path = f"models/tokenizers/{name}/tokenizer.json" - download_file_with_auth(url, token, save_path) - - # if downloaded file is less than 1KB, we likely need to download an LFS instead - if os.path.getsize(save_path) < 1024: - # remove the file - os.remove(save_path) - url = f"{repo}/resolve/main/tokenizer.json" - save_path = f"models/tokenizers/{name}/tokenizer.json" - download_file_with_auth(url, token, save_path) + os.makedirs(f"models/tokenizers/{name}", exist_ok=True) + files = ["config.json", "tokenizer.json", "tokenizer_config.json"] if tokt == TOKENIZER_TYPE.SPM: - url = f"{repo}/resolve/main/tokenizer.model" - save_path = f"models/tokenizers/{name}/tokenizer.model" - download_file_with_auth(url, token, save_path) + files.append("tokenizer.model") + + 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}") - url = f"{repo}/raw/main/tokenizer_config.json" - save_path = f"models/tokenizers/{name}/tokenizer_config.json" - download_file_with_auth(url, token, save_path) # generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function: -# TODO: auto-update convert-hf-to-gguf.py with the generated function src_ifs = "" for model in models: @@ -224,11 +209,18 @@ src_func = f""" return res """ -print(src_func) # noqa: NP100 +convert_py_pth = pathlib.Path("convert-hf-to-gguf.py") +convert_py = convert_py_pth.read_text() +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, +) -logger.info("\n") -logger.info("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!") -logger.info("\n") +convert_py_pth.write_text(convert_py) + +logger.info("+++ convert-hf-to-gguf.py was updated") # generate tests for each tokenizer model diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ec7f4dd75..bd303150a 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -240,23 +240,6 @@ class Model: return False def write_tensors(self): - # same as ggml_compute_fp32_to_bf16 in ggml-impl.h - def np_fp32_to_bf16(n: np.ndarray): - # force nan to quiet - n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n) - # flush subnormals to zero - n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n) - # round to nearest even - n = (n + (0x7fff + ((n >> 16) & 1))) >> 16 - return n.astype(np.int16) - - # Doing this row-wise is much, much faster than element-wise, hence the signature - v_fp32_to_bf16 = np.vectorize(np_fp32_to_bf16, otypes=[np.int16], signature="(n)->(n)") - if self.lazy: - # TODO: find a way to implicitly wrap np.vectorize functions - # NOTE: the type is changed to reflect otypes passed to np.vectorize above - v_fp32_to_bf16 = gguf.LazyNumpyTensor._wrap_fn(v_fp32_to_bf16, meta_noop=np.int16) - max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") for name, data_torch in self.get_tensors(): @@ -309,27 +292,31 @@ class Model: )) if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32: - if self.ftype == gguf.LlamaFileType.MOSTLY_F16: + if self.ftype == gguf.LlamaFileType.MOSTLY_BF16: + data = gguf.quantize_bf16(data) + assert data.dtype == np.int16 + data_qtype = gguf.GGMLQuantizationType.BF16 + + elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data): + data = gguf.quantize_q8_0(data) + assert data.dtype == np.uint8 + data_qtype = gguf.GGMLQuantizationType.Q8_0 + + else: # default to float16 for quantized tensors if data_dtype != np.float16: data = data.astype(np.float16) data_qtype = gguf.GGMLQuantizationType.F16 - elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16: - if data_dtype != np.float32: - data = data.astype(np.float32) - data = v_fp32_to_bf16(data.view(np.int32)) - assert data.dtype == np.int16 - data_qtype = gguf.GGMLQuantizationType.BF16 - - else: # by default, convert to float32 + if data_qtype is None: # by default, convert to float32 if data_dtype != np.float32: data = data.astype(np.float32) data_qtype = gguf.GGMLQuantizationType.F32 - assert data_qtype is not None - + block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype] # reverse shape to make it similar to the internal ggml dimension order - shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}" + shape_str = f"""{{{', '.join(str(n) for n in reversed( + (*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size)) + )}}}""" # 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}") @@ -415,6 +402,7 @@ class Model: # 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 @@ -458,6 +446,9 @@ class Model: if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454": # ref: https://huggingface.co/openai-community/gpt2 res = "gpt-2" + if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3": + # ref: https://huggingface.co/stabilityai/stablelm-2-1_6b + res = "stablelm2" if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": # ref: https://huggingface.co/smallcloudai/Refact-1_6-base res = "refact" @@ -475,13 +466,13 @@ class Model: res = "dbrx" if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en - res = "jina-en" + res = "jina-v2-en" if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es - res = "jina-es" + res = "jina-v2-es" if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de - res = "jina-de" + res = "jina-v2-de" if res is None: logger.warning("\n") @@ -502,6 +493,7 @@ class Model: logger.debug(f"chkhsh: {chkhsh}") return res + # Marker: End get_vocab_base_pre def _set_vocab_gpt2(self) -> None: tokens, toktypes, tokpre = self.get_vocab_base() @@ -539,7 +531,7 @@ class Model: # 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()} + 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: @@ -584,6 +576,10 @@ class Model: 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.UNKNOWN] * vocab_size + for token_id in range(tokenizer.vocab_size()): piece = tokenizer.IdToPiece(token_id) text = piece.encode("utf-8") @@ -599,21 +595,23 @@ class Model: elif tokenizer.IsByte(token_id): toktype = SentencePieceTokenTypes.BYTE - tokens.append(text) - scores.append(score) - toktypes.append(toktype) + 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: - key = key.encode("utf-8") - if key not in tokens: - tokens.append(key) - scores.append(-1000.0) - toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + 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) @@ -623,8 +621,6 @@ class Model: scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.UNUSED) - assert len(tokens) == vocab_size - self.gguf_writer.add_tokenizer_model("llama") self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) @@ -859,6 +855,7 @@ class BaichuanModel(Model): 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": @@ -981,6 +978,7 @@ class XverseModel(Model): 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": @@ -1215,6 +1213,7 @@ class StableLMModel(Model): 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 @@ -1591,6 +1590,7 @@ class QwenModel(Model): 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") @@ -1828,6 +1828,7 @@ class PlamoModel(Model): 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) @@ -2007,6 +2008,7 @@ in chat mode so that the conversation can end normally.") 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) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: num_heads = self.hparams["num_attention_heads"] @@ -2415,25 +2417,15 @@ class LazyTorchTensor(gguf.LazyBase): def numpy(self) -> gguf.LazyNumpyTensor: dtype = self._dtype_map[self.dtype] return gguf.LazyNumpyTensor( - meta=np.lib.stride_tricks.as_strided(np.zeros(1, dtype), self.shape, (0 for _ in self.shape)), + meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape), lazy=self._lazy, args=(self,), func=(lambda s: s[0].numpy()) ) @classmethod - def eager_to_meta(cls, t: Tensor) -> Tensor: - if t.is_meta: - return t - return t.detach().to("meta") - - @classmethod - def meta_with_dtype(cls, m: Tensor, dtype: torch.dtype) -> Tensor: - m = m.detach() - if not m.is_meta: - m = m.to("meta") - m.dtype = dtype - return m + def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: torch.Size) -> Tensor: + return torch.empty(size=shape, dtype=dtype, device="meta") @classmethod def __torch_function__(cls, func, types, args=(), kwargs=None): @@ -2464,8 +2456,8 @@ def parse_args() -> argparse.Namespace: 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", "auto"], default="f16", - help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", + "--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", @@ -2523,6 +2515,7 @@ def main() -> None: "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, } diff --git a/convert.py b/convert.py index 148bfd66a..da1247957 100755 --- a/convert.py +++ b/convert.py @@ -24,7 +24,7 @@ from abc import ABC, abstractmethod from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable +from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional import numpy as np from sentencepiece import SentencePieceProcessor @@ -344,10 +344,47 @@ class Params: return params +@dataclass +class Metadata: + name: Optional[str] = None + author: Optional[str] = None + version: Optional[str] = None + url: Optional[str] = None + description: Optional[str] = None + licence: Optional[str] = None + source_url: Optional[str] = None + source_hf_repo: Optional[str] = None + + @staticmethod + def load(metadata_path: Path) -> Metadata: + if metadata_path is None or not metadata_path.exists(): + return Metadata() + + with open(metadata_path, 'r') as file: + data = json.load(file) + + # Create a new Metadata instance + metadata = Metadata() + + # Assigning values to Metadata attributes if they exist in the JSON file + # This is based on LLM_KV_NAMES mapping in llama.cpp + metadata.name = data.get("general.name") + metadata.author = data.get("general.author") + metadata.version = data.get("general.version") + metadata.url = data.get("general.url") + metadata.description = data.get("general.description") + metadata.license = data.get("general.license") + metadata.source_url = data.get("general.source.url") + metadata.source_hf_repo = data.get("general.source.huggingface.repository") + + return metadata + + # # vocab # + @runtime_checkable class BaseVocab(Protocol): tokenizer_model: ClassVar[str] @@ -1066,21 +1103,42 @@ class OutputFile: 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: Metadata) -> 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_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_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.url is not None: + self.gguf.add_url(metadata.url) + if metadata.description is not None: + self.gguf.add_description(metadata.description) + if metadata.licence is not None: + self.gguf.add_licence(metadata.licence) + if metadata.source_url is not None: + self.gguf.add_source_url(metadata.source_url) + if metadata.source_hf_repo is not None: + self.gguf.add_source_hf_repo(metadata.source_hf_repo) + + 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) @@ -1183,13 +1241,14 @@ class OutputFile: @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: Metadata = 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) @@ -1216,12 +1275,14 @@ class OutputFile: 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: Metadata = 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) if isinstance(vocab, Vocab): of.add_meta_vocab(vocab) @@ -1257,6 +1318,37 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT raise ValueError(f"Unexpected combination of types: {name_to_type}") +def model_parameter_count(model: LazyModel) -> int: + total_model_parameters = 0 + for i, (name, lazy_tensor) in enumerate(model.items()): + sum_weights_in_tensor = 1 + for dim in lazy_tensor.shape: + sum_weights_in_tensor *= dim + total_model_parameters += sum_weights_in_tensor + return total_model_parameters + + +def model_parameter_count_rounded_notation(model_params_count: int) -> str: + if model_params_count > 1e12 : + # Trillions Of Parameters + scaled_model_params = model_params_count * 1e-12 + scale_suffix = "T" + elif model_params_count > 1e9 : + # Billions Of Parameters + scaled_model_params = model_params_count * 1e-9 + scale_suffix = "B" + elif model_params_count > 1e6 : + # Millions Of Parameters + scaled_model_params = model_params_count * 1e-6 + scale_suffix = "M" + else: + # Thousands Of Parameters + scaled_model_params = model_params_count * 1e-3 + scale_suffix = "K" + + return f"{round(scaled_model_params)}{scale_suffix}" + + def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: return {name: tensor.astype(output_type.type_for_tensor(name, tensor)) for (name, tensor) in model.items()} @@ -1436,13 +1528,35 @@ class VocabFactory: 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, params: Params, model_params_count: int, metadata: Metadata) -> str: + quantization = { + GGMLFileType.AllF32: "F32", + GGMLFileType.MostlyF16: "F16", + GGMLFileType.MostlyQ8_0: "Q8_0", }[file_type] - ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" + + parameters = model_parameter_count_rounded_notation(model_params_count) + + expert_count = "" + if params.n_experts is not None: + expert_count = f"{params.n_experts}x" + + version = "" + if metadata is not None and metadata.version is not None: + version = f"-{metadata.version}" + + name = "ggml-model" + if metadata is not None and metadata.name is not None: + name = metadata.name + elif params.path_model is not None: + name = params.path_model.name + + return f"{name}{version}-{expert_count}{parameters}-{quantization}" + + +def default_outfile(model_paths: list[Path], file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> Path: + default_filename = default_convention_outfile(file_type, params, model_params_count, metadata) + ret = model_paths[0].parent / f"{default_filename}.gguf" if ret in model_paths: logger.error( f"Error: Default output path ({ret}) would overwrite the input. " @@ -1480,17 +1594,30 @@ def main(args_in: list[str] | None = None) -> None: 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 a metadata file") + parser.add_argument("--get-outfile", action="store_true", help="get calculated default outfile name") args = parser.parse_args(args_in) if args.verbose: logging.basicConfig(level=logging.DEBUG) - elif args.dump_single or args.dump: + 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) + metadata = Metadata.load(args.metadata) + + if args.get_outfile: + model_plus = load_some_model(args.model) + params = Params.load(model_plus) + model = convert_model_names(model_plus.model, params, args.skip_unknown) + model_params_count = model_parameter_count(model_plus.model) + ftype = pick_output_type(model, args.outtype) + print(f"{default_convention_outfile(ftype, params, 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") @@ -1504,6 +1631,9 @@ def main(args_in: list[str] | None = None) -> None: else: model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None) + model_params_count = model_parameter_count(model_plus.model) + logger.info(f"model parameters count : {model_params_count} ({model_parameter_count_rounded_notation(model_params_count)})") + if args.dump: do_dump_model(model_plus) return @@ -1557,7 +1687,7 @@ def main(args_in: list[str] | None = None) -> None: f_norm_eps = 1e-5, ) OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, - endianess=endianess, pad_vocab=args.pad_vocab) + endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) logger.info(f"Wrote {outfile}") return @@ -1570,13 +1700,13 @@ def main(args_in: list[str] | None = None) -> None: 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, model_params_count, metadata) params.ftype = 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) + concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) logger.info(f"Wrote {outfile}") diff --git a/docs/debugging-tests.md b/docs/debugging-tests.md index 51a125e19..18407f688 100644 --- a/docs/debugging-tests.md +++ b/docs/debugging-tests.md @@ -1,6 +1,6 @@ # Debugging Tests Tips -## How to run & debug a specific test without anything else to keep the feedback loop short? +## 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. @@ -10,13 +10,27 @@ For example, running the following command will output an interactive list from 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.   @@ -41,7 +55,7 @@ cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON .. make -j ``` -#### Step 3.1: Identify Test Command for Debugging +#### Step 3: Find all tests available that matches REGEX The output of this command will give you the command & arguments needed to run GDB. @@ -69,11 +83,13 @@ Labels: main ... ``` -So for test #1 we can tell these two pieces of relevant information: +#### 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 3.2: Run GDB on test command +#### 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: diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index f421769cc..b40ee4ccb 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -49,4 +49,7 @@ else() add_subdirectory(server) endif() add_subdirectory(export-lora) + if (LLAMA_RPC) + add_subdirectory(rpc) + endif() endif() diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index c85a2da53..0c921ed69 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -211,6 +211,7 @@ int main(int argc, char ** argv) { // clean up llama_print_timings(ctx); + llama_batch_free(batch); llama_free(ctx); llama_free_model(model); llama_backend_free(); diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/app/src/main/cpp/CMakeLists.txt index 85139329a..4536974a5 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/app/src/main/cpp/CMakeLists.txt @@ -12,15 +12,20 @@ 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 -) +## Fetch latest llama.cpp from GitHub +#include(FetchContent) +#FetchContent_Declare( +# llama +# GIT_REPOSITORY https://github.com/ggerganov/llama.cpp +# GIT_TAG master +#) +# +## Also provides "common" +#FetchContent_MakeAvailable(llama) -# Also provides "common" -FetchContent_MakeAvailable(llama) +# llama.cpp CI uses the code from the current branch +# ref: https://github.com/ggerganov/llama.cpp/pull/7341#issuecomment-2117617700 +add_subdirectory(../../../../../../ build-llama) # Creates and names a library, sets it as either STATIC # or SHARED, and provides the relative paths to its source code. diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index da60ddf2f..a6d67e5d7 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -300,14 +300,10 @@ int main(int argc, char ** argv) { return 1; } - for (auto & image : params.image) { + if (prompt_contains_image(params.prompt)) { auto ctx_llava = llava_init_context(¶ms, model); - auto image_embed = load_image(ctx_llava, ¶ms, image); - if (!image_embed) { - std::cerr << "error: failed to load image " << image << ". Terminating\n\n"; - return 1; - } + auto image_embed = load_image(ctx_llava, ¶ms, ""); // process the prompt process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); @@ -316,7 +312,26 @@ int main(int argc, char ** argv) { 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) { + std::cerr << "error: failed to load image " << image << ". Terminating\n\n"; + return 1; + } + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_print_timings(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); + } } + llama_free_model(model); return 0; diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 9a990bb18..63878d176 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -88,7 +88,6 @@ 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 +149,6 @@ 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) { - LOG_TEE("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) { - LOG_TEE("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 diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md index c5e2bc5de..c2a3c5ce9 100644 --- a/examples/perplexity/README.md +++ b/examples/perplexity/README.md @@ -7,6 +7,8 @@ Also note that finetunes typically result in a higher perplexity value even thou 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. @@ -32,7 +34,13 @@ In addition to the KL divergence the following statistics are calculated with `- ## LLaMA 3 8b Scoreboard -Results are sorted by Kullback-Leibler divergence relative to FP16. +| 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). | Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp | @@ -89,6 +97,12 @@ K-quants score better on mean Δp than the legacy quants than e.g. KL divergence ## 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 | @@ -107,6 +121,50 @@ K-quants score better on mean Δp than the legacy quants than e.g. KL divergence | 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 diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index db6e0949d..bae014e6f 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1425,7 +1425,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params // Use all tasks tasks.resize(n_task); printf("%s: reading tasks", __func__); - int n_dot = n_task/100; + int n_dot = std::max((int) n_task/100, 1); int i = 0; for (auto& task : tasks) { ++i; @@ -1675,7 +1675,7 @@ 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)); diff --git a/examples/quantize/README.md b/examples/quantize/README.md index 8a10365c0..b78ece4e7 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -1,6 +1,8 @@ # 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. + +Note: It is synced from llama.cpp `main` every 6 hours. ## Llama 2 7B diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 909eab283..cbb452334 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -284,7 +284,7 @@ int main(int argc, char ** argv) { } else { usage(argv[0]); } - } else if (strcmp(argv[arg_idx], "--keep-split")) { + } else if (strcmp(argv[arg_idx], "--keep-split") == 0) { params.keep_split = true; } else { usage(argv[0]); 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..eeec71a8e --- /dev/null +++ b/examples/rpc/README.md @@ -0,0 +1,74 @@ +## Overview + +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] + ggml[llama.cpp]---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 `-DLLAMA_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 .. -DLLAMA_CUDA=ON -DLLAMA_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` only with `-DLLAMA_RPC=ON`: + +```bash +mkdir build-rpc +cd build-rpc +cmake .. -DLLAMA_RPC=ON +cmake --build . --config Release +``` + +Finally, use the `--rpc` option to specify the host and port of each `rpc-server`: + +```bash +$ bin/main -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 +``` diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp new file mode 100644 index 000000000..7c15d2aa4 --- /dev/null +++ b/examples/rpc/rpc-server.cpp @@ -0,0 +1,134 @@ +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include "ggml-rpc.h" +#ifdef _WIN32 +# include +#else +# include +#endif +#include +#include + +struct rpc_server_params { + std::string host = "0.0.0.0"; + 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__); + } +#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); +#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; + } + 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)); + start_rpc_server(backend, endpoint.c_str(), free_mem, total_mem); + ggml_backend_free(backend); + return 0; +} diff --git a/examples/server/README.md b/examples/server/README.md index 650317991..0c3db8c84 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -17,8 +17,9 @@ The project is under active development, and we are [looking for feedback and co **Command line options:** -- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend. -- `-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. Not used if model layers are offloaded to GPU. +- `-v`, `--verbose`: Enable verbose server output. When using the `/completion` endpoint, this includes the tokenized prompt, the full request and the full response. +- `-t N`, `--threads N`: Set the number of threads to use by CPU layers during generation. Not used by model layers that are offloaded to GPU. This option has no effect when using the maximum number of GPU layers. Default: `std::thread::hardware_concurrency()` (number of CPU cores). +- `-tb N, --threads-batch N`: Set the number of threads to use by CPU layers during batch and prompt processing (>= 32 tokens). This option has no effect if a GPU is available. Default: `--threads`. - `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)` - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused @@ -36,9 +37,7 @@ The project is under active development, and we are [looking for feedback and co - `--numa STRATEGY`: Attempt one of the below optimization strategies that may 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 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 may 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. @@ -48,8 +47,8 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/ - `--path`: Path from which to serve static files. Default: disabled - `--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` +- `--embeddings`: Enable embedding vector output and the OAI compatible endpoint /v1/embeddings. Physical batch size (`--ubatch-size`) must be carefully defined. Default: disabled +- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`. Values > 1 will allow for higher throughput with multiple parallel requests but the results will **not** be deterministic due to differences in rounding error. - `-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. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ceaeb1f76..7978f979d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -102,7 +102,6 @@ struct slot_params { bool stream = true; bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt - uint32_t seed = -1; // RNG seed 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 @@ -671,6 +670,13 @@ struct server_context { model = nullptr; } + // Clear any sampling context + for (server_slot & slot : slots) { + if (slot.ctx_sampling != nullptr) { + llama_sampling_free(slot.ctx_sampling); + } + } + llama_batch_free(batch); } @@ -1257,7 +1263,7 @@ struct server_context { {"n_ctx", slot.n_ctx}, {"n_predict", slot.n_predict}, {"model", params.model_alias}, - {"seed", slot.params.seed}, + {"seed", slot.sparams.seed}, {"temperature", slot.sparams.temp}, {"dynatemp_range", slot.sparams.dynatemp_range}, {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, @@ -2380,6 +2386,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co 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(" --rpc SERVERS comma separated list of RPC servers\n"); printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); 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"); @@ -2432,6 +2439,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } sparams.port = std::stoi(argv[i]); + } else if (arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers = argv[i]; } else if (arg == "--host") { if (++i >= argc) { invalid_param = true; diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature index aa0b8d0c6..5deb278c2 100644 --- a/examples/server/tests/features/results.feature +++ b/examples/server/tests/features/results.feature @@ -70,12 +70,48 @@ Feature: Results Then all predictions are equal Examples: | n_parallel | temp | - | 1 | 0.0 | - | 2 | 0.0 | - | 4 | 0.0 | - | 1 | 1.0 | - # FIXME: These tests fail on master. The problem seems to be the unified KV cache. + | 1 | 0.0 | + | 2 | 0.0 | + | 4 | 0.0 | + | 1 | 1.0 | + # FIXME: These tests fail on master. + # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism. # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 - # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 . - # | 2 | 1.0 | - # | 4 | 1.0 | + # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 + # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 2 | 1.0 | + # | 4 | 1.0 | + + Scenario Outline: consistent token probs with same seed and prompt + Given slots + And KV cache size + And 1.0 temperature + And max tokens to predict + Then the server is starting + Then the server is healthy + + Given 1 prompts "The meaning of life is" with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Given prompts "The meaning of life is" with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Then all token probabilities are equal + Examples: + | n_slots | n_kv | n_predict | n_parallel | + | 4 | 1024 | 1 | 1 | + | 4 | 1024 | 1 | 4 | + # FIXME: These tests fail on master. + # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism. + # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 + # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 + # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 4 | 1024 | 100 | 1 | + # This test still fails even the above patches; the first token probabilities are already different. + # | 4 | 1024 | 100 | 4 | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index f4b1ac1d7..7da503f2c 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -23,6 +23,7 @@ from prometheus_client import parser def step_server_config(context, server_fqdn, server_port): context.server_fqdn = server_fqdn context.server_port = int(server_port) + context.n_threads = None context.n_gpu_layer = None if 'PORT' in os.environ: context.server_port = int(os.environ['PORT']) @@ -109,6 +110,11 @@ def step_n_gpu_layer(context, ngl): context.n_gpu_layer = ngl +@step('{n_threads:d} threads') +def step_n_threads(context, n_threads): + context.n_thread = n_threads + + @step('{draft:d} as draft') def step_draft(context, draft): context.draft = draft @@ -274,13 +280,22 @@ async def step_predictions_equal(context): @step('all predictions are different') @async_run_until_complete -async def step_predictions_equal(context): +async def step_predictions_different(context): n_completions = await gather_tasks_results(context) assert n_completions >= 2, "need at least 2 completions" assert_all_predictions_different(context.tasks_result) context.tasks_result = [] +@step('all token probabilities are equal') +@async_run_until_complete +async def step_token_probabilities_equal(context): + n_completions = await gather_tasks_results(context) + assert n_completions >= 2, "need at least 2 completions" + assert_all_token_probabilities_equal(context.tasks_result) + context.tasks_result = [] + + @step('the completion is truncated') def step_assert_completion_truncated(context): step_assert_completion_truncated(context, '') @@ -869,6 +884,7 @@ async def request_completion(prompt, "id_slot": id_slot, "seed": seed if seed is not None else 42, "temperature": temperature if temperature is not None else "0.8f", + "n_probs": 2, }, headers=headers, timeout=3600) as response: @@ -887,6 +903,7 @@ async def oai_chat_completions(user_prompt, base_path, async_client, debug=False, + temperature=None, model=None, n_predict=None, enable_streaming=None, @@ -913,7 +930,8 @@ async def oai_chat_completions(user_prompt, "model": model, "max_tokens": n_predict, "stream": enable_streaming, - "seed": seed + "temperature": temperature if temperature is not None else 0.0, + "seed": seed, } if response_format is not None: payload['response_format'] = response_format @@ -978,7 +996,8 @@ async def oai_chat_completions(user_prompt, max_tokens=n_predict, stream=enable_streaming, response_format=payload.get('response_format'), - seed=seed + seed=seed, + temperature=payload['temperature'] ) except openai.error.AuthenticationError as e: if expect_api_error is not None and expect_api_error: @@ -1120,6 +1139,23 @@ def assert_all_predictions_different(completion_responses): assert content_i != content_j, "contents not different" +def assert_all_token_probabilities_equal(completion_responses): + n_predict = len(completion_responses[0]['completion_probabilities']) + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + for pos in range(n_predict): + for i, response_i in enumerate(completion_responses): + probs_i = response_i['completion_probabilities'][pos]['probs'] + print(f"pos {pos}, probs {i}: {probs_i}") + for pos in range(n_predict): + for i, response_i in enumerate(completion_responses): + probs_i = response_i['completion_probabilities'][pos]['probs'] + for j, response_j in enumerate(completion_responses): + if i == j: + continue + probs_j = response_j['completion_probabilities'][pos]['probs'] + assert probs_i == probs_j, "contents not equal" + + async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: @@ -1258,6 +1294,8 @@ def start_server_background(context): server_args.extend(['--batch-size', context.n_batch]) if context.n_ubatch: server_args.extend(['--ubatch-size', context.n_ubatch]) + if context.n_threads: + server_args.extend(['--threads', context.threads]) if context.n_gpu_layer: server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) if context.draft is not None: diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d872b63f5..d8a2286e4 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -371,7 +371,7 @@ static json oaicompat_completion_params_parse( 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["temperature"] = json_value(body, "temperature", 0.0); + llama_params["temperature"] = json_value(body, "temperature", 1.0); llama_params["top_p"] = json_value(body, "top_p", 1.0); // Apply chat template to the list of messages diff --git a/ggml-backend.c b/ggml-backend.c index dd090a583..9e35ce98d 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1895,7 +1895,6 @@ void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * t tensor->buffer = buffer; tensor->data = (char *)tensor->view_src->data + tensor->view_offs; - tensor->backend = tensor->view_src->backend; ggml_backend_buffer_init_tensor(buffer, tensor); } diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5b6c90919..b82167cbf 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -43,19 +43,59 @@ #include #include #include +#include +#include #include #include static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); +static void ggml_cuda_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { + GGML_UNUSED(level); + GGML_UNUSED(user_data); + fprintf(stderr, "%s", msg); +} + +ggml_log_callback ggml_cuda_log_callback = ggml_cuda_default_log_callback; +void * ggml_cuda_log_user_data = NULL; + +GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data) { + ggml_cuda_log_callback = log_callback; + ggml_cuda_log_user_data = user_data; +} + +#define GGML_CUDA_LOG_INFO(...) ggml_cuda_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__) +#define GGML_CUDA_LOG_WARN(...) ggml_cuda_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__) +#define GGML_CUDA_LOG_ERROR(...) ggml_cuda_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__) + +GGML_ATTRIBUTE_FORMAT(2, 3) +static void ggml_cuda_log(enum ggml_log_level level, const char * format, ...) { + if (ggml_cuda_log_callback != NULL) { + va_list args; + va_start(args, format); + char buffer[128]; + int len = vsnprintf(buffer, 128, format, args); + if (len < 128) { + ggml_cuda_log_callback(level, buffer, ggml_cuda_log_user_data); + } else { + std::vector buffer2(len + 1); // vsnprintf adds a null terminator + va_end(args); + va_start(args, format); + vsnprintf(&buffer2[0], buffer2.size(), format, args); + ggml_cuda_log_callback(level, buffer2.data(), ggml_cuda_log_user_data); + } + va_end(args); + } +} + [[noreturn]] void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) { int id = -1; // in case cudaGetDevice fails cudaGetDevice(&id); - fprintf(stderr, "CUDA error: %s\n", msg); - fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line); - fprintf(stderr, " %s\n", stmt); + GGML_CUDA_LOG_ERROR("CUDA error: %s\n", msg); + GGML_CUDA_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line); + GGML_CUDA_LOG_ERROR(" %s\n", stmt); // abort with GGML_ASSERT to get a stack trace GGML_ASSERT(!"CUDA error"); } @@ -91,7 +131,7 @@ static ggml_cuda_device_info ggml_cuda_init() { cudaError_t err = cudaGetDeviceCount(&info.device_count); if (err != cudaSuccess) { - fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); + GGML_CUDA_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); return info; } @@ -99,16 +139,16 @@ static ggml_cuda_device_info ggml_cuda_init() { int64_t total_vram = 0; #if defined(GGML_CUDA_FORCE_MMQ) - fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); + GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); #else - fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); + GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); #endif #if defined(CUDA_USE_TENSOR_CORES) - fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); + GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); #else - fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__); + GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: no\n", __func__); #endif - fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); + GGML_CUDA_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); for (int id = 0; id < info.device_count; ++id) { int device_vmm = 0; @@ -129,7 +169,7 @@ static ggml_cuda_device_info ggml_cuda_init() { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); + GGML_CUDA_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); info.default_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; @@ -235,8 +275,8 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { *actual_size = look_ahead_size; pool_size += look_ahead_size; #ifdef DEBUG_CUDA_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(pool_size/1024/1024), (uint32_t)(size/1024/1024)); + GGML_CUDA_LOG_INFO("%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, + (uint32_t)(max_size / 1024 / 1024), (uint32_t)(pool_size / 1024 / 1024), (uint32_t)(size / 1024 / 1024)); #endif return ptr; } @@ -250,7 +290,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { return; } } - fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); + GGML_CUDA_LOG_WARN("Cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); ggml_cuda_set_device(device); CUDA_CHECK(cudaFree(ptr)); pool_size -= size; @@ -499,7 +539,9 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe void * dev_ptr; cudaError_t err = cudaMalloc(&dev_ptr, size); if (err != cudaSuccess) { - fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err)); + // clear the error + cudaGetLastError(); + GGML_CUDA_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err)); return nullptr; } @@ -1002,8 +1044,8 @@ static void * ggml_cuda_host_malloc(size_t size) { if (err != cudaSuccess) { // clear the error cudaGetLastError(); - fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size/1024.0/1024.0, cudaGetErrorString(err)); + GGML_CUDA_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, + size / 1024.0 / 1024.0, cudaGetErrorString(err)); return nullptr; } @@ -2246,7 +2288,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg break; case GGML_OP_MUL_MAT: if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) { - fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]); + GGML_CUDA_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]); return false; } else { ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst); @@ -2300,7 +2342,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { - fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst)); + GGML_CUDA_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst)); CUDA_CHECK(err); } @@ -2476,7 +2518,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__); #endif } } @@ -2523,14 +2565,14 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) { use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__); #endif } if (node->op == GGML_OP_MUL_MAT_ID) { use_cuda_graph = false; // This node type is not supported by CUDA graph capture #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__); #endif } @@ -2539,7 +2581,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t // Changes in batch size or context size can cause changes to the grid size of some kernels. use_cuda_graph = false; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); #endif } @@ -2558,7 +2600,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates. - if (cuda_graph_update_required) { + if (use_cuda_graph && cuda_graph_update_required) { cuda_ctx->cuda_graph->number_consecutive_updates++; } else { cuda_ctx->cuda_graph->number_consecutive_updates = 0; @@ -2567,7 +2609,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) { cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); #endif } } @@ -2605,7 +2647,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); if (!ok) { - fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + GGML_CUDA_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); } @@ -2624,7 +2666,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t use_cuda_graph = false; cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__); #endif } else { graph_evaluated_or_captured = true; // CUDA graph has been captured @@ -2691,7 +2733,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); if (stat == cudaErrorGraphExecUpdateFailure) { #ifndef NDEBUG - fprintf(stderr, "%s: CUDA graph update failed\n", __func__); + GGML_CUDA_LOG_ERROR("%s: CUDA graph update failed\n", __func__); #endif // The pre-existing graph exec cannot be updated due to violated constraints // so instead clear error and re-instantiate @@ -2713,6 +2755,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context; switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -2840,8 +2883,16 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: - case GGML_OP_FLASH_ATTN_EXT: return true; + case GGML_OP_FLASH_ATTN_EXT: +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128; +#else + if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) { + return true; + } + return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) default: return false; } @@ -2939,13 +2990,13 @@ static ggml_guid_t ggml_backend_cuda_guid() { GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { if (device < 0 || device >= ggml_backend_cuda_get_device_count()) { - fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); + GGML_CUDA_LOG_ERROR("%s: invalid device %d\n", __func__, device); return nullptr; } ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device); if (ctx == nullptr) { - fprintf(stderr, "%s: error: failed to allocate context\n", __func__); + GGML_CUDA_LOG_ERROR("%s: failed to allocate context\n", __func__); return nullptr; } @@ -2989,8 +3040,8 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size // clear the error cudaGetLastError(); - fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__, - size/1024.0/1024.0, cudaGetErrorString(err)); + GGML_CUDA_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__, + size / 1024.0 / 1024.0, cudaGetErrorString(err)); return false; } return true; diff --git a/ggml-cuda.h b/ggml-cuda.h index 5eb4af40f..d7903c666 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -38,6 +38,7 @@ GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer); +GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data); #ifdef __cplusplus } #endif diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 44e67e040..8f6fd71cf 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -315,12 +315,30 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { #endif return c; } + +#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 +// __shfl_xor() for half2 was added in ROCm 5.6 +static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int width) { + typedef union half2_b32 { + half2 val; + int b32; + } half2_b32_t; + half2_b32_t tmp; + tmp.val = var; + tmp.b32 = __shfl_xor(tmp.b32, laneMask, width); + return tmp.val; +} +#endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 #endif // defined(GGML_USE_HIPBLAS) #define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL #define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +static bool fast_fp16_available(const int cc) { + return cc >= CC_PASCAL && cc != 610; +} + static bool fp16_mma_available(const int cc) { return cc < CC_OFFSET_AMD && cc >= CC_VOLTA; } @@ -459,6 +477,17 @@ static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, - typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v); +static __device__ __forceinline__ float get_alibi_slope( + const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1 +) { + if (max_bias <= 0.0f) { + return 1.0f; + } + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + return powf(base, exph); +} ////////////////////// diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh new file mode 100644 index 000000000..1dd519bde --- /dev/null +++ b/ggml-cuda/fattn-common.cuh @@ -0,0 +1,162 @@ +#include "common.cuh" + +#include + +#define FATTN_KQ_STRIDE 256 +#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. +#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. + +typedef void (* fattn_kernel_t)( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3); + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_combine_results( + const float * __restrict__ VKQ_parts, + const float2 * __restrict__ VKQ_meta, + float * __restrict__ dst) { + VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x; + VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x; + dst += D * gridDim.y*blockIdx.x; + + const int tid = threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float2 meta[parallel_blocks]; + if (tid < 2*parallel_blocks) { + ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid]; + } + + __syncthreads(); + + float kqmax = meta[0].x; +#pragma unroll + for (int l = 1; l < parallel_blocks; ++l) { + kqmax = max(kqmax, meta[l].x); + } + + float VKQ_numerator = 0.0f; + float VKQ_denominator = 0.0f; +#pragma unroll + for (int l = 0; l < parallel_blocks; ++l) { + const float diff = meta[l].x - kqmax; + const float KQ_max_scale = expf(diff); + const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD); + *((uint32_t *) &KQ_max_scale) &= ftz_mask; + + VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid]; + VKQ_denominator += KQ_max_scale * meta[l].y; + } + + dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; +} + +template +void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + GGML_ASSERT(Q->type == GGML_TYPE_F32); + GGML_ASSERT(K->type == GGML_TYPE_F16); + GGML_ASSERT(V->type == GGML_TYPE_F16); + GGML_ASSERT(KQV->type == GGML_TYPE_F32); + + GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); + GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && + "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); + + GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); + + ggml_cuda_pool & pool = ctx.pool(); + cudaStream_t main_stream = ctx.stream(); + + ggml_cuda_pool_alloc dst_tmp(pool); + ggml_cuda_pool_alloc dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + fattn_kernel<<>>( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, max_bias, m0, m1, n_head_log2, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if ((parallel_blocks) == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results + <<>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu new file mode 100644 index 000000000..4a07ac6ad --- /dev/null +++ b/ggml-cuda/fattn-tile-f16.cu @@ -0,0 +1,312 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f16.cuh" + +#define FATTN_KQ_STRIDE_TILE_F16 64 + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_tile_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV2 = nb11 / sizeof(half2); + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + + __shared__ half KQ[ncols*FATTN_KQ_STRIDE_TILE_F16]; + half2 * KQ2 = (half2 *) KQ; + + __shared__ half2 KV_tmp[FATTN_KQ_STRIDE_TILE_F16][D/2 + 1]; // Pad D to avoid memory bank conflicts. + + half kqmax[ncols/nwarps]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + kqmax[j0/nwarps] = -HALF_MAX_HALF; + } + half2 kqsum[ncols/nwarps] = {{0.0f, 0.0f}}; + + half2 VKQ[ncols/nwarps][(D/2)/WARP_SIZE] = {{{0.0f, 0.0f}}}; + + // Convert Q to half2 and store in registers: + __shared__ half2 Q_h2[ncols][D/2]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + + __syncthreads(); + + const int k_start = parallel_blocks == 1 ? 0 : ip*FATTN_KQ_STRIDE_TILE_F16; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE_TILE_F16) { + // Calculate KQ tile and keep track of new maximum KQ values: + + half kqmax_new[ncols/nwarps]; +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + kqmax_new[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + KV_tmp[i_KQ][k_KQ] = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; + } + } + + __syncthreads(); + + half2 sum2[FATTN_KQ_STRIDE_TILE_F16/WARP_SIZE][ncols/nwarps] = {{{0.0f, 0.0f}}}; + +#pragma unroll + for (int k_KQ = 0; k_KQ < D/2; ++k_KQ) { + half2 K_k[FATTN_KQ_STRIDE_TILE_F16/WARP_SIZE]; + half2 Q_k[ncols/nwarps]; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + + K_k[i_KQ_0/WARP_SIZE] = KV_tmp[i_KQ][k_KQ]; + } +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + Q_k[j_KQ_0/nwarps] = Q_h2[j_KQ][k_KQ]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += K_k[i_KQ_0/WARP_SIZE]*Q_k[j_KQ_0/nwarps]; + } + } + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + half sum = __low2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]) + __high2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]); + sum += mask ? slopeh*maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + kqmax_new[j_KQ_0/nwarps] = ggml_cuda_hmax(kqmax_new[j_KQ_0/nwarps], sum); + + KQ[j_KQ*FATTN_KQ_STRIDE_TILE_F16 + i_KQ] = sum; + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + kqmax_new[j0/nwarps] = warp_reduce_max(kqmax_new[j0/nwarps]); + const half2 KQ_max_scale = __half2half2(hexp(kqmax[j0/nwarps] - kqmax_new[j0/nwarps])); + kqmax[j0/nwarps] = kqmax_new[j0/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < FATTN_KQ_STRIDE_TILE_F16/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const half2 diff = KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + i] - __half2half2(kqmax[j0/nwarps]); + const half2 val = h2exp(diff); + kqsum[j0/nwarps] = kqsum[j0/nwarps]*KQ_max_scale + val; + KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + i] = val; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + VKQ[j0/nwarps][i0/WARP_SIZE] *= KQ_max_scale; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F16; k0 += nwarps) { + const int k = k0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + KV_tmp[k][i] = V_h2[(k_VKQ_0 + k)*stride_KV2 + i]; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F16; k0 += 2) { + half2 V_k[(D/2)/WARP_SIZE][2]; + half2 KQ_k[ncols/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + V_k[i0/WARP_SIZE][0] = KV_tmp[k0 + 0][i]; + V_k[i0/WARP_SIZE][1] = KV_tmp[k0 + 1][i]; + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + KQ_k[j0/nwarps] = KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + k0/2]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + VKQ[j0/nwarps][i0/WARP_SIZE] += V_k[i0/WARP_SIZE][0]* __low2half2(KQ_k[j0/nwarps]); + VKQ[j0/nwarps][i0/WARP_SIZE] += V_k[i0/WARP_SIZE][1]*__high2half2(KQ_k[j0/nwarps]); + } + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { + const int j_VKQ = j_VKQ_0 + threadIdx.y; + + half kqsum_j = __low2half(kqsum[j_VKQ_0/nwarps]) + __high2half(kqsum[j_VKQ_0/nwarps]); + kqsum_j = warp_reduce_sum(kqsum_j); + +#pragma unroll + for (int i00 = 0; i00 < D; i00 += 2*WARP_SIZE) { + const int i0 = i00 + 2*threadIdx.x; + + half2 dst_val = VKQ[j_VKQ_0/nwarps][i0/(2*WARP_SIZE)]; + if (parallel_blocks == 1) { + dst_val /= __half2half2(kqsum_j); + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 0] = __low2float(dst_val); + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 1] = __high2float(dst_val); + } + + if (parallel_blocks != 1 && threadIdx.x == 0) { + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j_VKQ_0/nwarps], kqsum_j); + } + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +template +void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + if (Q->ne[1] <= 16) { + constexpr int cols_per_block = 16; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f16_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f16_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 1; + launch_fattn_tile_f16_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-tile-f16.cuh b/ggml-cuda/fattn-tile-f16.cuh new file mode 100644 index 000000000..ffc587842 --- /dev/null +++ b/ggml-cuda/fattn-tile-f16.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu new file mode 100644 index 000000000..130e7cbdb --- /dev/null +++ b/ggml-cuda/fattn-tile-f32.cu @@ -0,0 +1,309 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f32.cuh" + +#define FATTN_KQ_STRIDE_TILE_F32 32 + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_tile_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV2 = nb11 / sizeof(half2); + + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + + __shared__ float KQ[ncols*FATTN_KQ_STRIDE_TILE_F32]; + + __shared__ float KV_tmp[FATTN_KQ_STRIDE_TILE_F32][D + 1]; // Pad D to avoid memory bank conflicts. + float2 * KV_tmp2 = (float2 *) KV_tmp; + + float kqmax[ncols/nwarps]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + kqmax[j0/nwarps] = -FLT_MAX/2.0f; + } + float kqsum[ncols/nwarps] = {0.0f}; + + float2 VKQ[ncols/nwarps][(D/2)/WARP_SIZE] = {{{0.0f, 0.0f}}}; + + // Convert Q to half2 and store in registers: + __shared__ float Q_f[ncols][D]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D; i0 += 2*WARP_SIZE) { + float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x]; + Q_f[j][i0 + 0*WARP_SIZE + threadIdx.x] = tmp.x * scale; + Q_f[j][i0 + 1*WARP_SIZE + threadIdx.x] = tmp.y * scale; + } + } + + __syncthreads(); + + const int k_start = parallel_blocks == 1 ? 0 : ip*FATTN_KQ_STRIDE_TILE_F32; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE_TILE_F32) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new[ncols/nwarps]; +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + kqmax_new[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 2*WARP_SIZE) { + const half2 tmp = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + threadIdx.x]; + KV_tmp[i_KQ][k_KQ_0 + 0*WARP_SIZE + threadIdx.x] = __low2float(tmp); + KV_tmp[i_KQ][k_KQ_0 + 1*WARP_SIZE + threadIdx.x] = __high2float(tmp); + } + } + + __syncthreads(); + + float sum[FATTN_KQ_STRIDE_TILE_F32/WARP_SIZE][ncols/nwarps] = {{0.0f}}; + +#pragma unroll + for (int k_KQ = 0; k_KQ < D; ++k_KQ) { + float K_k[FATTN_KQ_STRIDE_TILE_F32/WARP_SIZE]; + float Q_k[ncols/nwarps]; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + + K_k[i_KQ_0/WARP_SIZE] = KV_tmp[i_KQ][k_KQ]; + } +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + Q_k[j_KQ_0/nwarps] = Q_f[j_KQ][k_KQ]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += K_k[i_KQ_0/WARP_SIZE] * Q_k[j_KQ_0/nwarps]; + } + } + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new[j_KQ_0/nwarps] = fmaxf(kqmax_new[j_KQ_0/nwarps], sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]); + + KQ[j_KQ*FATTN_KQ_STRIDE_TILE_F32 + i_KQ] = sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + kqmax_new[j0/nwarps] = warp_reduce_max(kqmax_new[j0/nwarps]); + const float KQ_max_scale = expf(kqmax[j0/nwarps] - kqmax_new[j0/nwarps]); + kqmax[j0/nwarps] = kqmax_new[j0/nwarps]; + + float kqsum_add = 0.0f; +#pragma unroll + for (int i0 = 0; i0 < FATTN_KQ_STRIDE_TILE_F32; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float diff = KQ[j*FATTN_KQ_STRIDE_TILE_F32 + i] - kqmax[j0/nwarps]; + const float val = expf(diff); + kqsum_add += val; + KQ[j*FATTN_KQ_STRIDE_TILE_F32 + i] = val; + } + kqsum[j0/nwarps] = kqsum[j0/nwarps]*KQ_max_scale + kqsum_add; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + VKQ[j0/nwarps][i0/WARP_SIZE].x *= KQ_max_scale; + VKQ[j0/nwarps][i0/WARP_SIZE].y *= KQ_max_scale; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F32; k0 += nwarps) { + const int k = k0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + KV_tmp2[k*(D/2) + i].x = __low2float(V_h2[(k_VKQ_0 + k)*stride_KV2 + i]); + KV_tmp2[k*(D/2) + i].y = __high2float(V_h2[(k_VKQ_0 + k)*stride_KV2 + i]); + } + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < FATTN_KQ_STRIDE_TILE_F32; ++k) { + float2 V_k[(D/2)/WARP_SIZE]; + float KQ_k[ncols/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + V_k[i0/WARP_SIZE] = KV_tmp2[k*(D/2) + i]; + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + KQ_k[j0/nwarps] = KQ[j*FATTN_KQ_STRIDE_TILE_F32 + k]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + VKQ[j0/nwarps][i0/WARP_SIZE].x += V_k[i0/WARP_SIZE].x*KQ_k[j0/nwarps]; + VKQ[j0/nwarps][i0/WARP_SIZE].y += V_k[i0/WARP_SIZE].y*KQ_k[j0/nwarps]; + } + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { + const int j_VKQ = j_VKQ_0 + threadIdx.y; + + float kqsum_j = kqsum[j_VKQ_0/nwarps]; + kqsum_j = warp_reduce_sum(kqsum_j); + +#pragma unroll + for (int i00 = 0; i00 < D; i00 += 2*WARP_SIZE) { + const int i0 = i00 + 2*threadIdx.x; + + float2 dst_val = VKQ[j_VKQ_0/nwarps][i0/(2*WARP_SIZE)]; + if (parallel_blocks == 1) { + dst_val.x /= kqsum_j; + dst_val.y /= kqsum_j; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 0] = dst_val.x; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 1] = dst_val.y; + } + + if (parallel_blocks != 1 && threadIdx.x == 0) { + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j_VKQ_0/nwarps], kqsum_j); + } + } +} + +template +void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + if (Q->ne[1] <= 16) { + constexpr int cols_per_block = 16; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f32_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 1; + launch_fattn_tile_f32_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-tile-f32.cuh b/ggml-cuda/fattn-tile-f32.cuh new file mode 100644 index 000000000..b1c546c80 --- /dev/null +++ b/ggml-cuda/fattn-tile-f32.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu new file mode 100644 index 000000000..54e1ac5d1 --- /dev/null +++ b/ggml-cuda/fattn-vec-f16.cu @@ -0,0 +1,326 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-vec-f16.cuh" + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV = nb11 / sizeof(half); + const int stride_KV2 = nb11 / sizeof(half2); + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ half KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -HALF_MAX_HALF; + } + half2 * KQ2 = (half2 *) KQ; + + half kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -HALF_MAX_HALF; + } + half kqsum[ncols] = {0.0f}; + + __shared__ half kqmax_shared[ncols][WARP_SIZE]; + __shared__ half kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 and store in registers: + half2 Q_h2[ncols][D/(2*WARP_SIZE)]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + + half2 VKQ[ncols] = {{0.0f, 0.0f}}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, + // see https://github.com/ggerganov/llama.cpp/pull/7061 . + // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). + half kqmax_new = kqmax[0]; + half kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + + half2 sum2[ncols] = {{0.0f, 0.0f}}; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum2[j] = warp_reduce_sum(sum2[j]); + half sum = __low2half(sum2[j]) + __high2half(sum2[j]); + sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + if (ncols == 1) { + kqmax_new = ggml_cuda_hmax(kqmax_new, sum); + } else { + kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); + } + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const half val = hexp(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= __half2half2(KQ_max_scale); + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < D; k0 += 2) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { + break; + } + + half2 V_k; + reinterpret_cast(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; + reinterpret_cast(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * KQV = dst; + ggml_tensor * Q = dst->src[0]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 256: { + constexpr int D = 256; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: + GGML_ASSERT(false); + break; + } +} + +template +void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + if (Q->ne[1] == 1) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f16_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f16_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f16_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + launch_fattn_vec_f16_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh new file mode 100644 index 000000000..c7023610a --- /dev/null +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -0,0 +1,5 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu new file mode 100644 index 000000000..5bcabd092 --- /dev/null +++ b/ggml-cuda/fattn-vec-f32.cu @@ -0,0 +1,275 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-vec-f32.cuh" + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV = nb11 / sizeof(half); + const int stride_KV2 = nb11 / sizeof(half2); + + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -FLT_MAX/2.0f; + } + + float kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -FLT_MAX/2.0f; + } + float kqsum[ncols] = {0.0f}; + + __shared__ float kqmax_shared[ncols][WARP_SIZE]; + __shared__ float kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 and store in registers: + float2 Q_h2[ncols][D/(2*WARP_SIZE)]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_h2[j][i0/WARP_SIZE] = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i0/WARP_SIZE].x *= scale; + Q_h2[j][i0/WARP_SIZE].y *= scale; + } + } + + float VKQ[ncols] = {0.0f}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + + float sum[ncols] = {0.0f}; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x; + sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y; + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum[j] = warp_reduce_sum(sum[j]); + sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]); + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum[j]; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const float val = expf(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= KQ_max_scale; + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < D; ++k) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) { + break; + } + + const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_ki*KQ[j*D + k]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + float dst_val = VKQ[j_VKQ]; + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +} + +template +void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + launch_fattn_vec_f32_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh new file mode 100644 index 000000000..614d54ae3 --- /dev/null +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index ac5d6672b..af7c95232 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -1,4 +1,9 @@ #include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f16.cuh" +#include "fattn-tile-f32.cuh" +#include "fattn-vec-f16.cuh" +#include "fattn-vec-f32.cuh" #include "fattn.cuh" #include @@ -7,251 +12,6 @@ #include #endif -#define FATTN_KQ_STRIDE 256 -#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. -#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. - -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_vec_ext_f16( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { -#if FP16_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic0; - - const int stride_KV = nb11 / sizeof(half); - const int stride_KV2 = nb11 / sizeof(half2); - - half slopeh = __float2half(1.0f); - - // ALiBi - if (max_bias > 0.0f) { - const int h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slopeh = __float2half(powf(base, exph)); - } - - static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); - constexpr int nwarps = D / WARP_SIZE; - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < D); - - __shared__ half KQ[ncols*D]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - KQ[j*D + tid] = -HALF_MAX_HALF; - } - half2 * KQ2 = (half2 *) KQ; - - half kqmax[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax[j] = -HALF_MAX_HALF; - } - half kqsum[ncols] = {0.0f}; - - __shared__ half kqmax_shared[ncols][WARP_SIZE]; - __shared__ half kqsum_shared[ncols][WARP_SIZE]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - if (threadIdx.y == 0) { - kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; - kqsum_shared[j][threadIdx.x] = 0.0f; - } - } - __syncthreads(); - - // Convert Q to half2 and store in registers: - half2 Q_h2[ncols][D/(2*WARP_SIZE)]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - - const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; - Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); - } - } - - half2 VKQ[ncols] = {{0.0f, 0.0f}}; - - const int k_start = parallel_blocks == 1 ? 0 : ip*D; - for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { - // Calculate KQ tile and keep track of new maximum KQ values: - - // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, - // see https://github.com/ggerganov/llama.cpp/pull/7061 . - // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). - half kqmax_new = kqmax[0]; - half kqmax_new_arr[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax_new_arr[j] = kqmax[j]; - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - - if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { - break; - } - - half2 sum2[ncols] = {{0.0f, 0.0f}}; -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { - const int k_KQ = k_KQ_0 + threadIdx.x; - - const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] = warp_reduce_sum(sum2[j]); - half sum = __low2half(sum2[j]) + __high2half(sum2[j]); - sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); - - if (ncols == 1) { - kqmax_new = ggml_cuda_hmax(kqmax_new, sum); - } else { - kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); - } - - if (threadIdx.x == 0) { - KQ[j*D + i_KQ] = sum; - } - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; - - kqmax_new_j = warp_reduce_max(kqmax_new_j); - if (threadIdx.x == 0) { - kqmax_shared[j][threadIdx.y] = kqmax_new_j; - } - } - - __syncthreads(); - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = kqmax_shared[j][threadIdx.x]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); - - const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); - kqmax[j] = kqmax_new_j; - - const half val = hexp(KQ[j*D + tid] - kqmax[j]); - kqsum[j] = kqsum[j]*KQ_max_scale + val; - KQ[j*D + tid] = val; - - VKQ[j] *= __half2half2(KQ_max_scale); - } - - __syncthreads(); - -#pragma unroll - for (int k0 = 0; k0 < D; k0 += 2) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { - break; - } - - half2 V_k; - reinterpret_cast(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; - reinterpret_cast(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqsum[j] = warp_reduce_sum(kqsum[j]); - if (threadIdx.x == 0) { - kqsum_shared[j][threadIdx.y] = kqsum[j]; - } - } - - __syncthreads(); - -#pragma unroll - for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; - kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); - - half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); - if (parallel_blocks == 1) { - dst_val /= kqsum[j_VKQ]; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; - } - - if (parallel_blocks != 1 && tid != 0) { -#pragma unroll - for (int j = 0; j < ncols; ++j) { - dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); - } - } -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - // D == head size, VKQ_stride == num VKQ rows calculated in parallel: template #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) @@ -325,19 +85,9 @@ static __global__ void flash_attn_ext_f16( const int stride_Q = nb01 / sizeof(float); const int stride_KV = nb11 / sizeof(half); - half slopeh = __float2half(1.0f); - half2 slope2 = make_half2(1.0f, 1.0f); - - // ALiBi - if (max_bias > 0.0f) { - const int h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slopeh = __float2half(powf(base, exph)); - slope2 = make_half2(slopeh, slopeh); - } + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + const half2 slope2 = make_half2(slopef, slopef); frag_b Q_b[D/16][ncols/frag_n]; @@ -655,54 +405,6 @@ static __global__ void flash_attn_ext_f16( #endif // FP16_MMA_AVAILABLE } -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_combine_results( - const float * __restrict__ VKQ_parts, - const float2 * __restrict__ VKQ_meta, - float * __restrict__ dst) { -#if FP16_AVAILABLE - VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x; - VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x; - dst += D * gridDim.y*blockIdx.x; - - const int tid = threadIdx.x; - __builtin_assume(tid < D); - - __shared__ float2 meta[parallel_blocks]; - if (tid < 2*parallel_blocks) { - ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid]; - } - - __syncthreads(); - - float kqmax = meta[0].x; -#pragma unroll - for (int l = 1; l < parallel_blocks; ++l) { - kqmax = max(kqmax, meta[l].x); - } - - float VKQ_numerator = 0.0f; - float VKQ_denominator = 0.0f; -#pragma unroll - for (int l = 0; l < parallel_blocks; ++l) { - const float diff = meta[l].x - kqmax; - const float KQ_max_scale = expf(diff); - const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD); - *((uint32_t *) &KQ_max_scale) &= ftz_mask; - - VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid]; - VKQ_denominator += KQ_max_scale * meta[l].y; - } - - dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - constexpr int get_max_power_of_2(int x) { return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; } @@ -727,280 +429,94 @@ static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); -template void launch_fattn_vec_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); +template +void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); - } - - constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_vec_ext_f16 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if (parallel_blocks == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); -} - -template void launch_fattn_f16_impl( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); - } - - constexpr int frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_ext_f16 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if ((parallel_blocks) == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); -} - -template void launch_fattn_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream -) { + constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; + const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; if (4*blocks_num_pb1 < 2*nsm) { - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 4; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); return; } if (2*blocks_num_pb1 < 2*nsm) { - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 2; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); return; } - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 1; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); } void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - - ggml_tensor * KQV = dst; - - GGML_ASSERT(Q->type == GGML_TYPE_F32); - GGML_ASSERT(K->type == GGML_TYPE_F16); - GGML_ASSERT(V->type == GGML_TYPE_F16); - GGML_ASSERT(KQV->type == GGML_TYPE_F32); - - GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); - GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && - "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); - - GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; ggml_cuda_set_device(ctx.device); - - const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; - const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; - + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const int32_t precision = KQV->op_params[2]; - if (!fp16_mma_available(cc)) { - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - - if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; + // On AMD the tile kernels perform poorly, use the vec kernel instead: + if (cc >= CC_OFFSET_AMD) { + if (precision == GGML_PREC_DEFAULT) { + ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); } + return; + } + if (!fast_fp16_available(cc)) { if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f32(ctx, dst); } + return; + } - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; + if (!fp16_mma_available(cc)) { + if (Q->ne[1] <= 8) { + ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f16(ctx, dst); } return; } if (precision != GGML_PREC_DEFAULT) { + if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + return; + } + if (Q->ne[1] <= 32 || Q->ne[0] > 128) { constexpr int cols_per_block = 16; constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); break; default: GGML_ASSERT(false); @@ -1011,22 +527,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); break; // case 256: - // launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + // launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); // break; default: GGML_ASSERT(false); @@ -1037,22 +553,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 256: - launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); return; } @@ -1061,16 +562,16 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -1084,22 +585,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -1112,22 +613,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu index ca85285a3..ce64f2f2c 100644 --- a/ggml-cuda/softmax.cu +++ b/ggml-cuda/softmax.cu @@ -1,3 +1,4 @@ +#include "common.cuh" #include "softmax.cuh" template @@ -23,17 +24,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, float * dst const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const int h = rowx/nrows_y; // head index - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = powf(base, exph); - } + const float slope = get_alibi_slope(max_bias, rowx/nrows_y, n_head_log2, m0, m1); extern __shared__ float data_soft_max_f32[]; float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication diff --git a/ggml-cuda/upscale.cu b/ggml-cuda/upscale.cu index 2f62fed48..cf513c3ad 100644 --- a/ggml-cuda/upscale.cu +++ b/ggml-cuda/upscale.cu @@ -1,35 +1,36 @@ #include "upscale.cuh" -static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { - // blockIdx.z: idx of ne02*ne03 - // blockIdx.y: idx of ne01*scale_factor， aka ne1 - // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE - // ne00xne01: ne00 * ne01 - int ne0 = ne00 * scale_factor; - int nidx = threadIdx.x + blockIdx.x * blockDim.x; - if (nidx >= ne0) { +static __global__ void upscale_f32(const float * x, float * dst, + const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int ne13, + const float sf0, const float sf1, const float sf2, const float sf3) { + int index = threadIdx.x + blockIdx.x * blockDim.x; + if (index >= ne10 * ne11 * ne12 * ne13) { return; } - // operation - int i00 = nidx / scale_factor; - int i01 = blockIdx.y / scale_factor; - int offset_src = - i00 + - i01 * ne00 + - blockIdx.z * ne00xne01; - int offset_dst = - nidx + - blockIdx.y * ne0 + - blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; + + int i10 = index % ne10; + int i11 = (index / ne10) % ne11; + int i12 = (index / (ne10 * ne11)) % ne12; + int i13 = (index / (ne10 * ne11 * ne12)) % ne13; + + int i00 = i10 / sf0; + int i01 = i11 / sf1; + int i02 = i12 / sf2; + int i03 = i13 / sf3; + + dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); } -static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, - const int scale_factor, cudaStream_t stream) { - int ne0 = (ne00 * scale_factor); - int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; - dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); - upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); +static void upscale_f32_cuda(const float * x, float * dst, + const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int ne13, + const float sf0, const float sf1, const float sf2, const float sf3, + cudaStream_t stream) { + int dst_size = ne10 * ne11 * ne12 * ne13; + int num_blocks = (dst_size + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; + + upscale_f32<<>>(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3); } void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -39,10 +40,12 @@ void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { cudaStream_t stream = ctx.stream(); GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + GGML_ASSERT( dst->type == GGML_TYPE_F32); - const int scale_factor = dst->op_params[0]; + const float sf0 = (float)dst->ne[0]/src0->ne[0]; + const float sf1 = (float)dst->ne[1]/src0->ne[1]; + const float sf2 = (float)dst->ne[2]/src0->ne[2]; + const float sf3 = (float)dst->ne[3]/src0->ne[3]; - upscale_f32_cuda(src0_d, dst_d, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, stream); + upscale_f32_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, stream); } diff --git a/ggml-impl.h b/ggml-impl.h index d85b152bf..5ff014fe3 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -17,6 +17,18 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +#if defined(_WIN32) + +#define m512bh(p) p +#define m512i(p) p + +#else + +#define m512bh(p) (__m512bh)(p) +#define m512i(p) (__m512i)(p) + +#endif + /** * Converts brain16 to float32. * @@ -120,9 +132,16 @@ extern "C" { #ifndef __F16C__ #define __F16C__ #endif +#endif + +// __SSE3__ and __SSSE3__ are not defined in MSVC, but SSE3/SSSE3 are present when AVX/AVX2/AVX512 are available +#if defined(_MSC_VER) && (defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)) #ifndef __SSE3__ #define __SSE3__ #endif +#ifndef __SSSE3__ +#define __SSSE3__ +#endif #endif // 16-bit float diff --git a/ggml-metal.m b/ggml-metal.m index 28dec762a..b0b16dbf7 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1378,7 +1378,7 @@ static enum ggml_status ggml_metal_graph_compute( const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); if (ne00%4 == 0) { - while (nth < ne00/4 && nth < 256) { + while (nth < ne00/4 && nth*ne01*ne02*ne03 < 256) { nth *= 2; } if (use_f16) { @@ -1387,7 +1387,7 @@ static enum ggml_status ggml_metal_graph_compute( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline; } } else { - while (nth < ne00 && nth < 1024) { + while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { nth *= 2; } if (use_f16) { @@ -2353,7 +2353,10 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(src0->type == GGML_TYPE_F32); - const int sf = dst->op_params[0]; + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; @@ -2376,7 +2379,10 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; + [encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18]; + [encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19]; + [encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20]; + [encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); @@ -2512,13 +2518,14 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_FLASH_ATTN_EXT: { - GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ne11 % 32 == 0); + GGML_ASSERT(src0->type == GGML_TYPE_F32); - struct ggml_tensor * src3 = gf->nodes[i]->src[3]; + GGML_ASSERT(ggml_are_same_shape (src1, src2)); - GGML_ASSERT(ggml_are_same_shape(src1, src2)); - GGML_ASSERT(src3); + struct ggml_tensor * src3 = gf->nodes[i]->src[3]; size_t offs_src3 = 0; @@ -2528,6 +2535,11 @@ static enum ggml_status ggml_metal_graph_compute( GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) && "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); + const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); + const uint64_t nb21 = src2 ? src2->nb[1] : 0; + const uint64_t nb22 = src2 ? src2->nb[2] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; + const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); //const int64_t ne31 = src3 ? src3->ne[1] : 0; const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); @@ -2590,34 +2602,35 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + if (id_src3) { + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; + } [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:8]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:11]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:12]; - [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:15]; - [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:16]; - [encoder setBytes:&nb10 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:20]; - [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:21]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:22]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:23]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:24]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:25]; - [encoder setBytes:&scale length:sizeof( float) atIndex:26]; - [encoder setBytes:&max_bias length:sizeof( float) atIndex:27]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:28]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:29]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:30]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22]; + [encoder setBytes:&scale length:sizeof( float) atIndex:23]; + [encoder setBytes:&max_bias length:sizeof( float) atIndex:24]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:25]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:26]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27]; if (!use_vec_kernel) { // half8x8 kernel diff --git a/ggml-metal.metal b/ggml-metal.metal index 7af4e8f93..386e9195f 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1852,7 +1852,10 @@ kernel void kernel_upscale_f32( constant uint64_t & nb1, constant uint64_t & nb2, constant uint64_t & nb3, - constant int32_t & sf, + constant float & sf0, + constant float & sf1, + constant float & sf2, + constant float & sf3, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { @@ -1861,15 +1864,17 @@ kernel void kernel_upscale_f32( const int64_t i2 = tgpig.y; const int64_t i1 = tgpig.x; - const int64_t i03 = i3; - const int64_t i02 = i2; - const int64_t i01 = i1/sf; - - device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01); - device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1); + const int64_t i03 = i3/sf3; + const int64_t i02 = i2/sf2; + const int64_t i01 = i1/sf1; for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - dst_ptr[i0] = src0_ptr[i0/sf]; + const int64_t i00 = i0/sf0; + + device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_ptr[0] = src0_ptr[0]; } } @@ -2049,27 +2054,24 @@ typedef void (flash_attn_ext_f16_t)( device const char * v, device const char * mask, device float * dst, - constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, constant uint64_t & nb03, - constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, + constant uint64_t & nb21, + constant uint64_t & nb22, + constant uint64_t & nb23, constant uint64_t & nb31, - constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, - constant int64_t & ne3, constant float & scale, constant float & max_bias, constant float & m0, @@ -2090,27 +2092,24 @@ kernel void kernel_flash_attn_ext_f16( device const char * v, device const char * mask, device float * dst, - constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, constant uint64_t & nb03, - constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, + constant uint64_t & nb21, + constant uint64_t & nb22, + constant uint64_t & nb23, constant uint64_t & nb31, - constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, - constant int64_t & ne3, constant float & scale, constant float & max_bias, constant float & m0, @@ -2180,10 +2179,6 @@ kernel void kernel_flash_attn_ext_f16( const short ne22 = ne12; const short ne23 = ne13; - const uint nb21 = nb11; - const uint nb22 = nb12; - const uint nb23 = nb13; - // broadcast const short rk2 = ne02/ne12; const short rk3 = ne03/ne13; @@ -2247,11 +2242,16 @@ kernel void kernel_flash_attn_ext_f16( simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); } - // mqk = mqk*scale + mask*slope - simdgroup_half8x8 mm; - simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false); - simdgroup_multiply(mm, mslope, mm); - simdgroup_multiply_accumulate(mqk, mqk, mscale, mm); + if (mask != q) { + // mqk = mqk*scale + mask*slope + simdgroup_half8x8 mm; + simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false); + simdgroup_multiply(mm, mslope, mm); + simdgroup_multiply_accumulate(mqk, mqk, mscale, mm); + } else { + // mqk = mqk*scale + simdgroup_multiply(mqk, mscale, mqk); + } simdgroup_store(mqk, ss + 8*cc, TF, 0, false); } @@ -2425,27 +2425,24 @@ kernel void kernel_flash_attn_ext_vec_f16( device const char * v, device const char * mask, device float * dst, - constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, constant uint64_t & nb03, - constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, + constant uint64_t & nb21, + constant uint64_t & nb22, + constant uint64_t & nb23, constant uint64_t & nb31, - constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, - constant int64_t & ne3, constant float & scale, constant float & max_bias, constant float & m0, @@ -2521,10 +2518,6 @@ kernel void kernel_flash_attn_ext_vec_f16( const short ne22 = ne12; const short ne23 = ne13; - const uint nb21 = nb11; - const uint nb22 = nb12; - const uint nb23 = nb13; - // broadcast const short rk2 = ne02/ne12; const short rk3 = ne03/ne13; @@ -2589,8 +2582,7 @@ kernel void kernel_flash_attn_ext_vec_f16( // mqk = mqk*scale + mask*slope if (tiisg == 0) { - float4 mm = (float4) mp4[ic/4 + cc]; - mqk = mqk*scale + mm*slope; + mqk = mqk*scale + ((mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f); ss4[cc] = mqk; } diff --git a/ggml-mpi.c b/ggml-mpi.c deleted file mode 100644 index ae176d707..000000000 --- a/ggml-mpi.c +++ /dev/null @@ -1,216 +0,0 @@ -#include "ggml-mpi.h" - -#include "ggml.h" - -#include - -#include -#include - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) - -#define UNUSED GGML_UNUSED - -struct ggml_mpi_context { - int rank; - int size; -}; - -void ggml_mpi_backend_init(void) { - MPI_Init(NULL, NULL); -} - -void ggml_mpi_backend_free(void) { - MPI_Finalize(); -} - -struct ggml_mpi_context * ggml_mpi_init(void) { - struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context)); - - MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank); - MPI_Comm_size(MPI_COMM_WORLD, &ctx->size); - - return ctx; -} - -void ggml_mpi_free(struct ggml_mpi_context * ctx) { - free(ctx); -} - -int ggml_mpi_rank(struct ggml_mpi_context * ctx) { - return ctx->rank; -} - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads) { - UNUSED(ctx_mpi); - - // synchronize the worker node parameters with the root node - MPI_Barrier(MPI_COMM_WORLD); - - MPI_Bcast(n_tokens, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_past, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD); -} - -static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) { - struct ggml_tensor * t = ggml_graph_get_tensor(gf, name); - if (t == NULL) { - fprintf(stderr, "%s: tensor %s not found\n", __func__, name); - return -1; - } - - for (int i = 0; i < gf->n_nodes; i++) { - if (gf->nodes[i] == t) { - return i; - } - } - - fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name); - return -1; -} - -static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - MPI_Status status; UNUSED(status); - - const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -// TODO: there are many improvements that can be done to this implementation -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens"); - if (inp_tokens == NULL) { - fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__); - return; - } - - struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0"); - if (inp0 == NULL) { - fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__); - return; - } - - GGML_ASSERT(inp0 == gf->nodes[0]); - - // distribute the compute graph into slices across the MPI nodes - // - // the main node (0) processes the last layers + the remainder of the compute graph - // and is responsible to pass the input tokens to the first node (1) - // - // node 1: [( 0) * n_per_node, ( 1) * n_per_node) - // node 2: [( 1) * n_per_node, ( 2) * n_per_node) - // ... - // node n-1: [(n-2) * n_per_node, (n-1) * n_per_node) - // node 0: [(n-1) * n_per_node, n_nodes) - // - if (mpi_rank > 0) { - if (mpi_rank == 1) { - // the first node (1) receives the input tokens from the main node (0) - ggml_mpi_tensor_recv(inp_tokens, 0); - } else { - // recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph) - ggml_mpi_tensor_recv(inp0, mpi_rank - 1); - } - } else if (mpi_size > 1) { - // node 0 sends the input tokens to node 1 - ggml_mpi_tensor_send(inp_tokens, 1); - - // recv the output data from the last node - ggml_mpi_tensor_recv(inp0, mpi_size - 1); - } - - { - const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size; - - const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1; - - const int il0 = (mpi_idx + 0) * n_per_node; - const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node); - - char name_l0[GGML_MAX_NAME]; - char name_l1[GGML_MAX_NAME]; - - snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0); - snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1); - - const int idx_l0 = ggml_graph_get_node_idx(gf, name_l0); - const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes; - - if (idx_l0 < 0 || idx_l1 < 0) { - fprintf(stderr, "%s: layer input nodes not found\n", __func__); - return; - } - - // attach the input data to all nodes that need it - // TODO: not great - should be able to do this without modifying the compute graph (see next TODO below) - for (int i = idx_l0; i < idx_l1; i++) { - if (gf->nodes[i]->src[0] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[0] = inp0; - } - if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[1] = inp0; - } - } - - // TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph - for (int i = 1; i < idx_l1 - idx_l0; i++) { - gf->nodes[i] = gf->nodes[idx_l0 + i]; - gf->grads[i] = gf->grads[idx_l0 + i]; - } - - // the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node - if (mpi_idx != 0) { - gf->nodes[0]->op = GGML_OP_NONE; - } - - gf->n_nodes = idx_l1 - idx_l0; - - //fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1); - } -} - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - UNUSED(n_layers); - - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - // send the output data to the next node - if (mpi_rank > 0) { - ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size); - } -} diff --git a/ggml-mpi.h b/ggml-mpi.h deleted file mode 100644 index eda119d44..000000000 --- a/ggml-mpi.h +++ /dev/null @@ -1,39 +0,0 @@ -#pragma once - -struct ggml_context; -struct ggml_tensor; -struct ggml_cgraph; - -#ifdef __cplusplus -extern "C" { -#endif - -struct ggml_mpi_context; - -void ggml_mpi_backend_init(void); -void ggml_mpi_backend_free(void); - -struct ggml_mpi_context * ggml_mpi_init(void); -void ggml_mpi_free(struct ggml_mpi_context * ctx); - -int ggml_mpi_rank(struct ggml_mpi_context * ctx); - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads); - -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -#ifdef __cplusplus -} -#endif diff --git a/ggml-quants.c b/ggml-quants.c index 00334c5fe..ff1059863 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -14,6 +14,12 @@ #include // for qsort #include // for GGML_ASSERT +#define GROUP_MAX_EPS 1e-15f +#define GROUP_MAX_EPS_IQ3_XXS 1e-8f +#define GROUP_MAX_EPS_IQ2_S 1e-8f +#define GROUP_MAX_EPS_IQ1_M 1e-7f +#define GROUP_MAX_EPS_IQ1_S 1e-12f + #if defined(_MSC_VER) // disable "possible loss of data" to avoid warnings for hundreds of casts // we should just be careful :) @@ -241,7 +247,7 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #endif // __AVX__ || __AVX2__ || __AVX512F__ #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) -#if defined(__ARM_NEON) || defined(__wasm_simd128__) +#if defined(__ARM_NEON) || defined(__wasm_simd128__) || defined(__POWER9_VECTOR__) #define B1(c,s,n) 0x ## n ## c , 0x ## n ## s #define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) #define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) @@ -643,6 +649,38 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) // store result __riscv_vse8_v_i8m1(y[i].qs , vs, vl); } +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + } #else GGML_UNUSED(nb); // scalar @@ -898,6 +936,46 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); y[i].s = GGML_FP32_TO_FP16(sum*d); } +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + vector int accv = vec_splats(0); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + + accv = vec_add(accv, vi[j]); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + + accv = vec_add(accv, vec_sld(accv, accv, 4)); + accv = vec_add(accv, vec_sld(accv, accv, 8)); + y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); + } #else GGML_UNUSED(nb); // scalar @@ -1037,7 +1115,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * float ax = fabsf(x[i]); if (ax > amax) { amax = ax; max = x[i]; } } - if (amax < 1e-30f) { // all zero + if (amax < GROUP_MAX_EPS) { // all zero for (int i = 0; i < n; ++i) { L[i] = 0; } @@ -1071,7 +1149,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * sumlx += w*x[i]*l; suml2 += w*l*l; } - float scale = sumlx/suml2; + float scale = suml2 ? sumlx/suml2 : 0.0f; if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale; float best = scale * sumlx; for (int is = -9; is <= 9; ++is) { @@ -1105,7 +1183,7 @@ static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * float ax = fabsf(x[i]); if (ax > amax) { amax = ax; max = x[i]; } } - if (!amax) { // all zero + if (amax < GROUP_MAX_EPS) { // all zero for (int i = 0; i < n; ++i) { L[i] = 0; } return 0.f; } @@ -1574,7 +1652,7 @@ static float make_qp_quants(int n, int nmax, const float * restrict x, uint8_t * break; } } - return sumlx / suml2; + return sumlx/suml2; } static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restrict y, int k, const float * restrict quant_weights) { @@ -1914,7 +1992,7 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri for (int j = 0; j < QK_K/16; ++j) { if (quant_weights) { - const float * qw = quant_weights ? quant_weights + QK_K * i + 16*j : NULL; + const float * qw = quant_weights + QK_K * i + 16*j; for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j+l]*x[16*j+l]); } else { for (int l = 0; l < 16; ++l) weight[l] = x[16*j+l]*x[16*j+l]; @@ -2581,7 +2659,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict } - if (!max_abs_scale) { + if (max_abs_scale < GROUP_MAX_EPS) { memset(&y[i], 0, sizeof(block_q6_K)); y[i].d = GGML_FP32_TO_FP16(0.f); x += QK_K; @@ -2733,7 +2811,7 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri } - if (!max_abs_scale) { + if (max_abs_scale < GROUP_MAX_EPS) { memset(&y[i], 0, sizeof(block_q6_K)); y[i].d = GGML_FP32_TO_FP16(0.f); x += QK_K; @@ -3415,10 +3493,9 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q4_0 * restrict vx0 = vx; - const block_q4_0 * restrict vx1 = vx + bx; - + const block_q4_0 * restrict vx1 = (const block_q4_0 *) ((const uint8_t*)vx + bx); const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = vy + by; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); @@ -3452,10 +3529,12 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32_t _scale[4] = { GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -3740,6 +3819,46 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector signed char v8 = vec_splats((signed char)0x8); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; i++) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl(16, y[i].qs); + + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_sub(q4x0, v8); + q4x1 = vec_sub(q4x1, v8); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else // scalar float sumf = 0.0; @@ -3782,9 +3901,9 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q4_1 * restrict vx0 = vx; - const block_q4_1 * restrict vx1 = vx + bx; + const block_q4_1 * restrict vx1 = (const block_q4_1 *) ((const uint8_t*)vx + bx); const block_q8_1 * restrict vy0 = vy; - const block_q8_1 * restrict vy1 = vy + by; + const block_q8_1 * restrict vy1 = (const block_q8_1 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t summs0 = vdupq_n_f32(0.0f); @@ -3795,11 +3914,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const block_q8_1 * restrict b_y0 = &vy0[i]; const block_q8_1 * restrict b_y1 = &vy1[i]; - float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; - summs0 += summs_t; + float32_t summs_t[4] = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; + summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); const uint8x16_t m4b = vdupq_n_u8(0x0F); @@ -3819,10 +3938,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); // mmla into int32x4_t - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -3841,7 +3961,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - sumv2 = sumv2 + summs0; + sumv2 = vaddq_f32(sumv2, summs0); vst1_f32(s, vget_low_f32(sumv2)); vst1_f32(s + bs, vget_high_f32(sumv2)); @@ -3958,6 +4078,46 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r } *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; i++) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m)); + vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.0f, 0.0f, 0.0f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl(16, y[i].qs); + + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else // scalar float sumf = 0.0; @@ -4243,6 +4403,49 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[i].qh[0]]), (uint64_t)(table_b2b_1[x[i].qh[1]])}; + vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[i].qh[2]]), (uint64_t)(table_b2b_1[x[i].qh[3]])}; + + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; + + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); + + vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0); + vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1); + + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl( 16, y[i].qs); + + vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else // scalar float sumf = 0.0; @@ -4547,6 +4750,53 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r } *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m)); + vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.f, 0.f, 0.f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[i].qh[0]]), (uint64_t)(table_b2b_0[x[i].qh[1]])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[i].qh[2]]), (uint64_t)(table_b2b_0[x[i].qh[3]])}; + + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; + + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); + + vector signed char q5x0 = vec_or(vec_and(qxs, lowMask), qh0); + vector signed char q5x1 = vec_or(vec_sr(qxs, v4), qh1); + + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl( 16, y[i].qs); + + vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else // scalar float sumf = 0.0; @@ -4595,9 +4845,9 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q8_0 * restrict vx0 = vx; - const block_q8_0 * restrict vx1 = vx + bx; + const block_q8_0 * restrict vx1 = (const block_q8_0 *) ((const uint8_t*)vx + bx); const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = vy + by; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); @@ -4619,10 +4869,11 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -4722,6 +4973,45 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; +#elif defined(__POWER9_VECTOR__) + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; i++) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char q8x0 = vec_xl( 0, x[i].qs); + vector signed char q8x1 = vec_xl(16, x[i].qs); + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl(16, y[i].qs); + + vector signed short qv0 = vec_mule(q8x0, q8y0); + vector signed short qv1 = vec_mulo(q8x0, q8y0); + vector signed short qv2 = vec_mule(q8x1, q8y1); + vector signed short qv3 = vec_mulo(q8x1, q8y1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackh(qv1)); + vector signed int vsumi1 = vec_add(vec_unpackl(qv0), vec_unpackl(qv1)); + vector signed int vsumi2 = vec_add(vec_unpackh(qv2), vec_unpackh(qv3)); + vector signed int vsumi3 = vec_add(vec_unpackl(qv2), vec_unpackl(qv3)); + + vsumi0 = vec_add(vsumi0, vsumi2); + vsumi1 = vec_add(vsumi1, vsumi3); + + vsumi0 = vec_add(vsumi0, vsumi1); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else // scalar float sumf = 0.0; @@ -5077,6 +5367,147 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowScaleMask = vec_splats((signed char)0xF); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + vector signed char q2xmins = (vector signed char)vec_xl( 0, x[i].scales); + vector signed char vscales = vec_and(q2xmins, lowScaleMask); + + q2xmins = vec_sr(q2xmins, v4); + vector signed short q2xmins0 = vec_unpackh(q2xmins); + vector signed short q2xmins1 = vec_unpackl(q2xmins); + + vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); + vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); + vector signed int prod2 = vec_mule(q2xmins1, q8ysums1); + vector signed int prod3 = vec_mulo(q2xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q2); + vector signed char qxs1 = (vector signed char)vec_xl(16, q2); + q2 += 32; + + vector signed char q2x00 = vec_and(qxs0, lowMask); + vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask); + vector signed char q2x10 = vec_and(qxs1, lowMask); + vector signed char q2x11 = vec_and(vec_sr(qxs1, v2), lowMask); + vector signed char q2x12 = vec_and(vec_sr(qxs1, v4), lowMask); + vector signed char q2x13 = vec_and(vec_sr(qxs1, v6), lowMask); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00)); + vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01)); + vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02)); + vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03)); + vector signed short qv4 = vec_add(vec_mule(q2x10, q8y10), vec_mulo(q2x10, q8y10)); + vector signed short qv5 = vec_add(vec_mule(q2x11, q8y11), vec_mulo(q2x11, q8y11)); + vector signed short qv6 = vec_add(vec_mule(q2x12, q8y12), vec_mulo(q2x12, q8y12)); + vector signed short qv7 = vec_add(vec_mule(q2x13, q8y13), vec_mulo(q2x13, q8y13)); + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + vector signed short vs4 = vec_splat(vscales_h, 4); + vector signed short vs5 = vec_splat(vscales_h, 5); + vector signed short vs6 = vec_splat(vscales_h, 6); + vector signed short vs7 = vec_splat(vscales_h, 7); + vscales = vec_sld(vscales, vscales, 8); + + qv0 = vec_mul(qv0, vs0); + qv1 = vec_mul(qv1, vs2); + qv2 = vec_mul(qv2, vs4); + qv3 = vec_mul(qv3, vs6); + + qv0 = vec_madd(qv4, vs1, qv0); + qv1 = vec_madd(qv5, vs3, qv1); + qv2 = vec_madd(qv6, vs5, qv2); + qv3 = vec_madd(qv7, vs7, qv3); + + vsumi0 = vec_add(vec_unpackh(qv0), vsumi0); + vsumi1 = vec_add(vec_unpackh(qv1), vsumi1); + vsumi2 = vec_add(vec_unpackh(qv2), vsumi2); + vsumi3 = vec_add(vec_unpackh(qv3), vsumi3); + + vsumi4 = vec_add(vec_unpackl(qv0), vsumi4); + vsumi5 = vec_add(vec_unpackl(qv1), vsumi5); + vsumi6 = vec_add(vec_unpackl(qv2), vsumi6); + vsumi7 = vec_add(vec_unpackl(qv3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else float sumf = 0; @@ -5347,6 +5778,87 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowScaleMask = vec_splats((signed char)0xF); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + +#pragma GCC unroll 2 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl_len(y[i].bsums, 8); + + vector signed char q2xmins = (vector signed char)vec_xl_len(x[i].scales, 4); + vector signed char vscales = vec_and(q2xmins, lowScaleMask); + + q2xmins = vec_sr(q2xmins, v4); + vector signed short q2xmins0 = vec_unpackh((vector signed char)q2xmins); + + vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); + vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char q2x00 = vec_and(qxs0, lowMask); + vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask); + + vector signed char q8y00 = vec_xl( 0, y[i].qs); + vector signed char q8y01 = vec_xl( 16, y[i].qs); + vector signed char q8y02 = vec_xl( 32, y[i].qs); + vector signed char q8y03 = vec_xl( 48, y[i].qs); + + vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00)); + vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01)); + vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02)); + vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03)); + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + + vector signed int vsumi0 = vec_add(vec_mule(qv0, vs0), vec_mulo(qv0, vs0)); + vector signed int vsumi1 = vec_add(vec_mule(qv1, vs1), vec_mulo(qv1, vs1)); + vector signed int vsumi2 = vec_add(vec_mule(qv2, vs2), vec_mulo(qv2, vs2)); + vector signed int vsumi3 = vec_add(vec_mule(qv3, vs3), vec_mulo(qv3, vs3)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else float sumf = 0; @@ -5841,6 +6353,160 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char v1 = vec_splats((signed char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + uint32_t aux[3]; + uint32_t utmp[4]; + + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + + vector signed char vscales = (vector signed char)vec_xl( 0, utmp); + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask); + + vscales = vec_sub(vscales, off); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q3); + vector signed char qxs1 = (vector signed char)vec_xl(16, q3); + q3 += 32; + + //the low 2 bits + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char qxs02 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char qxs03 = vec_and(vec_sr(qxs0, v6), lowMask); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_and(vec_sr(qxs1, v2), lowMask); + vector signed char qxs12 = vec_and(vec_sr(qxs1, v4), lowMask); + vector signed char qxs13 = vec_and(vec_sr(qxs1, v6), lowMask); + + //the 3rd bit + vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); + vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, (vector unsigned char)v1)), v2); + vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); + vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v3)), v2); + vector signed char qxh10 = vec_sl(vec_andc(v1, qxhs1), v2); + vector signed char qxh11 = vec_sl(vec_andc(v1, vec_sr(qxhs1, (vector unsigned char)v1)), v2); + vector signed char qxh12 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v2)), v2); + vector signed char qxh13 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v3)), v2); + qxhs0 = vec_sr(qxhs0, v4); + qxhs1 = vec_sr(qxhs1, v4); + + vector signed char q3x00 = vec_sub(qxs00, qxh00); + vector signed char q3x01 = vec_sub(qxs01, qxh01); + vector signed char q3x02 = vec_sub(qxs02, qxh02); + vector signed char q3x03 = vec_sub(qxs03, qxh03); + vector signed char q3x10 = vec_sub(qxs10, qxh10); + vector signed char q3x11 = vec_sub(qxs11, qxh11); + vector signed char q3x12 = vec_sub(qxs12, qxh12); + vector signed char q3x13 = vec_sub(qxs13, qxh13); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + vector signed short vs4 = vec_splat(vscales_h, 4); + vector signed short vs5 = vec_splat(vscales_h, 5); + vector signed short vs6 = vec_splat(vscales_h, 6); + vector signed short vs7 = vec_splat(vscales_h, 7); + vscales = vec_sld(vscales, vscales, 8); + + vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); + vector signed short qv02 = vec_add(vec_mule(q3x02, q8y02), vec_mulo(q3x02, q8y02)); + vector signed short qv03 = vec_add(vec_mule(q3x03, q8y03), vec_mulo(q3x03, q8y03)); + vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); + vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12)); + vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13)); + + vector signed int vsum0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); + vector signed int vsum1 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); + vector signed int vsum2 = vec_add(vec_mule(qv02, vs4), vec_mulo(qv02, vs4)); + vector signed int vsum3 = vec_add(vec_mule(qv03, vs6), vec_mulo(qv03, vs6)); + vector signed int vsum4 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); + vector signed int vsum5 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); + vector signed int vsum6 = vec_add(vec_mule(qv12, vs5), vec_mulo(qv12, vs5)); + vector signed int vsum7 = vec_add(vec_mule(qv13, vs7), vec_mulo(qv13, vs7)); + + vsumi0 = vec_add(vsum0, vsumi0); + vsumi1 = vec_add(vsum1, vsumi1); + vsumi2 = vec_add(vsum2, vsumi2); + vsumi3 = vec_add(vsum3, vsumi3); + vsumi4 = vec_add(vsum4, vsumi4); + vsumi5 = vec_add(vsum5, vsumi5); + vsumi6 = vec_add(vsum6, vsumi6); + vsumi7 = vec_add(vsum7, vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else // scalar version // This function is written like this so the compiler can manage to vectorize most of it @@ -6207,6 +6873,95 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char v1 = vec_splats((signed char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x8); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + +#pragma GCC unroll 2 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + uint16_t aux16[2]; + int8_t * scales = (int8_t *)aux16; + + const uint16_t a = *(const uint16_t *)x[i].scales; + aux16[0] = a & 0x0f0f; + aux16[1] = (a >> 4) & 0x0f0f; + + vector signed char vscales = (vector signed char)vec_xl_len(scales, 8); + vector signed char qxhs0 = (vector signed char)vec_xl_len(x[i].hmask, 8); + qxhs0 = vec_or(qxhs0, vec_sr(vec_sld(qxhs0, qxhs0, 8), (vector unsigned char)v1)); + + vscales = vec_sub(vscales, off); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char qxs10 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char qxs11 = vec_and(vec_sr(qxs0, v6), lowMask); + + //the 3rd bit + vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); + vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); + vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v4)), v2); + vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v6)), v2); + qxhs0 = vec_sr(qxhs0, v4); + + vector signed char q3x00 = vec_sub(qxs00, qxh00); + vector signed char q3x01 = vec_sub(qxs01, qxh01); + vector signed char q3x10 = vec_sub(qxs10, qxh02); + vector signed char q3x11 = vec_sub(qxs11, qxh03); + + vector signed char q8y00 = vec_xl( 0, y[i].qs); + vector signed char q8y01 = vec_xl( 16, y[i].qs); + vector signed char q8y10 = vec_xl( 32, y[i].qs); + vector signed char q8y11 = vec_xl( 48, y[i].qs); + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + + vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); + vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); + vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); + vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); + + vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); + vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); + vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); + vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else int8_t aux8[QK_K]; @@ -6559,6 +7314,142 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + memcpy(utmp, x[i].scales, 12); + + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + vector signed char utmps = (vector signed char)vec_xl( 0, utmp); + vector signed short vscales = vec_unpackh(utmps); + vector signed short q4xmins = vec_unpackl(utmps); + vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins); + vector signed short q4xmins1 = vec_mergel(q4xmins, q4xmins); + + vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q4xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q4xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q4xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; j+=2) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + vector signed char qxs2 = (vector signed char)vec_xl(32, q4); + vector signed char qxs3 = (vector signed char)vec_xl(48, q4); + q4 += 64; + + vector signed char q4x00 = vec_and(qxs0, lowMask); + vector signed char q4x01 = vec_sr(qxs0, v4); + vector signed char q4x10 = vec_and(qxs1, lowMask); + vector signed char q4x11 = vec_sr(qxs1, v4); + vector signed char q4x20 = vec_and(qxs2, lowMask); + vector signed char q4x21 = vec_sr(qxs2, v4); + vector signed char q4x30 = vec_and(qxs3, lowMask); + vector signed char q4x31 = vec_sr(qxs3, v4); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y20 = vec_xl( 64, q8); + vector signed char q8y30 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01)); + vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11)); + vector signed short qv20 = vec_add(vec_mule(q4x20, q8y20), vec_mulo(q4x20, q8y20)); + vector signed short qv21 = vec_add(vec_mule(q4x21, q8y21), vec_mulo(q4x21, q8y21)); + vector signed short qv30 = vec_add(vec_mule(q4x30, q8y30), vec_mulo(q4x30, q8y30)); + vector signed short qv31 = vec_add(vec_mule(q4x31, q8y31), vec_mulo(q4x31, q8y31)); + + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vector signed short vs2 = vec_splat(vscales, 2); + vector signed short vs3 = vec_splat(vscales, 3); + vscales = vec_sld(vscales, vscales, 8); + + qv00 = vec_add(qv00, qv10); + qv10 = vec_add(qv01, qv11); + qv20 = vec_add(qv20, qv30); + qv30 = vec_add(qv21, qv31); + + vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); + vsumi2 = vec_add(vec_mule(qv10, vs1), vsumi2); + vsumi3 = vec_add(vec_mulo(qv10, vs1), vsumi3); + vsumi4 = vec_add(vec_mule(qv20, vs2), vsumi4); + vsumi5 = vec_add(vec_mulo(qv20, vs2), vsumi5); + vsumi6 = vec_add(vec_mule(qv30, vs3), vsumi6); + vsumi7 = vec_add(vec_mulo(qv30, vs3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else @@ -6825,6 +7716,87 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + +#pragma GCC unroll 2 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d[1])); + vector float vyd = vec_splats(y[i].d); + vector float vd= vec_mul(vxd, vyd); + + uint16_t s16[2]; + const uint8_t * scales = (const uint8_t *)s16; + + const uint16_t * restrict b = (const uint16_t *)x[i].scales; + s16[0] = b[0] & 0x0f0f; + s16[1] = (b[0] >> 4) & 0x0f0f; + + vector signed char utmps = (vector signed char)vec_xl_len(scales, 4); + vector signed short vscales = (vector signed short)vec_unpackh(utmps); + vector signed short q4xmins0 = vec_mergeh(vscales, vscales); + q4xmins0 = vec_sld(q4xmins0, q4xmins0, 8); + + vector signed short q8ysums0 = vec_xl_len((const int16_t *)(y[i].bsums), 8); + + vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); + vector signed int prod1 = vec_mulo(q4xmins0, q8ysums0); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vd, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vd, vsumf1); + + vd = vec_mul(vyd, vec_splats(GGML_FP16_TO_FP32(x[i].d[0]))); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs); + vector signed char q4x00 = vec_and(qxs0, lowMask); + vector signed char q4x01 = vec_sr(qxs0, v4); + vector signed char q4x10 = vec_and(qxs1, lowMask); + vector signed char q4x11 = vec_sr(qxs1, v4); + + vector signed char q8y00 = vec_xl( 0, y[i].qs); + vector signed char q8y10 = vec_xl(16, y[i].qs); + vector signed char q8y01 = vec_xl(32, y[i].qs); + vector signed char q8y11 = vec_xl(48, y[i].qs); + + vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01)); + vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11)); + + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + + vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); + vector signed int vsumi1 = vec_add(vec_mule(qv10, vs0), vec_mulo(qv10, vs0)); + vector signed int vsumi2 = vec_add(vec_mule(qv01, vs1), vec_mulo(qv01, vs1)); + vector signed int vsumi3 = vec_add(vec_mule(qv11, vs1), vec_mulo(qv11, vs1)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else uint8_t aux8[QK_K]; @@ -7226,6 +8198,130 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf+sums; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v1 = vec_splats((unsigned char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + memcpy(utmp, x[i].scales, 12); + + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + vector signed char utmps = (vector signed char)vec_xl( 0, utmp); + vector signed short vscales = vec_unpackh(utmps); + + vector signed short q5xmins = vec_unpackl(utmps); + vector signed short q5xmins0 = vec_mergeh(q5xmins, q5xmins); + vector signed short q5xmins1 = vec_mergel(q5xmins, q5xmins); + + vector signed int prod0 = vec_mule(q5xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q5xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q5xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q5xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q5, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q5); + vector signed char qxs1 = (vector signed char)vec_xl(16, q5); + q5 += 32; + + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + + vector signed char q5h00 = vec_sl(vec_and((vector signed char)v1, qxhs0), v4); + vector signed char q5h01 = vec_sl(vec_and((vector signed char)v2, qxhs0), v3); + vector signed char q5h10 = vec_sl(vec_and((vector signed char)v1, qxhs1), v4); + vector signed char q5h11 = vec_sl(vec_and((vector signed char)v2, qxhs1), v3); + qxhs0 = vec_sr(qxhs0, v2); + qxhs1 = vec_sr(qxhs1, v2); + + vector signed char q5x00 = vec_or(q5h00, qxs00); + vector signed char q5x01 = vec_or(q5h01, qxs01); + vector signed char q5x10 = vec_or(q5h10, qxs10); + vector signed char q5x11 = vec_or(q5h11, qxs11); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl(16, q8); + vector signed char q8y01 = vec_xl(32, q8); + vector signed char q8y11 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01)); + vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11)); + + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vscales = vec_sld(vscales, vscales, 12); + + qv00 = vec_add(qv00, qv10); + qv01 = vec_add(qv01, qv11); + + vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); + vsumi2 = vec_add(vec_mule(qv01, vs1), vsumi2); + vsumi3 = vec_add(vec_mulo(qv01, vs1), vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else const uint8_t * scales = (const uint8_t*)&utmp[0]; @@ -7523,6 +8619,83 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v1 = vec_splats((unsigned char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + +#pragma GCC unroll 2 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd= vec_mul(vxd, vyd); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs); + vector signed char qxs00 = (vector signed char)vec_and(qxs0, lowMask); + vector signed char qxs01 = (vector signed char)vec_sr(qxs0, v4); + vector signed char qxs10 = (vector signed char)vec_and(qxs1, lowMask); + vector signed char qxs11 = (vector signed char)vec_sr(qxs1, v4); + + vector signed char qxhs = (vector signed char)vec_xl_len(x[i].qh, 8); + vector signed char qxhs0 = vec_or(qxhs, vec_sr(vec_sld(qxhs, qxhs, 8), v1)); + vector signed char qxhs1 = vec_sr(qxhs0, v2); + vector signed char qxh00 = vec_sl(vec_andc((vector signed char)v1, qxhs0), v4); + vector signed char qxh10 = vec_sl(vec_andc((vector signed char)v1, qxhs1), v4); + vector signed char qxh01 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs0, v4)), v4); + vector signed char qxh11 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs1, v4)), v4); + + vector signed char q5x00 = vec_sub(qxs00, qxh00); + vector signed char q5x10 = vec_sub(qxs10, qxh10); + vector signed char q5x01 = vec_sub(qxs01, qxh01); + vector signed char q5x11 = vec_sub(qxs11, qxh11); + + vector signed char q8y00 = vec_xl( 0, y[i].qs); + vector signed char q8y10 = vec_xl(16, y[i].qs); + vector signed char q8y01 = vec_xl(32, y[i].qs); + vector signed char q8y11 = vec_xl(48, y[i].qs); + + vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01)); + vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11)); + + vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4)); + vector signed short vs0 = vec_splat(vs, 0); + vector signed short vs1 = vec_splat(vs, 1); + vector signed short vs2 = vec_splat(vs, 2); + vector signed short vs3 = vec_splat(vs, 3); + + vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); + vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); + vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); + vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else int8_t aux8[QK_K]; @@ -7953,6 +9126,151 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict qs = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q6, 0, 0); + __builtin_prefetch(qh, 0, 0); + __builtin_prefetch(q8, 0, 0); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q6); + vector signed char qxs1 = (vector signed char)vec_xl(16, q6); + vector signed char qxs2 = (vector signed char)vec_xl(32, q6); + vector signed char qxs3 = (vector signed char)vec_xl(48, q6); + q6 += 64; + + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + vector signed char qxs20 = vec_and(qxs2, lowMask); + vector signed char qxs21 = vec_sr(qxs2, v4); + vector signed char qxs30 = vec_and(qxs3, lowMask); + vector signed char qxs31 = vec_sr(qxs3, v4); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, qh); + qh += 32; + + vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); + vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); + vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, qxhs1), v4); + vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v4)), v4); + vector signed char qxh20 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); + vector signed char qxh21 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); + vector signed char qxh30 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v2)), v4); + vector signed char qxh31 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v6)), v4); + + vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); + vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); + vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); + vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); + vector signed char q6x20 = vec_sub(vec_or(qxh20, qxs20), off); + vector signed char q6x21 = vec_sub(vec_or(qxh21, qxs21), off); + vector signed char q6x30 = vec_sub(vec_or(qxh30, qxs30), off); + vector signed char q6x31 = vec_sub(vec_or(qxh31, qxs31), off); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y20 = vec_xl( 32, q8); + vector signed char q8y30 = vec_xl( 48, q8); + vector signed char q8y01 = vec_xl( 64, q8); + vector signed char q8y11 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); + vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); + vector signed short qv20 = vec_add(vec_mule(q6x20, q8y20), vec_mulo(q6x20, q8y20)); + vector signed short qv30 = vec_add(vec_mule(q6x30, q8y30), vec_mulo(q6x30, q8y30)); + vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); + vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); + vector signed short qv21 = vec_add(vec_mule(q6x21, q8y21), vec_mulo(q6x21, q8y21)); + vector signed short qv31 = vec_add(vec_mule(q6x31, q8y31), vec_mulo(q6x31, q8y31)); + + vector signed short vscales = vec_unpackh(vec_xl_len(qs, 8)); + qs += 8; + + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vector signed short vs2 = vec_splat(vscales, 2); + vector signed short vs3 = vec_splat(vscales, 3); + vector signed short vs4 = vec_splat(vscales, 4); + vector signed short vs5 = vec_splat(vscales, 5); + vector signed short vs6 = vec_splat(vscales, 6); + vector signed short vs7 = vec_splat(vscales, 7); + + vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); + vsumi2 = vec_add(vec_mule(qv01, vs4), vsumi2); + vsumi3 = vec_add(vec_mulo(qv01, vs4), vsumi3); + vsumi4 = vec_add(vec_mule(qv10, vs1), vsumi4); + vsumi5 = vec_add(vec_mulo(qv10, vs1), vsumi5); + vsumi6 = vec_add(vec_mule(qv11, vs5), vsumi6); + vsumi7 = vec_add(vec_mulo(qv11, vs5), vsumi7); + + vsumi0 = vec_add(vec_mule(qv20, vs2), vsumi0); + vsumi1 = vec_add(vec_mulo(qv20, vs2), vsumi1); + vsumi2 = vec_add(vec_mule(qv21, vs6), vsumi2); + vsumi3 = vec_add(vec_mulo(qv21, vs6), vsumi3); + vsumi4 = vec_add(vec_mule(qv30, vs3), vsumi4); + vsumi5 = vec_add(vec_mulo(qv30, vs3), vsumi5); + vsumi6 = vec_add(vec_mule(qv31, vs7), vsumi6); + vsumi7 = vec_add(vec_mulo(qv31, vs7), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else int8_t aux8[QK_K]; @@ -8259,6 +9577,85 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + +#pragma GCC unroll 2 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].ql, 0, 1); + __builtin_prefetch(x[i].qh, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd= vec_mul(vxd, vyd); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].ql); + vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].ql); + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); + + vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); + vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); + vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); + vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); + + vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); + vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); + vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); + vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); + + vector signed char q8y00 = vec_xl( 0, y[i].qs); + vector signed char q8y10 = vec_xl(16, y[i].qs); + vector signed char q8y01 = vec_xl(32, y[i].qs); + vector signed char q8y11 = vec_xl(48, y[i].qs); + + vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); + vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); + vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); + vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); + + vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4)); + vector signed short vs0 = vec_splat(vs, 0); + vector signed short vs1 = vec_splat(vs, 1); + vector signed short vs2 = vec_splat(vs, 2); + vector signed short vs3 = vec_splat(vs, 3); + + vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); + vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); + vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); + vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + #else int8_t aux8[QK_K]; @@ -8300,7 +9697,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif -#if defined (__AVX2__) || defined (__ARM_NEON) +#if defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) static const int8_t keven_signs_q2xs[1024] = { 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, @@ -8433,6 +9830,103 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void *s = 0.125f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + memcpy(aux32, q2, 4*sizeof(uint32_t)); + q2 += 8; + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xxs_grid + aux8[ 0]), *(const int64_t *)(iq2xxs_grid + aux8[ 1])}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xxs_grid + aux8[ 2]), *(const int64_t *)(iq2xxs_grid + aux8[ 3])}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xxs_grid + aux8[ 8]), *(const int64_t *)(iq2xxs_grid + aux8[ 9])}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xxs_grid + aux8[10]), *(const int64_t *)(iq2xxs_grid + aux8[11])}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((aux32[1] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 7) & 127))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((aux32[1] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 21) & 127))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((aux32[3] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 7) & 127))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((aux32[3] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 21) & 127))}; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = aux32[1] >> 28; + const uint16_t ls1 = aux32[3] >> 28; + + vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); #else uint32_t aux32[2]; @@ -8708,6 +10202,104 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * *s = 0.125f * hsum_float_8(accumf); #endif +#elif defined(__POWER9_VECTOR__) + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint16_t * restrict q2 = x[i].qs; + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xs_grid + (q2[0] & 511)), *(const int64_t *)(iq2xs_grid + (q2[1] & 511))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xs_grid + (q2[2] & 511)), *(const int64_t *)(iq2xs_grid + (q2[3] & 511))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xs_grid + (q2[4] & 511)), *(const int64_t *)(iq2xs_grid + (q2[5] & 511))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xs_grid + (q2[6] & 511)), *(const int64_t *)(iq2xs_grid + (q2[7] & 511))}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((q2[0] >> 9))), *(const int64_t *)(signs64 + ((q2[1] >> 9)))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((q2[2] >> 9))), *(const int64_t *)(signs64 + ((q2[3] >> 9)))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((q2[4] >> 9))), *(const int64_t *)(signs64 + ((q2[5] >> 9)))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((q2[6] >> 9))), *(const int64_t *)(signs64 + ((q2[7] >> 9)))}; + q2 += 8; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); #else float sumf = 0.f; @@ -8908,6 +10500,124 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * *s = 0.125f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q2 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2s_grid + (q2[0] | ((qh[0] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[1] | ((qh[0] << 6) & 0x300)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2s_grid + (q2[2] | ((qh[0] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[3] | ((qh[0] << 2) & 0x300)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2s_grid + (q2[4] | ((qh[1] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[5] | ((qh[1] << 6) & 0x300)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2s_grid + (q2[6] | ((qh[1] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[7] | ((qh[1] << 2) & 0x300)))}; + q2 += 8; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns23 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns23, vsigns23, mask0); + vector signed char vsigns3 = vec_perm(vsigns23, vsigns23, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q2x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux64x2_0), vsigns0); + vector signed char q2x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux64x2_1), vsigns1); + vector signed char q2x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux64x2_2), vsigns2); + vector signed char q2x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux64x2_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); #else float sumf = 0; @@ -9052,6 +10762,101 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void *s = 0.25f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q3 = x[i].qs; + const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4); + const int8_t * restrict q8 = y[i].qs; + +#pragma GCC unroll 1 + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; + vector unsigned int aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; + vector unsigned int aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; + vector unsigned int aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; + q3 += 16; + + vector unsigned long long aux64x2_0 = {(uint64_t)(signs64[(signs[0] >> 0) & 127]), (uint64_t)(signs64[(signs[0] >> 7) & 127])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(signs64[(signs[0] >> 14) & 127]), (uint64_t)(signs64[(signs[0] >> 21) & 127])}; + vector unsigned long long aux64x2_2 = {(uint64_t)(signs64[(signs[1] >> 0) & 127]), (uint64_t)(signs64[(signs[1] >> 7) & 127])}; + vector unsigned long long aux64x2_3 = {(uint64_t)(signs64[(signs[1] >> 14) & 127]), (uint64_t)(signs64[(signs[1] >> 21) & 127])}; + + vector signed char q3x0 = vec_mul((vector signed char)aux64x2_0, (vector signed char)aux32x4_0); + vector signed char q3x1 = vec_mul((vector signed char)aux64x2_1, (vector signed char)aux32x4_1); + vector signed char q3x2 = vec_mul((vector signed char)aux64x2_2, (vector signed char)aux32x4_2); + vector signed char q3x3 = vec_mul((vector signed char)aux64x2_3, (vector signed char)aux32x4_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(signs[0] >> 28); + const uint16_t ls1 = (uint16_t)(signs[1] >> 28); + signs += 2; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.25f * vec_extract(vsumf0, 0); #else uint32_t aux32; @@ -9279,6 +11084,124 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * *s = hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].signs); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3s_grid[q3[ 0] | ((qh[0] << 8) & 256)], iq3s_grid[q3[ 1] | ((qh[0] << 7) & 256)], + iq3s_grid[q3[ 2] | ((qh[0] << 6) & 256)], iq3s_grid[q3[ 3] | ((qh[0] << 5) & 256)]}; + vector unsigned int aux32x4_1 = {iq3s_grid[q3[ 4] | ((qh[0] << 4) & 256)], iq3s_grid[q3[ 5] | ((qh[0] << 3) & 256)], + iq3s_grid[q3[ 6] | ((qh[0] << 2) & 256)], iq3s_grid[q3[ 7] | ((qh[0] << 1) & 256)]}; + vector unsigned int aux32x4_2 = {iq3s_grid[q3[ 8] | ((qh[1] << 8) & 256)], iq3s_grid[q3[ 9] | ((qh[1] << 7) & 256)], + iq3s_grid[q3[10] | ((qh[1] << 6) & 256)], iq3s_grid[q3[11] | ((qh[1] << 5) & 256)]}; + vector unsigned int aux32x4_3 = {iq3s_grid[q3[12] | ((qh[1] << 4) & 256)], iq3s_grid[q3[13] | ((qh[1] << 3) & 256)], + iq3s_grid[q3[14] | ((qh[1] << 2) & 256)], iq3s_grid[q3[15] | ((qh[1] << 1) & 256)]}; + q3 += 16; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns02 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns02, vsigns02, mask0); + vector signed char vsigns3 = vec_perm(vsigns02, vsigns02, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q3x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux32x4_0), vsigns0); + vector signed char q3x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux32x4_1), vsigns1); + vector signed char q3x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux32x4_2), vsigns2); + vector signed char q3x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux32x4_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + sc ++; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else float sumf = 0.f; @@ -9433,6 +11356,113 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; +#elif defined(__POWER9_VECTOR__) + const vector unsigned char v0 = vec_splats((unsigned char)0x0); + const vector unsigned short vsign = vec_splats((unsigned short)0x8000); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi8 = vec_splats((int32_t)0); + + const uint8_t * restrict q1 = x[i].qs; + const uint16_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + const int16_t * restrict qs = y[i].bsums; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q1, 0, 1); + __builtin_prefetch(qh, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq1s_grid + (q1[0] | ((qh[0] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[1] | ((qh[0] << 5) & 0x700)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq1s_grid + (q1[2] | ((qh[0] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[3] | ((qh[0] >> 1) & 0x700)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq1s_grid + (q1[4] | ((qh[1] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[5] | ((qh[1] << 5) & 0x700)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq1s_grid + (q1[6] | ((qh[1] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[7] | ((qh[1] >> 1) & 0x700)))}; + q1 += 8; + + vector signed char q1x0 = (vector signed char)aux64x2_0; + vector signed char q1x1 = (vector signed char)aux64x2_1; + vector signed char q1x2 = (vector signed char)aux64x2_2; + vector signed char q1x3 = (vector signed char)aux64x2_3; + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q1x0, q8y0), vec_mulo(q1x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q1x1, q8y1), vec_mulo(q1x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q1x2, q8y2), vec_mulo(q1x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q1x3, q8y3), vec_mulo(q1x3, q8y3)); + + const uint16_t ls0 = (uint16_t)((qh[0] >> 12) & 7); + const uint16_t ls1 = (uint16_t)((qh[1] >> 12) & 7); + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + vector signed short vscales = vec_sld(vscales23, vscales01, 8); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + + vector signed short q8ysums = vec_xl_len(qs, 8); + qs += 4; + q8ysums = vec_mergeh(q8ysums, (vector signed short)v0); + + vector signed short qxh = (vector signed short)vec_sld(vec_splats(qh[1]), vec_splats(qh[0]), 8); + qh += 2; + vector __bool short vsel = vec_cmpge(qxh, (vector signed short)v0); + + vector signed short q8ysum = vec_sel((vector signed short)vec_xor((vector unsigned short)q8ysums, vsign), q8ysums, vsel); + + vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + + vsumf0 = vec_madd(vec_ctf(vsumi8, 0), vec_mul(vd, vec_splats(IQ1S_DELTA)), vsumf0); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else float sumf = 0; @@ -9789,6 +11819,51 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * *s = hsum_float_8(_mm256_add_ps(accum1, accum2)); +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + +#pragma GCC unroll 4 + for (int ib = 0; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_perm(values, values, (vector unsigned char)q4x0); + q4x1 = vec_perm(values, values, (vector unsigned char)q4x1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + vector signed int vsumi1 = vec_add(vec_unpackh(qv1), vec_unpackl(qv1)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + } + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else float sumf = 0; for (int ib = 0; ib < nb; ++ib) { @@ -9900,6 +11975,105 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * *s = hsum_float_8(accum); +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + + for (int ibl = 0; ibl < nb; ++ibl) { + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ibl].d)); + vector float vyd = vec_splats(y[ibl].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + uint16_t h = x[ibl].scales_h; + + const uint8_t * restrict q4 = x[ibl].qs; + const uint8_t * restrict sc = x[ibl].scales_l; + const int8_t * restrict q8 = y[ibl].qs; + + for (int ib = 0; ib < QK_K/64; ib ++ ) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + q4 += 32; + + vector signed char q4x00 = (vector signed char)vec_and(qxs0, lowMask); + vector signed char q4x01 = (vector signed char)vec_sr(qxs0, v4); + vector signed char q4x10 = (vector signed char)vec_and(qxs1, lowMask); + vector signed char q4x11 = (vector signed char)vec_sr(qxs1, v4); + + q4x00 = vec_perm(values, values, (vector unsigned char)q4x00); + q4x01 = vec_perm(values, values, (vector unsigned char)q4x01); + q4x10 = vec_perm(values, values, (vector unsigned char)q4x10); + q4x11 = vec_perm(values, values, (vector unsigned char)q4x11); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q4x00, q8y0), vec_mulo(q4x00, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x01, q8y1), vec_mulo(q4x01, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q4x10, q8y2), vec_mulo(q4x10, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q4x11, q8y3), vec_mulo(q4x11, q8y3)); + + const uint16_t ls0 = (uint16_t)(((sc[0] & 0xf) | ((h << 4) & 0x30)) - 32); + const uint16_t ls1 = (uint16_t)(((sc[0] >> 4) | ((h << 2) & 0x30)) - 32); + h >>= 4; + sc ++; + + vector signed short vscales01 = vec_splats((int16_t)ls0); + vector signed short vscales23 = vec_splats((int16_t)ls1); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); #else float sumf = 0; for (int ibl = 0; ibl < nb; ++ibl) { @@ -10431,7 +12605,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict } float max = xval[0]; for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS) { scales[ib] = 0; memset(L, 0, 32); continue; @@ -10607,7 +12781,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS) { scales[ib] = 0; memset(L, 0, 16); continue; @@ -11048,7 +13222,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v } float max = xval[0]; for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS_IQ3_XXS) { scales[ib] = 0; memset(L, 0, 32); continue; @@ -11588,7 +13762,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); float max = fabsf(xb[0]); for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); - if (!max) { + if (max < GROUP_MAX_EPS_IQ1_S) { scales[ib] = 0; memset(L, 1, block_size); continue; @@ -11776,7 +13950,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } float max = fabsf(xb[0]); for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); - if (!max) { + if (max < GROUP_MAX_EPS_IQ1_M) { scales[ib] = 0; memset(L, 1, block_size); continue; @@ -12040,7 +14214,7 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block amax = ax; max = xb[j]; } } - if (!amax) { + if (amax < GROUP_MAX_EPS) { scales[ib] = 0; continue; } @@ -12261,7 +14435,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS_IQ2_S) { scales[ib] = 0; continue; } diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp new file mode 100644 index 000000000..4a9bfa52d --- /dev/null +++ b/ggml-rpc.cpp @@ -0,0 +1,1032 @@ +#include "ggml-rpc.h" +#include "ggml.h" +#include "ggml-backend-impl.h" + +#include +#include +#include +#include +#include +#include +#ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +# include +#else +# include +# include +# include +# include +# include +# include +# include +#endif +#include + +#define UNUSED GGML_UNUSED + +#define GGML_DEBUG 0 +#if (GGML_DEBUG >= 1) +#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG(...) +#endif + +#ifdef _WIN32 +typedef SOCKET sockfd_t; +using ssize_t = __int64; +#else +typedef int sockfd_t; +#endif + +// cross-platform socket +struct socket_t { + sockfd_t fd; + socket_t(sockfd_t fd) : fd(fd) {} + ~socket_t() { +#ifdef _WIN32 + closesocket(this->fd); +#else + close(this->fd); +#endif + } +}; + +// ggml_tensor is serialized into rpc_tensor +struct rpc_tensor { + uint64_t id; + uint32_t type; + uint64_t buffer; + uint32_t ne[GGML_MAX_DIMS]; + uint32_t nb[GGML_MAX_DIMS]; + uint32_t op; + int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; + int32_t flags; + uint64_t src[GGML_MAX_SRC]; + uint64_t view_src; + uint64_t view_offs; + uint64_t data; + char name[GGML_MAX_NAME]; +}; + +// RPC commands +enum rpc_cmd { + ALLOC_BUFFER = 0, + GET_ALIGNMENT, + GET_MAX_SIZE, + BUFFER_GET_BASE, + FREE_BUFFER, + BUFFER_CLEAR, + SET_TENSOR, + GET_TENSOR, + COPY_TENSOR, + GRAPH_COMPUTE, + GET_DEVICE_MEMORY, +}; + +// RPC data structures + +static ggml_guid_t ggml_backend_rpc_guid() { + static ggml_guid guid = {0x99, 0x68, 0x5b, 0x6c, 0xd2, 0x83, 0x3d, 0x24, 0x25, 0x36, 0x72, 0xe1, 0x5b, 0x0e, 0x14, 0x03}; + return &guid; +} + +struct ggml_backend_rpc_buffer_type_context { + std::shared_ptr sock; + std::string name; + size_t alignment; + size_t max_size; +}; + +struct ggml_backend_rpc_context { + std::string endpoint; + std::string name; + std::shared_ptr sock; + ggml_backend_buffer_type_t buft; +}; + +struct ggml_backend_rpc_buffer_context { + std::shared_ptr sock; + std::unordered_map base_cache; + uint64_t remote_ptr; + std::string name; +}; + +// RPC helper functions + +static std::shared_ptr make_socket(sockfd_t fd) { +#ifdef _WIN32 + if (fd == INVALID_SOCKET) { + return nullptr; + } +#else + if (fd < 0) { + return nullptr; + } +#endif + return std::make_shared(fd); +} + +static bool set_no_delay(sockfd_t sockfd) { + int flag = 1; + // set TCP_NODELAY to disable Nagle's algorithm + int ret = setsockopt(sockfd, IPPROTO_TCP, TCP_NODELAY, (char *)&flag, sizeof(int)); + return ret == 0; +} + +static bool set_reuse_addr(sockfd_t sockfd) { + int flag = 1; + int ret = setsockopt(sockfd, SOL_SOCKET, SO_REUSEADDR, (char *)&flag, sizeof(int)); + return ret == 0; +} + +static std::shared_ptr socket_connect(const char * host, int port) { + struct sockaddr_in addr; + auto sockfd = socket(AF_INET, SOCK_STREAM, 0); + auto sock_ptr = make_socket(sockfd); + if (sock_ptr == nullptr) { + return nullptr; + } + if (!set_no_delay(sockfd)) { + fprintf(stderr, "Failed to set TCP_NODELAY\n"); + return nullptr; + } + addr.sin_family = AF_INET; + addr.sin_port = htons(port); + struct hostent * server = gethostbyname(host); + if (server == NULL) { + fprintf(stderr, "Cannot resolve host '%s'\n", host); + return nullptr; + } + memcpy(&addr.sin_addr.s_addr, server->h_addr, server->h_length); + if (connect(sock_ptr->fd, (struct sockaddr *)&addr, sizeof(addr)) < 0) { + return nullptr; + } + return sock_ptr; +} + +static std::shared_ptr socket_accept(sockfd_t srv_sockfd) { + auto client_socket_fd = accept(srv_sockfd, NULL, NULL); + auto client_socket = make_socket(client_socket_fd); + if (client_socket == nullptr) { + return nullptr; + } + if (!set_no_delay(client_socket_fd)) { + fprintf(stderr, "Failed to set TCP_NODELAY\n"); + return nullptr; + } + return client_socket; +} + +static std::shared_ptr create_server_socket(const char * host, int port) { + auto sockfd = socket(AF_INET, SOCK_STREAM, 0); + auto sock = make_socket(sockfd); + if (sock == nullptr) { + return nullptr; + } + if (!set_reuse_addr(sockfd)) { + fprintf(stderr, "Failed to set SO_REUSEADDR\n"); + return nullptr; + } + struct sockaddr_in serv_addr; + serv_addr.sin_family = AF_INET; + serv_addr.sin_addr.s_addr = inet_addr(host); + serv_addr.sin_port = htons(port); + + if (bind(sockfd, (struct sockaddr *) &serv_addr, sizeof(serv_addr)) < 0) { + return nullptr; + } + if (listen(sockfd, 1) < 0) { + return nullptr; + } + return sock; +} + +static bool send_data(sockfd_t sockfd, const void * data, size_t size) { + size_t bytes_sent = 0; + while (bytes_sent < size) { + ssize_t n = send(sockfd, (const char *)data + bytes_sent, size - bytes_sent, 0); + if (n < 0) { + return false; + } + bytes_sent += n; + } + return true; +} + +static bool recv_data(sockfd_t sockfd, void * data, size_t size) { + size_t bytes_recv = 0; + while (bytes_recv < size) { + ssize_t n = recv(sockfd, (char *)data + bytes_recv, size - bytes_recv, 0); + if (n <= 0) { + return false; + } + bytes_recv += n; + } + return true; +} + +static bool parse_endpoint(const char * endpoint, std::string & host, int & port) { + std::string str(endpoint); + size_t pos = str.find(':'); + if (pos == std::string::npos) { + return false; + } + host = str.substr(0, pos); + port = std::stoi(str.substr(pos + 1)); + return true; +} + +// RPC request : | rpc_cmd (1 byte) | request_size (8 bytes) | request_data (request_size bytes) | +// RPC response: | response_size (8 bytes) | response_data (response_size bytes) | +static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const std::vector & input, std::vector & output) { + uint8_t cmd_byte = cmd; + if (!send_data(sock->fd, &cmd_byte, sizeof(cmd_byte))) { + return false; + } + uint64_t input_size = input.size(); + if (!send_data(sock->fd, &input_size, sizeof(input_size))) { + return false; + } + if (!send_data(sock->fd, input.data(), input.size())) { + return false; + } + uint64_t output_size; + if (!recv_data(sock->fd, &output_size, sizeof(output_size))) { + return false; + } + if (output_size == 0) { + output.clear(); + return true; + } + output.resize(output_size); + if (!recv_data(sock->fd, output.data(), output_size)) { + return false; + } + return true; +} + +// RPC client-side implementation + +GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + return ctx->name.c_str(); +} + +GGML_CALL static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | remote_ptr (8 bytes) | + std::vector input(sizeof(uint64_t), 0); + uint64_t remote_ptr = ctx->remote_ptr; + memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, FREE_BUFFER, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.empty()); + delete ctx; +} + +GGML_CALL static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + if (ctx->base_cache.find(buffer) != ctx->base_cache.end()) { + return ctx->base_cache[buffer]; + } + // input serialization format: | remote_ptr (8 bytes) | + std::vector input(sizeof(uint64_t), 0); + uint64_t remote_ptr = ctx->remote_ptr; + memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, BUFFER_GET_BASE, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == sizeof(uint64_t)); + // output serialization format: | base_ptr (8 bytes) | + uint64_t base_ptr; + memcpy(&base_ptr, output.data(), sizeof(base_ptr)); + void * base = reinterpret_cast(base_ptr); + ctx->base_cache[buffer] = base; + return base; +} + +static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { + rpc_tensor result; + result.id = reinterpret_cast(tensor); + result.type = tensor->type; + if (tensor->buffer) { + ggml_backend_buffer_t buffer = tensor->buffer; + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + result.buffer = ctx->remote_ptr; + } else { + result.buffer = 0; + } + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { + result.ne[i] = tensor->ne[i]; + result.nb[i] = tensor->nb[i]; + } + result.op = tensor->op; + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { + result.op_params[i] = tensor->op_params[i]; + } + result.flags = tensor->flags; + for (uint32_t i = 0; i < GGML_MAX_SRC; i++) { + result.src[i] = reinterpret_cast(tensor->src[i]); + } + result.view_src = reinterpret_cast(tensor->view_src); + result.view_offs = tensor->view_offs; + result.data = reinterpret_cast(tensor->data); + snprintf(result.name, GGML_MAX_NAME, "%s", tensor->name); + return result; +} + +static ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) { + ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, + tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { + result->nb[i] = tensor->nb[i]; + } + result->buffer = reinterpret_cast(tensor->buffer); + result->op = (ggml_op) tensor->op; + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { + result->op_params[i] = tensor->op_params[i]; + } + result->flags = tensor->flags; + result->data = reinterpret_cast(tensor->data); + ggml_set_name(result, tensor->name); + return result; +} + +GGML_CALL static void ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + UNUSED(buffer); + if (ggml_is_quantized(tensor->type)) { + // TODO: this check is due to MATRIX_ROW_PADDING in CUDA and should be generalized + GGML_ASSERT(tensor->ne[0] % 512 == 0 && "unsupported quantized tensor"); + } +} + +GGML_CALL static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) | + size_t input_size = sizeof(rpc_tensor) + sizeof(uint64_t) + size; + std::vector input(input_size, 0); + rpc_tensor rpc_tensor = serialize_tensor(tensor); + memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); + memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); + memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), data, size); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, SET_TENSOR, input, output); + GGML_ASSERT(status); +} + +GGML_CALL static void ggml_backend_rpc_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | + int input_size = sizeof(rpc_tensor) + 2*sizeof(uint64_t); + std::vector input(input_size, 0); + rpc_tensor rpc_tensor = serialize_tensor(tensor); + memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); + memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); + memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), &size, sizeof(size)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, GET_TENSOR, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == size); + // output serialization format: | data (size bytes) | + memcpy(data, output.data(), size); +} + +GGML_CALL static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + // check if src and dst are on the same server + ggml_backend_buffer_t src_buffer = src->buffer; + ggml_backend_rpc_buffer_context * src_ctx = (ggml_backend_rpc_buffer_context *)src_buffer->context; + ggml_backend_buffer_t dst_buffer = dst->buffer; + ggml_backend_rpc_buffer_context * dst_ctx = (ggml_backend_rpc_buffer_context *)dst_buffer->context; + if (src_ctx->sock != dst_ctx->sock) { + return false; + } + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | rpc_tensor src | rpc_tensor dst | + int input_size = 2*sizeof(rpc_tensor); + std::vector input(input_size, 0); + rpc_tensor rpc_src = serialize_tensor(src); + rpc_tensor rpc_dst = serialize_tensor(dst); + memcpy(input.data(), &rpc_src, sizeof(rpc_src)); + memcpy(input.data() + sizeof(rpc_src), &rpc_dst, sizeof(rpc_dst)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, COPY_TENSOR, input, output); + GGML_ASSERT(status); + // output serialization format: | result (1 byte) | + GGML_ASSERT(output.size() == 1); + return output[0]; +} + +GGML_CALL static void ggml_backend_rpc_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // serialization format: | bufptr (8 bytes) | value (1 byte) | + int input_size = sizeof(uint64_t) + sizeof(uint8_t); + std::vector input(input_size, 0); + memcpy(input.data(), &ctx->remote_ptr, sizeof(ctx->remote_ptr)); + memcpy(input.data() + sizeof(ctx->remote_ptr), &value, sizeof(value)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, BUFFER_CLEAR, input, output); + GGML_ASSERT(status); +} + +static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = { + /* .get_name = */ ggml_backend_rpc_buffer_get_name, + /* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer, + /* .get_base = */ ggml_backend_rpc_buffer_get_base, + /* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_rpc_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_rpc_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_rpc_buffer_cpy_tensor, + /* .clear = */ ggml_backend_rpc_buffer_clear, + /* .reset = */ NULL, +}; + +GGML_CALL static const char * ggml_backend_rpc_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + return buft_ctx->name.c_str(); +} + +GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + // input serialization format: | size (8 bytes) | + int input_size = sizeof(uint64_t); + std::vector input(input_size, 0); + memcpy(input.data(), &size, sizeof(size)); + std::vector output; + bool status = send_rpc_cmd(buft_ctx->sock, ALLOC_BUFFER, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); + // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | + uint64_t remote_ptr; + memcpy(&remote_ptr, output.data(), sizeof(remote_ptr)); + size_t remote_size; + memcpy(&remote_size, output.data() + sizeof(uint64_t), sizeof(remote_size)); + + ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, + ggml_backend_rpc_buffer_interface, + new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, + remote_size); + + return buffer; +} + +static size_t get_alignment(const std::shared_ptr & sock) { + // input serialization format: | 0 bytes | + std::vector input; + std::vector output; + bool status = send_rpc_cmd(sock, GET_ALIGNMENT, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == sizeof(uint64_t)); + // output serialization format: | alignment (8 bytes) | + uint64_t alignment; + memcpy(&alignment, output.data(), sizeof(alignment)); + return alignment; +} + +GGML_CALL static size_t ggml_backend_rpc_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + return buft_ctx->alignment; +} + +static size_t get_max_size(const std::shared_ptr & sock) { + // input serialization format: | 0 bytes | + std::vector input; + std::vector output; + bool status = send_rpc_cmd(sock, GET_MAX_SIZE, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == sizeof(uint64_t)); + // output serialization format: | max_size (8 bytes) | + uint64_t max_size; + memcpy(&max_size, output.data(), sizeof(max_size)); + return max_size; +} + +GGML_CALL static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + return buft_ctx->max_size; +} + +GGML_CALL static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + UNUSED(buft); + return ggml_nbytes(tensor); +} + +GGML_CALL static bool ggml_backend_rpc_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + if (!ggml_backend_is_rpc(backend)) { + return false; + } + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + return buft_ctx->sock == rpc_ctx->sock; +} + +static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { + /* .get_name = */ ggml_backend_rpc_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_rpc_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_rpc_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_rpc_get_max_size, + /* .get_alloc_size = */ ggml_backend_rpc_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_rpc_buffer_type_supports_backend, + /* .is_host = */ NULL, +}; + + +GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + + return rpc_ctx->name.c_str(); +} + +GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) { + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)rpc_ctx->buft->context; + delete buft_ctx; + delete rpc_ctx->buft; + delete rpc_ctx; + delete backend; +} + +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; + return ctx->buft; +} + +GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { + UNUSED(backend); + // this is no-op because we don't have any async operations +} + +static void add_tensor(ggml_tensor * tensor, std::vector & tensors, std::unordered_set & visited) { + if (tensor == nullptr) { + return; + } + if (visited.find(tensor) != visited.end()) { + return; + } + visited.insert(tensor); + for (int i = 0; i < GGML_MAX_SRC; i++) { + add_tensor(tensor->src[i], tensors, visited); + } + add_tensor(tensor->view_src, tensors, visited); + tensors.push_back(serialize_tensor(tensor)); +} + +static void serialize_graph(const ggml_cgraph * cgraph, std::vector & output) { + uint32_t n_nodes = cgraph->n_nodes; + std::vector tensors; + std::unordered_set visited; + for (uint32_t i = 0; i < n_nodes; i++) { + add_tensor(cgraph->nodes[i], tensors, visited); + } + // serialization format: + // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | + uint32_t n_tensors = tensors.size(); + int output_size = sizeof(uint32_t) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t) + n_tensors * sizeof(rpc_tensor); + output.resize(output_size, 0); + memcpy(output.data(), &n_nodes, sizeof(n_nodes)); + uint64_t * out_nodes = (uint64_t *)(output.data() + sizeof(n_nodes)); + for (uint32_t i = 0; i < n_nodes; i++) { + out_nodes[i] = reinterpret_cast(cgraph->nodes[i]); + } + uint32_t * out_ntensors = (uint32_t *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t)); + *out_ntensors = n_tensors; + rpc_tensor * out_tensors = (rpc_tensor *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t)); + memcpy(out_tensors, tensors.data(), n_tensors * sizeof(rpc_tensor)); +} + +GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + std::vector input; + serialize_graph(cgraph, input); + std::vector output; + bool status = send_rpc_cmd(rpc_ctx->sock, GRAPH_COMPUTE, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == 1); + return (enum ggml_status)output[0]; +} + +GGML_CALL static bool ggml_backend_rpc_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + UNUSED(backend); + UNUSED(op); + GGML_ASSERT(false && "not implemented"); + return false; +} + +static ggml_backend_i ggml_backend_rpc_interface = { + /* .get_name = */ ggml_backend_rpc_name, + /* .free = */ ggml_backend_rpc_free, + /* .get_default_buffer_type = */ ggml_backend_rpc_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ ggml_backend_rpc_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_rpc_graph_compute, + /* .supports_op = */ ggml_backend_rpc_supports_op, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, +}; + +static std::unordered_map instances; + +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { + ggml_backend_t backend = ggml_backend_rpc_init(endpoint); + return backend != nullptr ? ggml_backend_rpc_get_default_buffer_type(backend) : nullptr; +} + +GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { + std::string endpoint_str(endpoint); + if (instances.find(endpoint_str) != instances.end()) { + return instances[endpoint_str]; + } +#ifdef _WIN32 + { + WSADATA wsaData; + int res = WSAStartup(MAKEWORD(2, 2), &wsaData); + if (res != 0) { + return nullptr; + } + } +#endif + GGML_PRINT_DEBUG("Connecting to %s\n", endpoint); + std::string host; + int port; + if (!parse_endpoint(endpoint, host, port)) { + return nullptr; + } + auto sock = socket_connect(host.c_str(), port); + if (sock == nullptr) { + return nullptr; + } + size_t alignment = get_alignment(sock); + size_t max_size = get_max_size(sock); + ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context { + /* .sock = */ sock, + /* .name = */ "RPC" + std::to_string(sock->fd), + /* .alignment = */ alignment, + /* .max_size = */ max_size + }; + + ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type { + /* .iface = */ ggml_backend_rpc_buffer_type_interface, + /* .context = */ buft_ctx + }; + + ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context { + /* .endpoint = */ endpoint, + /* .name = */ "RPC" + std::to_string(sock->fd), + /* .sock = */ sock, + /* .buft = */ buft + }; + + instances[endpoint] = new ggml_backend { + /* .guid = */ ggml_backend_rpc_guid(), + /* .interface = */ ggml_backend_rpc_interface, + /* .context = */ ctx + }; + + return instances[endpoint]; +} + +GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_rpc_guid()); +} + +static void get_device_memory(const std::shared_ptr & sock, size_t * free, size_t * total) { + // input serialization format: | 0 bytes | + std::vector input; + std::vector output; + bool status = send_rpc_cmd(sock, GET_DEVICE_MEMORY, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); + // output serialization format: | free (8 bytes) | total (8 bytes) | + uint64_t free_mem; + memcpy(&free_mem, output.data(), sizeof(free_mem)); + uint64_t total_mem; + memcpy(&total_mem, output.data() + sizeof(uint64_t), sizeof(total_mem)); + *free = free_mem; + *total = total_mem; +} + +GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { + ggml_backend_t backend = ggml_backend_rpc_init(endpoint); + if (backend == nullptr) { + *free = 0; + *total = 0; + return; + } + ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; + get_device_memory(ctx->sock, free, total); +} + +// RPC server-side implementation + +static void rpc_alloc_buffer(ggml_backend_t backend, const std::vector & input, std::vector & output) { + // input serialization format: | size (8 bytes) | + uint64_t size; + memcpy(&size, input.data(), sizeof(size)); + ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); + uint64_t remote_ptr = reinterpret_cast(buffer); + uint64_t remote_size = buffer->size; + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size); + // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | + output.resize(2*sizeof(uint64_t), 0); + memcpy(output.data(), &remote_ptr, sizeof(remote_ptr)); + memcpy(output.data() + sizeof(uint64_t), &remote_size, sizeof(remote_size)); +} + +static void rpc_get_alignment(ggml_backend_t backend, std::vector & output) { + ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); + size_t alignment = ggml_backend_buft_get_alignment(buft); + GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment); + // output serialization format: | alignment (8 bytes) | + output.resize(sizeof(uint64_t), 0); + memcpy(output.data(), &alignment, sizeof(alignment)); +} + +static void rpc_get_max_size(ggml_backend_t backend, std::vector & output) { + ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); + size_t max_size = ggml_backend_buft_get_max_size(buft); + GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size); + // output serialization format: | max_size (8 bytes) | + output.resize(sizeof(uint64_t), 0); + memcpy(output.data(), &max_size, sizeof(max_size)); +} + +static void rpc_buffer_get_base(const std::vector & input, std::vector & output) { + // input serialization format: | remote_ptr (8 bytes) | + uint64_t remote_ptr; + memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + void * base = ggml_backend_buffer_get_base(buffer); + // output serialization format: | base_ptr (8 bytes) | + uint64_t base_ptr = reinterpret_cast(base); + output.resize(sizeof(uint64_t), 0); + memcpy(output.data(), &base_ptr, sizeof(base_ptr)); +} + +static void rpc_free_buffer(const std::vector & input) { + // input serialization format: | remote_ptr (8 bytes) | + uint64_t remote_ptr; + memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + ggml_backend_buffer_free(buffer); +} + +static void rpc_buffer_clear(const std::vector & input) { + // input serialization format: | remote_ptr (8 bytes) | value (1 byte) | + uint64_t remote_ptr; + memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); + uint8_t value; + memcpy(&value, input.data() + sizeof(uint64_t), sizeof(value)); + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, remote_ptr, value); + ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + ggml_backend_buffer_clear(buffer, value); +} + +static void rpc_set_tensor(const std::vector & input) { + // serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) | + const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); + uint64_t offset; + memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); + size_t size = input.size() - sizeof(rpc_tensor) - sizeof(offset); + + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); + GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); + const void * data = input.data() + sizeof(rpc_tensor) + sizeof(offset); + ggml_backend_tensor_set(tensor, data, offset, size); + ggml_free(ctx); +} + +static void rpc_get_tensor(const std::vector & input, std::vector & output) { + // serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | + const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); + uint64_t offset; + memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); + uint64_t size; + memcpy(&size, input.data() + sizeof(rpc_tensor) + sizeof(offset), sizeof(size)); + + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); + GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); + // output serialization format: | data (size bytes) | + output.resize(size, 0); + ggml_backend_tensor_get(tensor, output.data(), offset, size); + ggml_free(ctx); +} + +static void rpc_copy_tensor(const std::vector & input, std::vector & output) { + // serialization format: | rpc_tensor src | rpc_tensor dst | + const rpc_tensor * rpc_src = (const rpc_tensor *)input.data(); + const rpc_tensor * rpc_dst = (const rpc_tensor *)(input.data() + sizeof(rpc_src)); + + struct ggml_init_params params { + /*.mem_size =*/ 2*ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + ggml_tensor * src = deserialize_tensor(ctx, rpc_src); + ggml_tensor * dst = deserialize_tensor(ctx, rpc_dst); + GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer); + bool result = ggml_backend_buffer_copy_tensor(src, dst); + // output serialization format: | result (1 byte) | + output.resize(1, 0); + output[0] = result; + ggml_free(ctx); +} + +static struct ggml_tensor * create_node(uint64_t id, + struct ggml_context * ctx, + const std::unordered_map & tensor_ptrs, + std::unordered_map & tensor_map) { + if (id == 0) { + return nullptr; + } + if (tensor_map.find(id) != tensor_map.end()) { + return tensor_map[id]; + } + const rpc_tensor * tensor = tensor_ptrs.at(id); + struct ggml_tensor * result = deserialize_tensor(ctx, tensor); + tensor_map[id] = result; + for (int i = 0; i < GGML_MAX_SRC; i++) { + result->src[i] = create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); + } + result->view_src = create_node(tensor->view_src, ctx, tensor_ptrs, tensor_map); + result->view_offs = tensor->view_offs; + return result; +} + +static void rpc_graph_compute(ggml_backend_t backend, const std::vector & input, std::vector & output) { + // serialization format: + // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | + uint32_t n_nodes; + memcpy(&n_nodes, input.data(), sizeof(n_nodes)); + const uint64_t * nodes = (const uint64_t *)(input.data() + sizeof(n_nodes)); + uint32_t n_tensors; + memcpy(&n_tensors, input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t), sizeof(n_tensors)); + const rpc_tensor * tensors = (const rpc_tensor *)(input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t) + sizeof(n_tensors)); + GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors); + + static size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false); + struct ggml_init_params params = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + struct ggml_cgraph * graph = ggml_new_graph_custom(ctx, n_nodes, false); + graph->n_nodes = n_nodes; + std::unordered_map tensor_ptrs; + for (uint32_t i = 0; i < n_tensors; i++) { + tensor_ptrs[tensors[i].id] = &tensors[i]; + } + std::unordered_map tensor_map; + for (uint32_t i = 0; i < n_nodes; i++) { + graph->nodes[i] = create_node(nodes[i], ctx, tensor_ptrs, tensor_map); + } + ggml_status status = ggml_backend_graph_compute(backend, graph); + // output serialization format: | status (1 byte) | + output.resize(1, 0); + output[0] = status; + ggml_free(ctx); +} + +static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t free_mem, size_t total_mem) { + while (true) { + uint8_t cmd; + if (!recv_data(sockfd, &cmd, 1)) { + break; + } + std::vector input; + std::vector output; + uint64_t input_size; + if (!recv_data(sockfd, &input_size, sizeof(input_size))) { + break; + } + input.resize(input_size); + if (!recv_data(sockfd, input.data(), input_size)) { + break; + } + switch (cmd) { + case ALLOC_BUFFER: { + rpc_alloc_buffer(backend, input, output); + break; + } + case GET_ALIGNMENT: { + rpc_get_alignment(backend, output); + break; + } + case GET_MAX_SIZE: { + rpc_get_max_size(backend, output); + break; + } + case BUFFER_GET_BASE: { + rpc_buffer_get_base(input, output); + break; + } + case FREE_BUFFER: { + rpc_free_buffer(input); + break; + } + case BUFFER_CLEAR: { + rpc_buffer_clear(input); + break; + } + case SET_TENSOR: { + rpc_set_tensor(input); + break; + } + case GET_TENSOR: { + rpc_get_tensor(input, output); + break; + } + case COPY_TENSOR: { + rpc_copy_tensor(input, output); + break; + } + case GRAPH_COMPUTE: { + rpc_graph_compute(backend, input, output); + break; + } + case GET_DEVICE_MEMORY: { + // output serialization format: | free (8 bytes) | total (8 bytes) | + output.resize(2*sizeof(uint64_t), 0); + memcpy(output.data(), &free_mem, sizeof(free_mem)); + memcpy(output.data() + sizeof(uint64_t), &total_mem, sizeof(total_mem)); + break; + } + default: { + fprintf(stderr, "Unknown command: %d\n", cmd); + return; + } + } + uint64_t output_size = output.size(); + if (!send_data(sockfd, &output_size, sizeof(output_size))) { + break; + } + if (!send_data(sockfd, output.data(), output_size)) { + break; + } + } +} + +void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem) { + std::string host; + int port; + if (!parse_endpoint(endpoint, host, port)) { + return; + } +#ifdef _WIN32 + { + WSADATA wsaData; + int res = WSAStartup(MAKEWORD(2, 2), &wsaData); + if (res != 0) { + fprintf(stderr, "WSAStartup failed: %d\n", res); + return; + } + } +#endif + auto server_socket = create_server_socket(host.c_str(), port); + if (server_socket == nullptr) { + fprintf(stderr, "Failed to create server socket\n"); + return; + } + while (true) { + auto client_socket = socket_accept(server_socket->fd); + if (client_socket == nullptr) { + fprintf(stderr, "Failed to accept client connection\n"); + return; + } + printf("Accepted client connection, free_mem=%zu, total_mem=%zu\n", free_mem, total_mem); + rpc_serve_client(backend, client_socket->fd, free_mem, total_mem); + printf("Client connection closed\n"); + } +#ifdef _WIN32 + WSACleanup(); +#endif +} diff --git a/ggml-rpc.h b/ggml-rpc.h new file mode 100644 index 000000000..aa144832a --- /dev/null +++ b/ggml-rpc.h @@ -0,0 +1,24 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define GGML_RPC_MAX_SERVERS 16 + +// backend API +GGML_API GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint); +GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend); + +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint); + +GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total); + +GGML_API GGML_CALL void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem); + +#ifdef __cplusplus +} +#endif diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index e93d2af63..19d22d637 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -13987,6 +13987,10 @@ inline void ggml_sycl_op_upscale(const ggml_tensor *src0, GGML_ASSERT(dst->type == GGML_TYPE_F32); GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors +#pragma message("TODO: generalize upscale operator") +#pragma message(" https://github.com/ggerganov/ggml/pull/814") + GGML_ASSERT(false && "TODO: generalize upscale operator"); + const int scale_factor = dst->op_params[0]; upscale_f32_sycl(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream); @@ -15564,26 +15568,6 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const int64_t r2 = ne12/ne02; const int64_t r3 = ne13/ne03; -#if 0 - // use syclGemmEx - { - for (int i13 = 0; i13 < ne13; ++i13) { - for (int i12 = 0; i12 < ne12; ++i12) { - int i03 = i13 / r3; - int i02 = i12 / r2; - - SYCL_CHECK( - syclGemmEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , SYCL_R_16F, nb01/sizeof(half), - (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F, nb11/sizeof(float), - beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - } - } - } -#else if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( @@ -15595,7 +15579,6 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb11 / nb10, nb12 / nb10, beta, (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); - g_sycl_handles[g_main_device]->wait(); } else { const int ne23 = ne12*ne13; @@ -15626,7 +15609,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb02, nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1); }); - }).wait(); + }); } SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, @@ -15637,9 +15620,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); - g_sycl_handles[g_main_device]->wait(); } -#endif if (no_mixed_dtypes) { const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index d3a2fcf38..70c4043d3 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -363,320 +363,349 @@ const uint64_t add_f32_len = 4276; unsigned char argsort_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x2b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x13,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x2b,0x00,0x00,0x00, -0x33,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x04,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x08,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -12002,9 +12031,9 @@ unsigned char matmul_f16_aligned_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, -0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00, -0x55,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00, +0x68,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 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+0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xd3,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x71,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd8,0x02,0x00,0x00, +0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_aligned_len = 10812; +const uint64_t matmul_f16_aligned_len = 11200; unsigned char matmul_f16_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -12908,8 +12969,8 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x50,0x01,0x00,0x00,0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x50,0x01,0x00,0x00,0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -12952,17 +13013,17 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00, +0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00, +0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00, +0x02,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x04,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x21,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x23,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x4d,0x01,0x00,0x00,0x06,0x00,0x00,0x00, 0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4e,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -12971,15 +13032,15 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x39,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x38,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x80,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x82,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x82,0x02,0x00,0x00,0x21,0x00,0x00,0x00, +0x7f,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x81,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -13030,16 +13091,16 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x68,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -13083,99 +13144,97 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf7,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 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-0x6e,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x74,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x73,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, +0x6f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x75,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x74,0x00,0x00,0x00, 0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7a,0x00,0x00,0x00, 0x4f,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, @@ -13267,7 +13326,7 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x9b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9e,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x85,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x00,0x00,0x00, -0x9e,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x9e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x17,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa4,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x84,0x00,0x05,0x00, @@ -13283,479 +13342,481 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0xac,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xaf,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x85,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00, -0xaf,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xaf,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0xb2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x00,0x00,0x00, -0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00, +0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00, 0x3f,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0xd1,0x00,0x00,0x00, 0xb3,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xc1,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xc1,0x00,0x00,0x00, 0xf6,0x00,0x04,0x00,0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 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-0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -14636,9 +14697,9 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, -0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00, -0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, +0x73,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00, +0xe2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_f32_aligned_len = 10940; +const uint64_t matmul_f16_f32_aligned_len = 11360; unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -15552,8 +15648,8 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x50,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -15596,25 +15692,25 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00, +0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00, +0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 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matmul_f16_f32_aligned_fp32_len = 10224; +const uint64_t matmul_f16_f32_aligned_fp32_len = 10240; unsigned char matmul_f16_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -18987,7 +19084,7 @@ const uint64_t matmul_f32_len = 10324; unsigned char matmul_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x17,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -18995,8 +19092,8 @@ unsigned char matmul_f32_aligned_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfc,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, -0x52,0x01,0x00,0x00,0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00, +0xfe,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, +0x71,0x01,0x00,0x00,0x78,0x02,0x00,0x00,0xc1,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -19039,37 +19136,4514 @@ unsigned char matmul_f32_aligned_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_f32_f16_aligned_fp32_len = 10240; + +unsigned char matmul_f32_f16_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, +0xfa,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x44,0x01,0x00,0x00, +0x50,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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-0x3d,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,0x4c,0x01,0x00,0x00, -0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,0x3f,0x01,0x00,0x00, -0x09,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0x3d,0x01,0x00,0x00, -0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x40,0x01,0x00,0x00, -0x3f,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00, -0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x2c,0x01,0x00,0x00, -0x3e,0x00,0x03,0x00,0x3f,0x01,0x00,0x00,0x41,0x01,0x00,0x00, -0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00, -0x4c,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x2f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x31,0x01,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t soft_max_f32_len = 4916; - -unsigned char soft_max_f32_f16_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x61,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 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soft_max_f32_f16_len = 5000; +const uint64_t soft_max_f32_f16_len = 4904; unsigned char split_k_reduce_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index b9449be03..aff451b63 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -114,6 +114,7 @@ struct vk_device { size_t idx; vk_matmul_pipeline pipeline_matmul_f32; + vk_matmul_pipeline pipeline_matmul_f32_f16; vk_matmul_pipeline pipeline_matmul_f16; vk_matmul_pipeline pipeline_matmul_f16_f32; vk_pipeline pipeline_matmul_split_k_reduce; @@ -294,7 +295,6 @@ struct vk_op_rope_neox_push_constants { struct vk_op_soft_max_push_constants { uint32_t KX; uint32_t KY; - uint32_t KZ; float scale; float max_bias; float m0; @@ -304,7 +304,8 @@ struct vk_op_soft_max_push_constants { struct vk_op_argsort_push_constants { uint32_t ncols; - bool ascending; + uint32_t ncols_pad; + int32_t order; }; // Allow pre-recording command buffers @@ -375,13 +376,12 @@ struct ggml_backend_vk_context { vk_context * compute_ctx; vk_context * transfer_ctx; - bool disable; bool initialized; size_t idx; }; -struct vk_instance { +struct vk_instance_t { vk::Instance instance; std::vector device_indices; @@ -423,7 +423,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); static bool vk_instance_initialized = false; -static vk_instance vk_instance; +static vk_instance_t vk_instance; GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); @@ -1013,6 +1013,7 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { uint32_t s_align = 32; ctx->device->pipeline_matmul_f32 = std::make_shared(); + ctx->device->pipeline_matmul_f32_f16 = std::make_shared(); ctx->device->pipeline_matmul_f16_f32 = std::make_shared(); ctx->device->pipeline_matmul_f16 = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); @@ -1048,6 +1049,13 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); @@ -1230,6 +1238,13 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); @@ -1501,8 +1516,8 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -1859,7 +1874,6 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->compute_ctx = nullptr; ctx->transfer_ctx = nullptr; - ctx->disable = false; ctx->initialized = true; ctx->idx = idx; @@ -1903,6 +1917,9 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f32; } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f32_f16; + } if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f16_f32; } @@ -2722,7 +2739,7 @@ static void ggml_vk_matmul( uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; + std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif ggml_vk_sync_buffers(subctx); if (split_k == 1) { @@ -2792,7 +2809,7 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; + std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; #endif const int tensor_type_size = ggml_type_size(tensor->type); @@ -2812,9 +2829,9 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -2982,19 +2999,13 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21, 0, 0, 0, 0, 1 ); // NOLINT - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) ((char *) dst->data); - ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); - } } static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -3147,12 +3158,11 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -3217,25 +3227,17 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_sync_buffers(subctx); - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); - } } static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -3302,26 +3304,6 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_sync_buffers(subctx); - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); - } -} - -static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) { - const uint64_t ne10 = src1->ne[0]; - - const uint64_t ne0 = dst->ne[0]; - const uint64_t ne1 = dst->ne[1]; - - // TODO: find the optimal values for these - return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && - (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) && - dst->type == GGML_TYPE_F32 && - ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU); } static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3711,8 +3693,6 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; @@ -3752,7 +3732,7 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx } -static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) { +static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { @@ -3834,7 +3814,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32; } - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && src2->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32_f16; } return nullptr; @@ -3900,16 +3880,13 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { } template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; } - if (src2 != nullptr) { - std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; - } - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; #endif GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT @@ -3926,13 +3903,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t ne13 = use_src1 ? src1->ne[3] : 0; const uint64_t ne1 = ne10 * ne11; // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0; - const uint64_t nb2 = dst->nb[2]; - const uint64_t nb3 = dst->nb[3]; - const bool use_src2 = src2 != nullptr; - const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0; - - vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -3955,18 +3927,15 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; vk_buffer d_X = nullptr; size_t x_buf_offset = 0; vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; vk_buffer d_Z = nullptr; - size_t z_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - bool src2_uma = false; if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); @@ -3975,21 +3944,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } - if (use_src2) { - ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset); - src2_uma = d_Z != nullptr; - } } uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment); uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; - uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; vk_buffer d_D = extra->buffer_gpu.lock(); // Workaround for tiny tensor inputs on ROPE - if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) { + if (use_src1 && y_sz > d_D->size) { y_sz = VK_WHOLE_SIZE; } @@ -4007,12 +3971,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c GGML_ASSERT(d_Y != nullptr); } - if (use_src2 && !src2_uma) { - d_Z = extra_src2->buffer_gpu.lock(); - z_buf_offset = extra_src2->offset; - GGML_ASSERT(d_Z != nullptr); - } - if (op_supports_incontiguous) { x_sz = ggml_nbytes(src0); y_sz = use_src1 ? ggml_nbytes(src1) : 0; @@ -4046,7 +4004,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; break; case GGML_OP_GET_ROWS: - elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + break; + case GGML_OP_ARGSORT: + elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 }; break; default: elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; @@ -4066,7 +4027,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } if (op == GGML_OP_SOFT_MAX) { - // Empty src1 and src2 are possible on soft_max, but the shader needs buffers + // Empty src1 is possible on soft_max, but the shader needs a buffer vk_subbuffer subbuf_y; if (use_src1) { subbuf_y = { d_Y, y_buf_offset, y_sz }; @@ -4074,15 +4035,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c subbuf_y = { d_X, 0, d_X->size }; } - vk_subbuffer subbuf_z; - if (use_src2) { - subbuf_z = { d_Z, z_buf_offset, z_sz }; - } else { - subbuf_z = { d_X, 0, d_X->size }; - } - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); @@ -4090,22 +4044,15 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } - if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) { - ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); - } else if(dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); - } } else { GGML_ASSERT(op != GGML_OP_SOFT_MAX); + GGML_ASSERT(op != GGML_OP_ARGSORT); ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03); switch (dst->op) { case GGML_OP_NORM: case GGML_OP_RMS_NORM: - case GGML_OP_SOFT_MAX: elements = { (uint32_t)ne01, 1, 1 }; break; case GGML_OP_DIAG_MASK_INF: @@ -4135,17 +4082,13 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); - } } } } } static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -4153,7 +4096,7 @@ static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4168,7 +4111,7 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4183,7 +4126,7 @@ static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4198,7 +4141,7 @@ static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4211,7 +4154,7 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4225,7 +4168,7 @@ static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4240,7 +4183,7 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t dst_type_size = ggml_type_size(dst->type); const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4252,24 +4195,24 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } -static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; float scale = op_params[0]; @@ -4285,13 +4228,9 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); -#pragma message("TODO: src2 is no longer used in soft_max - should be removed and ALiBi calculation should be updated") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") - - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, - src2 != nullptr ? (uint32_t)1 : (uint32_t)0, scale, max_bias, m0, m1, n_head_log2, @@ -4321,15 +4260,39 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { + (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims + }); } else { - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { + (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} + }); } } static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC }); + + uint32_t ncols = src0->ne[0]; + + uint32_t ncols_pad = 1; + while (ncols_pad < ncols) { + ncols_pad *= 2; + } + + GGML_ASSERT(ncols_pad <= 1024); + + std::cerr << "ncols=" << ncols << " ncols_pad=" << ncols_pad << " ascending=" << op_params[0] << std::endl; + + std::cerr << ((ggml_sort_order) op_params[0]) << " " << GGML_SORT_ORDER_ASC << std::endl; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_ARGSORT, { + ncols, + ncols_pad, + op_params[0], + }); } #ifdef GGML_VULKAN_RUN_TESTS @@ -4381,6 +4344,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_s; shname = "F32_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_s; + shname = "F32_F16_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_s; shname = "F16_F32_ALIGNED_S"; @@ -4394,6 +4360,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_m; shname = "F32_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_m; + shname = "F32_F16_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_m; shname = "F16_F32_ALIGNED_M"; @@ -4407,6 +4376,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_l; shname = "F32_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_l; + shname = "F32_F16_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_l; shname = "F16_F32_ALIGNED_L"; @@ -4427,6 +4399,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->s; shname = "F32_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->s; + shname = "F32_F16_S"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->s; shname = "F16_F32_S"; @@ -4438,6 +4413,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->m; shname = "F32_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->m; + shname = "F32_F16_M"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->m; shname = "F16_F32_M"; @@ -4449,6 +4427,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->l; shname = "F32_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->l; + shname = "F32_F16_L"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->l; shname = "F16_F32_L"; @@ -4561,15 +4542,11 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t src1_ggml->data = y; tensor_ggml->data = d_chk; - ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_ggml); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - ctx->disable = false; - ggml_free(ggml_ctx); double avg_err = 0.0; @@ -5049,15 +5026,11 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, src1_ggml->data = y; tensor_ggml->data = d_chk; - ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_ggml); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - ctx->disable = false; - ggml_free(ggml_ctx); double avg_err = 0.0; @@ -5134,12 +5107,12 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif - if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + + if (extra == nullptr) { return; } - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; - ggml_tensor * src0 = node->src[0]; ggml_tensor * src1 = node->src[1]; @@ -5244,9 +5217,6 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm } static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { - if (ctx->disable) { - return; - } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl; #endif @@ -5420,7 +5390,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { } static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ - if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(node)) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + + if (ggml_is_empty(node) || extra == nullptr) { return; } @@ -5432,9 +5404,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; - const ggml_tensor * src2 = node->src[2]; - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; switch (node->op) { case GGML_OP_UNARY: @@ -5547,7 +5516,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ROPE: @@ -5580,7 +5549,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod last_node = true; #endif - if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) { + if (last_node) { ggml_vk_ctx_end(ctx->compute_ctx); ctx->compute_ctx->exit_tensor = node; ctx->compute_ctx = nullptr; @@ -5588,10 +5557,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){ - if (ctx->disable) { - return false; - } - ggml_tensor_extra_gpu * extra = nullptr; switch (tensor->op) { @@ -5650,7 +5615,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ } #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; + std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; #endif #ifdef GGML_VULKAN_CHECK_RESULTS @@ -5690,9 +5655,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ // Clean up after graph processing is done static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { - if (ctx->disable) { - return; - } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif @@ -5865,7 +5827,6 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; } - tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; } @@ -5873,8 +5834,6 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -5888,8 +5847,6 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6032,6 +5989,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif + size += 32; // Behave like the CPU buffer type void * ptr = nullptr; try { ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size); @@ -6119,7 +6077,6 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g #endif ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6140,7 +6097,6 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c #endif ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6206,6 +6162,10 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { ctx->transfer_ctx = nullptr; } +static bool ggml_vk_is_empty(ggml_tensor * node) { + return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE; +} + GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)" << std::endl; @@ -6220,7 +6180,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen int last_node = cgraph->n_nodes - 1; // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly - while (last_node > 0 && (cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(cgraph->nodes[last_node]))) { + while (last_node > 0 && ggml_vk_is_empty(cgraph->nodes[last_node])) { last_node -= 1; } @@ -6234,7 +6194,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + if (ggml_vk_is_empty(node)) { continue; } @@ -6548,7 +6508,7 @@ static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vectortype != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) { return; } i0 = std::max(i0, 5); @@ -6569,6 +6529,8 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); } else if (tensor->type == GGML_TYPE_F16) { val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); + } else if (tensor->type == GGML_TYPE_I32) { + val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); } else { GGML_ASSERT(false); } @@ -6671,7 +6633,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; - ggml_tensor * src2 = tensor->src[2]; struct ggml_init_params iparams = { /*.mem_size =*/ 1024*1024*1024, @@ -6798,66 +6759,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); } - if (src2 != nullptr) { - src2_clone = ggml_dup_tensor(ggml_ctx, src2); - - src2_size = ggml_nbytes(src2); - - src2_buffer = malloc(src2_size); - src2_clone->data = src2_buffer; - if (src2->backend == GGML_BACKEND_TYPE_CPU) { - memcpy(src2_clone->data, src2->data, src2_size); - memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src2->backend == GGML_BACKEND_TYPE_GPU) { - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; - vk_buffer buf = extra->buffer_gpu.lock(); - uint64_t offset = extra->offset; - if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { - for (int i3 = 0; i3 < src2->ne[3]; i3++) { - for (int i2 = 0; i2 < src2->ne[2]; i2++) { - const int idx = i3*src2->ne[2] + i2; - ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); - } - } - - src2_clone->nb[0] = src2->nb[0]; - src2_clone->nb[1] = src2->nb[1]; - for (int i = 2; i < GGML_MAX_DIMS; i++) { - src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; - } - } else { - if (offset + src2_size >= buf->size) { - src2_size = buf->size - offset; - } - ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size); - memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); - } - } else { - GGML_ASSERT(false); - } - - if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(ctx, src2, "src2"); - std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; - if (src2->src[0] != nullptr) { - std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; - } - if (src2->src[1] != nullptr) { - std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; - } - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0); - std::cerr << std::endl; - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(src2_clone, done); - } - - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone); - } if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); @@ -6877,7 +6778,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); } @@ -6937,16 +6838,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ GGML_ASSERT(false); } - // Disable vulkan here to avoid the hooks in ggml.c - ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_clone); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - ctx->disable = false; - ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone); if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone"); @@ -6964,9 +6860,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { free(src1_buffer); } - if (src2 != nullptr) { - free(src2_buffer); - } ggml_free(ggml_ctx); } @@ -7026,8 +6919,11 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->type == GGML_TYPE_F16) { correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0])); result = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); + } else if (tensor->type == GGML_TYPE_I32) { + correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); + result = *(int32_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); } else { - std::cerr << "comp_size=" << comp_size << " but required is " << (i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]) << std::endl; + std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl; } } else { std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl; diff --git a/ggml.c b/ggml.c index b96a82a41..53da231ee 100644 --- a/ggml.c +++ b/ggml.c @@ -112,6 +112,8 @@ typedef void * thread_ret_t; #endif +typedef pthread_t ggml_thread_t; + #ifdef GGML_USE_CPU_HBM #include #endif @@ -163,9 +165,6 @@ void ggml_print_backtrace(void) { #define GGML_DEBUG 0 #define GGML_GELU_FP16 #define GGML_GELU_QUICK_FP16 -#define GGML_SILU_FP16 -// #define GGML_CROSS_ENTROPY_EXP_FP16 -// #define GGML_FLASH_ATTN_EXP_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 @@ -316,12 +315,6 @@ static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; // precomputed quick gelu table for f16 (128 KB) static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; -// precomputed silu table for f16 (128 KB) -static ggml_fp16_t ggml_table_silu_f16[1 << 16]; - -// precomputed exp table for f16 (128 KB) -static ggml_fp16_t ggml_table_exp_f16[1 << 16]; - // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; @@ -413,10 +406,10 @@ void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) { int i = 0; #if defined(__AVX512BF16__) for (; i + 32 <= n; i += 32) { - _mm512_storeu_ps( - (__m512 *)(y + i), - (__m512)_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), - _mm512_loadu_ps(x + i))); + _mm512_storeu_si512( + (__m512i *)(y + i), + m512i(_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), + _mm512_loadu_ps(x + i)))); } #endif for (; i < n; i++) { @@ -1306,6 +1299,8 @@ static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F16_VEC_ZERO GGML_F32x4_ZERO #define GGML_F16_VEC_SET1 GGML_F32x4_SET1 #define GGML_F16_VEC_FMA GGML_F32x4_FMA +#define GGML_F16_VEC_ADD GGML_F32x4_ADD +#define GGML_F16_VEC_MUL GGML_F32x4_MUL #define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE // Use vec_xl, not vec_ld, in case the load address is not aligned. #define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ @@ -1537,6 +1532,59 @@ static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { #define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) #endif +// +// ggml context +// + +struct ggml_context { + size_t mem_size; + void* mem_buffer; + bool mem_buffer_owned; + bool no_alloc; + bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers + + int n_objects; + + struct ggml_object* objects_begin; + struct ggml_object* objects_end; + + struct ggml_scratch scratch; + struct ggml_scratch scratch_save; +}; + +struct ggml_context_container { + bool used; + + struct ggml_context context; +}; + +struct ggml_compute_state_shared { + const struct ggml_cgraph* cgraph; + const struct ggml_cplan* cplan; + + int64_t perf_node_start_cycles; + int64_t perf_node_start_time_us; + + const int n_threads; + + // synchronization primitives + atomic_int n_active; // num active threads + atomic_int node_n; // active graph node + atomic_int node_task; // active graph node task phase + + ggml_abort_callback abort_callback; // abort ggml_graph_compute when true + void* abort_callback_data; + + atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. +}; + +struct ggml_compute_state { + ggml_thread_t thrd; + int ith; + struct ggml_compute_state_shared* shared; + enum ggml_status ec; +}; + // // fundamental operations // @@ -1618,10 +1666,10 @@ static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t __m512 c1 = _mm512_setzero_ps(); __m512 c2 = _mm512_setzero_ps(); for (; i + 64 <= n; i += 64) { - c1 = _mm512_dpbf16_ps(c1, (__m512bh)_mm512_loadu_ps((const float *)(x + i)), - (__m512bh)_mm512_loadu_ps((const float *)(y + i))); - c2 = _mm512_dpbf16_ps(c2, (__m512bh)_mm512_loadu_ps((const float *)(x + i + 32)), - (__m512bh)_mm512_loadu_ps((const float *)(y + i + 32))); + c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), + m512bh(_mm512_loadu_si512((y + i)))); + c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), + m512bh(_mm512_loadu_si512((y + i + 32)))); } sumf += (ggml_float)_mm512_reduce_add_ps(c1); sumf += (ggml_float)_mm512_reduce_add_ps(c2); @@ -2028,52 +2076,291 @@ inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } -//inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { -// const uint16_t * i16 = (const uint16_t *) x; -// for (int i = 0; i < n; ++i) { -// y[i] = ggml_table_silu_f16[i16[i]]; -// } -//} +#if defined(__ARM_NEON) && defined(__aarch64__) -#ifdef GGML_SILU_FP16 -inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_silu_f16[t]); - } +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static float32x4_t ggml_v_expf(float32x4_t x) { + const float32x4_t r = vdupq_n_f32(0x1.8p23f); + const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); + const float32x4_t n = vsubq_f32(z, r); + const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, + vdupq_n_f32(0x1.7f7d1cp-20f)); + const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); + const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); + const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); + const float32x4_t u = vmulq_f32(b, b); + const float32x4_t j = vfmaq_f32( + vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), + vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), + vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); + if (!vpaddd_u64(vreinterpretq_u64_u32(c))) + return vfmaq_f32(k, j, k); + const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); + const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); + const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); + return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), + vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); } + +// computes silu x/(1+exp(-x)) in single precision vector +inline static float32x4_t ggml_v_silu(float32x4_t x) { + const float32x4_t one = vdupq_n_f32(1.0f); + const float32x4_t zero = vdupq_n_f32(0.0f); + const float32x4_t neg_x = vsubq_f32(zero, x); + const float32x4_t exp_neg_x = ggml_v_expf(neg_x); + const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); + return vdivq_f32(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX512F__) && defined(__AVX512DQ__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m512 ggml_v_expf(__m512 x) { + const __m512 r = _mm512_set1_ps(0x1.8p23f); + const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); + const __m512 n = _mm512_sub_ps(z, r); + const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); + const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23); + const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1)))); + const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b)); + if (_mm512_kortestz(c, c)) + return _mm512_fmadd_ps(j, k, k); + const __m512i g = _mm512_and_si512( + _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)), + _mm512_set1_epi32(0x82000000u)); + const __m512 s1 = + _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u))); + const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g)); + const __mmask16 d = + _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); + return _mm512_mask_blend_ps( + d, _mm512_mask_blend_ps( + c, _mm512_fmadd_ps(k, j, k), + _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)), + _mm512_mul_ps(s1, s1)); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m512 ggml_v_silu(__m512 x) { + const __m512 one = _mm512_set1_ps(1); + const __m512 zero = _mm512_setzero_ps(); + const __m512 neg_x = _mm512_sub_ps(zero, x); + const __m512 exp_neg_x = ggml_v_expf(neg_x); + const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); + return _mm512_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX2__) && defined(__FMA__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m256 ggml_v_expf(__m256 x) { + const __m256 r = _mm256_set1_ps(0x1.8p23f); + const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); + const __m256 n = _mm256_sub_ps(z, r); + const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), + _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); + const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); + const __m256 k = _mm256_castsi256_ps( + _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); + const __m256i c = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(126), _CMP_GT_OQ)); + const __m256 u = _mm256_mul_ps(b, b); + const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, + _mm256_set1_ps(0x1.573e2ep-5f)), u, + _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, + _mm256_set1_ps(0x1.fffdb6p-2f))), + u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) + return _mm256_fmadd_ps(j, k, k); + const __m256i g = _mm256_and_si256( + _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), + _mm256_set1_epi32(0x82000000u)); + const __m256 s1 = + _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); + const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); + const __m256i d = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(192), _CMP_GT_OQ)); + return _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), + _mm256_andnot_ps( + _mm256_castsi256_ps(d), + _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(c), + _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), + _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m256 ggml_v_silu(__m256 x) { + const __m256 one = _mm256_set1_ps(1); + const __m256 zero = _mm256_setzero_ps(); + const __m256 neg_x = _mm256_sub_ps(zero, x); + const __m256 exp_neg_x = ggml_v_expf(neg_x); + const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); + return _mm256_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON + +#if defined(__FMA__) +#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) +#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) #else -inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { +#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) +#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) +#endif + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m128 ggml_v_expf(__m128 x) { + const __m128 r = _mm_set1_ps(0x1.8p23f); + const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); + const __m128 n = _mm_sub_ps(z, r); + const __m128 b = + NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); + const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); + const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); + const __m128i c = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); + const __m128 u = _mm_mul_ps(b, b); + const __m128 j = + MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, + MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), + u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm_movemask_epi8(c)) + return MADD128(j, k, k); + const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), + _mm_set1_epi32(0x82000000u)); + const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); + const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); + const __m128i d = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); + return _mm_or_ps( + _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), + _mm_andnot_ps(_mm_castsi128_ps(d), + _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), + _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m128 ggml_v_silu(__m128 x) { + const __m128 one = _mm_set1_ps(1); + const __m128 zero = _mm_setzero_ps(); + const __m128 neg_x = _mm_sub_ps(zero, x); + const __m128 exp_neg_x = ggml_v_expf(neg_x); + const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); + return _mm_div_ps(x, one_plus_exp_neg_x); +} + +#endif // __ARM_NEON / __AVX2__ / __SSE2__ + +static void ggml_vec_silu_f32(const int n, float * y, const float * x) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); + } +#endif + for (; i < n; ++i) { y[i] = ggml_silu_f32(x[i]); } } + +static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { + int i = 0; + ggml_float sum = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), + _mm512_set1_ps(max))); + _mm512_storeu_ps(y + i, val); + sum += (ggml_float)_mm512_reduce_add_ps(val); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), + _mm256_set1_ps(max))); + _mm256_storeu_ps(y + i, val); + __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), + _mm256_castps256_ps128(val)); + val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); + val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); + sum += (ggml_float)_mm_cvtss_f32(val2); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), + _mm_set1_ps(max))); + _mm_storeu_ps(y + i, val); +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + val = _mm_add_ps(val, _mm_movehl_ps(val, val)); + val = _mm_add_ss(val, _mm_movehdup_ps(val)); +#else + __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); + val = _mm_add_ps(val, tmp); + tmp = _mm_movehl_ps(tmp, val); + val = _mm_add_ss(val, tmp); #endif + sum += (ggml_float)_mm_cvtss_f32(val); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), + vdupq_n_f32(max))); + vst1q_f32(y + i, val); + sum += (ggml_float)vaddvq_f32(val); + } +#endif + for (; i < n; ++i) { + float val = expf(x[i] - max); + sum += (ggml_float)val; + y[i] = val; + } + return sum; +} inline static float ggml_silu_backward_f32(float x, float dy) { const float s = 1.0f/(1.0f + expf(-x)); return dy*s*(1.0f + x*(1.0f - s)); } -#ifdef GGML_SILU_FP16 -inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { - for (int i = 0; i < n; ++i) { - // we did not use x[i] to compute forward silu but its f16 equivalent - // take derivative at f16 of x[i]: - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - float usedx = GGML_FP16_TO_FP32(fp16); - dx[i] = ggml_silu_backward_f32(usedx, dy[i]); - } -} -#else inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { for (int i = 0; i < n; ++i) { dx[i] = ggml_silu_backward_f32(x[i], dy[i]); } } -#endif inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE @@ -2383,32 +2670,6 @@ static void ggml_setup_op_has_task_pass(void) { } } -// -// ggml context -// - -struct ggml_context { - size_t mem_size; - void * mem_buffer; - bool mem_buffer_owned; - bool no_alloc; - bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers - - int n_objects; - - struct ggml_object * objects_begin; - struct ggml_object * objects_end; - - struct ggml_scratch scratch; - struct ggml_scratch scratch_save; -}; - -struct ggml_context_container { - bool used; - - struct ggml_context context; -}; - // // NUMA support // @@ -2822,6 +3083,16 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor (t0->ne[3] == t1->ne[3] ); } +bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return + (t0->nb[0] == t1->nb[0] ) && + (t0->nb[1] == t1->nb[1] ) && + (t0->nb[2] == t1->nb[2] ) && + (t0->nb[3] == t1->nb[3] ); +} + // check if t1 can be represented as a repeatition of t0 static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); @@ -2881,8 +3152,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); - ggml_table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); - ggml_table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); @@ -3166,6 +3435,12 @@ static struct ggml_tensor * ggml_new_tensor_impl( struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); +#ifdef __clang__ + // temporary until ggml_tensor::backend is removed + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif + *result = (struct ggml_tensor) { /*.type =*/ type, /*.backend =*/ GGML_BACKEND_TYPE_CPU, @@ -3188,6 +3463,10 @@ static struct ggml_tensor * ggml_new_tensor_impl( /*.padding =*/ { 0 }, }; +#ifdef __clang__ + #pragma clang diagnostic pop +#endif + // TODO: this should not be needed as long as we don't rely on aligned SIMD loads //ggml_assert_aligned(result->data); @@ -6281,7 +6560,10 @@ struct ggml_tensor * ggml_pool_2d( static struct ggml_tensor * ggml_upscale_impl( struct ggml_context * ctx, struct ggml_tensor * a, - int scale_factor) { + int ne0, + int ne1, + int ne2, + int ne3) { bool is_node = false; if (a->grad) { @@ -6289,19 +6571,45 @@ static struct ggml_tensor * ggml_upscale_impl( is_node = true; } + GGML_ASSERT(a->ne[0] <= ne0); + GGML_ASSERT(a->ne[1] <= ne1); + GGML_ASSERT(a->ne[2] <= ne2); + GGML_ASSERT(a->ne[3] <= ne3); + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, - a->ne[0] * scale_factor, - a->ne[1] * scale_factor, - a->ne[2], a->ne[3]); + ne0, + ne1, + ne2, + ne3 + ); result->op = GGML_OP_UPSCALE; - result->op_params[0] = scale_factor; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } +struct ggml_tensor * ggml_upscale( + struct ggml_context * ctx, + struct ggml_tensor * a, + int scale_factor) { + return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]); +} + +struct ggml_tensor * ggml_upscale_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3) { + return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3); +} + +// ggml_pad + struct ggml_tensor * ggml_pad( struct ggml_context * ctx, struct ggml_tensor * a, @@ -6326,12 +6634,7 @@ struct ggml_tensor * ggml_pad( return result; } -struct ggml_tensor * ggml_upscale( - struct ggml_context * ctx, - struct ggml_tensor * a, - int scale_factor) { - return ggml_upscale_impl(ctx, a, scale_factor); -} +// ggml_arange struct ggml_tensor * ggml_arange( struct ggml_context * ctx, @@ -6353,6 +6656,8 @@ struct ggml_tensor * ggml_arange( return result; } +// ggml_timestep_embedding + struct ggml_tensor * ggml_timestep_embedding( struct ggml_context * ctx, struct ggml_tensor * timesteps, @@ -11767,9 +12072,101 @@ static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) { } #endif +static void ggml_compute_forward_mul_mat_one_chunk( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const int64_t num_rows_per_vec_dot, + const int64_t ir0_start, + const int64_t ir0_end, + const int64_t ir1_start, + const int64_t ir1_end) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const enum ggml_type type = src0->type; + + const bool src1_cont = ggml_is_contiguous(src1); + + ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; + + // broadcast factors + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + + //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); + + // threads with no work simply yield (not sure if it helps) + if (ir0_start >= ir0_end || ir1_start >= ir1_end) { + return; + } + + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + assert(ne12 % ne02 == 0); + assert(ne13 % ne03 == 0); + + // block-tiling attempt + const int64_t blck_0 = 16; + const int64_t blck_1 = 16; + + const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + + // attempt to reduce false-sharing (does not seem to make a difference) + // 16 * 2, accounting for mmla kernels + float tmp[32]; + + for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { + for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { + const int64_t i13 = (ir1 / (ne12 * ne1)); + const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; + const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); + + // broadcast src0 into src1 + const int64_t i03 = i13 / r3; + const int64_t i02 = i12 / r2; + + const int64_t i1 = i11; + const int64_t i2 = i12; + const int64_t i3 = i13; + + const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); + + // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides + // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using + // the original src1 data pointer, so we should index using the indices directly + // TODO: this is a bit of a hack, we should probably have a better way to handle this + const char * src1_col = (const char*)wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size + : (i11 * nb11 + i12 * nb12 + i13 * nb13)); + float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); + + //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { + // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); + //} + + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { + vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); + } + + for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { + memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); + } + } + } + } +} + static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, - struct ggml_tensor * dst) { + struct ggml_tensor * dst, + struct ggml_compute_state * state) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; @@ -11784,9 +12181,6 @@ static void ggml_compute_forward_mul_mat( const enum ggml_type type = src0->type; - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; int64_t const vec_dot_num_rows = type_traits[type].nrows; @@ -11807,8 +12201,10 @@ static void ggml_compute_forward_mul_mat( GGML_ASSERT(nb2 <= nb3); // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + UNUSED(r2); + UNUSED(r3); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows @@ -11890,6 +12286,8 @@ static void ggml_compute_forward_mul_mat( #endif #if GGML_USE_LLAMAFILE + const bool src1_cont = ggml_is_contiguous(src1); + if (src1_cont) { for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) @@ -11915,6 +12313,8 @@ UseGgmlGemm1:; if (ith != 0) { return; } + // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. + atomic_store(&state->shared->current_chunk, nth); if (src1->type != vec_dot_type) { char * wdata = params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); @@ -11939,11 +12339,11 @@ UseGgmlGemm1:; return; } - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - #if GGML_USE_LLAMAFILE if (src1->type != vec_dot_type) { + const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), @@ -11964,98 +12364,87 @@ UseGgmlGemm1:; UseGgmlGemm2:; #endif - const int64_t nr0 = ne01; // src0 rows - const int64_t nr1 = ne1*ne12*ne13; // src1 rows +#ifdef GGML_PERF + int chunks_executed = 0; + UNUSED(chunks_executed); +#endif - //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1); + // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) + const int64_t nr0 = ne0; - // distribute the thread work across the inner or outer loop based on which one is larger - - const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - - const int64_t ith0 = ith % nth0; - const int64_t ith1 = ith / nth0; - - const int64_t dr0 = (nr0 + nth0 - 1)/nth0; - const int64_t dr1 = (nr1 + nth1 - 1)/nth1; - - const int64_t ir010 = dr0*ith0; - const int64_t ir011 = MIN(ir010 + dr0, nr0); - - const int64_t ir110 = dr1*ith1; - const int64_t ir111 = MIN(ir110 + dr1, nr1); - - //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111); - - // threads with no work simply yield (not sure if it helps) - if (ir010 >= ir011 || ir110 >= ir111) { - sched_yield(); - return; - } - - assert(ne12 % ne02 == 0); - assert(ne13 % ne03 == 0); - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; + // This is the size of the rest of the dimensions of the result + const int64_t nr1 = ne1 * ne2 * ne3; // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols - int64_t nrc = vec_dot_num_rows; + int64_t num_rows_per_vec_dot = vec_dot_num_rows; // TODO: currently the mmla kernels support only even numbered rows/cols. // this check can be removed once they are extended to support odd numbered rows/cols too if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { - nrc = 1; + num_rows_per_vec_dot = 1; } - const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + // Now select a reasonable chunk size. + int chunk_size = 16; - // attempt to reduce false-sharing (does not seem to make a difference) - // 16 * 2, accounting for mmla kernels - float tmp[32]; + // We need to step up the size if it's small + if (nr0 == 1 || nr1 == 1) { + chunk_size = 64; + } - for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { - for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ir1 += nrc) { - const int64_t i13 = (ir1/(ne12*ne1)); - const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1; - const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1); + // distribute the work across the inner or outer loop based on which one is larger + // The number of chunks in the 0/1 dim. + // CEIL(nr0/chunk_size) + int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; + int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; - // broadcast src0 into src1 - const int64_t i03 = i13/r3; - const int64_t i02 = i12/r2; + // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. + // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 + // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. + if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { + // distribute the thread work across the inner or outer loop based on which one is larger + nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows + nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows + } - const int64_t i1 = i11; - const int64_t i2 = i12; - const int64_t i3 = i13; + // The number of elements in each chunk + const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; + const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; - const char * src0_row = (const char *) src0->data + (0 + i02*nb02 + i03*nb03); + //if (ith == 0) + // printf("MUL_MAT = [%d, %d, %d, %d] x [%d, %d, %d, %d] = %d x %d = %d. Fp Ops/Ch %d\n", ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nchunk0, nchunk1, nchunk0 * nchunk1, ne00 * nr0 * nr1 / nchunk0 / nchunk1); - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12*ne11 + i13*ne12*ne11)*row_size - : (i11*nb11 + i12*nb12 + i13*nb13)); - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); + // The first chunk comes from our thread_id, the rest will get auto-assigned. + int current_chunk = ith; - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} + while (current_chunk < nchunk0 * nchunk1) { + const int64_t ith0 = current_chunk % nchunk0; + const int64_t ith1 = current_chunk / nchunk0; - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ir0 += nrc) { - vec_dot(ne00, &tmp[ir0 - iir0], (nrc>1 ? 16 : 0), src0_row + ir0*nb01, (nrc>1 ? nb01 : 0), src1_col, (nrc>1 ? src1_col_stride : 0), nrc); - } + const int64_t ir0_start = dr0 * ith0; + const int64_t ir0_end = MIN(ir0_start + dr0, nr0); - for (int cn = 0; cn < nrc; ++cn) { - memcpy(&dst_col[iir0 + cn*nb1/nb0], tmp + (cn*16), (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); - } - } + const int64_t ir1_start = dr1 * ith1; + const int64_t ir1_end = MIN(ir1_start + dr1, nr1); + + ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); + +#ifdef GGML_PERF + chunks_executed++; +#endif + + if (nth >= nchunk0 * nchunk1) { + break; } + + current_chunk = atomic_fetch_add(&state->shared->current_chunk, 1); } + +#ifdef GGML_PERF + // These numbers are useful when trying to measure how well the threading scheduling works. + //int64_t workSize = (ne01 * ne11 * ne12 * ne13 * ne00) / nchunk0 / nchunk1; + //float time = (ggml_perf_time_us() - t0); + //printf("MUL_MAT = %f ms, [%d, %d, %d, %d] x [%d, %d, %d, %d] = %I64u, %f ops/usec in %d chunks.\n", time / 1000.0, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, workSize, (float)workSize/time, chunks_executed); +#endif } // ggml_compute_forward_mul_mat_id @@ -13439,22 +13828,7 @@ static void ggml_compute_forward_soft_max_f32( float max = -INFINITY; ggml_vec_max_f32(nc, &max, wp); - ggml_float sum = 0.0; - - uint16_t scvt; - for (int i = 0; i < nc; i++) { - if (wp[i] == -INFINITY) { - dp[i] = 0.0f; - } else { - // const float val = (wp[i] == -INFINITY) ? 0.0 : exp(wp[i] - max); - ggml_fp16_t s = GGML_FP32_TO_FP16(wp[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); - sum += (ggml_float)val; - dp[i] = val; - } - } - + ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); assert(sum > 0.0); sum = 1.0/sum; @@ -14808,25 +15182,28 @@ static void ggml_compute_forward_upscale_f32( return; } - GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS - const int scale_factor = dst->op_params[0]; + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; // TODO: optimize for (int64_t i3 = 0; i3 < ne3; i3++) { - const int64_t i03 = i3; + const int64_t i03 = i3 / sf3; for (int64_t i2 = ith; i2 < ne2; i2 += nth) { - const int64_t i02 = i2; + const int64_t i02 = i2 / sf2; for (int64_t i1 = 0; i1 < ne1; i1++) { - const int64_t i01 = i1 / scale_factor; + const int64_t i01 = i1 / sf1; for (int64_t i0 = 0; i0 < ne0; i0++) { - const int64_t i00 = i0 / scale_factor; + const int64_t i00 = i0 / sf0; const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); @@ -14856,6 +15233,7 @@ static void ggml_compute_forward_upscale( } } + // ggml_compute_forward_pad static void ggml_compute_forward_pad_f32( @@ -15209,37 +15587,7 @@ static void ggml_compute_forward_flash_attn_f32( vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - if (i >= masked_begin) { - break; - } - float * SS = S + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (i + j >= masked_begin) { - break; - } else if (SS[j] == -INFINITY) { - SS[j] = 0.0f; - } else { -#ifndef GGML_FLASH_ATTN_EXP_FP16 - const float val = expf(SS[j] - max); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); -#endif - sump[j] += (ggml_float)val; - SS[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } + sum = ggml_vec_soft_max_f32(Mup, S, S, max); #endif } @@ -15421,28 +15769,7 @@ static void ggml_compute_forward_flash_attn_f16( vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - float * SS = S + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (SS[j] == -INFINITY) { - SS[j] = 0.0f; - } else { - ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); - sump[j] += (ggml_float)val; - SS[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } + sum = ggml_vec_soft_max_f32(Mup, S, S, max); #endif } @@ -15555,9 +15882,10 @@ static void ggml_compute_forward_flash_attn_ext_f16( GGML_ASSERT(ne0 == D); GGML_ASSERT(ne2 == N); - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); @@ -15611,6 +15939,11 @@ static void ggml_compute_forward_flash_attn_ext_f16( const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + enum ggml_type const k_vec_dot_type = type_traits[k->type].vec_dot_type; + ggml_from_float_t const q_to_vec_dot = type_traits[k_vec_dot_type].from_float; + ggml_vec_dot_t const kq_vec_dot = type_traits[k->type].vec_dot; + ggml_to_float_t const v_to_float = type_traits[v->type].to_float; + // loop over n_batch and n_head for (int ir = ir0; ir < ir1; ++ir) { // q indices @@ -15618,17 +15951,22 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - const uint32_t h = iq2; // head + const uint32_t h = iq2; // head index const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - float S = 0.0f; - float M = -INFINITY; + float S = 0.0f; // sum + float M = -INFINITY; // maximum KQ value - float * V32 = (float *) params->wdata + ith*(2*D + CACHE_LINE_SIZE_F32); - ggml_fp16_t * Q16 = (ggml_fp16_t *) (V32); // reuse memory - ggml_fp16_t * V16 = (ggml_fp16_t *) (V32 + D); + float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator + float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer + ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator + ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 - memset(V16, 0, D*sizeof(ggml_fp16_t)); + if (v->type == GGML_TYPE_F16) { + memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); + } else { + memset(VKQ32, 0, D*sizeof(float)); + } const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; @@ -15640,6 +15978,9 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int iv3 = iq3 / rv3; const int iv2 = iq2 / rv2; + const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + q_to_vec_dot(pq, Q_q, D); + // online softmax / attention // loop over n_kv and n_head_kv // ref: https://arxiv.org/pdf/2112.05682.pdf @@ -15649,51 +15990,66 @@ static void ggml_compute_forward_flash_attn_ext_f16( continue; } - float s; + float s; // KQ value - // convert Q to F16 in V32 - { - const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); + kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); - for (int64_t d = 0; d < D; ++d) { - Q16[d] = GGML_FP32_TO_FP16(pq[d]); - } - } - - ggml_vec_dot_f16(D, - &s, 0, - (ggml_fp16_t *) ((char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - Q16, 0, 1); - - s = s*scale + mv; + s = s*scale + mv; // scale KQ value and apply mask const float Mold = M; - float ms = 1.0f; - float vs = 1.0f; + float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value + float vs = 1.0f; // post-softmax KQ value, expf(s - M) - if (s > M) { - M = s; - ms = expf(Mold - M); + const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); - // V = V*expf(Mold - M) - ggml_vec_scale_f16(D, V16, ms); + if (v->type== GGML_TYPE_F16) { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f16(D, VKQ16, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + // V += v*expf(s - M) + ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); } else { - vs = expf(s - M); + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f32(D, VKQ32, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + v_to_float(v_data, V32, D); + + // V += v*expf(s - M) + ggml_vec_mad_f32(D, VKQ32, V32, vs); } - const ggml_fp16_t * v16 = (const ggml_fp16_t *) ((char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); + S = S*ms + vs; // scale and increment sum with partial sum + } - // V += v*expf(s - M) - ggml_vec_mad_f16(D, V16, v16, vs); - - S = S*ms + vs; + if (v->type == GGML_TYPE_F16) { + for (int64_t d = 0; d < D; ++d) { + VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); + } } // V /= S - for (int64_t d = 0; d < D; ++d) { - V32[d] = GGML_FP16_TO_FP32(V16[d])/S; - } + const float S_inv = 1.0f/S; + ggml_vec_scale_f32(D, VKQ32, S_inv); // dst indices const int i1 = iq1; @@ -15704,7 +16060,7 @@ static void ggml_compute_forward_flash_attn_ext_f16( //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, V32, nb1); + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); } } @@ -16069,38 +16425,7 @@ static void ggml_compute_forward_flash_attn_back_f32( vvexpf(SM, SM, &Mup); ggml_vec_sum_f32(Mup, &sum, SM); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - if (i >= masked_begin) { - break; - } - float * SR = S + i; - float * SW = SM + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (i + j >= masked_begin) { - break; - } else if (SR[j] == -INFINITY) { - SW[j] = 0.0f; - } else { -#ifndef GGML_FLASH_ATTN_EXP_FP16 - const float val = expf(SR[j] - max); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); -#endif - sump[j] += (ggml_float)val; - SW[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } + sum = ggml_vec_soft_max_f32(Mup, SM, S, max); #endif } @@ -17126,35 +17451,15 @@ static void ggml_compute_forward_cross_entropy_loss_f32( assert(!isnan(s1[i])); } #endif + // soft_max - ggml_float sum = 0.0; - { - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - - uint16_t scvt; UNUSED(scvt); - for (int i = 0; i < nc; i++) { - if (s0[i] == -INFINITY) { - st[i] = 0.0f; - } else { -#ifndef GGML_CROSS_ENTROPY_EXP_FP16 - const float s = s0[i] - max; - const float val = expf(s); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); -#endif - sum += (ggml_float)val; - st[i] = val; - } - } - - assert(sum > 0.0); - // sum = 1.0/sum; - } - // avoid log(0) by rescaling from [0..1] to [eps..1] + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, st, s0, max); + assert(sum > 0.0); sum = (1.0 - eps) / sum; + + // avoid log(0) by rescaling from [0..1] to [eps..1] ggml_vec_scale_f32(nc, st, sum); ggml_vec_add1_f32(nc, st, st, eps); ggml_vec_log_f32(nc, st, st); @@ -17244,32 +17549,11 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( #endif // soft_max - ggml_float sum = 0.0; - { - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - - uint16_t scvt; UNUSED(scvt); - for (int i = 0; i < nc; i++) { - if (s0[i] == -INFINITY) { - ds0[i] = 0.0f; - } else { -#ifndef GGML_CROSS_ENTROPY_EXP_FP16 - const float s = s0[i] - max; - const float val = expf(s); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); -#endif - sum += (ggml_float)val; - ds0[i] = val; - } - } - - assert(sum > 0.0); - sum = (1.0 - eps)/sum; - } + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); + assert(sum > 0.0); + sum = (1.0 - eps) / sum; // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr ggml_vec_scale_f32(nc, ds0, sum); @@ -17306,7 +17590,7 @@ static void ggml_compute_forward_cross_entropy_loss_back( ///////////////////////////////// -static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { +static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor, struct ggml_compute_state * state) { GGML_ASSERT(params); if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { @@ -17404,7 +17688,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_MUL_MAT: { - ggml_compute_forward_mul_mat(params, tensor); + ggml_compute_forward_mul_mat(params, tensor, state); } break; case GGML_OP_MUL_MAT_ID: { @@ -19020,8 +19304,6 @@ typedef int ggml_lock_t; #define GGML_LOCK_INITIALIZER 0 -typedef pthread_t ggml_thread_t; - #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join @@ -19047,8 +19329,6 @@ typedef int ggml_lock_t; #define GGML_LOCK_INITIALIZER 0 -typedef pthread_t ggml_thread_t; - #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join @@ -19128,31 +19408,6 @@ static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } static void clear_numa_thread_affinity(void) {} #endif -struct ggml_compute_state_shared { - const struct ggml_cgraph * cgraph; - const struct ggml_cplan * cplan; - - int64_t perf_node_start_cycles; - int64_t perf_node_start_time_us; - - const int n_threads; - - // synchronization primitives - atomic_int n_active; // num active threads - atomic_int node_n; // active graph node - atomic_int node_task; // active graph node task phase - - ggml_abort_callback abort_callback; // abort ggml_graph_compute when true - void * abort_callback_data; -}; - -struct ggml_compute_state { - ggml_thread_t thrd; - int ith; - struct ggml_compute_state_shared * shared; - enum ggml_status ec; -}; - static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles; int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us; @@ -19425,6 +19680,10 @@ static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_comput * node_n = atomic_load(&state->shared->node_n); if (* node_n != last_node_n) break; +#if defined(__SSE3__) + // Tell the processor we're spinning. It's a processor hint for spinlocks. + _mm_pause(); +#endif } } @@ -19439,6 +19698,10 @@ static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_co * task_phase = atomic_load(&state->shared->node_task); if (* task_phase != last_task_phase) break; +#if defined(__SSE3__) + // Tell the processor we're spinning. It's a processor hint for spinlocks. + _mm_pause(); +#endif } } @@ -19478,7 +19741,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_tensor * node = cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); } @@ -19498,17 +19761,17 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { /* INIT */ if (GGML_OP_HAS_INIT[node->op]) { params.type = GGML_TASK_TYPE_INIT; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, // they do something more efficient than spinning (?) params.type = GGML_TASK_TYPE_COMPUTE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); if (GGML_OP_HAS_FINALIZE[node->op]) { params.type = GGML_TASK_TYPE_FINALIZE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); @@ -19547,7 +19810,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (state->ith < n_tasks) { if (GGML_OP_HAS_INIT[node->op]) { - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } } @@ -19568,7 +19831,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (state->ith < n_tasks) { params.type = GGML_TASK_TYPE_COMPUTE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { @@ -19738,7 +20001,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa { const int64_t ne00 = node->src[0]->ne[0]; // D - cur = 2*sizeof(float)*ne00*n_tasks; // 2x head size + cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread } break; case GGML_OP_FLASH_FF: { @@ -19819,6 +20082,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl /*.node_task =*/ GGML_TASK_TYPE_FINALIZE, /*.abort_callback =*/ NULL, /*.abort_callback_data =*/ NULL, + /*.current_chunk; =*/ 0, }; struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); @@ -22873,6 +23137,14 @@ int ggml_cpu_has_avx512_vnni(void) { #endif } +int ggml_cpu_has_avx512_bf16(void) { +#if defined(__AVX512BF16__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; diff --git a/ggml.h b/ggml.h index 3fe95ed57..774757101 100644 --- a/ggml.h +++ b/ggml.h @@ -565,7 +565,8 @@ extern "C" { // n-dimensional tensor struct ggml_tensor { enum ggml_type type; - enum ggml_backend_type backend; + + GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor"); struct ggml_backend_buffer * buffer; @@ -766,7 +767,8 @@ extern "C" { GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars - GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1); + GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1); + GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1); // use this to compute the memory overhead of a tensor GGML_API size_t ggml_tensor_overhead(void); @@ -1673,12 +1675,24 @@ extern "C" { float p1); // nearest interpolate + // multiplies ne0 and ne1 by scale factor // used in stable-diffusion GGML_API struct ggml_tensor * ggml_upscale( struct ggml_context * ctx, struct ggml_tensor * a, int scale_factor); + // nearest interpolate + // nearest interpolate to specified dimensions + // used in tortoise.cpp + GGML_API struct ggml_tensor * ggml_upscale_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3); + // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0] GGML_API struct ggml_tensor * ggml_pad( struct ggml_context * ctx, @@ -2376,6 +2390,7 @@ extern "C" { GGML_API int ggml_cpu_has_avx512 (void); GGML_API int ggml_cpu_has_avx512_vbmi(void); GGML_API int ggml_cpu_has_avx512_vnni(void); + GGML_API int ggml_cpu_has_avx512_bf16(void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); GGML_API int ggml_cpu_has_arm_fma (void); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 162cf5c6e..8096c03b7 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -2432,7 +2432,6 @@ layout (push_constant) uniform parameter { uint KX; uint KY; - uint KZ; float scale; float max_bias; float m0; @@ -2449,8 +2448,7 @@ layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) readonly buffer Z {C_TYPE data_c[];}; -layout (binding = 3) buffer D {D_TYPE data_d[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; shared FLOAT_TYPE vals[BLOCK_SIZE]; @@ -2459,7 +2457,7 @@ void main() { const uint rowx = gl_WorkGroupID.x; const uint rowy = rowx % p.KY; - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (p.max_bias > 0.0f) { @@ -2472,11 +2470,18 @@ void main() { } // Find max - vals[tid] = uintBitsToFloat(0xFF800000); + FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * FLOAT_TYPE(data_c[col]) : 0.0f)); + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); } + vals[tid] = max_val; barrier(); [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { @@ -2486,15 +2491,21 @@ void main() { barrier(); } - const FLOAT_TYPE max_val = vals[0]; + max_val = vals[0]; barrier(); // Sum up values vals[tid] = FLOAT_TYPE(0.0f); - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); vals[tid] += val; data_d[i] = D_TYPE(val); } @@ -2509,7 +2520,13 @@ void main() { const D_TYPE divisor = D_TYPE(vals[0]); - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + data_d[rowx*p.KX + col] /= divisor; } } @@ -2672,20 +2689,26 @@ argsort_src = """ #extension GL_EXT_shader_16bit_storage : require -layout(local_size_x = 1024, local_size_y = 1, local_size_z = 1) in; +#define BLOCK_SIZE 1024 +#define ASC 0 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) buffer D {int data_d[];}; layout (push_constant) uniform parameter { uint ncols; - bool ascending; + uint ncols_pad; + uint order; } p; +shared int dst_row[BLOCK_SIZE]; + void swap(uint idx0, uint idx1) { - int tmp = data_d[idx0]; - data_d[idx0] = data_d[idx1]; - data_d[idx1] = tmp; + int tmp = dst_row[idx0]; + dst_row[idx0] = dst_row[idx1]; + dst_row[idx1] = tmp; } void main() { @@ -2693,36 +2716,45 @@ void main() { const int col = int(gl_LocalInvocationID.x); const uint row = gl_WorkGroupID.y; - if (col >= p.ncols) { + if (col >= p.ncols_pad) { return; } - const uint a_idx = row * p.ncols; - const uint d_idx = row * p.ncols; + const uint row_offset = row * p.ncols; // initialize indices - if (col < p.ncols) { - data_d[col] = col; - } + dst_row[col] = col; barrier(); - for (uint k = 2; k <= p.ncols; k *= 2) { + for (uint k = 2; k <= p.ncols_pad; k *= 2) { for (uint j = k / 2; j > 0; j /= 2) { const uint ixj = col ^ j; if (ixj > col) { if ((col & k) == 0) { - if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]]) { - swap(d_idx + col, d_idx + ixj); + if (dst_row[col] >= p.ncols || + (dst_row[ixj] < p.ncols && (p.order == ASC ? + data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] : + data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]])) + ) { + swap(col, ixj); } } else { - if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]]) { - swap(d_idx + col, d_idx + ixj); + if (dst_row[ixj] >= p.ncols || + (dst_row[col] < p.ncols && (p.order == ASC ? + data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] : + data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]])) + ) { + swap(col, ixj); } } } barrier(); } } + + if (col < p.ncols) { + data_d[row_offset + col] = dst_row[col]; + } } """ @@ -2845,13 +2877,16 @@ async def main(): stream.clear() stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + tasks.append(string_to_spv("matmul_f32_f16", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) stream.clear() stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py index e5d5806c8..ea5146b16 100644 --- a/gguf-py/gguf/__init__.py +++ b/gguf-py/gguf/__init__.py @@ -2,5 +2,6 @@ from .constants import * from .lazy import * from .gguf_reader import * from .gguf_writer import * +from .quants import * from .tensor_mapping import * from .vocab import * diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 96574358d..d5e323a52 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -13,6 +13,7 @@ from string import ascii_letters, digits import numpy as np from .constants import ( + GGML_QUANT_SIZES, GGUF_DEFAULT_ALIGNMENT, GGUF_MAGIC, GGUF_VERSION, @@ -195,7 +196,7 @@ class GGUFWriter: return ((x + n - 1) // n) * n def add_tensor_info( - self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], + self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype, tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, ) -> None: if self.state is not WriterState.EMPTY: @@ -208,10 +209,6 @@ class GGUFWriter: encoded_name = name.encode("utf-8") self.ti_data += self._pack("Q", len(encoded_name)) self.ti_data += encoded_name - n_dims = len(tensor_shape) - self.ti_data += self._pack("I", n_dims) - for i in range(n_dims): - self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) if raw_dtype is None: if tensor_dtype == np.float16: dtype = GGMLQuantizationType.F16 @@ -231,6 +228,15 @@ class GGUFWriter: raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now") else: dtype = raw_dtype + if tensor_dtype == np.uint8: + block_size, type_size = GGML_QUANT_SIZES[raw_dtype] + if tensor_shape[-1] % type_size != 0: + raise ValueError(f"Quantized tensor row size ({tensor_shape[-1]}) is not a multiple of {dtype.name} type size ({type_size})") + tensor_shape = tuple(tensor_shape[:-1]) + (tensor_shape[-1] // type_size * block_size,) + n_dims = len(tensor_shape) + self.ti_data += self._pack("I", n_dims) + for i in range(n_dims): + self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) self.ti_data += self._pack("I", dtype) self.ti_data += self._pack("Q", self.offset_tensor) self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py index 650bea11c..1167335b8 100644 --- a/gguf-py/gguf/lazy.py +++ b/gguf-py/gguf/lazy.py @@ -6,6 +6,7 @@ from typing import Any, Callable from collections import deque import numpy as np +from numpy._typing import _Shape from numpy.typing import DTypeLike @@ -110,7 +111,7 @@ class LazyBase(ABC, metaclass=LazyMeta): return o @classmethod - def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike = False) -> Callable[[Any], Any]: + def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike | tuple[DTypeLike, Callable[[tuple[int, ...]], tuple[int, ...]]] = False) -> Callable[[Any], Any]: def wrapped_fn(*args, **kwargs): if kwargs is None: kwargs = {} @@ -130,9 +131,14 @@ class LazyBase(ABC, metaclass=LazyMeta): res = args[0] assert isinstance(res, cls) res = res._meta - # allow operations to override the dtype + # allow operations to override the dtype and shape if meta_noop is not True: - res = cls.meta_with_dtype(res, meta_noop) + if isinstance(meta_noop, tuple): + dtype, shape = meta_noop + assert callable(shape) + res = cls.meta_with_dtype_and_shape(dtype, shape(res.shape)) + else: + res = cls.meta_with_dtype_and_shape(meta_noop, res.shape) if isinstance(res, cls._tensor_type): def collect_replace(t: LazyBase): @@ -168,7 +174,12 @@ class LazyBase(ABC, metaclass=LazyMeta): while _t._data is None: lt = _t._lazy.popleft() if lt._data is not None: - raise ValueError(f"{lt} did not belong in the lazy queue") + # Lazy tensor did not belong in the lazy queue. + # Weirdly only happens with Bloom models... + # likely because tensors aren't unique in the queue. + # The final output is still the same as in eager mode, + # so it's safe to ignore this. + continue assert lt._func is not None lt._args = cls._recurse_apply(lt._args, already_eager_to_eager) lt._data = lt._func(lt._args) @@ -183,12 +194,12 @@ class LazyBase(ABC, metaclass=LazyMeta): @classmethod def eager_to_meta(cls, t: Any) -> Any: - return cls.meta_with_dtype(t, t.dtype) + return cls.meta_with_dtype_and_shape(t.dtype, t.shape) # must be overridden, meta tensor init is backend-specific @classmethod @abstractmethod - def meta_with_dtype(cls, m: Any, dtype: Any) -> Any: pass + def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass @classmethod def from_eager(cls, t: Any) -> Any: @@ -205,15 +216,15 @@ class LazyNumpyTensor(LazyBase): _tensor_type = np.ndarray @classmethod - def meta_with_dtype(cls, m: np.ndarray[Any, Any], dtype: DTypeLike) -> np.ndarray[Any, Any]: + def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: _Shape) -> np.ndarray[Any, Any]: # The initial idea was to use np.nan as the fill value, # but non-float types like np.int16 can't use that. # So zero it is. cheat = np.zeros(1, dtype) - return np.lib.stride_tricks.as_strided(cheat, m.shape, (0 for _ in m.shape)) + return np.lib.stride_tricks.as_strided(cheat, shape, (0 for _ in shape)) def astype(self, dtype, *args, **kwargs): - meta = type(self).meta_with_dtype(self._meta, dtype) + meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape) full_args = (self, dtype,) + args # very important to pass the shared _lazy deque, or else there's an infinite loop somewhere. return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs))) diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py new file mode 100644 index 000000000..e7fc0eae3 --- /dev/null +++ b/gguf-py/gguf/quants.py @@ -0,0 +1,109 @@ +from __future__ import annotations +from typing import Callable + +from numpy.typing import DTypeLike + +from .constants import GGML_QUANT_SIZES, GGMLQuantizationType +from .lazy import LazyNumpyTensor + +import numpy as np + + +# same as ggml_compute_fp32_to_bf16 in ggml-impl.h +def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray: + n = n.astype(np.float32, copy=False).view(np.int32) + # force nan to quiet + n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n) + # flush subnormals to zero + n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n) + # round to nearest even + n = (n + (0x7fff + ((n >> 16) & 1))) >> 16 + return n.astype(np.int16) + + +# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time +def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray: + rows = arr.reshape((-1, arr.shape[-1])) + osize = 1 + for dim in oshape: + osize *= dim + out = np.empty(shape=osize, dtype=otype) + # compute over groups of 16 rows (arbitrary, but seems good for performance) + n_groups = rows.shape[0] // 16 + np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out) + return out.reshape(oshape) + + +def __quantize_bf16_array(n: np.ndarray) -> np.ndarray: + return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.int16, oshape=n.shape) + + +__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.int16) + + +def quantize_bf16(n: np.ndarray): + if type(n) is LazyNumpyTensor: + return __quantize_bf16_lazy(n) + else: + return __quantize_bf16_array(n) + + +__q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0] + + +def can_quantize_to_q8_0(n: np.ndarray) -> bool: + return n.shape[-1] % __q8_block_size == 0 + + +# round away from zero +# ref: https://stackoverflow.com/a/59143326/22827863 +def np_roundf(n: np.ndarray) -> np.ndarray: + a = abs(n) + floored = np.floor(a) + b = floored + np.floor(2 * (a - floored)) + return np.sign(n) * b + + +def __quantize_q8_0_shape_change(s: tuple[int, ...]) -> tuple[int, ...]: + return (*s[:-1], s[-1] // __q8_block_size * __q8_type_size) + + +# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c +def __quantize_q8_0_rows(n: np.ndarray) -> np.ndarray: + shape = n.shape + assert shape[-1] % __q8_block_size == 0 + + n_blocks = n.size // __q8_block_size + + blocks = n.reshape((n_blocks, __q8_block_size)).astype(np.float32, copy=False) + + d = abs(blocks).max(axis=1, keepdims=True) / 127 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np_roundf(blocks * id) + + # (n_blocks, 2) + d = d.astype(np.float16).view(np.uint8) + # (n_blocks, block_size) + qs = qs.astype(np.int8).view(np.uint8) + + assert d.shape[1] + qs.shape[1] == __q8_type_size + + return np.concatenate([d, qs], axis=1).reshape(__quantize_q8_0_shape_change(shape)) + + +def __quantize_q8_0_array(n: np.ndarray) -> np.ndarray: + return __apply_over_grouped_rows(__quantize_q8_0_rows, arr=n, otype=np.uint8, oshape=__quantize_q8_0_shape_change(n.shape)) + + +__quantize_q8_0_lazy = LazyNumpyTensor._wrap_fn( + __quantize_q8_0_array, + meta_noop=(np.uint8, __quantize_q8_0_shape_change), +) + + +def quantize_q8_0(data: np.ndarray): + if type(data) is LazyNumpyTensor: + return __quantize_q8_0_lazy(data) + else: + return __quantize_q8_0_array(data) diff --git a/llama.cpp b/llama.cpp index e91ad7285..ca3e9fcc0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7,6 +7,10 @@ #include "ggml-alloc.h" #include "ggml-backend.h" +#ifdef GGML_USE_RPC +# include "ggml-rpc.h" +#endif + #ifdef GGML_USE_CUDA # include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) @@ -22,9 +26,6 @@ #ifdef GGML_USE_METAL # include "ggml-metal.h" #endif -#ifdef GGML_USE_MPI -# include "ggml-mpi.h" -#endif #ifndef QK_K # ifdef GGML_QKK_64 # define QK_K 64 @@ -1685,91 +1686,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer GGML_UNUSED(host_buffer); } -static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { - ggml_backend_buffer_type_t buft = nullptr; - -#ifdef GGML_USE_METAL - buft = ggml_backend_metal_buffer_type(); -#elif defined(GGML_USE_CUDA) - buft = ggml_backend_cuda_buffer_type(gpu); -#elif defined(GGML_USE_VULKAN) - buft = ggml_backend_vk_buffer_type(gpu); -#elif defined(GGML_USE_SYCL) - buft = ggml_backend_sycl_buffer_type(gpu); -#elif defined(GGML_USE_CLBLAST) - buft = ggml_backend_opencl_buffer_type(); -#elif defined(GGML_USE_KOMPUTE) - buft = ggml_backend_kompute_buffer_type(gpu); - if (buft == nullptr) { - LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu); - } -#endif - - if (buft == nullptr) { - buft = llama_default_buffer_type_cpu(true); - } - return buft; - - GGML_UNUSED(gpu); -} - -static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) { - ggml_backend_buffer_type_t buft = nullptr; - -#ifdef GGML_USE_CUDA - if (ggml_backend_cuda_get_device_count() > 1) { - buft = ggml_backend_cuda_split_buffer_type(tensor_split); - } -#endif - -#ifdef GGML_USE_SYCL - if (ggml_backend_sycl_get_device_count() > 1) { - buft = ggml_backend_sycl_split_buffer_type(tensor_split); - } -#endif - - if (buft == nullptr) { - buft = llama_default_buffer_type_offload(fallback_gpu); - } - return buft; - - GGML_UNUSED(tensor_split); -} - -static size_t llama_get_device_count() { -#if defined(GGML_USE_CUDA) - return ggml_backend_cuda_get_device_count(); -#elif defined(GGML_USE_SYCL) - return ggml_backend_sycl_get_device_count(); -#elif defined(GGML_USE_VULKAN) - return ggml_backend_vk_get_device_count(); -#else - return 1; -#endif -} - -static size_t llama_get_device_memory(int device) { -#if defined(GGML_USE_CUDA) - size_t total; - size_t free; - ggml_backend_cuda_get_device_memory(device, &free, &total); - return free; -#elif defined(GGML_USE_SYCL) - size_t total; - size_t free; - ggml_backend_sycl_get_device_memory(device, &free, &total); - return free; -#elif defined(GGML_USE_VULKAN) - size_t total; - size_t free; - ggml_backend_vk_get_device_memory(device, &free, &total); - return free; -#else - return 1; - GGML_UNUSED(device); -#endif -} - // // globals // @@ -1778,6 +1694,8 @@ struct llama_state { llama_state() { #ifdef GGML_USE_METAL ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data); +#elif defined(GGML_USE_CUDA) + ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data); #endif } @@ -2210,6 +2128,8 @@ struct llama_model { int main_gpu; int n_gpu_layers; + std::vector rpc_servers; + // gguf metadata std::unordered_map gguf_kv; @@ -2347,12 +2267,106 @@ struct llama_context { // control vectors struct llama_control_vector cvec; - -#ifdef GGML_USE_MPI - ggml_mpi_context * ctx_mpi = NULL; -#endif }; +static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) { + ggml_backend_buffer_type_t buft = nullptr; + +#ifdef GGML_USE_RPC + std::string endpoint = model.rpc_servers[gpu]; + buft = ggml_backend_rpc_buffer_type(endpoint.c_str()); +#elif defined(GGML_USE_METAL) + buft = ggml_backend_metal_buffer_type(); +#elif defined(GGML_USE_CUDA) + buft = ggml_backend_cuda_buffer_type(gpu); +#elif defined(GGML_USE_VULKAN) + buft = ggml_backend_vk_buffer_type(gpu); +#elif defined(GGML_USE_SYCL) + buft = ggml_backend_sycl_buffer_type(gpu); +#elif defined(GGML_USE_CLBLAST) + buft = ggml_backend_opencl_buffer_type(); +#elif defined(GGML_USE_KOMPUTE) + buft = ggml_backend_kompute_buffer_type(gpu); + if (buft == nullptr) { + LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu); + } +#endif + + if (buft == nullptr) { + buft = llama_default_buffer_type_cpu(true); + } + return buft; + GGML_UNUSED(model); + GGML_UNUSED(gpu); +} + +static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) { + ggml_backend_buffer_type_t buft = nullptr; + +#ifdef GGML_USE_CUDA + if (ggml_backend_cuda_get_device_count() > 1) { + buft = ggml_backend_cuda_split_buffer_type(tensor_split); + } +#endif + +#ifdef GGML_USE_SYCL + if (ggml_backend_sycl_get_device_count() > 1) { + buft = ggml_backend_sycl_split_buffer_type(tensor_split); + } +#endif + + if (buft == nullptr) { + buft = llama_default_buffer_type_offload(model, fallback_gpu); + } + return buft; + + GGML_UNUSED(tensor_split); +} + +static size_t llama_get_device_count(const llama_model & model) { +#if defined(GGML_USE_RPC) + return model.rpc_servers.size(); +#elif defined(GGML_USE_CUDA) + return ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_SYCL) + return ggml_backend_sycl_get_device_count(); +#elif defined(GGML_USE_VULKAN) + return ggml_backend_vk_get_device_count(); +#else + return 1; +#endif + GGML_UNUSED(model); +} + +static size_t llama_get_device_memory(const llama_model & model, int device) { +#if defined(GGML_USE_RPC) + size_t total; + size_t free; + std::string endpoint = model.rpc_servers[device]; + ggml_backend_rpc_get_device_memory(endpoint.c_str(), &free, &total); + return free; +#elif defined(GGML_USE_CUDA) + size_t total; + size_t free; + ggml_backend_cuda_get_device_memory(device, &free, &total); + return free; +#elif defined(GGML_USE_SYCL) + size_t total; + size_t free; + ggml_backend_sycl_get_device_memory(device, &free, &total); + return free; +#elif defined(GGML_USE_VULKAN) + size_t total; + size_t free; + ggml_backend_vk_get_device_memory(device, &free, &total); + return free; +#else + return 1; +#endif + GGML_UNUSED(model); + GGML_UNUSED(device); +} + // // kv cache helpers // @@ -2805,6 +2819,11 @@ static void llama_kv_cache_defrag(struct llama_kv_cache & cache) { cache.do_defrag = true; } +static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) { + // the FA kernels require padding to avoid extra runtime boundary checks + return cparams.flash_attn ? 256u : 32u; +} + // // model loading and saving // @@ -4424,7 +4443,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "gpt-2" || tokenizer_pre == "jina-es" || - tokenizer_pre == "jina-de") { + tokenizer_pre == "jina-de" || + tokenizer_pre == "jina-v2-es" || + tokenizer_pre == "jina-v2-de") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "refact") { @@ -4435,6 +4456,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "qwen2") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2; + } else if ( + tokenizer_pre == "stablelm2") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2; } else if ( tokenizer_pre == "olmo") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO; @@ -4784,13 +4808,13 @@ static bool llm_load_tensors( if (split_mode == LLAMA_SPLIT_MODE_LAYER) { // calculate the split points - int device_count = llama_get_device_count(); + int device_count = llama_get_device_count(model); bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); std::vector splits(device_count); if (all_zero) { // default split, by free memory for (int i = 0; i < device_count; ++i) { - splits[i] = llama_get_device_memory(i); + splits[i] = llama_get_device_memory(model, i); } } else { std::copy(tensor_split, tensor_split + device_count, splits.begin()); @@ -4810,35 +4834,35 @@ static bool llm_load_tensors( int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); for (int64_t i = i_gpu_start; i < n_layer; ++i) { int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); - model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu); + model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu); } // assign the output layer if (n_gpu_layers > n_layer) { int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin(); - model.buft_output = llama_default_buffer_type_offload(layer_gpu); + model.buft_output = llama_default_buffer_type_offload(model, layer_gpu); } else { model.buft_output = llama_default_buffer_type_cpu(true); } } else { ggml_backend_buffer_type_t split_buft; if (split_mode == LLAMA_SPLIT_MODE_ROW) { - split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); + split_buft = llama_default_buffer_type_split(model, main_gpu, tensor_split); } else { // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported - split_buft = llama_default_buffer_type_offload(main_gpu); + split_buft = llama_default_buffer_type_offload(model, main_gpu); } // assign the repeating layers for (int64_t i = i_gpu_start; i < n_layer; ++i) { model.buft_layer[i] = { split_buft, - llama_default_buffer_type_offload(main_gpu) + llama_default_buffer_type_offload(model, main_gpu) }; } // assign the output layer if (n_gpu_layers > n_layer) { model.buft_output = { split_buft, - llama_default_buffer_type_offload(main_gpu) + llama_default_buffer_type_offload(model, main_gpu) }; } else { model.buft_output = llama_default_buffer_type_cpu(true); @@ -5162,7 +5186,14 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + if (!model.output) { + // needs to be on GPU + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + } for (int i = 0; i < n_layer; ++i) { @@ -6298,10 +6329,7 @@ static struct ggml_tensor * llm_build_inp_embd( inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); inpL = lctx.inp_embd; ggml_set_input(lctx.inp_embd); } @@ -6596,6 +6624,7 @@ static struct ggml_tensor * llm_build_kqv( const int64_t n_embd_head_k = hparams.n_embd_head_k; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3); cb(q, "q", il); @@ -6618,8 +6647,8 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * v = ggml_view_3d(ctx, kv.v_l[il], n_embd_head_v, n_kv, n_head_kv, - ggml_row_size(kv.v_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv.v_l[il]->type, n_embd_head_k), + ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa), + ggml_row_size(kv.v_l[il]->type, n_embd_head_v), 0); cb(v, "v", il); @@ -6629,7 +6658,7 @@ static struct ggml_tensor * llm_build_kqv( ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); } - cur = ggml_reshape_2d(ctx, cur, n_embd_head_k*n_head, n_tokens); + cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens); } else { struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); @@ -6674,7 +6703,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); cb(kqv_merged, "kqv_merged", il); - cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens); + cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens); cb(cur, "kqv_merged_cont", il); } @@ -11312,11 +11341,6 @@ static void llama_graph_compute( llama_context & lctx, ggml_cgraph * gf, int n_threads) { -#ifdef GGML_USE_MPI - const int64_t n_layer = lctx.model.hparams.n_layer; - ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); -#endif - #ifdef GGML_USE_METAL if (ggml_backend_is_metal(lctx.backend_metal)) { ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); @@ -11331,10 +11355,6 @@ static void llama_graph_compute( ggml_backend_sched_graph_compute_async(lctx.sched, gf); // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); - -#ifdef GGML_USE_MPI - ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); -#endif } // decode a batch of tokens by evaluating the transformer @@ -11372,12 +11392,6 @@ static int llama_decode_internal( } lctx.n_queued_tokens += n_tokens_all; -#ifdef GGML_USE_MPI - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads); -#endif - auto & kv_self = lctx.kv_self; const int64_t n_embd = hparams.n_embd; @@ -11508,7 +11522,8 @@ static int llama_decode_internal( // a heuristic, to avoid attending the full cache if it is not yet utilized // after enough generations, the benefit from this heuristic disappears // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(kv_self.size, std::max(256u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 256))); + const uint32_t pad = llama_kv_cache_get_padding(cparams); + kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad))); //kv_self.n = llama_kv_cache_cell_max(kv_self); } } @@ -12326,6 +12341,7 @@ struct llm_tokenizer_bpe { "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", }); break; + case LLAMA_VOCAB_PRE_TYPE_STABLELM2: case LLAMA_VOCAB_PRE_TYPE_QWEN2: word_collection = unicode_regex_split(text, { // original regex from tokenizer.json @@ -12548,16 +12564,16 @@ struct llm_tokenizer_wpm { // to lowercase, pad chinese characters, pad punctuation std::string new_str = ""; for (uint32_t code : cpts_nfd) { - int type = unicode_cpt_type(code); - if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) { + const codepoint_flags flags = unicode_cpt_flags(code); + if (flags.is_accent_mark || flags.is_control) { continue; } code = unicode_tolower(code); - if (type == CODEPOINT_TYPE_SEPARATOR) { + if (flags.is_separator || flags.is_whitespace) { //####FIXME: is_separator ? code = ' '; } std::string s = unicode_cpt_to_utf8(code); - if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) { + if (flags.is_punctuation || is_ascii_punct(code) || is_chinese_char(code)) { new_str += " "; new_str += s; new_str += " "; @@ -12791,6 +12807,13 @@ static std::vector llama_tokenize_internal(const llama_vocab & } } + if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) { + LLAMA_LOG_WARN( + "%s: Added a BOS token to the prompt as specified by the model but the prompt " + "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. " + "Are you sure this is what you want?\n", __FUNCTION__); + } + if (add_special && vocab.special_add_eos == 1) { GGML_ASSERT(vocab.special_eos_id != -1); output.push_back(vocab.special_eos_id); @@ -12817,6 +12840,13 @@ static std::vector llama_tokenize_internal(const llama_vocab & } } + if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) { + LLAMA_LOG_WARN( + "%s: Added a BOS token to the prompt as specified by the model but the prompt " + "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. " + "Are you sure this is what you want?\n", __FUNCTION__); + } + if (add_special && vocab.special_add_eos == 1) { GGML_ASSERT(vocab.special_add_eos != -1); output.push_back(vocab.special_eos_id); @@ -13174,6 +13204,58 @@ static std::vector llama_grammar_reject_candidates( return rejects; } +static bool llama_grammar_detect_left_recursion( + const std::vector> & rules, + size_t rule_index, + std::vector * rules_visited, + std::vector * rules_in_progress, + std::vector * rules_may_be_empty) { + if ((*rules_in_progress)[rule_index]) { + return true; + } + + (*rules_in_progress)[rule_index] = true; + + const std::vector & rule = rules[rule_index]; + + // First check if the rule might produce the empty string. This could be done combined with the second + // step but it's more readable as two steps. + bool at_rule_start = true; + for (size_t i = 0; i < rule.size(); i++) { + if (llama_grammar_is_end_of_sequence(&rule[i])) { + if (at_rule_start) { + (*rules_may_be_empty)[rule_index] = true; + break; + } + at_rule_start = true; + } else { + at_rule_start = false; + } + } + + // Second, recurse into leftmost nonterminals (or next-leftmost as long as the previous nonterminal may + // be empty) + bool recurse_into_nonterminal = true; + for (size_t i = 0; i < rule.size(); i++) { + if (rule[i].type == LLAMA_GRETYPE_RULE_REF && recurse_into_nonterminal) { + if (llama_grammar_detect_left_recursion(rules, (size_t)rule[i].value, rules_visited, rules_in_progress, rules_may_be_empty)) { + return true; + } + if (!((*rules_may_be_empty)[(size_t)rule[i].value])) { + recurse_into_nonterminal = false; + } + } else if (llama_grammar_is_end_of_sequence(&rule[i])) { + recurse_into_nonterminal = true; + } else { + recurse_into_nonterminal = false; + } + } + + (*rules_in_progress)[rule_index] = false; + (*rules_visited)[rule_index] = true; + return false; +} + // // grammar - external // @@ -13193,6 +13275,19 @@ struct llama_grammar * llama_grammar_init( vec_rules[i].push_back({LLAMA_GRETYPE_END, 0}); } + // Check for left recursion + std::vector rules_visited(n_rules); + std::vector rules_in_progress(n_rules); + std::vector rules_may_be_empty(n_rules); + for (size_t i = 0; i < n_rules; i++) { + if (rules_visited[i]) { + continue; + } + if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) { + throw std::runtime_error(format("unsupported grammar, left recursion detected for nonterminal at index %zu", i)); + } + } + // loop over alternates of start rule to build initial stacks std::vector> stacks; pos = vec_rules[start_rule_index].data(); @@ -13215,6 +13310,9 @@ struct llama_grammar * llama_grammar_init( } } while (true); + // Important: vec_rules has to be moved here, not copied, because stacks contains + // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar + // then the pointers would be invalidated when the local vec_rules goes out of scope. return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} }; } @@ -13809,9 +13907,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ // Sample the next word X using top-k sampling llama_sample_top_k(nullptr, candidates, int(k), 1); - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; llama_token X = llama_sample_token(ctx, candidates); t_start_sample_us = ggml_time_us(); @@ -13825,9 +13921,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ // Update mu using the learning rate and error *mu = *mu - eta * e; - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; return X; } @@ -15314,6 +15408,7 @@ struct llama_model_params llama_model_default_params() { /*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, + /*.rpc_servers =*/ nullptr, /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.kv_overrides =*/ nullptr, @@ -15384,7 +15479,9 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { } size_t llama_max_devices(void) { -#if defined(GGML_USE_METAL) +#if defined(GGML_USE_RPC) + return GGML_RPC_MAX_SERVERS; +#elif defined(GGML_USE_METAL) return 1; #elif defined(GGML_USE_CUDA) return GGML_CUDA_MAX_DEVICES; @@ -15407,7 +15504,7 @@ bool llama_supports_mlock(void) { bool llama_supports_gpu_offload(void) { #if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. return true; #else @@ -15424,10 +15521,6 @@ void llama_backend_init(void) { struct ggml_context * ctx = ggml_init(params); ggml_free(ctx); } - -#ifdef GGML_USE_MPI - ggml_mpi_backend_init(); -#endif } void llama_numa_init(enum ggml_numa_strategy numa) { @@ -15437,9 +15530,6 @@ void llama_numa_init(enum ggml_numa_strategy numa) { } void llama_backend_free(void) { -#ifdef GGML_USE_MPI - ggml_mpi_backend_free(); -#endif ggml_quantize_free(); } @@ -15470,7 +15560,17 @@ struct llama_model * llama_load_model_from_file( return true; }; } - + if (params.rpc_servers != nullptr) { + // split the servers set them into model->rpc_servers + std::string servers(params.rpc_servers); + size_t pos = 0; + while ((pos = servers.find(",")) != std::string::npos) { + std::string server = servers.substr(0, pos); + model->rpc_servers.push_back(server); + servers.erase(0, pos + 1); + } + model->rpc_servers.push_back(servers); + } int status = llama_model_load(path_model, *model, params); GGML_ASSERT(status <= 0); if (status < 0) { @@ -15509,6 +15609,11 @@ struct llama_context * llama_new_context_with_model( return nullptr; } + if (params.flash_attn && model->arch == LLM_ARCH_GROK) { + LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__); + params.flash_attn = false; + } + llama_context * ctx = new llama_context(*model); const auto & hparams = model->hparams; @@ -15532,7 +15637,7 @@ struct llama_context * llama_new_context_with_model( cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale; // this is necessary due to kv_self.n being padded later during inference - cparams.n_ctx = GGML_PAD(cparams.n_ctx, 256); + cparams.n_ctx = GGML_PAD(cparams.n_ctx, llama_kv_cache_get_padding(cparams)); // with causal attention, the batch size is limited by the context size cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; @@ -15577,11 +15682,6 @@ struct llama_context * llama_new_context_with_model( } } - if (cparams.flash_attn && model->arch == LLM_ARCH_GROK) { - LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__); - cparams.flash_attn = false; - } - if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } @@ -15617,7 +15717,17 @@ struct llama_context * llama_new_context_with_model( if (!hparams.vocab_only) { // initialize backends -#ifdef GGML_USE_METAL +#if defined(GGML_USE_RPC) + for (auto & server : model->rpc_servers) { + ggml_backend_t backend = ggml_backend_rpc_init(server.c_str()); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to connect RPC backend to %s\n", __func__, server.c_str()); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } +#elif defined(GGML_USE_METAL) if (model->n_gpu_layers > 0) { ctx->backend_metal = ggml_backend_metal_init(); if (ctx->backend_metal == nullptr) { @@ -15773,7 +15883,11 @@ struct llama_context * llama_new_context_with_model( ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false)); // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary - bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER; + bool pipeline_parallel = + llama_get_device_count(*model) > 1 && + model->n_gpu_layers > (int)model->hparams.n_layer && + model->split_mode == LLAMA_SPLIT_MODE_LAYER && + params.offload_kqv; #ifndef GGML_USE_CUDA // pipeline parallelism requires support for async compute and events // currently this is only implemented in the CUDA backend @@ -15816,20 +15930,6 @@ struct llama_context * llama_new_context_with_model( } } -#ifdef GGML_USE_MPI - ctx->ctx_mpi = ggml_mpi_init(); - - if (ggml_mpi_rank(ctx->ctx_mpi) > 0) { - // Enter a blocking eval loop with dummy input, letting rank=0 drive the process - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //const std::vector tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx)); - //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {}; - llama_backend_free(); - exit(1); - } -#endif - return ctx; } @@ -16893,13 +16993,13 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam } else { if (cell_range_begin != kv_self.size) { - cell_ranges.push_back({ cell_range_begin, i }); + cell_ranges.emplace_back(cell_range_begin, i); cell_range_begin = kv_self.size; } } } if (cell_range_begin != kv_self.size) { - cell_ranges.push_back({ cell_range_begin, kv_self.size }); + cell_ranges.emplace_back(cell_range_begin, kv_self.size); } // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count @@ -17974,6 +18074,7 @@ const char * llama_print_system_info(void) { s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | "; s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | "; + s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | "; @@ -18034,6 +18135,8 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) { g_state.log_callback_user_data = user_data; #ifdef GGML_USE_METAL ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); +#elif defined(GGML_USE_CUDA) + ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); #endif } diff --git a/llama.h b/llama.h index 0b2e708d0..b7bf2afcb 100644 --- a/llama.h +++ b/llama.h @@ -81,9 +81,10 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, LLAMA_VOCAB_PRE_TYPE_REFACT = 8, LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, - LLAMA_VOCAB_PRE_TYPE_QWEN2 = 10, - LLAMA_VOCAB_PRE_TYPE_OLMO = 11, - LLAMA_VOCAB_PRE_TYPE_DBRX = 12, + LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10, + LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11, + LLAMA_VOCAB_PRE_TYPE_OLMO = 12, + LLAMA_VOCAB_PRE_TYPE_DBRX = 13, }; // note: these values should be synchronized with ggml_rope @@ -242,6 +243,9 @@ extern "C" { // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() const float * tensor_split; + // comma separated list of RPC servers to use for offloading + const char * rpc_servers; + // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. // If the provided progress_callback returns true, model loading continues. // If it returns false, model loading is immediately aborted. diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in index f842c7137..92e39708b 100644 --- a/scripts/LlamaConfig.cmake.in +++ b/scripts/LlamaConfig.cmake.in @@ -5,7 +5,6 @@ set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) set(LLAMA_BLAS @LLAMA_BLAS@) set(LLAMA_CUDA @LLAMA_CUDA@) set(LLAMA_METAL @LLAMA_METAL@) -set(LLAMA_MPI @LLAMA_MPI@) set(LLAMA_CLBLAST @LLAMA_CLBLAST@) set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@) set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@) @@ -37,10 +36,6 @@ if (LLAMA_METAL) find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) endif() -if (LLAMA_MPI) - find_package(MPI REQUIRED) -endif() - if (LLAMA_CLBLAST) find_package(CLBlast REQUIRED) endif() diff --git a/scripts/debug-test.sh b/scripts/debug-test.sh index 231a23d69..7b2b601a9 100755 --- a/scripts/debug-test.sh +++ b/scripts/debug-test.sh @@ -1,117 +1,203 @@ #!/bin/bash -test_suite=${1:-} -test_number=${2:-} PROG=${0##*/} build_dir="build-ci-debug" -if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then - echo "Usage: $PROG [OPTION]... (test_number)" - echo "Debug specific ctest program." - echo - echo "Options:" - echo " -h, --help Display this help and exit" - echo - echo "Arguments:" - echo " (Mandatory) Supply one regex to the script to filter tests" - echo " (test_number) (Optional) Test number to run a specific test" - echo - echo "Example:" - echo " $PROG test-tokenizer" - echo " $PROG test-tokenizer 3" - echo - exit 0 -fi +# Print Color Commands +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +blue=$(tput setaf 4) +magenta=$(tput setaf 5) +cyan=$(tput setaf 6) +normal=$(tput sgr0) -# Function to select and debug a test -function select_test() { - test_suite=${1:-test} - test_number=${2:-} - # Sanity Check If Tests Is Detected - printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n" - tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1')) - if [ ${#tests[@]} -eq 0 ] - then - echo "No tests avaliable... check your compliation process..." - echo "Exiting." - exit 1 - fi +# Print Help Message +#################### - if [ -z $test_number ] - then - # List out avaliable tests - printf "Which test would you like to debug?\n" - id=0 - for s in "${tests[@]}" - do - echo "Test# ${id}" - echo " $s" - ((id++)) - done +print_full_help() { + cat << EOF +Usage: $PROG [OPTION]... (test_number) +Debug specific ctest program. - # Prompt user which test they wanted to run - printf "\nRun test#? " - read test_number - else - printf "\nUser Already Requested #${test_number}" - fi +Options: + -h, --help display this help and exit + -g run in gdb mode - # Start GDB with the requested test binary and arguments - printf "Debugging(GDB) test: ${tests[test_number]}\n" - # Change IFS (Internal Field Separator) - sIFS=$IFS - IFS=$'\n' +Arguments: + (Mandatory) Supply one regex to the script to filter tests + (test_number) (Optional) Test number to run a specific test - # Get test args - gdb_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' )) - IFS=$sIFS - printf "Debug arguments: ${gdb_args[test_number]}\n\n" - - # Expand paths if needed - args=() - for x in $(echo ${gdb_args[test_number]} | sed -e 's/"\/\"//') - do - args+=($(echo $x | sed -e 's/.*\/..\//..\//')) - done - - # Execute debugger - echo "gdb args: ${args[@]}" - gdb --args ${args[@]} +Example: + $PROG test-tokenizer + $PROG test-tokenizer 3 +EOF } +abort() { + echo "Error: $1" >&2 + cat << EOF >&2 +Usage: $PROG [OPTION]... (test_number) +Debug specific ctest program. +Refer to --help for full instructions. +EOF + exit 1 +} + + +# Dependency Sanity Check +######################### + +check_dependency() { + command -v "$1" >/dev/null 2>&1 || { + abort "$1 is required but not found. Please install it and try again." + } +} + +check_dependency ctest +check_dependency cmake + + # Step 0: Check the args -if [ -z "$test_suite" ] -then - echo "Usage: $PROG [OPTION]... (test_number)" - echo "Supply one regex to the script to filter tests," - echo "and optionally a test number to run a specific test." - echo "Use --help flag for full instructions" - exit 1 +######################## + +if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then + print_full_help >&2 + exit 0 fi +# Parse command-line options +gdb_mode=false +while getopts "g" opt; do + case $opt in + g) + gdb_mode=true + echo "gdb_mode Mode Enabled" + ;; + esac +done + +# Shift the option parameters +shift $((OPTIND - 1)) + +# Positionial Argument Processing : +if [ -z "${1}" ]; then + abort "Test regex is required" +else + test_suite=${1:-} +fi + +# Positionial Argument Processing : (test_number) +test_number=${2:-} + + # Step 1: Reset and Setup folder context +######################################## + ## Sanity check that we are actually in a git repo repo_root=$(git rev-parse --show-toplevel) if [ ! -d "$repo_root" ]; then - echo "Error: Not in a Git repository." - exit 1 + abort "Not in a Git repository." fi -## Reset folder to root context of git repo -pushd "$repo_root" || exit 1 +## Reset folder to root context of git repo and Create and enter build directory +pushd "$repo_root" +rm -rf "$build_dir" && mkdir "$build_dir" || abort "Failed to make $build_dir" -## Create and enter build directory -rm -rf "$build_dir" && mkdir "$build_dir" || exit 1 # Step 2: Setup Build Environment and Compile Test Binaries -cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON || exit 1 -pushd "$build_dir" && make -j || exit 1 +########################################################### -# Step 3: Debug the Test -select_test "$test_suite" "$test_number" +# Note: test-eval-callback requires -DLLAMA_CURL +cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_CURL=1 || abort "Failed to build enviroment" +pushd "$build_dir" +make -j || abort "Failed to compile" +popd > /dev/null || exit 1 -# Step 4: Return to the directory from which the user ran the command. -popd || exit 1 -popd || exit 1 -popd || exit 1 + +# Step 3: Find all tests available that matches REGEX +#################################################### + +# Ctest Gather Tests +# `-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 +printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n" +pushd "$build_dir" +tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1')) +if [ ${#tests[@]} -eq 0 ]; then + abort "No tests avaliable... check your compliation process..." +fi +popd > /dev/null || exit 1 + + +# Step 4: Identify Test Command for Debugging +############################################# + +# Select test number +if [ -z $test_number ]; then + # List out avaliable tests + printf "Which test would you like to debug?\n" + id=0 + for s in "${tests[@]}" + do + echo "Test# ${id}" + echo " $s" + ((id++)) + done + + # Prompt user which test they wanted to run + printf "\nRun test#? " + read test_number + +else + printf "\nUser Already Requested #${test_number}\n" + +fi + +# Grab all tests commands +pushd "$build_dir" +sIFS=$IFS # Save Initial IFS (Internal Field Separator) +IFS=$'\n' # Change IFS (Internal Field Separator) (So we split ctest output by newline rather than by spaces) +test_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' )) # Get test args +IFS=$sIFS # Reset IFS (Internal Field Separator) +popd > /dev/null || exit 1 + +# Grab specific test command +single_test_name="${tests[test_number]}" +single_test_command="${test_args[test_number]}" + + +# Step 5: Execute or GDB Debug +############################## + +printf "${magenta}Running Test #${test_number}: ${single_test_name}${normal}\n" +printf "${cyan}single_test_command: ${single_test_command}${normal}\n" + +if [ "$gdb_mode" = "true" ]; then + # Execute debugger + pushd "$repo_root" || exit 1 + eval "gdb --args ${single_test_command}" + popd > /dev/null || exit 1 + +else + # Execute Test + pushd "$repo_root" || exit 1 + eval "${single_test_command}" + exit_code=$? + popd > /dev/null || exit 1 + + # Print Result + printf "${blue}Ran Test #${test_number}: ${single_test_name}${normal}\n" + printf "${yellow}Command: ${single_test_command}${normal}\n" + if [ $exit_code -eq 0 ]; then + printf "${green}TEST PASS${normal}\n" + else + printf "${red}TEST FAIL${normal}\n" + fi + +fi + +# Return to the directory from which the user ran the command. +popd > /dev/null || exit 1 diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py index 37d1e396c..744873c2a 100644 --- a/scripts/gen-unicode-data.py +++ b/scripts/gen-unicode-data.py @@ -1,64 +1,134 @@ import regex +import ctypes +import unicodedata -def get_matches(regex_expr): - regex_expr_compiled = regex.compile(regex_expr) - unicode_ranges = [] - current_range = None - - for codepoint in range(0x110000): - char = chr(codepoint) - if regex_expr_compiled.match(char): - if current_range is None: - current_range = [codepoint, codepoint] - else: - current_range[1] = codepoint - elif current_range is not None: - unicode_ranges.append(tuple(current_range)) - current_range = None - - if current_range is not None: - unicode_ranges.append(tuple(current_range)) - - return unicode_ranges +class CoodepointFlags (ctypes.Structure): + _fields_ = [ # see definition in unicode.h + ("is_undefined", ctypes.c_uint16, 1), + ("is_number", ctypes.c_uint16, 1), # regex: \p{N} + ("is_letter", ctypes.c_uint16, 1), # regex: \p{L} + ("is_separator", ctypes.c_uint16, 1), # regex: \p{Z} + ("is_accent_mark", ctypes.c_uint16, 1), # regex: \p{M} + ("is_punctuation", ctypes.c_uint16, 1), # regex: \p{P} + ("is_symbol", ctypes.c_uint16, 1), # regex: \p{S} + ("is_control", ctypes.c_uint16, 1), # regex: \p{C} + ] -def print_cat(mode, cat, ranges): - if mode == "range": - print("const std::vector> unicode_ranges_{} = {{".format(cat)) # noqa: NP100 - if mode == "map": - print("const std::map unicode_map_{} = {{".format(cat)) # noqa: NP100 - for i, values in enumerate(ranges): - end = ",\n" if (i % 4 == 3 or i + 1 == len(ranges)) else ", " - values = ["0x%08X" % value for value in values] - print("{" + ", ".join(values) + "}", end=end) # noqa: NP100 - print("};") # noqa: NP100 - print("") # noqa: NP100 +assert (ctypes.sizeof(CoodepointFlags) == 2) -print_cat("range", "number", get_matches(r'\p{N}')) -print_cat("range", "letter", get_matches(r'\p{L}')) -print_cat("range", "separator", get_matches(r'\p{Z}')) -print_cat("range", "accent_mark", get_matches(r'\p{M}')) -print_cat("range", "punctuation", get_matches(r'\p{P}')) -print_cat("range", "symbol", get_matches(r'\p{S}')) -print_cat("range", "control", get_matches(r'\p{C}')) +MAX_CODEPOINTS = 0x110000 -print_cat("range", "whitespace", get_matches(r'\s')) +regex_number = regex.compile(r'\p{N}') +regex_letter = regex.compile(r'\p{L}') +regex_separator = regex.compile(r'\p{Z}') +regex_accent_mark = regex.compile(r'\p{M}') +regex_punctuation = regex.compile(r'\p{P}') +regex_symbol = regex.compile(r'\p{S}') +regex_control = regex.compile(r'\p{C}') +regex_whitespace = regex.compile(r'\s') +codepoint_flags = (CoodepointFlags * MAX_CODEPOINTS)() +table_whitespace = [] +table_lowercase = [] +table_uppercase = [] +table_nfd = [] -map_lowercase = [] -map_uppercase = [] -for codepoint in range(0x110000): +for codepoint in range(MAX_CODEPOINTS): + # convert codepoint to unicode character char = chr(codepoint) + + # regex categories + flags = codepoint_flags[codepoint] + flags.is_number = bool(regex_number.match(char)) + flags.is_letter = bool(regex_letter.match(char)) + flags.is_separator = bool(regex_separator.match(char)) + flags.is_accent_mark = bool(regex_accent_mark.match(char)) + flags.is_punctuation = bool(regex_punctuation.match(char)) + flags.is_symbol = bool(regex_symbol.match(char)) + flags.is_control = bool(regex_control.match(char)) + flags.is_undefined = bytes(flags)[0] == 0 + assert (not flags.is_undefined) + + # whitespaces + if bool(regex_whitespace.match(char)): + table_whitespace.append(codepoint) + + # lowercase conversion lower = ord(char.lower()[0]) - upper = ord(char.upper()[0]) if codepoint != lower: - map_lowercase.append((codepoint, lower)) + table_lowercase.append((codepoint, lower)) + + # uppercase conversion + upper = ord(char.upper()[0]) if codepoint != upper: - map_uppercase.append((codepoint, upper)) -print_cat("map", "lowercase", map_lowercase) -print_cat("map", "uppercase", map_uppercase) + table_uppercase.append((codepoint, upper)) + + # NFD normalization + norm = ord(unicodedata.normalize('NFD', char)[0]) + if codepoint != norm: + table_nfd.append((codepoint, norm)) -# TODO: generate unicode_map_nfd +# group ranges with same flags +ranges_flags = [(0, codepoint_flags[0])] # start, flags +for codepoint, flags in enumerate(codepoint_flags): + if bytes(flags) != bytes(ranges_flags[-1][1]): + ranges_flags.append((codepoint, flags)) +ranges_flags.append((MAX_CODEPOINTS, CoodepointFlags())) + + +# group ranges with same nfd +ranges_nfd = [(0, 0, 0)] # start, last, nfd +for codepoint, norm in table_nfd: + start = ranges_nfd[-1][0] + if ranges_nfd[-1] != (start, codepoint - 1, norm): + ranges_nfd.append(None) + start = codepoint + ranges_nfd[-1] = (start, codepoint, norm) + + +# Generate 'unicode-data.cpp' + + +def out(line=""): + print(line, end='\n') # noqa + + +out("""\ +// generated with scripts/gen-unicode-data.py + +#include "unicode-data.h" + +#include +#include +#include +#include +""") + +out("const std::vector> unicode_ranges_flags = { // start, flags // last=next_start-1") +for codepoint, flags in ranges_flags: + flags = int.from_bytes(bytes(flags), "little") + out("{0x%06X, 0x%04X}," % (codepoint, flags)) +out("};\n") + +out("const std::unordered_set unicode_set_whitespace = {") +out(", ".join("0x%06X" % cpt for cpt in table_whitespace)) +out("};\n") + +out("const std::unordered_map unicode_map_lowercase = {") +for tuple in table_lowercase: + out("{0x%06X, 0x%06X}," % tuple) +out("};\n") + +out("const std::unordered_map unicode_map_uppercase = {") +for tuple in table_uppercase: + out("{0x%06X, 0x%06X}," % tuple) +out("};\n") + +out("const std::vector unicode_ranges_nfd = { // start, last, nfd") +for triple in ranges_nfd: + out("{0x%06X, 0x%06X, 0x%06X}," % triple) +out("};\n") diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index 7c157eb4c..cf22afc41 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -112,6 +112,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-opencl.h -> ggml-opencl.h # src/ggml-quants.c -> ggml-quants.c # src/ggml-quants.h -> ggml-quants.h + # src/ggml-rpc.cpp -> ggml-rpc.cpp + # src/ggml-rpc.h -> ggml-rpc.h # src/ggml-sycl.cpp -> ggml-sycl.cpp # src/ggml-sycl.h -> ggml-sycl.h # src/ggml-vulkan.cpp -> ggml-vulkan.cpp @@ -149,6 +151,8 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \ -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \ -e 's/src\/ggml-quants\.h/ggml-quants.h/g' \ + -e 's/src\/ggml-rpc\.cpp/ggml-rpc.cpp/g' \ + -e 's/src\/ggml-rpc\.h/ggml-rpc.h/g' \ -e 's/src\/ggml-sycl\.cpp/ggml-sycl.cpp/g' \ -e 's/src\/ggml-sycl\.h/ggml-sycl.h/g' \ -e 's/src\/ggml-vulkan\.cpp/ggml-vulkan.cpp/g' \ diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 1ea320429..57bede67b 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -30f54cbb3ada3e4c5bc6924de3e5918e5be4ff11 +126d34985705a5a2222723c145cb4e125ac689f3 diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index 80c43976f..ec47fb27c 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -20,6 +20,8 @@ cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h cp -rpv ../ggml/src/ggml-quants.c ./ggml-quants.c cp -rpv ../ggml/src/ggml-quants.h ./ggml-quants.h +cp -rpv ../ggml/src/ggml-rpc.cpp ./ggml-rpc.cpp +cp -rpv ../ggml/src/ggml-rpc.h ./ggml-rpc.h cp -rpv ../ggml/src/ggml-sycl.cpp ./ggml-sycl.cpp cp -rpv ../ggml/src/ggml-sycl.h ./ggml-sycl.h cp -rpv ../ggml/src/ggml-vulkan.cpp ./ggml-vulkan.cpp diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 731788b95..c74e253db 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2,6 +2,7 @@ #include #include #include + #include #include #include @@ -15,6 +16,7 @@ #include #include + static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) { // static RNG initialization (revisit if n_threads stops being constant) static const size_t n_threads = std::thread::hardware_concurrency(); @@ -48,6 +50,22 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m t.join(); } +#if 0 + const char * val_str = getenv("GGML_TEST_EPS"); + float val = 1e-9f; + if (val_str != nullptr) { + val = std::stof(val_str); + printf("GGML_TEST_EPS=%e\n", val); + } + + // test quantization with very small values that may result in nan scales due to division by zero + if (ggml_is_quantized(tensor->type)) { + for (int i = 0; i < 256; i++) { + data[i] = val; + } + } +#endif + if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) { ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float)); } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) { @@ -63,6 +81,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m } } ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im); + GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size())); ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { // This is going to create some weird integers though. @@ -1328,23 +1347,47 @@ struct test_upscale : public test_case { const ggml_type type; const std::array ne; const int32_t scale_factor; + const bool transpose; std::string vars() override { - return VARS_TO_STR3(type, ne, scale_factor); + return VARS_TO_STR4(type, ne, scale_factor, transpose); } test_upscale(ggml_type type = GGML_TYPE_F32, std::array ne = {512, 512, 3, 1}, - int32_t scale_factor = 2) - : type(type), ne(ne), scale_factor(scale_factor) {} + int32_t scale_factor = 2, bool transpose = false) + : type(type), ne(ne), scale_factor(scale_factor), transpose(transpose) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + if (transpose) a = ggml_transpose(ctx, a); ggml_tensor * out = ggml_upscale(ctx, a, scale_factor); return out; } }; +// GGML_OP_UPSCALE (ext) +struct test_upscale_ext : public test_case { + const ggml_type type; + const std::array ne; + const std::array ne_tgt; + + std::string vars() override { + return VARS_TO_STR3(type, ne, ne_tgt); + } + + test_upscale_ext(ggml_type type = GGML_TYPE_F32, + std::array ne = {2, 5, 7, 11}, + std::array ne_tgt = {5, 7, 11, 13}) + : type(type), ne(ne), ne_tgt(ne_tgt) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_tensor * out = ggml_upscale_ext(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3]); + return out; + } +}; + // GGML_OP_GROUP_NORM struct test_group_norm : public test_case { const ggml_type type; @@ -1486,25 +1529,27 @@ struct test_flash_attn_ext : public test_case { const int64_t kv; // kv size const int64_t nb; // batch size + const bool mask; // use mask + const float max_bias; // ALiBi std::string vars() override { - return VARS_TO_STR5(hs, nh, kv, nb, max_bias); + return VARS_TO_STR6(hs, nh, kv, nb, mask, max_bias); } double max_nmse_err() override { return 5e-4; } - test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, float max_bias = 0.0f) - : hs(hs), nh(nh), kv(kv), nb(nb), max_bias(max_bias) {} + test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f) + : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1); ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); - ggml_tensor * mask = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1); - ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, mask, 1.0f/sqrtf(hs), max_bias); + ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr; + ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias); return out; } }; @@ -2166,6 +2211,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_sum_rows()); test_cases.emplace_back(new test_upscale()); + test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, { 512, 512, 3, 1 }, 2, true)); + test_cases.emplace_back(new test_upscale_ext()); test_cases.emplace_back(new test_group_norm()); test_cases.emplace_back(new test_acc()); test_cases.emplace_back(new test_pad()); @@ -2173,16 +2220,15 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_timestep_embedding()); test_cases.emplace_back(new test_leaky_relu()); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - for (int hs : { 64, 128, }) { // other head sizes not implemented -#else for (int hs : { 64, 80, 128, 256, }) { -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - for (float max_bias : {0.0f, 8.0f}) { - for (int nh : { 32, }) { - for (int kv : { 512, 1024, }) { - for (int nb : { 1, 2, 4, 8, }) { - test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, max_bias)); + for (bool mask : { true, false } ) { + for (float max_bias : { 0.0f, 8.0f }) { + if (!mask && max_bias > 0.0f) continue; + for (int nh : { 32, }) { + for (int kv : { 512, 1024, }) { + for (int nb : { 1, 2, 4, 8, }) { + test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias)); + } } } } diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 1a4004e2a..01c5bb27a 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -28,6 +28,19 @@ static llama_grammar* build_grammar(const std::string & grammar_str) { return grammar; } +static bool test_build_grammar_fails(const std::string & grammar_str) { + fprintf(stderr, "⚫ Testing failure for grammar: %s\n", grammar_str.c_str()); + bool grammar_fails = false; + try { + build_grammar(grammar_str); + fprintf(stderr, " ❌ Expected build failure, but succeeded\n"); + } catch (const std::exception & err) { + grammar_fails = true; + fprintf(stdout, " ✅︎\n"); + } + return grammar_fails; +} + static bool match_string(const std::string & input, llama_grammar* grammar) { auto decoded = decode_utf8(input, {}); @@ -320,6 +333,38 @@ number ::= [0-9]+)"""; fprintf(stderr, " ✅︎ Passed\n"); } +static void test_failure_left_recursion() { + fprintf(stderr, "⚫ Testing left recursion detection:\n"); + + // Test simple left recursion detection + const std::string simple_str = R"""(root ::= "a" | root "a")"""; + assert(test_build_grammar_fails(simple_str)); + + // Test more complicated left recursion detection + const std::string medium_str = R"""( +root ::= asdf +asdf ::= "a" | asdf "a" +)"""; + assert(test_build_grammar_fails(medium_str)); + + // Test even more complicated left recursion detection + const std::string hard_str = R"""( +root ::= asdf +asdf ::= "a" | foo "b" +foo ::= "c" | asdf "d" | "e")"""; + assert(test_build_grammar_fails(hard_str)); + + // Test yet even more complicated left recursion detection + const std::string hardest_str = R"""( +root ::= asdf +asdf ::= "a" | foo "b" +foo ::= "c" | empty asdf "d" | "e" +empty ::= "blah" | )"""; + assert(test_build_grammar_fails(hardest_str)); + + fprintf(stderr, " ✅︎ Passed\n"); +} + int main() { fprintf(stdout, "Running grammar integration tests...\n"); test_simple_grammar(); @@ -327,6 +372,7 @@ int main() { test_quantifiers(); test_failure_missing_root(); test_failure_missing_reference(); + test_failure_left_recursion(); fprintf(stdout, "All tests passed.\n"); return 0; } diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py index 5b2ab8ef7..d5a6f185f 100644 --- a/tests/test-tokenizer-random.py +++ b/tests/test-tokenizer-random.py @@ -1,5 +1,5 @@ # Test libllama tokenizer == AutoTokenizer. -# Brute force random tokens/text generation. +# Brute force random words/text generation. # # Sample usage: # @@ -12,10 +12,10 @@ import argparse import subprocess import random -from typing import Iterator +from typing import Callable, Iterator import cffi -from transformers import AutoTokenizer, PreTrainedTokenizerBase +from transformers import AutoTokenizer logger = logging.getLogger("test-tokenizer-random-bpe") @@ -145,28 +145,35 @@ def generator_custom_text() -> Iterator[str]: def generator_custom_text_edge_cases() -> Iterator[str]: """Edge cases found while debugging""" yield from [ - '\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F} - '¼-a', # unicode_ranges_digit, 0x00BC - '½-a', # unicode_ranges_digit, 0x00BD - '¾-a', # unicode_ranges_digit, 0x00BE - 'a 〇b', # unicode_ranges_digit, 0x3007 - 'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms - '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) - 'a' # TODO: Phi-3 fail + '\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F} + '¼-a', # unicode_ranges_digit, 0x00BC + '½-a', # unicode_ranges_digit, 0x00BD + '¾-a', # unicode_ranges_digit, 0x00BE + 'a 〇b', # unicode_ranges_digit, 0x3007 + 'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms + '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) + 'Cửa Việt', # llama-3, ignore_merges = true + 'a', # TODO: Phi-3 fail + 'a\na', # TODO: Bert fail ] -def generator_random_chars(iterations = 100) -> Iterator[str]: +def generator_vocab_words(vocab: list[str]) -> Iterator[str]: + """Brute force check all vocab words""" + yield from vocab + + +def generator_random_chars(iterations=100) -> Iterator[str]: """Brute force random text with simple characters""" WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) - CHARS = list(set(""" + CHARS = list(sorted(set(""" ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ áéíóúàèìòùâêîôûäëïöü .-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_ - """)) + """))) rand = random.Random() for m in range(iterations): @@ -181,13 +188,13 @@ def generator_random_chars(iterations = 100) -> Iterator[str]: yield "".join(text) -def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: +def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[str]: """Brute force random text with vocab characters""" - vocab_ids = list(tokenizer.vocab.values()) - vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True) - vocab_chars = list(set(vocab_text)) - del vocab_ids, vocab_text + vocab_chars = set() + for word in vocab: + vocab_chars.update(word) + vocab_chars = list(sorted(vocab_chars)) rand = random.Random() for m in range(iterations): @@ -196,19 +203,11 @@ def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations yield "".join(text) -def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: - """Brute force random text from vocab tokens""" +def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[str]: + """Brute force random text from vocab words""" - space_id = tokenizer.encode(" ", add_special_tokens=False)[0] - vocab_ids = list(tokenizer.vocab.values()) - vocab_ids = list(sorted(vocab_ids + vocab_ids)) - for i in range(1, len(vocab_ids), 2): - vocab_ids[i] = space_id - vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True) - vocab_tokens = vocab_tokens.split(" ") - del vocab_ids - - yield from vocab_tokens + vocab = [w.strip() for w in vocab] + yield from vocab rand = random.Random() for m in range(iterations): @@ -217,14 +216,13 @@ def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations num_words = rand.randint(300, 400) for i in range(num_words): k = rand.randint(1, 3) - tokens = rand.choices(vocab_tokens, k=k) - tokens = [t.strip(" \n\r\t") for t in tokens] + words = rand.choices(vocab, k=k) sep = rand.choice(" \n\r\t") - text.append("".join(tokens) + sep) + text.append("".join(words) + sep) yield "".join(text) -def generator_random_bytes(iterations = 100) -> Iterator[str]: +def generator_random_bytes(iterations=100) -> Iterator[str]: """Brute force random bytes""" WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) @@ -242,10 +240,10 @@ def generator_random_bytes(iterations = 100) -> Iterator[str]: yield "".join(text) -def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, generator: Iterator[str]): +def test_compare_tokenizer(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]): def find_first_mismatch(ids1: list[int], ids2: list[int]): - for i, (a,b) in enumerate(zip(ids1, ids2)): + for i, (a, b) in enumerate(zip(ids1, ids2)): if a != b: return i if len(ids1) == len(ids2): @@ -255,15 +253,12 @@ def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerB t0 = time.perf_counter() logger.info("%s: %s" % (generator.__name__, "ini")) for text in generator: - ids1 = model.tokenize(text, add_special=False, parse_special=False) - ids2 = tokenizer.encode(text, add_special_tokens=False) + ids1 = func_tokenize1(text) + ids2 = func_tokenize2(text) if ids1 != ids2: i = find_first_mismatch(ids1, ids2) ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1] ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1] - text2 = tokenizer.decode(ids2, skip_special_tokens=True) - assert (text2 in text) - logger.info(" Text: " + repr(text2)) logger.info(" TokenIDs: " + str(ids1)) logger.info(" Expected: " + str(ids2)) raise Exception() @@ -271,25 +266,37 @@ def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerB logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0)) -if __name__ == "__main__": - +def main(argv: list[str] = None): parser = argparse.ArgumentParser() parser.add_argument("vocab_file", help="path to vocab 'gguf' file") parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - args = parser.parse_args() + args = parser.parse_args(argv) logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=2048)) - + model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) - test_compare_tokenizer(model, tokenizer, generator_custom_text()) - test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases()) - test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000)) - test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000)) - test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000)) - # test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL + def func_tokenize2(text: str): + return tokenizer.encode(text, add_special_tokens=False) + + parse_special = all(len(func_tokenize2(t)) == 1 for t in tokenizer.all_special_tokens) + + def func_tokenize1(text: str): + return model.tokenize(text, add_special=False, parse_special=parse_special) + + vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 10_000)) + # test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_bytes(10_000)) # FAIL model.free() + + +if __name__ == "__main__": + main() diff --git a/unicode-data.cpp b/unicode-data.cpp index c54175fc3..d7c1c898d 100644 --- a/unicode-data.cpp +++ b/unicode-data.cpp @@ -1,2183 +1,6983 @@ -#include "unicode-data.h" +// generated with scripts/gen-unicode-data.py + +#include "unicode-data.h" #include -#include -#include #include +#include +#include -// generated with scripts/gen-unicode-data.py -// -// TODO: generate unicode_map_nfd - -const std::vector> unicode_ranges_number = { -{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x000000BC, 0x000000BE}, -{0x00000660, 0x00000669}, {0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F}, -{0x000009E6, 0x000009EF}, {0x000009F4, 0x000009F9}, {0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF}, -{0x00000B66, 0x00000B6F}, {0x00000B72, 0x00000B77}, {0x00000BE6, 0x00000BF2}, {0x00000C66, 0x00000C6F}, -{0x00000C78, 0x00000C7E}, {0x00000CE6, 0x00000CEF}, {0x00000D58, 0x00000D5E}, {0x00000D66, 0x00000D78}, -{0x00000DE6, 0x00000DEF}, {0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F33}, -{0x00001040, 0x00001049}, {0x00001090, 0x00001099}, {0x00001369, 0x0000137C}, {0x000016EE, 0x000016F0}, -{0x000017E0, 0x000017E9}, {0x000017F0, 0x000017F9}, {0x00001810, 0x00001819}, {0x00001946, 0x0000194F}, -{0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, {0x00001B50, 0x00001B59}, -{0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, {0x00002070, 0x00002070}, -{0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002150, 0x00002182}, {0x00002185, 0x00002189}, -{0x00002460, 0x0000249B}, {0x000024EA, 0x000024FF}, {0x00002776, 0x00002793}, {0x00002CFD, 0x00002CFD}, -{0x00003007, 0x00003007}, {0x00003021, 0x00003029}, {0x00003038, 0x0000303A}, {0x00003192, 0x00003195}, -{0x00003220, 0x00003229}, {0x00003248, 0x0000324F}, {0x00003251, 0x0000325F}, {0x00003280, 0x00003289}, -{0x000032B1, 0x000032BF}, {0x0000A620, 0x0000A629}, {0x0000A6E6, 0x0000A6EF}, {0x0000A830, 0x0000A835}, -{0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, {0x0000A9F0, 0x0000A9F9}, -{0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, {0x00010107, 0x00010133}, -{0x00010140, 0x00010178}, {0x0001018A, 0x0001018B}, {0x000102E1, 0x000102FB}, {0x00010320, 0x00010323}, -{0x00010341, 0x00010341}, {0x0001034A, 0x0001034A}, {0x000103D1, 0x000103D5}, {0x000104A0, 0x000104A9}, -{0x00010858, 0x0001085F}, {0x00010879, 0x0001087F}, {0x000108A7, 0x000108AF}, {0x000108FB, 0x000108FF}, -{0x00010916, 0x0001091B}, {0x000109BC, 0x000109BD}, {0x000109C0, 0x000109CF}, {0x000109D2, 0x000109FF}, -{0x00010A40, 0x00010A48}, {0x00010A7D, 0x00010A7E}, {0x00010A9D, 0x00010A9F}, {0x00010AEB, 0x00010AEF}, -{0x00010B58, 0x00010B5F}, {0x00010B78, 0x00010B7F}, {0x00010BA9, 0x00010BAF}, {0x00010CFA, 0x00010CFF}, -{0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E7E}, {0x00010F1D, 0x00010F26}, {0x00010F51, 0x00010F54}, -{0x00010FC5, 0x00010FCB}, {0x00011052, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F}, -{0x000111D0, 0x000111D9}, {0x000111E1, 0x000111F4}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459}, -{0x000114D0, 0x000114D9}, {0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x0001173B}, -{0x000118E0, 0x000118F2}, {0x00011950, 0x00011959}, {0x00011C50, 0x00011C6C}, {0x00011D50, 0x00011D59}, -{0x00011DA0, 0x00011DA9}, {0x00011F50, 0x00011F59}, {0x00011FC0, 0x00011FD4}, {0x00012400, 0x0001246E}, -{0x00016A60, 0x00016A69}, {0x00016AC0, 0x00016AC9}, {0x00016B50, 0x00016B59}, {0x00016B5B, 0x00016B61}, -{0x00016E80, 0x00016E96}, {0x0001D2C0, 0x0001D2D3}, {0x0001D2E0, 0x0001D2F3}, {0x0001D360, 0x0001D378}, -{0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, {0x0001E2F0, 0x0001E2F9}, {0x0001E4F0, 0x0001E4F9}, -{0x0001E8C7, 0x0001E8CF}, {0x0001E950, 0x0001E959}, {0x0001EC71, 0x0001ECAB}, {0x0001ECAD, 0x0001ECAF}, -{0x0001ECB1, 0x0001ECB4}, {0x0001ED01, 0x0001ED2D}, {0x0001ED2F, 0x0001ED3D}, {0x0001F100, 0x0001F10C}, -{0x0001FBF0, 0x0001FBF9}, +const std::vector> unicode_ranges_flags = { // start, flags // last=next_start-1 +{0x000000, 0x0080}, +{0x000020, 0x0008}, +{0x000021, 0x0020}, +{0x000024, 0x0040}, +{0x000025, 0x0020}, +{0x00002B, 0x0040}, +{0x00002C, 0x0020}, +{0x000030, 0x0002}, +{0x00003A, 0x0020}, +{0x00003C, 0x0040}, +{0x00003F, 0x0020}, +{0x000041, 0x0004}, +{0x00005B, 0x0020}, +{0x00005E, 0x0040}, +{0x00005F, 0x0020}, +{0x000060, 0x0040}, +{0x000061, 0x0004}, +{0x00007B, 0x0020}, +{0x00007C, 0x0040}, +{0x00007D, 0x0020}, +{0x00007E, 0x0040}, +{0x00007F, 0x0080}, +{0x0000A0, 0x0008}, +{0x0000A1, 0x0020}, +{0x0000A2, 0x0040}, +{0x0000A7, 0x0020}, +{0x0000A8, 0x0040}, +{0x0000AA, 0x0004}, +{0x0000AB, 0x0020}, +{0x0000AC, 0x0040}, +{0x0000AD, 0x0080}, +{0x0000AE, 0x0040}, +{0x0000B2, 0x0002}, +{0x0000B4, 0x0040}, +{0x0000B5, 0x0004}, +{0x0000B6, 0x0020}, +{0x0000B8, 0x0040}, +{0x0000B9, 0x0002}, +{0x0000BA, 0x0004}, +{0x0000BB, 0x0020}, +{0x0000BC, 0x0002}, +{0x0000BF, 0x0020}, +{0x0000C0, 0x0004}, +{0x0000D7, 0x0040}, +{0x0000D8, 0x0004}, +{0x0000F7, 0x0040}, +{0x0000F8, 0x0004}, +{0x0002C2, 0x0040}, +{0x0002C6, 0x0004}, +{0x0002D2, 0x0040}, +{0x0002E0, 0x0004}, +{0x0002E5, 0x0040}, +{0x0002EC, 0x0004}, +{0x0002ED, 0x0040}, +{0x0002EE, 0x0004}, +{0x0002EF, 0x0040}, +{0x000300, 0x0010}, +{0x000370, 0x0004}, +{0x000375, 0x0040}, +{0x000376, 0x0004}, +{0x000378, 0x0080}, +{0x00037A, 0x0004}, +{0x00037E, 0x0020}, +{0x00037F, 0x0004}, +{0x000380, 0x0080}, +{0x000384, 0x0040}, +{0x000386, 0x0004}, +{0x000387, 0x0020}, +{0x000388, 0x0004}, +{0x00038B, 0x0080}, +{0x00038C, 0x0004}, +{0x00038D, 0x0080}, +{0x00038E, 0x0004}, +{0x0003A2, 0x0080}, +{0x0003A3, 0x0004}, +{0x0003F6, 0x0040}, +{0x0003F7, 0x0004}, +{0x000482, 0x0040}, +{0x000483, 0x0010}, +{0x00048A, 0x0004}, +{0x000530, 0x0080}, +{0x000531, 0x0004}, +{0x000557, 0x0080}, +{0x000559, 0x0004}, +{0x00055A, 0x0020}, +{0x000560, 0x0004}, +{0x000589, 0x0020}, +{0x00058B, 0x0080}, +{0x00058D, 0x0040}, +{0x000590, 0x0080}, +{0x000591, 0x0010}, +{0x0005BE, 0x0020}, +{0x0005BF, 0x0010}, +{0x0005C0, 0x0020}, +{0x0005C1, 0x0010}, +{0x0005C3, 0x0020}, +{0x0005C4, 0x0010}, +{0x0005C6, 0x0020}, +{0x0005C7, 0x0010}, +{0x0005C8, 0x0080}, +{0x0005D0, 0x0004}, +{0x0005EB, 0x0080}, +{0x0005EF, 0x0004}, +{0x0005F3, 0x0020}, +{0x0005F5, 0x0080}, +{0x000606, 0x0040}, +{0x000609, 0x0020}, +{0x00060B, 0x0040}, +{0x00060C, 0x0020}, +{0x00060E, 0x0040}, +{0x000610, 0x0010}, +{0x00061B, 0x0020}, +{0x00061C, 0x0080}, +{0x00061D, 0x0020}, +{0x000620, 0x0004}, +{0x00064B, 0x0010}, +{0x000660, 0x0002}, +{0x00066A, 0x0020}, +{0x00066E, 0x0004}, +{0x000670, 0x0010}, +{0x000671, 0x0004}, +{0x0006D4, 0x0020}, +{0x0006D5, 0x0004}, +{0x0006D6, 0x0010}, +{0x0006DD, 0x0080}, +{0x0006DE, 0x0040}, +{0x0006DF, 0x0010}, +{0x0006E5, 0x0004}, +{0x0006E7, 0x0010}, +{0x0006E9, 0x0040}, +{0x0006EA, 0x0010}, +{0x0006EE, 0x0004}, +{0x0006F0, 0x0002}, +{0x0006FA, 0x0004}, +{0x0006FD, 0x0040}, +{0x0006FF, 0x0004}, +{0x000700, 0x0020}, +{0x00070E, 0x0080}, +{0x000710, 0x0004}, +{0x000711, 0x0010}, +{0x000712, 0x0004}, +{0x000730, 0x0010}, +{0x00074B, 0x0080}, +{0x00074D, 0x0004}, +{0x0007A6, 0x0010}, +{0x0007B1, 0x0004}, +{0x0007B2, 0x0080}, +{0x0007C0, 0x0002}, +{0x0007CA, 0x0004}, +{0x0007EB, 0x0010}, +{0x0007F4, 0x0004}, +{0x0007F6, 0x0040}, +{0x0007F7, 0x0020}, +{0x0007FA, 0x0004}, +{0x0007FB, 0x0080}, +{0x0007FD, 0x0010}, +{0x0007FE, 0x0040}, +{0x000800, 0x0004}, +{0x000816, 0x0010}, +{0x00081A, 0x0004}, +{0x00081B, 0x0010}, +{0x000824, 0x0004}, +{0x000825, 0x0010}, +{0x000828, 0x0004}, +{0x000829, 0x0010}, +{0x00082E, 0x0080}, +{0x000830, 0x0020}, +{0x00083F, 0x0080}, +{0x000840, 0x0004}, +{0x000859, 0x0010}, +{0x00085C, 0x0080}, +{0x00085E, 0x0020}, +{0x00085F, 0x0080}, +{0x000860, 0x0004}, +{0x00086B, 0x0080}, +{0x000870, 0x0004}, +{0x000888, 0x0040}, +{0x000889, 0x0004}, +{0x00088F, 0x0080}, +{0x000898, 0x0010}, +{0x0008A0, 0x0004}, +{0x0008CA, 0x0010}, +{0x0008E2, 0x0080}, +{0x0008E3, 0x0010}, +{0x000904, 0x0004}, +{0x00093A, 0x0010}, +{0x00093D, 0x0004}, +{0x00093E, 0x0010}, +{0x000950, 0x0004}, +{0x000951, 0x0010}, +{0x000958, 0x0004}, +{0x000962, 0x0010}, +{0x000964, 0x0020}, +{0x000966, 0x0002}, +{0x000970, 0x0020}, +{0x000971, 0x0004}, +{0x000981, 0x0010}, +{0x000984, 0x0080}, +{0x000985, 0x0004}, +{0x00098D, 0x0080}, +{0x00098F, 0x0004}, +{0x000991, 0x0080}, +{0x000993, 0x0004}, +{0x0009A9, 0x0080}, +{0x0009AA, 0x0004}, +{0x0009B1, 0x0080}, +{0x0009B2, 0x0004}, +{0x0009B3, 0x0080}, +{0x0009B6, 0x0004}, +{0x0009BA, 0x0080}, +{0x0009BC, 0x0010}, +{0x0009BD, 0x0004}, +{0x0009BE, 0x0010}, +{0x0009C5, 0x0080}, +{0x0009C7, 0x0010}, +{0x0009C9, 0x0080}, +{0x0009CB, 0x0010}, +{0x0009CE, 0x0004}, +{0x0009CF, 0x0080}, +{0x0009D7, 0x0010}, +{0x0009D8, 0x0080}, +{0x0009DC, 0x0004}, +{0x0009DE, 0x0080}, +{0x0009DF, 0x0004}, +{0x0009E2, 0x0010}, +{0x0009E4, 0x0080}, +{0x0009E6, 0x0002}, +{0x0009F0, 0x0004}, +{0x0009F2, 0x0040}, +{0x0009F4, 0x0002}, +{0x0009FA, 0x0040}, +{0x0009FC, 0x0004}, +{0x0009FD, 0x0020}, +{0x0009FE, 0x0010}, +{0x0009FF, 0x0080}, +{0x000A01, 0x0010}, +{0x000A04, 0x0080}, +{0x000A05, 0x0004}, +{0x000A0B, 0x0080}, +{0x000A0F, 0x0004}, +{0x000A11, 0x0080}, +{0x000A13, 0x0004}, +{0x000A29, 0x0080}, +{0x000A2A, 0x0004}, +{0x000A31, 0x0080}, +{0x000A32, 0x0004}, +{0x000A34, 0x0080}, +{0x000A35, 0x0004}, +{0x000A37, 0x0080}, +{0x000A38, 0x0004}, +{0x000A3A, 0x0080}, +{0x000A3C, 0x0010}, +{0x000A3D, 0x0080}, +{0x000A3E, 0x0010}, +{0x000A43, 0x0080}, +{0x000A47, 0x0010}, +{0x000A49, 0x0080}, +{0x000A4B, 0x0010}, +{0x000A4E, 0x0080}, +{0x000A51, 0x0010}, +{0x000A52, 0x0080}, +{0x000A59, 0x0004}, +{0x000A5D, 0x0080}, +{0x000A5E, 0x0004}, +{0x000A5F, 0x0080}, +{0x000A66, 0x0002}, +{0x000A70, 0x0010}, +{0x000A72, 0x0004}, +{0x000A75, 0x0010}, +{0x000A76, 0x0020}, +{0x000A77, 0x0080}, +{0x000A81, 0x0010}, +{0x000A84, 0x0080}, +{0x000A85, 0x0004}, +{0x000A8E, 0x0080}, +{0x000A8F, 0x0004}, +{0x000A92, 0x0080}, +{0x000A93, 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0x0080}, +{0x010A05, 0x0010}, +{0x010A07, 0x0080}, +{0x010A0C, 0x0010}, +{0x010A10, 0x0004}, +{0x010A14, 0x0080}, +{0x010A15, 0x0004}, +{0x010A18, 0x0080}, +{0x010A19, 0x0004}, +{0x010A36, 0x0080}, +{0x010A38, 0x0010}, +{0x010A3B, 0x0080}, +{0x010A3F, 0x0010}, +{0x010A40, 0x0002}, +{0x010A49, 0x0080}, +{0x010A50, 0x0020}, +{0x010A59, 0x0080}, +{0x010A60, 0x0004}, +{0x010A7D, 0x0002}, +{0x010A7F, 0x0020}, +{0x010A80, 0x0004}, +{0x010A9D, 0x0002}, +{0x010AA0, 0x0080}, +{0x010AC0, 0x0004}, +{0x010AC8, 0x0040}, +{0x010AC9, 0x0004}, +{0x010AE5, 0x0010}, +{0x010AE7, 0x0080}, +{0x010AEB, 0x0002}, +{0x010AF0, 0x0020}, +{0x010AF7, 0x0080}, +{0x010B00, 0x0004}, +{0x010B36, 0x0080}, +{0x010B39, 0x0020}, +{0x010B40, 0x0004}, +{0x010B56, 0x0080}, +{0x010B58, 0x0002}, +{0x010B60, 0x0004}, +{0x010B73, 0x0080}, +{0x010B78, 0x0002}, +{0x010B80, 0x0004}, +{0x010B92, 0x0080}, +{0x010B99, 0x0020}, +{0x010B9D, 0x0080}, +{0x010BA9, 0x0002}, +{0x010BB0, 0x0080}, +{0x010C00, 0x0004}, +{0x010C49, 0x0080}, 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0x0004}, +{0x011076, 0x0080}, +{0x01107F, 0x0010}, +{0x011083, 0x0004}, +{0x0110B0, 0x0010}, +{0x0110BB, 0x0020}, +{0x0110BD, 0x0080}, +{0x0110BE, 0x0020}, +{0x0110C2, 0x0010}, +{0x0110C3, 0x0080}, +{0x0110D0, 0x0004}, +{0x0110E9, 0x0080}, +{0x0110F0, 0x0002}, +{0x0110FA, 0x0080}, +{0x011100, 0x0010}, +{0x011103, 0x0004}, +{0x011127, 0x0010}, +{0x011135, 0x0080}, +{0x011136, 0x0002}, +{0x011140, 0x0020}, +{0x011144, 0x0004}, +{0x011145, 0x0010}, +{0x011147, 0x0004}, +{0x011148, 0x0080}, +{0x011150, 0x0004}, +{0x011173, 0x0010}, +{0x011174, 0x0020}, +{0x011176, 0x0004}, +{0x011177, 0x0080}, +{0x011180, 0x0010}, +{0x011183, 0x0004}, +{0x0111B3, 0x0010}, +{0x0111C1, 0x0004}, +{0x0111C5, 0x0020}, +{0x0111C9, 0x0010}, +{0x0111CD, 0x0020}, +{0x0111CE, 0x0010}, +{0x0111D0, 0x0002}, +{0x0111DA, 0x0004}, +{0x0111DB, 0x0020}, +{0x0111DC, 0x0004}, +{0x0111DD, 0x0020}, +{0x0111E0, 0x0080}, +{0x0111E1, 0x0002}, +{0x0111F5, 0x0080}, +{0x011200, 0x0004}, +{0x011212, 0x0080}, +{0x011213, 0x0004}, +{0x01122C, 0x0010}, +{0x011238, 0x0020}, +{0x01123E, 0x0010}, +{0x01123F, 0x0004}, +{0x011241, 0x0010}, +{0x011242, 0x0080}, +{0x011280, 0x0004}, +{0x011287, 0x0080}, +{0x011288, 0x0004}, +{0x011289, 0x0080}, +{0x01128A, 0x0004}, +{0x01128E, 0x0080}, +{0x01128F, 0x0004}, +{0x01129E, 0x0080}, +{0x01129F, 0x0004}, +{0x0112A9, 0x0020}, +{0x0112AA, 0x0080}, +{0x0112B0, 0x0004}, +{0x0112DF, 0x0010}, +{0x0112EB, 0x0080}, +{0x0112F0, 0x0002}, +{0x0112FA, 0x0080}, +{0x011300, 0x0010}, +{0x011304, 0x0080}, +{0x011305, 0x0004}, +{0x01130D, 0x0080}, +{0x01130F, 0x0004}, +{0x011311, 0x0080}, +{0x011313, 0x0004}, +{0x011329, 0x0080}, +{0x01132A, 0x0004}, +{0x011331, 0x0080}, +{0x011332, 0x0004}, +{0x011334, 0x0080}, +{0x011335, 0x0004}, +{0x01133A, 0x0080}, +{0x01133B, 0x0010}, +{0x01133D, 0x0004}, +{0x01133E, 0x0010}, +{0x011345, 0x0080}, +{0x011347, 0x0010}, +{0x011349, 0x0080}, +{0x01134B, 0x0010}, +{0x01134E, 0x0080}, +{0x011350, 0x0004}, +{0x011351, 0x0080}, +{0x011357, 0x0010}, +{0x011358, 0x0080}, +{0x01135D, 0x0004}, +{0x011362, 0x0010}, +{0x011364, 0x0080}, +{0x011366, 0x0010}, +{0x01136D, 0x0080}, +{0x011370, 0x0010}, +{0x011375, 0x0080}, +{0x011400, 0x0004}, +{0x011435, 0x0010}, +{0x011447, 0x0004}, +{0x01144B, 0x0020}, +{0x011450, 0x0002}, +{0x01145A, 0x0020}, +{0x01145C, 0x0080}, +{0x01145D, 0x0020}, +{0x01145E, 0x0010}, +{0x01145F, 0x0004}, +{0x011462, 0x0080}, +{0x011480, 0x0004}, +{0x0114B0, 0x0010}, +{0x0114C4, 0x0004}, +{0x0114C6, 0x0020}, +{0x0114C7, 0x0004}, +{0x0114C8, 0x0080}, +{0x0114D0, 0x0002}, +{0x0114DA, 0x0080}, +{0x011580, 0x0004}, +{0x0115AF, 0x0010}, +{0x0115B6, 0x0080}, +{0x0115B8, 0x0010}, +{0x0115C1, 0x0020}, +{0x0115D8, 0x0004}, +{0x0115DC, 0x0010}, +{0x0115DE, 0x0080}, +{0x011600, 0x0004}, +{0x011630, 0x0010}, +{0x011641, 0x0020}, +{0x011644, 0x0004}, +{0x011645, 0x0080}, +{0x011650, 0x0002}, +{0x01165A, 0x0080}, +{0x011660, 0x0020}, +{0x01166D, 0x0080}, +{0x011680, 0x0004}, +{0x0116AB, 0x0010}, +{0x0116B8, 0x0004}, +{0x0116B9, 0x0020}, +{0x0116BA, 0x0080}, +{0x0116C0, 0x0002}, +{0x0116CA, 0x0080}, +{0x011700, 0x0004}, +{0x01171B, 0x0080}, +{0x01171D, 0x0010}, +{0x01172C, 0x0080}, +{0x011730, 0x0002}, +{0x01173C, 0x0020}, +{0x01173F, 0x0040}, +{0x011740, 0x0004}, +{0x011747, 0x0080}, +{0x011800, 0x0004}, +{0x01182C, 0x0010}, +{0x01183B, 0x0020}, +{0x01183C, 0x0080}, +{0x0118A0, 0x0004}, +{0x0118E0, 0x0002}, +{0x0118F3, 0x0080}, +{0x0118FF, 0x0004}, +{0x011907, 0x0080}, +{0x011909, 0x0004}, +{0x01190A, 0x0080}, +{0x01190C, 0x0004}, +{0x011914, 0x0080}, +{0x011915, 0x0004}, +{0x011917, 0x0080}, +{0x011918, 0x0004}, +{0x011930, 0x0010}, +{0x011936, 0x0080}, +{0x011937, 0x0010}, +{0x011939, 0x0080}, +{0x01193B, 0x0010}, +{0x01193F, 0x0004}, +{0x011940, 0x0010}, +{0x011941, 0x0004}, +{0x011942, 0x0010}, +{0x011944, 0x0020}, +{0x011947, 0x0080}, +{0x011950, 0x0002}, +{0x01195A, 0x0080}, +{0x0119A0, 0x0004}, +{0x0119A8, 0x0080}, +{0x0119AA, 0x0004}, +{0x0119D1, 0x0010}, +{0x0119D8, 0x0080}, +{0x0119DA, 0x0010}, +{0x0119E1, 0x0004}, +{0x0119E2, 0x0020}, +{0x0119E3, 0x0004}, +{0x0119E4, 0x0010}, +{0x0119E5, 0x0080}, +{0x011A00, 0x0004}, +{0x011A01, 0x0010}, +{0x011A0B, 0x0004}, +{0x011A33, 0x0010}, +{0x011A3A, 0x0004}, +{0x011A3B, 0x0010}, +{0x011A3F, 0x0020}, +{0x011A47, 0x0010}, +{0x011A48, 0x0080}, +{0x011A50, 0x0004}, +{0x011A51, 0x0010}, +{0x011A5C, 0x0004}, +{0x011A8A, 0x0010}, +{0x011A9A, 0x0020}, +{0x011A9D, 0x0004}, +{0x011A9E, 0x0020}, +{0x011AA3, 0x0080}, +{0x011AB0, 0x0004}, +{0x011AF9, 0x0080}, +{0x011B00, 0x0020}, +{0x011B0A, 0x0080}, +{0x011C00, 0x0004}, +{0x011C09, 0x0080}, +{0x011C0A, 0x0004}, +{0x011C2F, 0x0010}, +{0x011C37, 0x0080}, +{0x011C38, 0x0010}, +{0x011C40, 0x0004}, +{0x011C41, 0x0020}, +{0x011C46, 0x0080}, +{0x011C50, 0x0002}, +{0x011C6D, 0x0080}, +{0x011C70, 0x0020}, +{0x011C72, 0x0004}, +{0x011C90, 0x0080}, +{0x011C92, 0x0010}, +{0x011CA8, 0x0080}, +{0x011CA9, 0x0010}, +{0x011CB7, 0x0080}, +{0x011D00, 0x0004}, +{0x011D07, 0x0080}, +{0x011D08, 0x0004}, +{0x011D0A, 0x0080}, 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0x0080}, +{0x011FFF, 0x0020}, +{0x012000, 0x0004}, +{0x01239A, 0x0080}, +{0x012400, 0x0002}, +{0x01246F, 0x0080}, +{0x012470, 0x0020}, +{0x012475, 0x0080}, +{0x012480, 0x0004}, +{0x012544, 0x0080}, +{0x012F90, 0x0004}, +{0x012FF1, 0x0020}, +{0x012FF3, 0x0080}, +{0x013000, 0x0004}, +{0x013430, 0x0080}, +{0x013440, 0x0010}, +{0x013441, 0x0004}, +{0x013447, 0x0010}, +{0x013456, 0x0080}, +{0x014400, 0x0004}, +{0x014647, 0x0080}, +{0x016800, 0x0004}, +{0x016A39, 0x0080}, +{0x016A40, 0x0004}, +{0x016A5F, 0x0080}, +{0x016A60, 0x0002}, +{0x016A6A, 0x0080}, +{0x016A6E, 0x0020}, +{0x016A70, 0x0004}, +{0x016ABF, 0x0080}, +{0x016AC0, 0x0002}, +{0x016ACA, 0x0080}, +{0x016AD0, 0x0004}, +{0x016AEE, 0x0080}, +{0x016AF0, 0x0010}, +{0x016AF5, 0x0020}, +{0x016AF6, 0x0080}, +{0x016B00, 0x0004}, +{0x016B30, 0x0010}, +{0x016B37, 0x0020}, +{0x016B3C, 0x0040}, +{0x016B40, 0x0004}, +{0x016B44, 0x0020}, +{0x016B45, 0x0040}, +{0x016B46, 0x0080}, +{0x016B50, 0x0002}, +{0x016B5A, 0x0080}, +{0x016B5B, 0x0002}, 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0x0004}, +{0x01B2FC, 0x0080}, +{0x01BC00, 0x0004}, +{0x01BC6B, 0x0080}, +{0x01BC70, 0x0004}, +{0x01BC7D, 0x0080}, +{0x01BC80, 0x0004}, +{0x01BC89, 0x0080}, +{0x01BC90, 0x0004}, +{0x01BC9A, 0x0080}, +{0x01BC9C, 0x0040}, +{0x01BC9D, 0x0010}, +{0x01BC9F, 0x0020}, +{0x01BCA0, 0x0080}, +{0x01CF00, 0x0010}, +{0x01CF2E, 0x0080}, +{0x01CF30, 0x0010}, +{0x01CF47, 0x0080}, +{0x01CF50, 0x0040}, +{0x01CFC4, 0x0080}, +{0x01D000, 0x0040}, +{0x01D0F6, 0x0080}, +{0x01D100, 0x0040}, +{0x01D127, 0x0080}, +{0x01D129, 0x0040}, +{0x01D165, 0x0010}, +{0x01D16A, 0x0040}, +{0x01D16D, 0x0010}, +{0x01D173, 0x0080}, +{0x01D17B, 0x0010}, +{0x01D183, 0x0040}, +{0x01D185, 0x0010}, +{0x01D18C, 0x0040}, +{0x01D1AA, 0x0010}, +{0x01D1AE, 0x0040}, +{0x01D1EB, 0x0080}, +{0x01D200, 0x0040}, +{0x01D242, 0x0010}, +{0x01D245, 0x0040}, +{0x01D246, 0x0080}, +{0x01D2C0, 0x0002}, +{0x01D2D4, 0x0080}, +{0x01D2E0, 0x0002}, +{0x01D2F4, 0x0080}, +{0x01D300, 0x0040}, +{0x01D357, 0x0080}, +{0x01D360, 0x0002}, +{0x01D379, 0x0080}, +{0x01D400, 0x0004}, +{0x01D455, 0x0080}, +{0x01D456, 0x0004}, +{0x01D49D, 0x0080}, +{0x01D49E, 0x0004}, +{0x01D4A0, 0x0080}, +{0x01D4A2, 0x0004}, +{0x01D4A3, 0x0080}, +{0x01D4A5, 0x0004}, +{0x01D4A7, 0x0080}, +{0x01D4A9, 0x0004}, +{0x01D4AD, 0x0080}, +{0x01D4AE, 0x0004}, +{0x01D4BA, 0x0080}, +{0x01D4BB, 0x0004}, +{0x01D4BC, 0x0080}, +{0x01D4BD, 0x0004}, +{0x01D4C4, 0x0080}, +{0x01D4C5, 0x0004}, +{0x01D506, 0x0080}, +{0x01D507, 0x0004}, +{0x01D50B, 0x0080}, +{0x01D50D, 0x0004}, +{0x01D515, 0x0080}, +{0x01D516, 0x0004}, +{0x01D51D, 0x0080}, +{0x01D51E, 0x0004}, +{0x01D53A, 0x0080}, +{0x01D53B, 0x0004}, +{0x01D53F, 0x0080}, +{0x01D540, 0x0004}, +{0x01D545, 0x0080}, +{0x01D546, 0x0004}, +{0x01D547, 0x0080}, +{0x01D54A, 0x0004}, +{0x01D551, 0x0080}, +{0x01D552, 0x0004}, +{0x01D6A6, 0x0080}, +{0x01D6A8, 0x0004}, +{0x01D6C1, 0x0040}, +{0x01D6C2, 0x0004}, +{0x01D6DB, 0x0040}, +{0x01D6DC, 0x0004}, +{0x01D6FB, 0x0040}, +{0x01D6FC, 0x0004}, +{0x01D715, 0x0040}, +{0x01D716, 0x0004}, +{0x01D735, 0x0040}, +{0x01D736, 0x0004}, +{0x01D74F, 0x0040}, +{0x01D750, 0x0004}, +{0x01D76F, 0x0040}, +{0x01D770, 0x0004}, +{0x01D789, 0x0040}, +{0x01D78A, 0x0004}, +{0x01D7A9, 0x0040}, +{0x01D7AA, 0x0004}, +{0x01D7C3, 0x0040}, +{0x01D7C4, 0x0004}, +{0x01D7CC, 0x0080}, +{0x01D7CE, 0x0002}, +{0x01D800, 0x0040}, +{0x01DA00, 0x0010}, +{0x01DA37, 0x0040}, +{0x01DA3B, 0x0010}, +{0x01DA6D, 0x0040}, +{0x01DA75, 0x0010}, +{0x01DA76, 0x0040}, +{0x01DA84, 0x0010}, +{0x01DA85, 0x0040}, +{0x01DA87, 0x0020}, +{0x01DA8C, 0x0080}, +{0x01DA9B, 0x0010}, +{0x01DAA0, 0x0080}, +{0x01DAA1, 0x0010}, +{0x01DAB0, 0x0080}, +{0x01DF00, 0x0004}, +{0x01DF1F, 0x0080}, +{0x01DF25, 0x0004}, +{0x01DF2B, 0x0080}, +{0x01E000, 0x0010}, +{0x01E007, 0x0080}, +{0x01E008, 0x0010}, +{0x01E019, 0x0080}, +{0x01E01B, 0x0010}, +{0x01E022, 0x0080}, +{0x01E023, 0x0010}, +{0x01E025, 0x0080}, +{0x01E026, 0x0010}, +{0x01E02B, 0x0080}, +{0x01E030, 0x0004}, +{0x01E06E, 0x0080}, +{0x01E08F, 0x0010}, +{0x01E090, 0x0080}, +{0x01E100, 0x0004}, +{0x01E12D, 0x0080}, +{0x01E130, 0x0010}, +{0x01E137, 0x0004}, +{0x01E13E, 0x0080}, +{0x01E140, 0x0002}, +{0x01E14A, 0x0080}, +{0x01E14E, 0x0004}, +{0x01E14F, 0x0040}, +{0x01E150, 0x0080}, +{0x01E290, 0x0004}, +{0x01E2AE, 0x0010}, +{0x01E2AF, 0x0080}, +{0x01E2C0, 0x0004}, +{0x01E2EC, 0x0010}, +{0x01E2F0, 0x0002}, +{0x01E2FA, 0x0080}, +{0x01E2FF, 0x0040}, +{0x01E300, 0x0080}, +{0x01E4D0, 0x0004}, +{0x01E4EC, 0x0010}, +{0x01E4F0, 0x0002}, +{0x01E4FA, 0x0080}, +{0x01E7E0, 0x0004}, +{0x01E7E7, 0x0080}, +{0x01E7E8, 0x0004}, +{0x01E7EC, 0x0080}, +{0x01E7ED, 0x0004}, +{0x01E7EF, 0x0080}, +{0x01E7F0, 0x0004}, +{0x01E7FF, 0x0080}, +{0x01E800, 0x0004}, +{0x01E8C5, 0x0080}, +{0x01E8C7, 0x0002}, +{0x01E8D0, 0x0010}, +{0x01E8D7, 0x0080}, +{0x01E900, 0x0004}, +{0x01E944, 0x0010}, +{0x01E94B, 0x0004}, +{0x01E94C, 0x0080}, +{0x01E950, 0x0002}, +{0x01E95A, 0x0080}, +{0x01E95E, 0x0020}, +{0x01E960, 0x0080}, +{0x01EC71, 0x0002}, +{0x01ECAC, 0x0040}, +{0x01ECAD, 0x0002}, +{0x01ECB0, 0x0040}, +{0x01ECB1, 0x0002}, +{0x01ECB5, 0x0080}, +{0x01ED01, 0x0002}, +{0x01ED2E, 0x0040}, +{0x01ED2F, 0x0002}, +{0x01ED3E, 0x0080}, +{0x01EE00, 0x0004}, +{0x01EE04, 0x0080}, +{0x01EE05, 0x0004}, +{0x01EE20, 0x0080}, +{0x01EE21, 0x0004}, +{0x01EE23, 0x0080}, +{0x01EE24, 0x0004}, +{0x01EE25, 0x0080}, +{0x01EE27, 0x0004}, +{0x01EE28, 0x0080}, +{0x01EE29, 0x0004}, +{0x01EE33, 0x0080}, +{0x01EE34, 0x0004}, +{0x01EE38, 0x0080}, +{0x01EE39, 0x0004}, +{0x01EE3A, 0x0080}, +{0x01EE3B, 0x0004}, +{0x01EE3C, 0x0080}, +{0x01EE42, 0x0004}, +{0x01EE43, 0x0080}, +{0x01EE47, 0x0004}, +{0x01EE48, 0x0080}, +{0x01EE49, 0x0004}, +{0x01EE4A, 0x0080}, +{0x01EE4B, 0x0004}, +{0x01EE4C, 0x0080}, +{0x01EE4D, 0x0004}, +{0x01EE50, 0x0080}, +{0x01EE51, 0x0004}, +{0x01EE53, 0x0080}, +{0x01EE54, 0x0004}, +{0x01EE55, 0x0080}, +{0x01EE57, 0x0004}, +{0x01EE58, 0x0080}, +{0x01EE59, 0x0004}, +{0x01EE5A, 0x0080}, +{0x01EE5B, 0x0004}, +{0x01EE5C, 0x0080}, +{0x01EE5D, 0x0004}, +{0x01EE5E, 0x0080}, +{0x01EE5F, 0x0004}, +{0x01EE60, 0x0080}, +{0x01EE61, 0x0004}, +{0x01EE63, 0x0080}, +{0x01EE64, 0x0004}, +{0x01EE65, 0x0080}, +{0x01EE67, 0x0004}, +{0x01EE6B, 0x0080}, +{0x01EE6C, 0x0004}, +{0x01EE73, 0x0080}, +{0x01EE74, 0x0004}, +{0x01EE78, 0x0080}, +{0x01EE79, 0x0004}, +{0x01EE7D, 0x0080}, +{0x01EE7E, 0x0004}, +{0x01EE7F, 0x0080}, +{0x01EE80, 0x0004}, +{0x01EE8A, 0x0080}, +{0x01EE8B, 0x0004}, +{0x01EE9C, 0x0080}, +{0x01EEA1, 0x0004}, +{0x01EEA4, 0x0080}, +{0x01EEA5, 0x0004}, +{0x01EEAA, 0x0080}, +{0x01EEAB, 0x0004}, +{0x01EEBC, 0x0080}, +{0x01EEF0, 0x0040}, +{0x01EEF2, 0x0080}, +{0x01F000, 0x0040}, +{0x01F02C, 0x0080}, +{0x01F030, 0x0040}, +{0x01F094, 0x0080}, +{0x01F0A0, 0x0040}, +{0x01F0AF, 0x0080}, +{0x01F0B1, 0x0040}, +{0x01F0C0, 0x0080}, +{0x01F0C1, 0x0040}, +{0x01F0D0, 0x0080}, +{0x01F0D1, 0x0040}, +{0x01F0F6, 0x0080}, +{0x01F100, 0x0002}, +{0x01F10D, 0x0040}, +{0x01F1AE, 0x0080}, +{0x01F1E6, 0x0040}, +{0x01F203, 0x0080}, +{0x01F210, 0x0040}, +{0x01F23C, 0x0080}, +{0x01F240, 0x0040}, +{0x01F249, 0x0080}, +{0x01F250, 0x0040}, +{0x01F252, 0x0080}, +{0x01F260, 0x0040}, +{0x01F266, 0x0080}, +{0x01F300, 0x0040}, +{0x01F6D8, 0x0080}, +{0x01F6DC, 0x0040}, +{0x01F6ED, 0x0080}, +{0x01F6F0, 0x0040}, +{0x01F6FD, 0x0080}, +{0x01F700, 0x0040}, +{0x01F777, 0x0080}, +{0x01F77B, 0x0040}, +{0x01F7DA, 0x0080}, +{0x01F7E0, 0x0040}, +{0x01F7EC, 0x0080}, +{0x01F7F0, 0x0040}, +{0x01F7F1, 0x0080}, +{0x01F800, 0x0040}, +{0x01F80C, 0x0080}, +{0x01F810, 0x0040}, +{0x01F848, 0x0080}, +{0x01F850, 0x0040}, +{0x01F85A, 0x0080}, +{0x01F860, 0x0040}, +{0x01F888, 0x0080}, +{0x01F890, 0x0040}, +{0x01F8AE, 0x0080}, +{0x01F8B0, 0x0040}, +{0x01F8B2, 0x0080}, +{0x01F900, 0x0040}, +{0x01FA54, 0x0080}, +{0x01FA60, 0x0040}, +{0x01FA6E, 0x0080}, +{0x01FA70, 0x0040}, +{0x01FA7D, 0x0080}, +{0x01FA80, 0x0040}, +{0x01FA89, 0x0080}, +{0x01FA90, 0x0040}, +{0x01FABE, 0x0080}, +{0x01FABF, 0x0040}, +{0x01FAC6, 0x0080}, +{0x01FACE, 0x0040}, +{0x01FADC, 0x0080}, +{0x01FAE0, 0x0040}, +{0x01FAE9, 0x0080}, +{0x01FAF0, 0x0040}, +{0x01FAF9, 0x0080}, +{0x01FB00, 0x0040}, +{0x01FB93, 0x0080}, +{0x01FB94, 0x0040}, +{0x01FBCB, 0x0080}, +{0x01FBF0, 0x0002}, +{0x01FBFA, 0x0080}, +{0x020000, 0x0004}, +{0x02A6E0, 0x0080}, +{0x02A700, 0x0004}, +{0x02B73A, 0x0080}, +{0x02B740, 0x0004}, +{0x02B81E, 0x0080}, +{0x02B820, 0x0004}, +{0x02CEA2, 0x0080}, +{0x02CEB0, 0x0004}, +{0x02EBE1, 0x0080}, +{0x02EBF0, 0x0004}, +{0x02EE5E, 0x0080}, +{0x02F800, 0x0004}, +{0x02FA1E, 0x0080}, +{0x030000, 0x0004}, +{0x03134B, 0x0080}, +{0x031350, 0x0004}, +{0x0323B0, 0x0080}, +{0x0E0100, 0x0010}, +{0x0E01F0, 0x0080}, +{0x110000, 0x0000}, }; -const std::vector> unicode_ranges_letter = { -{0x00000041, 0x0000005A}, {0x00000061, 0x0000007A}, {0x000000AA, 0x000000AA}, {0x000000B5, 0x000000B5}, -{0x000000BA, 0x000000BA}, {0x000000C0, 0x000000D6}, {0x000000D8, 0x000000F6}, {0x000000F8, 0x000002C1}, -{0x000002C6, 0x000002D1}, {0x000002E0, 0x000002E4}, {0x000002EC, 0x000002EC}, {0x000002EE, 0x000002EE}, -{0x00000370, 0x00000374}, {0x00000376, 0x00000377}, {0x0000037A, 0x0000037D}, {0x0000037F, 0x0000037F}, -{0x00000386, 0x00000386}, {0x00000388, 0x0000038A}, {0x0000038C, 0x0000038C}, {0x0000038E, 0x000003A1}, -{0x000003A3, 0x000003F5}, {0x000003F7, 0x00000481}, {0x0000048A, 0x0000052F}, {0x00000531, 0x00000556}, -{0x00000559, 0x00000559}, {0x00000560, 0x00000588}, {0x000005D0, 0x000005EA}, {0x000005EF, 0x000005F2}, -{0x00000620, 0x0000064A}, {0x0000066E, 0x0000066F}, {0x00000671, 0x000006D3}, {0x000006D5, 0x000006D5}, -{0x000006E5, 0x000006E6}, {0x000006EE, 0x000006EF}, {0x000006FA, 0x000006FC}, {0x000006FF, 0x000006FF}, -{0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1}, -{0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815}, -{0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858}, -{0x00000860, 0x0000086A}, {0x00000870, 0x00000887}, {0x00000889, 0x0000088E}, {0x000008A0, 0x000008C9}, -{0x00000904, 0x00000939}, {0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961}, -{0x00000971, 0x00000980}, {0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8}, -{0x000009AA, 0x000009B0}, {0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD}, -{0x000009CE, 0x000009CE}, {0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1}, -{0x000009FC, 0x000009FC}, {0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28}, -{0x00000A2A, 0x00000A30}, {0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39}, -{0x00000A59, 0x00000A5C}, {0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D}, -{0x00000A8F, 0x00000A91}, {0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3}, -{0x00000AB5, 0x00000AB9}, {0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1}, -{0x00000AF9, 0x00000AF9}, {0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28}, -{0x00000B2A, 0x00000B30}, {0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D}, -{0x00000B5C, 0x00000B5D}, {0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83}, -{0x00000B85, 0x00000B8A}, {0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A}, -{0x00000B9C, 0x00000B9C}, {0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA}, -{0x00000BAE, 0x00000BB9}, {0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10}, -{0x00000C12, 0x00000C28}, {0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A}, -{0x00000C5D, 0x00000C5D}, {0x00000C60, 0x00000C61}, {0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C}, -{0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, {0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9}, -{0x00000CBD, 0x00000CBD}, {0x00000CDD, 0x00000CDE}, {0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2}, -{0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, {0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D}, -{0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, {0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F}, -{0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, {0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD}, -{0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, {0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46}, -{0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, {0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3}, -{0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, {0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD}, -{0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, {0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00}, -{0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, {0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A}, -{0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, {0x0000105A, 0x0000105D}, {0x00001061, 0x00001061}, -{0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, {0x00001075, 0x00001081}, {0x0000108E, 0x0000108E}, -{0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, {0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA}, -{0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, {0x00001250, 0x00001256}, {0x00001258, 0x00001258}, -{0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, {0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0}, -{0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, {0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5}, -{0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, {0x00001312, 0x00001315}, {0x00001318, 0x0000135A}, -{0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, {0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C}, -{0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, {0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8}, -{0x00001700, 0x00001711}, {0x0000171F, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C}, -{0x0000176E, 0x00001770}, {0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC}, -{0x00001820, 0x00001878}, {0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA}, -{0x000018B0, 0x000018F5}, {0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974}, -{0x00001980, 0x000019AB}, {0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54}, -{0x00001AA7, 0x00001AA7}, {0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4C}, {0x00001B83, 0x00001BA0}, -{0x00001BAE, 0x00001BAF}, {0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F}, -{0x00001C5A, 0x00001C7D}, {0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF}, -{0x00001CE9, 0x00001CEC}, {0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA}, -{0x00001D00, 0x00001DBF}, {0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45}, -{0x00001F48, 0x00001F4D}, {0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B}, -{0x00001F5D, 0x00001F5D}, {0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC}, -{0x00001FBE, 0x00001FBE}, {0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3}, -{0x00001FD6, 0x00001FDB}, {0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC}, -{0x00002071, 0x00002071}, {0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102}, -{0x00002107, 0x00002107}, {0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D}, -{0x00002124, 0x00002124}, {0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D}, -{0x0000212F, 0x00002139}, {0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E}, -{0x00002183, 0x00002184}, {0x00002C00, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, {0x00002CF2, 0x00002CF3}, -{0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, {0x00002D30, 0x00002D67}, -{0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, {0x00002DA8, 0x00002DAE}, -{0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, {0x00002DC8, 0x00002DCE}, -{0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, {0x00003005, 0x00003006}, -{0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, {0x0000309D, 0x0000309F}, -{0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, {0x00003131, 0x0000318E}, -{0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, {0x00004E00, 0x0000A48C}, -{0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, {0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B}, -{0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, {0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F}, -{0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7CA}, {0x0000A7D0, 0x0000A7D1}, {0x0000A7D3, 0x0000A7D3}, -{0x0000A7D5, 0x0000A7D9}, {0x0000A7F2, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A}, -{0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7}, -{0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946}, -{0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4}, -{0x0000A9E6, 0x0000A9EF}, {0x0000A9FA, 0x0000A9FE}, {0x0000AA00, 0x0000AA28}, {0x0000AA40, 0x0000AA42}, -{0x0000AA44, 0x0000AA4B}, {0x0000AA60, 0x0000AA76}, {0x0000AA7A, 0x0000AA7A}, {0x0000AA7E, 0x0000AAAF}, -{0x0000AAB1, 0x0000AAB1}, {0x0000AAB5, 0x0000AAB6}, {0x0000AAB9, 0x0000AABD}, {0x0000AAC0, 0x0000AAC0}, -{0x0000AAC2, 0x0000AAC2}, {0x0000AADB, 0x0000AADD}, {0x0000AAE0, 0x0000AAEA}, {0x0000AAF2, 0x0000AAF4}, -{0x0000AB01, 0x0000AB06}, {0x0000AB09, 0x0000AB0E}, {0x0000AB11, 0x0000AB16}, {0x0000AB20, 0x0000AB26}, -{0x0000AB28, 0x0000AB2E}, {0x0000AB30, 0x0000AB5A}, {0x0000AB5C, 0x0000AB69}, {0x0000AB70, 0x0000ABE2}, -{0x0000AC00, 0x0000D7A3}, {0x0000D7B0, 0x0000D7C6}, {0x0000D7CB, 0x0000D7FB}, {0x0000F900, 0x0000FA6D}, -{0x0000FA70, 0x0000FAD9}, {0x0000FB00, 0x0000FB06}, {0x0000FB13, 0x0000FB17}, {0x0000FB1D, 0x0000FB1D}, -{0x0000FB1F, 0x0000FB28}, {0x0000FB2A, 0x0000FB36}, {0x0000FB38, 0x0000FB3C}, {0x0000FB3E, 0x0000FB3E}, -{0x0000FB40, 0x0000FB41}, {0x0000FB43, 0x0000FB44}, {0x0000FB46, 0x0000FBB1}, {0x0000FBD3, 0x0000FD3D}, -{0x0000FD50, 0x0000FD8F}, {0x0000FD92, 0x0000FDC7}, {0x0000FDF0, 0x0000FDFB}, {0x0000FE70, 0x0000FE74}, -{0x0000FE76, 0x0000FEFC}, {0x0000FF21, 0x0000FF3A}, {0x0000FF41, 0x0000FF5A}, {0x0000FF66, 0x0000FFBE}, -{0x0000FFC2, 0x0000FFC7}, {0x0000FFCA, 0x0000FFCF}, {0x0000FFD2, 0x0000FFD7}, {0x0000FFDA, 0x0000FFDC}, -{0x00010000, 0x0001000B}, {0x0001000D, 0x00010026}, {0x00010028, 0x0001003A}, {0x0001003C, 0x0001003D}, -{0x0001003F, 0x0001004D}, {0x00010050, 0x0001005D}, {0x00010080, 0x000100FA}, {0x00010280, 0x0001029C}, -{0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349}, -{0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF}, -{0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527}, -{0x00010530, 0x00010563}, {0x00010570, 0x0001057A}, {0x0001057C, 0x0001058A}, {0x0001058C, 0x00010592}, -{0x00010594, 0x00010595}, {0x00010597, 0x000105A1}, {0x000105A3, 0x000105B1}, {0x000105B3, 0x000105B9}, -{0x000105BB, 0x000105BC}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767}, -{0x00010780, 0x00010785}, {0x00010787, 0x000107B0}, {0x000107B2, 0x000107BA}, {0x00010800, 0x00010805}, -{0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, {0x0001083C, 0x0001083C}, -{0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, {0x000108E0, 0x000108F2}, -{0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, {0x00010980, 0x000109B7}, -{0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, {0x00010A15, 0x00010A17}, -{0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, {0x00010AC0, 0x00010AC7}, -{0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, {0x00010B60, 0x00010B72}, -{0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, {0x00010CC0, 0x00010CF2}, -{0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, {0x00010F00, 0x00010F1C}, -{0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010F70, 0x00010F81}, {0x00010FB0, 0x00010FC4}, -{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011071, 0x00011072}, {0x00011075, 0x00011075}, -{0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, {0x00011103, 0x00011126}, {0x00011144, 0x00011144}, -{0x00011147, 0x00011147}, {0x00011150, 0x00011172}, {0x00011176, 0x00011176}, {0x00011183, 0x000111B2}, -{0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, {0x000111DC, 0x000111DC}, {0x00011200, 0x00011211}, -{0x00011213, 0x0001122B}, {0x0001123F, 0x00011240}, {0x00011280, 0x00011286}, {0x00011288, 0x00011288}, -{0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, {0x000112B0, 0x000112DE}, -{0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, {0x0001132A, 0x00011330}, -{0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, {0x00011350, 0x00011350}, -{0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, {0x0001145F, 0x00011461}, -{0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, {0x00011580, 0x000115AE}, -{0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, {0x00011680, 0x000116AA}, -{0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011740, 0x00011746}, {0x00011800, 0x0001182B}, -{0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913}, -{0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941}, -{0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3}, -{0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50}, -{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AB0, 0x00011AF8}, {0x00011C00, 0x00011C08}, -{0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06}, -{0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65}, -{0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2}, -{0x00011F02, 0x00011F02}, {0x00011F04, 0x00011F10}, {0x00011F12, 0x00011F33}, {0x00011FB0, 0x00011FB0}, -{0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00012F90, 0x00012FF0}, {0x00013000, 0x0001342F}, -{0x00013441, 0x00013446}, {0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E}, -{0x00016A70, 0x00016ABE}, {0x00016AD0, 0x00016AED}, {0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43}, -{0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, {0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A}, -{0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, {0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3}, -{0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, {0x00018D00, 0x00018D08}, {0x0001AFF0, 0x0001AFF3}, -{0x0001AFF5, 0x0001AFFB}, {0x0001AFFD, 0x0001AFFE}, {0x0001B000, 0x0001B122}, {0x0001B132, 0x0001B132}, -{0x0001B150, 0x0001B152}, {0x0001B155, 0x0001B155}, {0x0001B164, 0x0001B167}, {0x0001B170, 0x0001B2FB}, -{0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, {0x0001BC90, 0x0001BC99}, -{0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, {0x0001D4A2, 0x0001D4A2}, -{0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, {0x0001D4BB, 0x0001D4BB}, -{0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, {0x0001D50D, 0x0001D514}, -{0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, {0x0001D540, 0x0001D544}, -{0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, {0x0001D6A8, 0x0001D6C0}, -{0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, {0x0001D716, 0x0001D734}, -{0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, {0x0001D78A, 0x0001D7A8}, -{0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001DF00, 0x0001DF1E}, {0x0001DF25, 0x0001DF2A}, -{0x0001E030, 0x0001E06D}, {0x0001E100, 0x0001E12C}, {0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E}, -{0x0001E290, 0x0001E2AD}, {0x0001E2C0, 0x0001E2EB}, {0x0001E4D0, 0x0001E4EB}, {0x0001E7E0, 0x0001E7E6}, -{0x0001E7E8, 0x0001E7EB}, {0x0001E7ED, 0x0001E7EE}, {0x0001E7F0, 0x0001E7FE}, {0x0001E800, 0x0001E8C4}, -{0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F}, -{0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32}, -{0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42}, -{0x0001EE47, 0x0001EE47}, {0x0001EE49, 0x0001EE49}, {0x0001EE4B, 0x0001EE4B}, {0x0001EE4D, 0x0001EE4F}, -{0x0001EE51, 0x0001EE52}, {0x0001EE54, 0x0001EE54}, {0x0001EE57, 0x0001EE57}, {0x0001EE59, 0x0001EE59}, -{0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62}, -{0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77}, -{0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B}, -{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DF}, -{0x0002A700, 0x0002B739}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0}, -{0x0002EBF0, 0x0002EE5D}, {0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF}, +const std::unordered_set unicode_set_whitespace = { +0x000009, 0x00000A, 0x00000B, 0x00000C, 0x00000D, 0x000020, 0x000085, 0x0000A0, 0x001680, 0x002000, 0x002001, 0x002002, 0x002003, 0x002004, 0x002005, 0x002006, 0x002007, 0x002008, 0x002009, 0x00200A, 0x002028, 0x002029, 0x00202F, 0x00205F, 0x003000 }; -const std::vector> unicode_ranges_separator = { -{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, -{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, +const std::unordered_map unicode_map_lowercase = { +{0x000041, 0x000061}, +{0x000042, 0x000062}, +{0x000043, 0x000063}, +{0x000044, 0x000064}, +{0x000045, 0x000065}, +{0x000046, 0x000066}, +{0x000047, 0x000067}, +{0x000048, 0x000068}, +{0x000049, 0x000069}, +{0x00004A, 0x00006A}, +{0x00004B, 0x00006B}, +{0x00004C, 0x00006C}, +{0x00004D, 0x00006D}, +{0x00004E, 0x00006E}, +{0x00004F, 0x00006F}, +{0x000050, 0x000070}, +{0x000051, 0x000071}, +{0x000052, 0x000072}, +{0x000053, 0x000073}, +{0x000054, 0x000074}, +{0x000055, 0x000075}, +{0x000056, 0x000076}, +{0x000057, 0x000077}, +{0x000058, 0x000078}, +{0x000059, 0x000079}, +{0x00005A, 0x00007A}, +{0x0000C0, 0x0000E0}, +{0x0000C1, 0x0000E1}, +{0x0000C2, 0x0000E2}, +{0x0000C3, 0x0000E3}, +{0x0000C4, 0x0000E4}, +{0x0000C5, 0x0000E5}, +{0x0000C6, 0x0000E6}, +{0x0000C7, 0x0000E7}, +{0x0000C8, 0x0000E8}, +{0x0000C9, 0x0000E9}, +{0x0000CA, 0x0000EA}, +{0x0000CB, 0x0000EB}, +{0x0000CC, 0x0000EC}, +{0x0000CD, 0x0000ED}, +{0x0000CE, 0x0000EE}, +{0x0000CF, 0x0000EF}, +{0x0000D0, 0x0000F0}, +{0x0000D1, 0x0000F1}, +{0x0000D2, 0x0000F2}, +{0x0000D3, 0x0000F3}, +{0x0000D4, 0x0000F4}, +{0x0000D5, 0x0000F5}, +{0x0000D6, 0x0000F6}, +{0x0000D8, 0x0000F8}, +{0x0000D9, 0x0000F9}, +{0x0000DA, 0x0000FA}, +{0x0000DB, 0x0000FB}, +{0x0000DC, 0x0000FC}, +{0x0000DD, 0x0000FD}, +{0x0000DE, 0x0000FE}, +{0x000100, 0x000101}, +{0x000102, 0x000103}, +{0x000104, 0x000105}, +{0x000106, 0x000107}, +{0x000108, 0x000109}, +{0x00010A, 0x00010B}, +{0x00010C, 0x00010D}, +{0x00010E, 0x00010F}, +{0x000110, 0x000111}, +{0x000112, 0x000113}, +{0x000114, 0x000115}, +{0x000116, 0x000117}, +{0x000118, 0x000119}, +{0x00011A, 0x00011B}, +{0x00011C, 0x00011D}, +{0x00011E, 0x00011F}, +{0x000120, 0x000121}, +{0x000122, 0x000123}, +{0x000124, 0x000125}, +{0x000126, 0x000127}, +{0x000128, 0x000129}, +{0x00012A, 0x00012B}, +{0x00012C, 0x00012D}, +{0x00012E, 0x00012F}, +{0x000130, 0x000069}, +{0x000132, 0x000133}, +{0x000134, 0x000135}, +{0x000136, 0x000137}, +{0x000139, 0x00013A}, +{0x00013B, 0x00013C}, +{0x00013D, 0x00013E}, +{0x00013F, 0x000140}, +{0x000141, 0x000142}, +{0x000143, 0x000144}, +{0x000145, 0x000146}, +{0x000147, 0x000148}, +{0x00014A, 0x00014B}, +{0x00014C, 0x00014D}, +{0x00014E, 0x00014F}, +{0x000150, 0x000151}, +{0x000152, 0x000153}, +{0x000154, 0x000155}, +{0x000156, 0x000157}, +{0x000158, 0x000159}, +{0x00015A, 0x00015B}, +{0x00015C, 0x00015D}, +{0x00015E, 0x00015F}, +{0x000160, 0x000161}, +{0x000162, 0x000163}, +{0x000164, 0x000165}, +{0x000166, 0x000167}, +{0x000168, 0x000169}, +{0x00016A, 0x00016B}, +{0x00016C, 0x00016D}, +{0x00016E, 0x00016F}, +{0x000170, 0x000171}, +{0x000172, 0x000173}, +{0x000174, 0x000175}, +{0x000176, 0x000177}, +{0x000178, 0x0000FF}, +{0x000179, 0x00017A}, +{0x00017B, 0x00017C}, +{0x00017D, 0x00017E}, +{0x000181, 0x000253}, +{0x000182, 0x000183}, +{0x000184, 0x000185}, +{0x000186, 0x000254}, +{0x000187, 0x000188}, +{0x000189, 0x000256}, +{0x00018A, 0x000257}, +{0x00018B, 0x00018C}, +{0x00018E, 0x0001DD}, +{0x00018F, 0x000259}, +{0x000190, 0x00025B}, +{0x000191, 0x000192}, +{0x000193, 0x000260}, +{0x000194, 0x000263}, +{0x000196, 0x000269}, +{0x000197, 0x000268}, +{0x000198, 0x000199}, +{0x00019C, 0x00026F}, +{0x00019D, 0x000272}, +{0x00019F, 0x000275}, +{0x0001A0, 0x0001A1}, +{0x0001A2, 0x0001A3}, +{0x0001A4, 0x0001A5}, +{0x0001A6, 0x000280}, +{0x0001A7, 0x0001A8}, +{0x0001A9, 0x000283}, +{0x0001AC, 0x0001AD}, +{0x0001AE, 0x000288}, +{0x0001AF, 0x0001B0}, +{0x0001B1, 0x00028A}, +{0x0001B2, 0x00028B}, +{0x0001B3, 0x0001B4}, +{0x0001B5, 0x0001B6}, +{0x0001B7, 0x000292}, +{0x0001B8, 0x0001B9}, +{0x0001BC, 0x0001BD}, +{0x0001C4, 0x0001C6}, +{0x0001C5, 0x0001C6}, +{0x0001C7, 0x0001C9}, +{0x0001C8, 0x0001C9}, +{0x0001CA, 0x0001CC}, +{0x0001CB, 0x0001CC}, +{0x0001CD, 0x0001CE}, +{0x0001CF, 0x0001D0}, +{0x0001D1, 0x0001D2}, +{0x0001D3, 0x0001D4}, +{0x0001D5, 0x0001D6}, +{0x0001D7, 0x0001D8}, +{0x0001D9, 0x0001DA}, +{0x0001DB, 0x0001DC}, +{0x0001DE, 0x0001DF}, +{0x0001E0, 0x0001E1}, +{0x0001E2, 0x0001E3}, +{0x0001E4, 0x0001E5}, +{0x0001E6, 0x0001E7}, +{0x0001E8, 0x0001E9}, +{0x0001EA, 0x0001EB}, +{0x0001EC, 0x0001ED}, +{0x0001EE, 0x0001EF}, +{0x0001F1, 0x0001F3}, +{0x0001F2, 0x0001F3}, +{0x0001F4, 0x0001F5}, +{0x0001F6, 0x000195}, +{0x0001F7, 0x0001BF}, +{0x0001F8, 0x0001F9}, +{0x0001FA, 0x0001FB}, +{0x0001FC, 0x0001FD}, +{0x0001FE, 0x0001FF}, +{0x000200, 0x000201}, +{0x000202, 0x000203}, +{0x000204, 0x000205}, +{0x000206, 0x000207}, +{0x000208, 0x000209}, +{0x00020A, 0x00020B}, +{0x00020C, 0x00020D}, +{0x00020E, 0x00020F}, +{0x000210, 0x000211}, +{0x000212, 0x000213}, +{0x000214, 0x000215}, +{0x000216, 0x000217}, +{0x000218, 0x000219}, +{0x00021A, 0x00021B}, +{0x00021C, 0x00021D}, +{0x00021E, 0x00021F}, +{0x000220, 0x00019E}, +{0x000222, 0x000223}, +{0x000224, 0x000225}, +{0x000226, 0x000227}, +{0x000228, 0x000229}, +{0x00022A, 0x00022B}, +{0x00022C, 0x00022D}, +{0x00022E, 0x00022F}, +{0x000230, 0x000231}, +{0x000232, 0x000233}, +{0x00023A, 0x002C65}, +{0x00023B, 0x00023C}, +{0x00023D, 0x00019A}, +{0x00023E, 0x002C66}, +{0x000241, 0x000242}, +{0x000243, 0x000180}, +{0x000244, 0x000289}, +{0x000245, 0x00028C}, +{0x000246, 0x000247}, +{0x000248, 0x000249}, +{0x00024A, 0x00024B}, +{0x00024C, 0x00024D}, +{0x00024E, 0x00024F}, +{0x000370, 0x000371}, +{0x000372, 0x000373}, +{0x000376, 0x000377}, +{0x00037F, 0x0003F3}, +{0x000386, 0x0003AC}, +{0x000388, 0x0003AD}, +{0x000389, 0x0003AE}, +{0x00038A, 0x0003AF}, +{0x00038C, 0x0003CC}, +{0x00038E, 0x0003CD}, +{0x00038F, 0x0003CE}, +{0x000391, 0x0003B1}, +{0x000392, 0x0003B2}, +{0x000393, 0x0003B3}, +{0x000394, 0x0003B4}, +{0x000395, 0x0003B5}, +{0x000396, 0x0003B6}, +{0x000397, 0x0003B7}, +{0x000398, 0x0003B8}, +{0x000399, 0x0003B9}, +{0x00039A, 0x0003BA}, +{0x00039B, 0x0003BB}, +{0x00039C, 0x0003BC}, +{0x00039D, 0x0003BD}, +{0x00039E, 0x0003BE}, +{0x00039F, 0x0003BF}, +{0x0003A0, 0x0003C0}, +{0x0003A1, 0x0003C1}, +{0x0003A3, 0x0003C3}, +{0x0003A4, 0x0003C4}, +{0x0003A5, 0x0003C5}, +{0x0003A6, 0x0003C6}, +{0x0003A7, 0x0003C7}, +{0x0003A8, 0x0003C8}, +{0x0003A9, 0x0003C9}, +{0x0003AA, 0x0003CA}, +{0x0003AB, 0x0003CB}, +{0x0003CF, 0x0003D7}, +{0x0003D8, 0x0003D9}, +{0x0003DA, 0x0003DB}, +{0x0003DC, 0x0003DD}, +{0x0003DE, 0x0003DF}, +{0x0003E0, 0x0003E1}, +{0x0003E2, 0x0003E3}, +{0x0003E4, 0x0003E5}, +{0x0003E6, 0x0003E7}, +{0x0003E8, 0x0003E9}, +{0x0003EA, 0x0003EB}, +{0x0003EC, 0x0003ED}, +{0x0003EE, 0x0003EF}, +{0x0003F4, 0x0003B8}, +{0x0003F7, 0x0003F8}, +{0x0003F9, 0x0003F2}, +{0x0003FA, 0x0003FB}, +{0x0003FD, 0x00037B}, +{0x0003FE, 0x00037C}, +{0x0003FF, 0x00037D}, +{0x000400, 0x000450}, +{0x000401, 0x000451}, +{0x000402, 0x000452}, +{0x000403, 0x000453}, +{0x000404, 0x000454}, +{0x000405, 0x000455}, +{0x000406, 0x000456}, +{0x000407, 0x000457}, +{0x000408, 0x000458}, +{0x000409, 0x000459}, +{0x00040A, 0x00045A}, +{0x00040B, 0x00045B}, +{0x00040C, 0x00045C}, +{0x00040D, 0x00045D}, +{0x00040E, 0x00045E}, +{0x00040F, 0x00045F}, +{0x000410, 0x000430}, +{0x000411, 0x000431}, +{0x000412, 0x000432}, +{0x000413, 0x000433}, +{0x000414, 0x000434}, +{0x000415, 0x000435}, +{0x000416, 0x000436}, +{0x000417, 0x000437}, +{0x000418, 0x000438}, +{0x000419, 0x000439}, +{0x00041A, 0x00043A}, +{0x00041B, 0x00043B}, +{0x00041C, 0x00043C}, +{0x00041D, 0x00043D}, +{0x00041E, 0x00043E}, +{0x00041F, 0x00043F}, +{0x000420, 0x000440}, +{0x000421, 0x000441}, +{0x000422, 0x000442}, +{0x000423, 0x000443}, +{0x000424, 0x000444}, +{0x000425, 0x000445}, +{0x000426, 0x000446}, +{0x000427, 0x000447}, +{0x000428, 0x000448}, +{0x000429, 0x000449}, +{0x00042A, 0x00044A}, +{0x00042B, 0x00044B}, +{0x00042C, 0x00044C}, +{0x00042D, 0x00044D}, +{0x00042E, 0x00044E}, +{0x00042F, 0x00044F}, +{0x000460, 0x000461}, +{0x000462, 0x000463}, +{0x000464, 0x000465}, +{0x000466, 0x000467}, +{0x000468, 0x000469}, +{0x00046A, 0x00046B}, +{0x00046C, 0x00046D}, +{0x00046E, 0x00046F}, +{0x000470, 0x000471}, +{0x000472, 0x000473}, +{0x000474, 0x000475}, +{0x000476, 0x000477}, +{0x000478, 0x000479}, +{0x00047A, 0x00047B}, +{0x00047C, 0x00047D}, +{0x00047E, 0x00047F}, +{0x000480, 0x000481}, +{0x00048A, 0x00048B}, +{0x00048C, 0x00048D}, +{0x00048E, 0x00048F}, +{0x000490, 0x000491}, +{0x000492, 0x000493}, +{0x000494, 0x000495}, +{0x000496, 0x000497}, +{0x000498, 0x000499}, +{0x00049A, 0x00049B}, +{0x00049C, 0x00049D}, +{0x00049E, 0x00049F}, +{0x0004A0, 0x0004A1}, +{0x0004A2, 0x0004A3}, +{0x0004A4, 0x0004A5}, +{0x0004A6, 0x0004A7}, +{0x0004A8, 0x0004A9}, +{0x0004AA, 0x0004AB}, +{0x0004AC, 0x0004AD}, +{0x0004AE, 0x0004AF}, +{0x0004B0, 0x0004B1}, +{0x0004B2, 0x0004B3}, +{0x0004B4, 0x0004B5}, +{0x0004B6, 0x0004B7}, +{0x0004B8, 0x0004B9}, +{0x0004BA, 0x0004BB}, +{0x0004BC, 0x0004BD}, +{0x0004BE, 0x0004BF}, +{0x0004C0, 0x0004CF}, +{0x0004C1, 0x0004C2}, +{0x0004C3, 0x0004C4}, +{0x0004C5, 0x0004C6}, +{0x0004C7, 0x0004C8}, +{0x0004C9, 0x0004CA}, +{0x0004CB, 0x0004CC}, +{0x0004CD, 0x0004CE}, +{0x0004D0, 0x0004D1}, +{0x0004D2, 0x0004D3}, +{0x0004D4, 0x0004D5}, +{0x0004D6, 0x0004D7}, +{0x0004D8, 0x0004D9}, +{0x0004DA, 0x0004DB}, +{0x0004DC, 0x0004DD}, +{0x0004DE, 0x0004DF}, +{0x0004E0, 0x0004E1}, +{0x0004E2, 0x0004E3}, +{0x0004E4, 0x0004E5}, +{0x0004E6, 0x0004E7}, +{0x0004E8, 0x0004E9}, +{0x0004EA, 0x0004EB}, +{0x0004EC, 0x0004ED}, +{0x0004EE, 0x0004EF}, +{0x0004F0, 0x0004F1}, +{0x0004F2, 0x0004F3}, +{0x0004F4, 0x0004F5}, +{0x0004F6, 0x0004F7}, +{0x0004F8, 0x0004F9}, +{0x0004FA, 0x0004FB}, +{0x0004FC, 0x0004FD}, +{0x0004FE, 0x0004FF}, +{0x000500, 0x000501}, +{0x000502, 0x000503}, +{0x000504, 0x000505}, +{0x000506, 0x000507}, +{0x000508, 0x000509}, +{0x00050A, 0x00050B}, +{0x00050C, 0x00050D}, +{0x00050E, 0x00050F}, +{0x000510, 0x000511}, +{0x000512, 0x000513}, +{0x000514, 0x000515}, +{0x000516, 0x000517}, +{0x000518, 0x000519}, +{0x00051A, 0x00051B}, +{0x00051C, 0x00051D}, +{0x00051E, 0x00051F}, +{0x000520, 0x000521}, +{0x000522, 0x000523}, +{0x000524, 0x000525}, +{0x000526, 0x000527}, +{0x000528, 0x000529}, +{0x00052A, 0x00052B}, +{0x00052C, 0x00052D}, +{0x00052E, 0x00052F}, +{0x000531, 0x000561}, +{0x000532, 0x000562}, +{0x000533, 0x000563}, +{0x000534, 0x000564}, +{0x000535, 0x000565}, +{0x000536, 0x000566}, +{0x000537, 0x000567}, +{0x000538, 0x000568}, +{0x000539, 0x000569}, +{0x00053A, 0x00056A}, +{0x00053B, 0x00056B}, +{0x00053C, 0x00056C}, +{0x00053D, 0x00056D}, +{0x00053E, 0x00056E}, +{0x00053F, 0x00056F}, +{0x000540, 0x000570}, +{0x000541, 0x000571}, +{0x000542, 0x000572}, +{0x000543, 0x000573}, +{0x000544, 0x000574}, +{0x000545, 0x000575}, +{0x000546, 0x000576}, +{0x000547, 0x000577}, +{0x000548, 0x000578}, +{0x000549, 0x000579}, +{0x00054A, 0x00057A}, +{0x00054B, 0x00057B}, +{0x00054C, 0x00057C}, +{0x00054D, 0x00057D}, +{0x00054E, 0x00057E}, +{0x00054F, 0x00057F}, +{0x000550, 0x000580}, +{0x000551, 0x000581}, +{0x000552, 0x000582}, +{0x000553, 0x000583}, +{0x000554, 0x000584}, +{0x000555, 0x000585}, +{0x000556, 0x000586}, +{0x0010A0, 0x002D00}, +{0x0010A1, 0x002D01}, +{0x0010A2, 0x002D02}, +{0x0010A3, 0x002D03}, +{0x0010A4, 0x002D04}, +{0x0010A5, 0x002D05}, +{0x0010A6, 0x002D06}, +{0x0010A7, 0x002D07}, +{0x0010A8, 0x002D08}, +{0x0010A9, 0x002D09}, +{0x0010AA, 0x002D0A}, +{0x0010AB, 0x002D0B}, +{0x0010AC, 0x002D0C}, +{0x0010AD, 0x002D0D}, +{0x0010AE, 0x002D0E}, +{0x0010AF, 0x002D0F}, +{0x0010B0, 0x002D10}, +{0x0010B1, 0x002D11}, +{0x0010B2, 0x002D12}, +{0x0010B3, 0x002D13}, +{0x0010B4, 0x002D14}, +{0x0010B5, 0x002D15}, +{0x0010B6, 0x002D16}, +{0x0010B7, 0x002D17}, +{0x0010B8, 0x002D18}, +{0x0010B9, 0x002D19}, +{0x0010BA, 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+{0x00A724, 0x00A725}, +{0x00A726, 0x00A727}, +{0x00A728, 0x00A729}, +{0x00A72A, 0x00A72B}, +{0x00A72C, 0x00A72D}, +{0x00A72E, 0x00A72F}, +{0x00A732, 0x00A733}, +{0x00A734, 0x00A735}, +{0x00A736, 0x00A737}, +{0x00A738, 0x00A739}, +{0x00A73A, 0x00A73B}, +{0x00A73C, 0x00A73D}, +{0x00A73E, 0x00A73F}, +{0x00A740, 0x00A741}, +{0x00A742, 0x00A743}, +{0x00A744, 0x00A745}, +{0x00A746, 0x00A747}, +{0x00A748, 0x00A749}, +{0x00A74A, 0x00A74B}, +{0x00A74C, 0x00A74D}, +{0x00A74E, 0x00A74F}, +{0x00A750, 0x00A751}, +{0x00A752, 0x00A753}, +{0x00A754, 0x00A755}, +{0x00A756, 0x00A757}, +{0x00A758, 0x00A759}, +{0x00A75A, 0x00A75B}, +{0x00A75C, 0x00A75D}, +{0x00A75E, 0x00A75F}, +{0x00A760, 0x00A761}, +{0x00A762, 0x00A763}, +{0x00A764, 0x00A765}, +{0x00A766, 0x00A767}, +{0x00A768, 0x00A769}, +{0x00A76A, 0x00A76B}, +{0x00A76C, 0x00A76D}, +{0x00A76E, 0x00A76F}, +{0x00A779, 0x00A77A}, +{0x00A77B, 0x00A77C}, +{0x00A77D, 0x001D79}, +{0x00A77E, 0x00A77F}, +{0x00A780, 0x00A781}, +{0x00A782, 0x00A783}, +{0x00A784, 0x00A785}, +{0x00A786, 0x00A787}, +{0x00A78B, 0x00A78C}, +{0x00A78D, 0x000265}, +{0x00A790, 0x00A791}, +{0x00A792, 0x00A793}, +{0x00A796, 0x00A797}, +{0x00A798, 0x00A799}, +{0x00A79A, 0x00A79B}, +{0x00A79C, 0x00A79D}, +{0x00A79E, 0x00A79F}, +{0x00A7A0, 0x00A7A1}, +{0x00A7A2, 0x00A7A3}, +{0x00A7A4, 0x00A7A5}, +{0x00A7A6, 0x00A7A7}, +{0x00A7A8, 0x00A7A9}, +{0x00A7AA, 0x000266}, +{0x00A7AB, 0x00025C}, +{0x00A7AC, 0x000261}, +{0x00A7AD, 0x00026C}, +{0x00A7AE, 0x00026A}, +{0x00A7B0, 0x00029E}, +{0x00A7B1, 0x000287}, +{0x00A7B2, 0x00029D}, +{0x00A7B3, 0x00AB53}, +{0x00A7B4, 0x00A7B5}, +{0x00A7B6, 0x00A7B7}, +{0x00A7B8, 0x00A7B9}, +{0x00A7BA, 0x00A7BB}, +{0x00A7BC, 0x00A7BD}, +{0x00A7BE, 0x00A7BF}, +{0x00A7C2, 0x00A7C3}, +{0x00A7C4, 0x00A794}, +{0x00A7C5, 0x000282}, +{0x00A7C6, 0x001D8E}, +{0x00A7C7, 0x00A7C8}, +{0x00A7C9, 0x00A7CA}, +{0x00A7F5, 0x00A7F6}, +{0x00FF21, 0x00FF41}, +{0x00FF22, 0x00FF42}, +{0x00FF23, 0x00FF43}, +{0x00FF24, 0x00FF44}, +{0x00FF25, 0x00FF45}, +{0x00FF26, 0x00FF46}, +{0x00FF27, 0x00FF47}, +{0x00FF28, 0x00FF48}, +{0x00FF29, 0x00FF49}, +{0x00FF2A, 0x00FF4A}, +{0x00FF2B, 0x00FF4B}, +{0x00FF2C, 0x00FF4C}, +{0x00FF2D, 0x00FF4D}, +{0x00FF2E, 0x00FF4E}, +{0x00FF2F, 0x00FF4F}, +{0x00FF30, 0x00FF50}, +{0x00FF31, 0x00FF51}, +{0x00FF32, 0x00FF52}, +{0x00FF33, 0x00FF53}, +{0x00FF34, 0x00FF54}, +{0x00FF35, 0x00FF55}, +{0x00FF36, 0x00FF56}, +{0x00FF37, 0x00FF57}, +{0x00FF38, 0x00FF58}, +{0x00FF39, 0x00FF59}, +{0x00FF3A, 0x00FF5A}, +{0x010400, 0x010428}, +{0x010401, 0x010429}, +{0x010402, 0x01042A}, +{0x010403, 0x01042B}, +{0x010404, 0x01042C}, +{0x010405, 0x01042D}, +{0x010406, 0x01042E}, +{0x010407, 0x01042F}, +{0x010408, 0x010430}, +{0x010409, 0x010431}, +{0x01040A, 0x010432}, +{0x01040B, 0x010433}, +{0x01040C, 0x010434}, +{0x01040D, 0x010435}, +{0x01040E, 0x010436}, +{0x01040F, 0x010437}, +{0x010410, 0x010438}, +{0x010411, 0x010439}, +{0x010412, 0x01043A}, +{0x010413, 0x01043B}, +{0x010414, 0x01043C}, +{0x010415, 0x01043D}, +{0x010416, 0x01043E}, +{0x010417, 0x01043F}, +{0x010418, 0x010440}, +{0x010419, 0x010441}, +{0x01041A, 0x010442}, +{0x01041B, 0x010443}, +{0x01041C, 0x010444}, +{0x01041D, 0x010445}, +{0x01041E, 0x010446}, +{0x01041F, 0x010447}, +{0x010420, 0x010448}, +{0x010421, 0x010449}, +{0x010422, 0x01044A}, +{0x010423, 0x01044B}, +{0x010424, 0x01044C}, +{0x010425, 0x01044D}, +{0x010426, 0x01044E}, +{0x010427, 0x01044F}, +{0x0104B0, 0x0104D8}, +{0x0104B1, 0x0104D9}, +{0x0104B2, 0x0104DA}, +{0x0104B3, 0x0104DB}, +{0x0104B4, 0x0104DC}, +{0x0104B5, 0x0104DD}, +{0x0104B6, 0x0104DE}, +{0x0104B7, 0x0104DF}, +{0x0104B8, 0x0104E0}, +{0x0104B9, 0x0104E1}, +{0x0104BA, 0x0104E2}, +{0x0104BB, 0x0104E3}, +{0x0104BC, 0x0104E4}, +{0x0104BD, 0x0104E5}, +{0x0104BE, 0x0104E6}, +{0x0104BF, 0x0104E7}, +{0x0104C0, 0x0104E8}, +{0x0104C1, 0x0104E9}, +{0x0104C2, 0x0104EA}, +{0x0104C3, 0x0104EB}, +{0x0104C4, 0x0104EC}, +{0x0104C5, 0x0104ED}, +{0x0104C6, 0x0104EE}, +{0x0104C7, 0x0104EF}, +{0x0104C8, 0x0104F0}, +{0x0104C9, 0x0104F1}, +{0x0104CA, 0x0104F2}, +{0x0104CB, 0x0104F3}, +{0x0104CC, 0x0104F4}, +{0x0104CD, 0x0104F5}, +{0x0104CE, 0x0104F6}, +{0x0104CF, 0x0104F7}, +{0x0104D0, 0x0104F8}, +{0x0104D1, 0x0104F9}, +{0x0104D2, 0x0104FA}, +{0x0104D3, 0x0104FB}, +{0x010C80, 0x010CC0}, +{0x010C81, 0x010CC1}, +{0x010C82, 0x010CC2}, +{0x010C83, 0x010CC3}, +{0x010C84, 0x010CC4}, +{0x010C85, 0x010CC5}, +{0x010C86, 0x010CC6}, +{0x010C87, 0x010CC7}, +{0x010C88, 0x010CC8}, +{0x010C89, 0x010CC9}, +{0x010C8A, 0x010CCA}, +{0x010C8B, 0x010CCB}, +{0x010C8C, 0x010CCC}, +{0x010C8D, 0x010CCD}, +{0x010C8E, 0x010CCE}, +{0x010C8F, 0x010CCF}, +{0x010C90, 0x010CD0}, +{0x010C91, 0x010CD1}, +{0x010C92, 0x010CD2}, +{0x010C93, 0x010CD3}, +{0x010C94, 0x010CD4}, +{0x010C95, 0x010CD5}, +{0x010C96, 0x010CD6}, +{0x010C97, 0x010CD7}, +{0x010C98, 0x010CD8}, +{0x010C99, 0x010CD9}, +{0x010C9A, 0x010CDA}, +{0x010C9B, 0x010CDB}, +{0x010C9C, 0x010CDC}, +{0x010C9D, 0x010CDD}, +{0x010C9E, 0x010CDE}, +{0x010C9F, 0x010CDF}, +{0x010CA0, 0x010CE0}, +{0x010CA1, 0x010CE1}, +{0x010CA2, 0x010CE2}, +{0x010CA3, 0x010CE3}, +{0x010CA4, 0x010CE4}, +{0x010CA5, 0x010CE5}, +{0x010CA6, 0x010CE6}, +{0x010CA7, 0x010CE7}, +{0x010CA8, 0x010CE8}, +{0x010CA9, 0x010CE9}, +{0x010CAA, 0x010CEA}, +{0x010CAB, 0x010CEB}, +{0x010CAC, 0x010CEC}, +{0x010CAD, 0x010CED}, +{0x010CAE, 0x010CEE}, +{0x010CAF, 0x010CEF}, +{0x010CB0, 0x010CF0}, +{0x010CB1, 0x010CF1}, +{0x010CB2, 0x010CF2}, +{0x0118A0, 0x0118C0}, +{0x0118A1, 0x0118C1}, +{0x0118A2, 0x0118C2}, +{0x0118A3, 0x0118C3}, +{0x0118A4, 0x0118C4}, +{0x0118A5, 0x0118C5}, +{0x0118A6, 0x0118C6}, +{0x0118A7, 0x0118C7}, +{0x0118A8, 0x0118C8}, +{0x0118A9, 0x0118C9}, +{0x0118AA, 0x0118CA}, +{0x0118AB, 0x0118CB}, +{0x0118AC, 0x0118CC}, +{0x0118AD, 0x0118CD}, +{0x0118AE, 0x0118CE}, +{0x0118AF, 0x0118CF}, +{0x0118B0, 0x0118D0}, +{0x0118B1, 0x0118D1}, +{0x0118B2, 0x0118D2}, +{0x0118B3, 0x0118D3}, +{0x0118B4, 0x0118D4}, +{0x0118B5, 0x0118D5}, +{0x0118B6, 0x0118D6}, +{0x0118B7, 0x0118D7}, +{0x0118B8, 0x0118D8}, +{0x0118B9, 0x0118D9}, +{0x0118BA, 0x0118DA}, +{0x0118BB, 0x0118DB}, +{0x0118BC, 0x0118DC}, +{0x0118BD, 0x0118DD}, +{0x0118BE, 0x0118DE}, +{0x0118BF, 0x0118DF}, +{0x016E40, 0x016E60}, +{0x016E41, 0x016E61}, +{0x016E42, 0x016E62}, +{0x016E43, 0x016E63}, +{0x016E44, 0x016E64}, +{0x016E45, 0x016E65}, +{0x016E46, 0x016E66}, +{0x016E47, 0x016E67}, +{0x016E48, 0x016E68}, +{0x016E49, 0x016E69}, +{0x016E4A, 0x016E6A}, +{0x016E4B, 0x016E6B}, +{0x016E4C, 0x016E6C}, +{0x016E4D, 0x016E6D}, +{0x016E4E, 0x016E6E}, +{0x016E4F, 0x016E6F}, +{0x016E50, 0x016E70}, +{0x016E51, 0x016E71}, +{0x016E52, 0x016E72}, +{0x016E53, 0x016E73}, +{0x016E54, 0x016E74}, +{0x016E55, 0x016E75}, +{0x016E56, 0x016E76}, +{0x016E57, 0x016E77}, +{0x016E58, 0x016E78}, +{0x016E59, 0x016E79}, +{0x016E5A, 0x016E7A}, +{0x016E5B, 0x016E7B}, +{0x016E5C, 0x016E7C}, +{0x016E5D, 0x016E7D}, +{0x016E5E, 0x016E7E}, +{0x016E5F, 0x016E7F}, +{0x01E900, 0x01E922}, +{0x01E901, 0x01E923}, +{0x01E902, 0x01E924}, +{0x01E903, 0x01E925}, +{0x01E904, 0x01E926}, +{0x01E905, 0x01E927}, +{0x01E906, 0x01E928}, +{0x01E907, 0x01E929}, +{0x01E908, 0x01E92A}, +{0x01E909, 0x01E92B}, +{0x01E90A, 0x01E92C}, +{0x01E90B, 0x01E92D}, +{0x01E90C, 0x01E92E}, +{0x01E90D, 0x01E92F}, +{0x01E90E, 0x01E930}, +{0x01E90F, 0x01E931}, +{0x01E910, 0x01E932}, +{0x01E911, 0x01E933}, +{0x01E912, 0x01E934}, +{0x01E913, 0x01E935}, +{0x01E914, 0x01E936}, +{0x01E915, 0x01E937}, +{0x01E916, 0x01E938}, +{0x01E917, 0x01E939}, +{0x01E918, 0x01E93A}, +{0x01E919, 0x01E93B}, +{0x01E91A, 0x01E93C}, +{0x01E91B, 0x01E93D}, +{0x01E91C, 0x01E93E}, +{0x01E91D, 0x01E93F}, +{0x01E91E, 0x01E940}, +{0x01E91F, 0x01E941}, +{0x01E920, 0x01E942}, +{0x01E921, 0x01E943}, }; -const std::vector> unicode_ranges_whitespace = { -{0x00000009, 0x0000000D}, {0x00000020, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0}, -{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, -{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, +const std::unordered_map unicode_map_uppercase = { +{0x000061, 0x000041}, +{0x000062, 0x000042}, +{0x000063, 0x000043}, +{0x000064, 0x000044}, +{0x000065, 0x000045}, +{0x000066, 0x000046}, +{0x000067, 0x000047}, +{0x000068, 0x000048}, +{0x000069, 0x000049}, +{0x00006A, 0x00004A}, +{0x00006B, 0x00004B}, +{0x00006C, 0x00004C}, +{0x00006D, 0x00004D}, +{0x00006E, 0x00004E}, +{0x00006F, 0x00004F}, +{0x000070, 0x000050}, +{0x000071, 0x000051}, +{0x000072, 0x000052}, +{0x000073, 0x000053}, +{0x000074, 0x000054}, +{0x000075, 0x000055}, +{0x000076, 0x000056}, +{0x000077, 0x000057}, +{0x000078, 0x000058}, +{0x000079, 0x000059}, +{0x00007A, 0x00005A}, +{0x0000B5, 0x00039C}, +{0x0000DF, 0x000053}, +{0x0000E0, 0x0000C0}, +{0x0000E1, 0x0000C1}, +{0x0000E2, 0x0000C2}, +{0x0000E3, 0x0000C3}, +{0x0000E4, 0x0000C4}, +{0x0000E5, 0x0000C5}, +{0x0000E6, 0x0000C6}, +{0x0000E7, 0x0000C7}, +{0x0000E8, 0x0000C8}, +{0x0000E9, 0x0000C9}, +{0x0000EA, 0x0000CA}, +{0x0000EB, 0x0000CB}, +{0x0000EC, 0x0000CC}, +{0x0000ED, 0x0000CD}, +{0x0000EE, 0x0000CE}, +{0x0000EF, 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+{0x00A7B5, 0x00A7B4}, +{0x00A7B7, 0x00A7B6}, +{0x00A7B9, 0x00A7B8}, +{0x00A7BB, 0x00A7BA}, +{0x00A7BD, 0x00A7BC}, +{0x00A7BF, 0x00A7BE}, +{0x00A7C3, 0x00A7C2}, +{0x00A7C8, 0x00A7C7}, +{0x00A7CA, 0x00A7C9}, +{0x00A7F6, 0x00A7F5}, +{0x00AB53, 0x00A7B3}, +{0x00AB70, 0x0013A0}, +{0x00AB71, 0x0013A1}, +{0x00AB72, 0x0013A2}, +{0x00AB73, 0x0013A3}, +{0x00AB74, 0x0013A4}, +{0x00AB75, 0x0013A5}, +{0x00AB76, 0x0013A6}, +{0x00AB77, 0x0013A7}, +{0x00AB78, 0x0013A8}, +{0x00AB79, 0x0013A9}, +{0x00AB7A, 0x0013AA}, +{0x00AB7B, 0x0013AB}, +{0x00AB7C, 0x0013AC}, +{0x00AB7D, 0x0013AD}, +{0x00AB7E, 0x0013AE}, +{0x00AB7F, 0x0013AF}, +{0x00AB80, 0x0013B0}, +{0x00AB81, 0x0013B1}, +{0x00AB82, 0x0013B2}, +{0x00AB83, 0x0013B3}, +{0x00AB84, 0x0013B4}, +{0x00AB85, 0x0013B5}, +{0x00AB86, 0x0013B6}, +{0x00AB87, 0x0013B7}, +{0x00AB88, 0x0013B8}, +{0x00AB89, 0x0013B9}, +{0x00AB8A, 0x0013BA}, +{0x00AB8B, 0x0013BB}, +{0x00AB8C, 0x0013BC}, +{0x00AB8D, 0x0013BD}, +{0x00AB8E, 0x0013BE}, +{0x00AB8F, 0x0013BF}, +{0x00AB90, 0x0013C0}, +{0x00AB91, 0x0013C1}, +{0x00AB92, 0x0013C2}, +{0x00AB93, 0x0013C3}, +{0x00AB94, 0x0013C4}, +{0x00AB95, 0x0013C5}, +{0x00AB96, 0x0013C6}, +{0x00AB97, 0x0013C7}, +{0x00AB98, 0x0013C8}, +{0x00AB99, 0x0013C9}, +{0x00AB9A, 0x0013CA}, +{0x00AB9B, 0x0013CB}, +{0x00AB9C, 0x0013CC}, +{0x00AB9D, 0x0013CD}, +{0x00AB9E, 0x0013CE}, +{0x00AB9F, 0x0013CF}, +{0x00ABA0, 0x0013D0}, +{0x00ABA1, 0x0013D1}, +{0x00ABA2, 0x0013D2}, +{0x00ABA3, 0x0013D3}, +{0x00ABA4, 0x0013D4}, +{0x00ABA5, 0x0013D5}, +{0x00ABA6, 0x0013D6}, +{0x00ABA7, 0x0013D7}, +{0x00ABA8, 0x0013D8}, +{0x00ABA9, 0x0013D9}, +{0x00ABAA, 0x0013DA}, +{0x00ABAB, 0x0013DB}, +{0x00ABAC, 0x0013DC}, +{0x00ABAD, 0x0013DD}, +{0x00ABAE, 0x0013DE}, +{0x00ABAF, 0x0013DF}, +{0x00ABB0, 0x0013E0}, +{0x00ABB1, 0x0013E1}, +{0x00ABB2, 0x0013E2}, +{0x00ABB3, 0x0013E3}, +{0x00ABB4, 0x0013E4}, +{0x00ABB5, 0x0013E5}, +{0x00ABB6, 0x0013E6}, +{0x00ABB7, 0x0013E7}, +{0x00ABB8, 0x0013E8}, +{0x00ABB9, 0x0013E9}, +{0x00ABBA, 0x0013EA}, +{0x00ABBB, 0x0013EB}, +{0x00ABBC, 0x0013EC}, +{0x00ABBD, 0x0013ED}, +{0x00ABBE, 0x0013EE}, +{0x00ABBF, 0x0013EF}, +{0x00FB00, 0x000046}, +{0x00FB01, 0x000046}, +{0x00FB02, 0x000046}, +{0x00FB03, 0x000046}, +{0x00FB04, 0x000046}, +{0x00FB05, 0x000053}, +{0x00FB06, 0x000053}, +{0x00FB13, 0x000544}, +{0x00FB14, 0x000544}, +{0x00FB15, 0x000544}, +{0x00FB16, 0x00054E}, +{0x00FB17, 0x000544}, +{0x00FF41, 0x00FF21}, +{0x00FF42, 0x00FF22}, +{0x00FF43, 0x00FF23}, +{0x00FF44, 0x00FF24}, +{0x00FF45, 0x00FF25}, +{0x00FF46, 0x00FF26}, +{0x00FF47, 0x00FF27}, +{0x00FF48, 0x00FF28}, +{0x00FF49, 0x00FF29}, +{0x00FF4A, 0x00FF2A}, +{0x00FF4B, 0x00FF2B}, +{0x00FF4C, 0x00FF2C}, +{0x00FF4D, 0x00FF2D}, +{0x00FF4E, 0x00FF2E}, +{0x00FF4F, 0x00FF2F}, +{0x00FF50, 0x00FF30}, +{0x00FF51, 0x00FF31}, +{0x00FF52, 0x00FF32}, +{0x00FF53, 0x00FF33}, +{0x00FF54, 0x00FF34}, +{0x00FF55, 0x00FF35}, +{0x00FF56, 0x00FF36}, +{0x00FF57, 0x00FF37}, +{0x00FF58, 0x00FF38}, +{0x00FF59, 0x00FF39}, +{0x00FF5A, 0x00FF3A}, +{0x010428, 0x010400}, +{0x010429, 0x010401}, +{0x01042A, 0x010402}, +{0x01042B, 0x010403}, +{0x01042C, 0x010404}, +{0x01042D, 0x010405}, +{0x01042E, 0x010406}, +{0x01042F, 0x010407}, +{0x010430, 0x010408}, +{0x010431, 0x010409}, +{0x010432, 0x01040A}, +{0x010433, 0x01040B}, +{0x010434, 0x01040C}, +{0x010435, 0x01040D}, +{0x010436, 0x01040E}, +{0x010437, 0x01040F}, +{0x010438, 0x010410}, +{0x010439, 0x010411}, +{0x01043A, 0x010412}, +{0x01043B, 0x010413}, +{0x01043C, 0x010414}, +{0x01043D, 0x010415}, +{0x01043E, 0x010416}, +{0x01043F, 0x010417}, +{0x010440, 0x010418}, +{0x010441, 0x010419}, +{0x010442, 0x01041A}, +{0x010443, 0x01041B}, +{0x010444, 0x01041C}, +{0x010445, 0x01041D}, +{0x010446, 0x01041E}, +{0x010447, 0x01041F}, +{0x010448, 0x010420}, +{0x010449, 0x010421}, +{0x01044A, 0x010422}, +{0x01044B, 0x010423}, +{0x01044C, 0x010424}, +{0x01044D, 0x010425}, +{0x01044E, 0x010426}, +{0x01044F, 0x010427}, +{0x0104D8, 0x0104B0}, +{0x0104D9, 0x0104B1}, +{0x0104DA, 0x0104B2}, +{0x0104DB, 0x0104B3}, +{0x0104DC, 0x0104B4}, +{0x0104DD, 0x0104B5}, +{0x0104DE, 0x0104B6}, +{0x0104DF, 0x0104B7}, +{0x0104E0, 0x0104B8}, +{0x0104E1, 0x0104B9}, +{0x0104E2, 0x0104BA}, +{0x0104E3, 0x0104BB}, +{0x0104E4, 0x0104BC}, +{0x0104E5, 0x0104BD}, +{0x0104E6, 0x0104BE}, +{0x0104E7, 0x0104BF}, +{0x0104E8, 0x0104C0}, +{0x0104E9, 0x0104C1}, +{0x0104EA, 0x0104C2}, +{0x0104EB, 0x0104C3}, +{0x0104EC, 0x0104C4}, +{0x0104ED, 0x0104C5}, +{0x0104EE, 0x0104C6}, +{0x0104EF, 0x0104C7}, +{0x0104F0, 0x0104C8}, +{0x0104F1, 0x0104C9}, +{0x0104F2, 0x0104CA}, +{0x0104F3, 0x0104CB}, +{0x0104F4, 0x0104CC}, +{0x0104F5, 0x0104CD}, +{0x0104F6, 0x0104CE}, +{0x0104F7, 0x0104CF}, +{0x0104F8, 0x0104D0}, +{0x0104F9, 0x0104D1}, +{0x0104FA, 0x0104D2}, +{0x0104FB, 0x0104D3}, +{0x010CC0, 0x010C80}, +{0x010CC1, 0x010C81}, +{0x010CC2, 0x010C82}, +{0x010CC3, 0x010C83}, +{0x010CC4, 0x010C84}, +{0x010CC5, 0x010C85}, +{0x010CC6, 0x010C86}, +{0x010CC7, 0x010C87}, +{0x010CC8, 0x010C88}, +{0x010CC9, 0x010C89}, +{0x010CCA, 0x010C8A}, +{0x010CCB, 0x010C8B}, +{0x010CCC, 0x010C8C}, +{0x010CCD, 0x010C8D}, +{0x010CCE, 0x010C8E}, +{0x010CCF, 0x010C8F}, +{0x010CD0, 0x010C90}, +{0x010CD1, 0x010C91}, +{0x010CD2, 0x010C92}, +{0x010CD3, 0x010C93}, +{0x010CD4, 0x010C94}, +{0x010CD5, 0x010C95}, +{0x010CD6, 0x010C96}, +{0x010CD7, 0x010C97}, +{0x010CD8, 0x010C98}, +{0x010CD9, 0x010C99}, +{0x010CDA, 0x010C9A}, +{0x010CDB, 0x010C9B}, +{0x010CDC, 0x010C9C}, +{0x010CDD, 0x010C9D}, +{0x010CDE, 0x010C9E}, +{0x010CDF, 0x010C9F}, +{0x010CE0, 0x010CA0}, +{0x010CE1, 0x010CA1}, +{0x010CE2, 0x010CA2}, +{0x010CE3, 0x010CA3}, +{0x010CE4, 0x010CA4}, +{0x010CE5, 0x010CA5}, +{0x010CE6, 0x010CA6}, +{0x010CE7, 0x010CA7}, +{0x010CE8, 0x010CA8}, +{0x010CE9, 0x010CA9}, +{0x010CEA, 0x010CAA}, +{0x010CEB, 0x010CAB}, +{0x010CEC, 0x010CAC}, +{0x010CED, 0x010CAD}, +{0x010CEE, 0x010CAE}, +{0x010CEF, 0x010CAF}, +{0x010CF0, 0x010CB0}, +{0x010CF1, 0x010CB1}, +{0x010CF2, 0x010CB2}, +{0x0118C0, 0x0118A0}, +{0x0118C1, 0x0118A1}, +{0x0118C2, 0x0118A2}, +{0x0118C3, 0x0118A3}, +{0x0118C4, 0x0118A4}, +{0x0118C5, 0x0118A5}, +{0x0118C6, 0x0118A6}, +{0x0118C7, 0x0118A7}, +{0x0118C8, 0x0118A8}, +{0x0118C9, 0x0118A9}, +{0x0118CA, 0x0118AA}, +{0x0118CB, 0x0118AB}, +{0x0118CC, 0x0118AC}, +{0x0118CD, 0x0118AD}, +{0x0118CE, 0x0118AE}, +{0x0118CF, 0x0118AF}, +{0x0118D0, 0x0118B0}, +{0x0118D1, 0x0118B1}, +{0x0118D2, 0x0118B2}, +{0x0118D3, 0x0118B3}, +{0x0118D4, 0x0118B4}, +{0x0118D5, 0x0118B5}, +{0x0118D6, 0x0118B6}, +{0x0118D7, 0x0118B7}, +{0x0118D8, 0x0118B8}, +{0x0118D9, 0x0118B9}, +{0x0118DA, 0x0118BA}, +{0x0118DB, 0x0118BB}, +{0x0118DC, 0x0118BC}, +{0x0118DD, 0x0118BD}, +{0x0118DE, 0x0118BE}, +{0x0118DF, 0x0118BF}, +{0x016E60, 0x016E40}, +{0x016E61, 0x016E41}, +{0x016E62, 0x016E42}, +{0x016E63, 0x016E43}, +{0x016E64, 0x016E44}, +{0x016E65, 0x016E45}, +{0x016E66, 0x016E46}, +{0x016E67, 0x016E47}, +{0x016E68, 0x016E48}, +{0x016E69, 0x016E49}, +{0x016E6A, 0x016E4A}, +{0x016E6B, 0x016E4B}, +{0x016E6C, 0x016E4C}, +{0x016E6D, 0x016E4D}, +{0x016E6E, 0x016E4E}, +{0x016E6F, 0x016E4F}, +{0x016E70, 0x016E50}, +{0x016E71, 0x016E51}, +{0x016E72, 0x016E52}, +{0x016E73, 0x016E53}, +{0x016E74, 0x016E54}, +{0x016E75, 0x016E55}, +{0x016E76, 0x016E56}, +{0x016E77, 0x016E57}, +{0x016E78, 0x016E58}, +{0x016E79, 0x016E59}, +{0x016E7A, 0x016E5A}, +{0x016E7B, 0x016E5B}, +{0x016E7C, 0x016E5C}, +{0x016E7D, 0x016E5D}, +{0x016E7E, 0x016E5E}, +{0x016E7F, 0x016E5F}, +{0x01E922, 0x01E900}, +{0x01E923, 0x01E901}, +{0x01E924, 0x01E902}, +{0x01E925, 0x01E903}, +{0x01E926, 0x01E904}, +{0x01E927, 0x01E905}, +{0x01E928, 0x01E906}, +{0x01E929, 0x01E907}, +{0x01E92A, 0x01E908}, +{0x01E92B, 0x01E909}, +{0x01E92C, 0x01E90A}, +{0x01E92D, 0x01E90B}, +{0x01E92E, 0x01E90C}, +{0x01E92F, 0x01E90D}, +{0x01E930, 0x01E90E}, +{0x01E931, 0x01E90F}, +{0x01E932, 0x01E910}, +{0x01E933, 0x01E911}, +{0x01E934, 0x01E912}, +{0x01E935, 0x01E913}, +{0x01E936, 0x01E914}, +{0x01E937, 0x01E915}, +{0x01E938, 0x01E916}, +{0x01E939, 0x01E917}, +{0x01E93A, 0x01E918}, +{0x01E93B, 0x01E919}, +{0x01E93C, 0x01E91A}, +{0x01E93D, 0x01E91B}, +{0x01E93E, 0x01E91C}, +{0x01E93F, 0x01E91D}, +{0x01E940, 0x01E91E}, +{0x01E941, 0x01E91F}, +{0x01E942, 0x01E920}, +{0x01E943, 0x01E921}, }; -const std::vector> unicode_ranges_accent_mark = { -{0x00000300, 0x0000036F}, {0x00000483, 0x00000489}, {0x00000591, 0x000005BD}, {0x000005BF, 0x000005BF}, -{0x000005C1, 0x000005C2}, {0x000005C4, 0x000005C5}, {0x000005C7, 0x000005C7}, {0x00000610, 0x0000061A}, -{0x0000064B, 0x0000065F}, {0x00000670, 0x00000670}, {0x000006D6, 0x000006DC}, {0x000006DF, 0x000006E4}, -{0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A}, -{0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819}, -{0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B}, -{0x00000898, 0x0000089F}, {0x000008CA, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C}, -{0x0000093E, 0x0000094F}, {0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983}, -{0x000009BC, 0x000009BC}, {0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD}, -{0x000009D7, 0x000009D7}, {0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03}, -{0x00000A3C, 0x00000A3C}, {0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D}, -{0x00000A51, 0x00000A51}, {0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83}, -{0x00000ABC, 0x00000ABC}, {0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD}, -{0x00000AE2, 0x00000AE3}, {0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C}, -{0x00000B3E, 0x00000B44}, {0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57}, -{0x00000B62, 0x00000B63}, {0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8}, -{0x00000BCA, 0x00000BCD}, {0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3C, 0x00000C3C}, -{0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, {0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56}, -{0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, {0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4}, -{0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, {0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3}, -{0x00000CF3, 0x00000CF3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, {0x00000D3E, 0x00000D44}, -{0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, {0x00000D62, 0x00000D63}, -{0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, {0x00000DD6, 0x00000DD6}, -{0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, {0x00000E34, 0x00000E3A}, -{0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, {0x00000EC8, 0x00000ECE}, -{0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, {0x00000F39, 0x00000F39}, -{0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, {0x00000F8D, 0x00000F97}, -{0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, {0x00001056, 0x00001059}, -{0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, {0x00001071, 0x00001074}, -{0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, {0x0000135D, 0x0000135F}, -{0x00001712, 0x00001715}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, {0x00001772, 0x00001773}, -{0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, {0x0000180F, 0x0000180F}, -{0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B}, -{0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F}, -{0x00001AB0, 0x00001ACE}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73}, -{0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37}, -{0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4}, -{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DFF}, {0x000020D0, 0x000020F0}, {0x00002CEF, 0x00002CF1}, -{0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, {0x00003099, 0x0000309A}, -{0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, {0x0000A6F0, 0x0000A6F1}, -{0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, {0x0000A823, 0x0000A827}, -{0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, {0x0000A8E0, 0x0000A8F1}, -{0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, {0x0000A980, 0x0000A983}, -{0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, {0x0000AA43, 0x0000AA43}, -{0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, {0x0000AAB2, 0x0000AAB4}, -{0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, {0x0000AAEB, 0x0000AAEF}, -{0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, {0x0000FB1E, 0x0000FB1E}, -{0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, {0x000102E0, 0x000102E0}, -{0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, {0x00010A0C, 0x00010A0F}, -{0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, {0x00010D24, 0x00010D27}, -{0x00010EAB, 0x00010EAC}, {0x00010EFD, 0x00010EFF}, {0x00010F46, 0x00010F50}, {0x00010F82, 0x00010F85}, -{0x00011000, 0x00011002}, {0x00011038, 0x00011046}, {0x00011070, 0x00011070}, {0x00011073, 0x00011074}, -{0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x000110C2, 0x000110C2}, {0x00011100, 0x00011102}, -{0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182}, -{0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237}, -{0x0001123E, 0x0001123E}, {0x00011241, 0x00011241}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303}, -{0x0001133B, 0x0001133C}, {0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D}, -{0x00011357, 0x00011357}, {0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374}, -{0x00011435, 0x00011446}, {0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5}, -{0x000115B8, 0x000115C0}, {0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7}, -{0x0001171D, 0x0001172B}, {0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938}, -{0x0001193B, 0x0001193E}, {0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7}, -{0x000119DA, 0x000119E0}, {0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39}, -{0x00011A3B, 0x00011A3E}, {0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99}, -{0x00011C2F, 0x00011C36}, {0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6}, -{0x00011D31, 0x00011D36}, {0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45}, -{0x00011D47, 0x00011D47}, {0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97}, -{0x00011EF3, 0x00011EF6}, {0x00011F00, 0x00011F01}, {0x00011F03, 0x00011F03}, {0x00011F34, 0x00011F3A}, -{0x00011F3E, 0x00011F42}, {0x00013440, 0x00013440}, {0x00013447, 0x00013455}, {0x00016AF0, 0x00016AF4}, -{0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, {0x00016F8F, 0x00016F92}, -{0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, {0x0001CF00, 0x0001CF2D}, -{0x0001CF30, 0x0001CF46}, {0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182}, -{0x0001D185, 0x0001D18B}, {0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36}, -{0x0001DA3B, 0x0001DA6C}, {0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F}, -{0x0001DAA1, 0x0001DAAF}, {0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021}, -{0x0001E023, 0x0001E024}, {0x0001E026, 0x0001E02A}, {0x0001E08F, 0x0001E08F}, {0x0001E130, 0x0001E136}, -{0x0001E2AE, 0x0001E2AE}, {0x0001E2EC, 0x0001E2EF}, {0x0001E4EC, 0x0001E4EF}, {0x0001E8D0, 0x0001E8D6}, -{0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF}, +const std::vector unicode_ranges_nfd = { // start, last, nfd +{0x000000, 0x000000, 0x000000}, +{0x0000C0, 0x0000C5, 0x000041}, +{0x0000C7, 0x0000C7, 0x000043}, +{0x0000C8, 0x0000CB, 0x000045}, +{0x0000CC, 0x0000CF, 0x000049}, +{0x0000D1, 0x0000D1, 0x00004E}, +{0x0000D2, 0x0000D6, 0x00004F}, +{0x0000D9, 0x0000DC, 0x000055}, +{0x0000DD, 0x0000DD, 0x000059}, +{0x0000E0, 0x0000E5, 0x000061}, +{0x0000E7, 0x0000E7, 0x000063}, +{0x0000E8, 0x0000EB, 0x000065}, +{0x0000EC, 0x0000EF, 0x000069}, +{0x0000F1, 0x0000F1, 0x00006E}, +{0x0000F2, 0x0000F6, 0x00006F}, +{0x0000F9, 0x0000FC, 0x000075}, +{0x0000FD, 0x0000FD, 0x000079}, +{0x0000FF, 0x0000FF, 0x000079}, +{0x000100, 0x000100, 0x000041}, +{0x000101, 0x000101, 0x000061}, +{0x000102, 0x000102, 0x000041}, +{0x000103, 0x000103, 0x000061}, +{0x000104, 0x000104, 0x000041}, +{0x000105, 0x000105, 0x000061}, +{0x000106, 0x000106, 0x000043}, +{0x000107, 0x000107, 0x000063}, +{0x000108, 0x000108, 0x000043}, +{0x000109, 0x000109, 0x000063}, +{0x00010A, 0x00010A, 0x000043}, +{0x00010B, 0x00010B, 0x000063}, +{0x00010C, 0x00010C, 0x000043}, +{0x00010D, 0x00010D, 0x000063}, +{0x00010E, 0x00010E, 0x000044}, +{0x00010F, 0x00010F, 0x000064}, +{0x000112, 0x000112, 0x000045}, +{0x000113, 0x000113, 0x000065}, +{0x000114, 0x000114, 0x000045}, +{0x000115, 0x000115, 0x000065}, +{0x000116, 0x000116, 0x000045}, +{0x000117, 0x000117, 0x000065}, +{0x000118, 0x000118, 0x000045}, +{0x000119, 0x000119, 0x000065}, +{0x00011A, 0x00011A, 0x000045}, +{0x00011B, 0x00011B, 0x000065}, +{0x00011C, 0x00011C, 0x000047}, +{0x00011D, 0x00011D, 0x000067}, +{0x00011E, 0x00011E, 0x000047}, +{0x00011F, 0x00011F, 0x000067}, +{0x000120, 0x000120, 0x000047}, +{0x000121, 0x000121, 0x000067}, +{0x000122, 0x000122, 0x000047}, +{0x000123, 0x000123, 0x000067}, +{0x000124, 0x000124, 0x000048}, +{0x000125, 0x000125, 0x000068}, +{0x000128, 0x000128, 0x000049}, +{0x000129, 0x000129, 0x000069}, +{0x00012A, 0x00012A, 0x000049}, +{0x00012B, 0x00012B, 0x000069}, +{0x00012C, 0x00012C, 0x000049}, +{0x00012D, 0x00012D, 0x000069}, +{0x00012E, 0x00012E, 0x000049}, +{0x00012F, 0x00012F, 0x000069}, +{0x000130, 0x000130, 0x000049}, +{0x000134, 0x000134, 0x00004A}, +{0x000135, 0x000135, 0x00006A}, +{0x000136, 0x000136, 0x00004B}, +{0x000137, 0x000137, 0x00006B}, +{0x000139, 0x000139, 0x00004C}, +{0x00013A, 0x00013A, 0x00006C}, +{0x00013B, 0x00013B, 0x00004C}, +{0x00013C, 0x00013C, 0x00006C}, +{0x00013D, 0x00013D, 0x00004C}, +{0x00013E, 0x00013E, 0x00006C}, +{0x000143, 0x000143, 0x00004E}, +{0x000144, 0x000144, 0x00006E}, +{0x000145, 0x000145, 0x00004E}, +{0x000146, 0x000146, 0x00006E}, +{0x000147, 0x000147, 0x00004E}, +{0x000148, 0x000148, 0x00006E}, +{0x00014C, 0x00014C, 0x00004F}, +{0x00014D, 0x00014D, 0x00006F}, +{0x00014E, 0x00014E, 0x00004F}, +{0x00014F, 0x00014F, 0x00006F}, +{0x000150, 0x000150, 0x00004F}, +{0x000151, 0x000151, 0x00006F}, +{0x000154, 0x000154, 0x000052}, +{0x000155, 0x000155, 0x000072}, +{0x000156, 0x000156, 0x000052}, +{0x000157, 0x000157, 0x000072}, +{0x000158, 0x000158, 0x000052}, +{0x000159, 0x000159, 0x000072}, +{0x00015A, 0x00015A, 0x000053}, +{0x00015B, 0x00015B, 0x000073}, +{0x00015C, 0x00015C, 0x000053}, +{0x00015D, 0x00015D, 0x000073}, +{0x00015E, 0x00015E, 0x000053}, +{0x00015F, 0x00015F, 0x000073}, +{0x000160, 0x000160, 0x000053}, +{0x000161, 0x000161, 0x000073}, +{0x000162, 0x000162, 0x000054}, +{0x000163, 0x000163, 0x000074}, +{0x000164, 0x000164, 0x000054}, +{0x000165, 0x000165, 0x000074}, +{0x000168, 0x000168, 0x000055}, +{0x000169, 0x000169, 0x000075}, +{0x00016A, 0x00016A, 0x000055}, +{0x00016B, 0x00016B, 0x000075}, +{0x00016C, 0x00016C, 0x000055}, +{0x00016D, 0x00016D, 0x000075}, +{0x00016E, 0x00016E, 0x000055}, +{0x00016F, 0x00016F, 0x000075}, +{0x000170, 0x000170, 0x000055}, +{0x000171, 0x000171, 0x000075}, +{0x000172, 0x000172, 0x000055}, +{0x000173, 0x000173, 0x000075}, +{0x000174, 0x000174, 0x000057}, +{0x000175, 0x000175, 0x000077}, +{0x000176, 0x000176, 0x000059}, +{0x000177, 0x000177, 0x000079}, +{0x000178, 0x000178, 0x000059}, +{0x000179, 0x000179, 0x00005A}, +{0x00017A, 0x00017A, 0x00007A}, +{0x00017B, 0x00017B, 0x00005A}, +{0x00017C, 0x00017C, 0x00007A}, +{0x00017D, 0x00017D, 0x00005A}, +{0x00017E, 0x00017E, 0x00007A}, +{0x0001A0, 0x0001A0, 0x00004F}, +{0x0001A1, 0x0001A1, 0x00006F}, +{0x0001AF, 0x0001AF, 0x000055}, +{0x0001B0, 0x0001B0, 0x000075}, +{0x0001CD, 0x0001CD, 0x000041}, +{0x0001CE, 0x0001CE, 0x000061}, +{0x0001CF, 0x0001CF, 0x000049}, +{0x0001D0, 0x0001D0, 0x000069}, +{0x0001D1, 0x0001D1, 0x00004F}, +{0x0001D2, 0x0001D2, 0x00006F}, +{0x0001D3, 0x0001D3, 0x000055}, +{0x0001D4, 0x0001D4, 0x000075}, +{0x0001D5, 0x0001D5, 0x000055}, +{0x0001D6, 0x0001D6, 0x000075}, +{0x0001D7, 0x0001D7, 0x000055}, +{0x0001D8, 0x0001D8, 0x000075}, +{0x0001D9, 0x0001D9, 0x000055}, +{0x0001DA, 0x0001DA, 0x000075}, +{0x0001DB, 0x0001DB, 0x000055}, +{0x0001DC, 0x0001DC, 0x000075}, +{0x0001DE, 0x0001DE, 0x000041}, +{0x0001DF, 0x0001DF, 0x000061}, +{0x0001E0, 0x0001E0, 0x000041}, +{0x0001E1, 0x0001E1, 0x000061}, +{0x0001E2, 0x0001E2, 0x0000C6}, +{0x0001E3, 0x0001E3, 0x0000E6}, +{0x0001E6, 0x0001E6, 0x000047}, +{0x0001E7, 0x0001E7, 0x000067}, +{0x0001E8, 0x0001E8, 0x00004B}, +{0x0001E9, 0x0001E9, 0x00006B}, +{0x0001EA, 0x0001EA, 0x00004F}, +{0x0001EB, 0x0001EB, 0x00006F}, +{0x0001EC, 0x0001EC, 0x00004F}, +{0x0001ED, 0x0001ED, 0x00006F}, +{0x0001EE, 0x0001EE, 0x0001B7}, +{0x0001EF, 0x0001EF, 0x000292}, +{0x0001F0, 0x0001F0, 0x00006A}, +{0x0001F4, 0x0001F4, 0x000047}, +{0x0001F5, 0x0001F5, 0x000067}, +{0x0001F8, 0x0001F8, 0x00004E}, +{0x0001F9, 0x0001F9, 0x00006E}, +{0x0001FA, 0x0001FA, 0x000041}, +{0x0001FB, 0x0001FB, 0x000061}, +{0x0001FC, 0x0001FC, 0x0000C6}, +{0x0001FD, 0x0001FD, 0x0000E6}, +{0x0001FE, 0x0001FE, 0x0000D8}, +{0x0001FF, 0x0001FF, 0x0000F8}, +{0x000200, 0x000200, 0x000041}, +{0x000201, 0x000201, 0x000061}, +{0x000202, 0x000202, 0x000041}, +{0x000203, 0x000203, 0x000061}, +{0x000204, 0x000204, 0x000045}, +{0x000205, 0x000205, 0x000065}, +{0x000206, 0x000206, 0x000045}, +{0x000207, 0x000207, 0x000065}, +{0x000208, 0x000208, 0x000049}, +{0x000209, 0x000209, 0x000069}, +{0x00020A, 0x00020A, 0x000049}, +{0x00020B, 0x00020B, 0x000069}, +{0x00020C, 0x00020C, 0x00004F}, +{0x00020D, 0x00020D, 0x00006F}, +{0x00020E, 0x00020E, 0x00004F}, +{0x00020F, 0x00020F, 0x00006F}, +{0x000210, 0x000210, 0x000052}, +{0x000211, 0x000211, 0x000072}, +{0x000212, 0x000212, 0x000052}, +{0x000213, 0x000213, 0x000072}, +{0x000214, 0x000214, 0x000055}, +{0x000215, 0x000215, 0x000075}, +{0x000216, 0x000216, 0x000055}, +{0x000217, 0x000217, 0x000075}, +{0x000218, 0x000218, 0x000053}, +{0x000219, 0x000219, 0x000073}, +{0x00021A, 0x00021A, 0x000054}, +{0x00021B, 0x00021B, 0x000074}, +{0x00021E, 0x00021E, 0x000048}, +{0x00021F, 0x00021F, 0x000068}, +{0x000226, 0x000226, 0x000041}, +{0x000227, 0x000227, 0x000061}, +{0x000228, 0x000228, 0x000045}, +{0x000229, 0x000229, 0x000065}, +{0x00022A, 0x00022A, 0x00004F}, +{0x00022B, 0x00022B, 0x00006F}, +{0x00022C, 0x00022C, 0x00004F}, +{0x00022D, 0x00022D, 0x00006F}, +{0x00022E, 0x00022E, 0x00004F}, +{0x00022F, 0x00022F, 0x00006F}, +{0x000230, 0x000230, 0x00004F}, +{0x000231, 0x000231, 0x00006F}, +{0x000232, 0x000232, 0x000059}, +{0x000233, 0x000233, 0x000079}, +{0x000340, 0x000340, 0x000300}, +{0x000341, 0x000341, 0x000301}, +{0x000343, 0x000343, 0x000313}, +{0x000344, 0x000344, 0x000308}, +{0x000374, 0x000374, 0x0002B9}, +{0x00037E, 0x00037E, 0x00003B}, +{0x000385, 0x000385, 0x0000A8}, +{0x000386, 0x000386, 0x000391}, +{0x000387, 0x000387, 0x0000B7}, +{0x000388, 0x000388, 0x000395}, +{0x000389, 0x000389, 0x000397}, 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+{0x02F9D7, 0x02F9D7, 0x008D77}, +{0x02F9D8, 0x02F9D8, 0x027F2F}, +{0x02F9D9, 0x02F9D9, 0x020804}, +{0x02F9DA, 0x02F9DA, 0x008DCB}, +{0x02F9DB, 0x02F9DB, 0x008DBC}, +{0x02F9DC, 0x02F9DC, 0x008DF0}, +{0x02F9DD, 0x02F9DD, 0x0208DE}, +{0x02F9DE, 0x02F9DE, 0x008ED4}, +{0x02F9DF, 0x02F9DF, 0x008F38}, +{0x02F9E0, 0x02F9E0, 0x0285D2}, +{0x02F9E1, 0x02F9E1, 0x0285ED}, +{0x02F9E2, 0x02F9E2, 0x009094}, +{0x02F9E3, 0x02F9E3, 0x0090F1}, +{0x02F9E4, 0x02F9E4, 0x009111}, +{0x02F9E5, 0x02F9E5, 0x02872E}, +{0x02F9E6, 0x02F9E6, 0x00911B}, +{0x02F9E7, 0x02F9E7, 0x009238}, +{0x02F9E8, 0x02F9E8, 0x0092D7}, +{0x02F9E9, 0x02F9E9, 0x0092D8}, +{0x02F9EA, 0x02F9EA, 0x00927C}, +{0x02F9EB, 0x02F9EB, 0x0093F9}, +{0x02F9EC, 0x02F9EC, 0x009415}, +{0x02F9ED, 0x02F9ED, 0x028BFA}, +{0x02F9EE, 0x02F9EE, 0x00958B}, +{0x02F9EF, 0x02F9EF, 0x004995}, +{0x02F9F0, 0x02F9F0, 0x0095B7}, +{0x02F9F1, 0x02F9F1, 0x028D77}, +{0x02F9F2, 0x02F9F2, 0x0049E6}, +{0x02F9F3, 0x02F9F3, 0x0096C3}, +{0x02F9F4, 0x02F9F4, 0x005DB2}, +{0x02F9F5, 0x02F9F5, 0x009723}, +{0x02F9F6, 0x02F9F6, 0x029145}, +{0x02F9F7, 0x02F9F7, 0x02921A}, +{0x02F9F8, 0x02F9F8, 0x004A6E}, +{0x02F9F9, 0x02F9F9, 0x004A76}, +{0x02F9FA, 0x02F9FA, 0x0097E0}, +{0x02F9FB, 0x02F9FB, 0x02940A}, +{0x02F9FC, 0x02F9FC, 0x004AB2}, +{0x02F9FD, 0x02F9FD, 0x029496}, +{0x02F9FE, 0x02F9FF, 0x00980B}, +{0x02FA00, 0x02FA00, 0x009829}, +{0x02FA01, 0x02FA01, 0x0295B6}, +{0x02FA02, 0x02FA02, 0x0098E2}, +{0x02FA03, 0x02FA03, 0x004B33}, +{0x02FA04, 0x02FA04, 0x009929}, +{0x02FA05, 0x02FA05, 0x0099A7}, +{0x02FA06, 0x02FA06, 0x0099C2}, +{0x02FA07, 0x02FA07, 0x0099FE}, +{0x02FA08, 0x02FA08, 0x004BCE}, +{0x02FA09, 0x02FA09, 0x029B30}, +{0x02FA0A, 0x02FA0A, 0x009B12}, +{0x02FA0B, 0x02FA0B, 0x009C40}, +{0x02FA0C, 0x02FA0C, 0x009CFD}, +{0x02FA0D, 0x02FA0D, 0x004CCE}, +{0x02FA0E, 0x02FA0E, 0x004CED}, +{0x02FA0F, 0x02FA0F, 0x009D67}, +{0x02FA10, 0x02FA10, 0x02A0CE}, +{0x02FA11, 0x02FA11, 0x004CF8}, +{0x02FA12, 0x02FA12, 0x02A105}, +{0x02FA13, 0x02FA13, 0x02A20E}, +{0x02FA14, 0x02FA14, 0x02A291}, +{0x02FA15, 0x02FA15, 0x009EBB}, +{0x02FA16, 0x02FA16, 0x004D56}, +{0x02FA17, 0x02FA17, 0x009EF9}, +{0x02FA18, 0x02FA18, 0x009EFE}, +{0x02FA19, 0x02FA19, 0x009F05}, +{0x02FA1A, 0x02FA1A, 0x009F0F}, +{0x02FA1B, 0x02FA1B, 0x009F16}, +{0x02FA1C, 0x02FA1C, 0x009F3B}, +{0x02FA1D, 0x02FA1D, 0x02A600}, }; -const std::vector> unicode_ranges_punctuation = { -{0x00000021, 0x00000023}, {0x00000025, 0x0000002A}, {0x0000002C, 0x0000002F}, {0x0000003A, 0x0000003B}, -{0x0000003F, 0x00000040}, {0x0000005B, 0x0000005D}, {0x0000005F, 0x0000005F}, {0x0000007B, 0x0000007B}, -{0x0000007D, 0x0000007D}, {0x000000A1, 0x000000A1}, {0x000000A7, 0x000000A7}, {0x000000AB, 0x000000AB}, -{0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E}, -{0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE}, -{0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4}, -{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061D, 0x0000061F}, -{0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9}, -{0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970}, -{0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77}, -{0x00000C84, 0x00000C84}, {0x00000DF4, 0x00000DF4}, {0x00000E4F, 0x00000E4F}, {0x00000E5A, 0x00000E5B}, -{0x00000F04, 0x00000F12}, {0x00000F14, 0x00000F14}, {0x00000F3A, 0x00000F3D}, {0x00000F85, 0x00000F85}, -{0x00000FD0, 0x00000FD4}, {0x00000FD9, 0x00000FDA}, {0x0000104A, 0x0000104F}, {0x000010FB, 0x000010FB}, -{0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C}, -{0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA}, -{0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6}, -{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001B7D, 0x00001B7E}, {0x00001BFC, 0x00001BFF}, -{0x00001C3B, 0x00001C3F}, {0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3}, -{0x00002010, 0x00002027}, {0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E}, -{0x0000207D, 0x0000207E}, {0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A}, -{0x00002768, 0x00002775}, {0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998}, -{0x000029D8, 0x000029DB}, {0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF}, -{0x00002D70, 0x00002D70}, {0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E5D}, -{0x00003001, 0x00003003}, {0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030}, -{0x0000303D, 0x0000303D}, {0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF}, -{0x0000A60D, 0x0000A60F}, {0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7}, -{0x0000A874, 0x0000A877}, {0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC}, -{0x0000A92E, 0x0000A92F}, {0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF}, -{0x0000AA5C, 0x0000AA5F}, {0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB}, -{0x0000FD3E, 0x0000FD3F}, {0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61}, -{0x0000FE63, 0x0000FE63}, {0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03}, -{0x0000FF05, 0x0000FF0A}, {0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20}, -{0x0000FF3B, 0x0000FF3D}, {0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D}, -{0x0000FF5F, 0x0000FF65}, {0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0}, -{0x0001056F, 0x0001056F}, {0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F}, -{0x00010A50, 0x00010A58}, {0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F}, -{0x00010B99, 0x00010B9C}, {0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00010F86, 0x00010F89}, -{0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, {0x000110BE, 0x000110C1}, {0x00011140, 0x00011143}, -{0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, {0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB}, -{0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, {0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F}, -{0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, {0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7}, -{0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, {0x000116B9, 0x000116B9}, {0x0001173C, 0x0001173E}, -{0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, {0x00011A3F, 0x00011A46}, -{0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011B00, 0x00011B09}, {0x00011C41, 0x00011C45}, -{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011F43, 0x00011F4F}, {0x00011FFF, 0x00011FFF}, -{0x00012470, 0x00012474}, {0x00012FF1, 0x00012FF2}, {0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5}, -{0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, {0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2}, -{0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, {0x0001E95E, 0x0001E95F}, -}; - -const std::vector> unicode_ranges_symbol = { -{0x00000024, 0x00000024}, {0x0000002B, 0x0000002B}, {0x0000003C, 0x0000003E}, {0x0000005E, 0x0000005E}, -{0x00000060, 0x00000060}, {0x0000007C, 0x0000007C}, {0x0000007E, 0x0000007E}, {0x000000A2, 0x000000A6}, -{0x000000A8, 0x000000A9}, {0x000000AC, 0x000000AC}, {0x000000AE, 0x000000B1}, {0x000000B4, 0x000000B4}, -{0x000000B8, 0x000000B8}, {0x000000D7, 0x000000D7}, {0x000000F7, 0x000000F7}, {0x000002C2, 0x000002C5}, -{0x000002D2, 0x000002DF}, {0x000002E5, 0x000002EB}, {0x000002ED, 0x000002ED}, {0x000002EF, 0x000002FF}, -{0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482}, -{0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F}, -{0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6}, -{0x000007FE, 0x000007FF}, {0x00000888, 0x00000888}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB}, -{0x00000AF1, 0x00000AF1}, {0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F}, -{0x00000D4F, 0x00000D4F}, {0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03}, -{0x00000F13, 0x00000F13}, {0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34}, -{0x00000F36, 0x00000F36}, {0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC}, -{0x00000FCE, 0x00000FCF}, {0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399}, -{0x0000166D, 0x0000166D}, {0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF}, -{0x00001B61, 0x00001B6A}, {0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1}, -{0x00001FCD, 0x00001FCF}, {0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE}, -{0x00002044, 0x00002044}, {0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C}, -{0x000020A0, 0x000020C0}, {0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109}, -{0x00002114, 0x00002114}, {0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125}, -{0x00002127, 0x00002127}, {0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B}, -{0x00002140, 0x00002144}, {0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B}, -{0x00002190, 0x00002307}, {0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A}, -{0x0000249C, 0x000024E9}, {0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, -{0x000027F0, 0x00002982}, {0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, -{0x00002B76, 0x00002B95}, {0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, -{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFF}, -{0x00003004, 0x00003004}, {0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, -{0x0000303E, 0x0000303F}, {0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, -{0x000031C0, 0x000031E3}, {0x000031EF, 0x000031EF}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, -{0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, -{0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, -{0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, -{0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2}, -{0x0000FD40, 0x0000FD4F}, {0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62}, -{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, -{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, -{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, -{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, -{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, -{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, -{0x0001BC9C, 0x0001BC9C}, {0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, -{0x0001D129, 0x0001D164}, {0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, -{0x0001D1AE, 0x0001D1EA}, {0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, -{0x0001D6C1, 0x0001D6C1}, {0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, -{0x0001D735, 0x0001D735}, {0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, -{0x0001D7A9, 0x0001D7A9}, {0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A}, -{0x0001DA6D, 0x0001DA74}, {0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F}, -{0x0001E2FF, 0x0001E2FF}, {0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E}, -{0x0001EEF0, 0x0001EEF1}, {0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, -{0x0001F0B1, 0x0001F0BF}, {0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, -{0x0001F1E6, 0x0001F202}, {0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, -{0x0001F260, 0x0001F265}, {0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, -{0x0001F700, 0x0001F776}, {0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0}, -{0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, -{0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, -{0x0001FA70, 0x0001FA7C}, {0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5}, -{0x0001FACE, 0x0001FADB}, {0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92}, -{0x0001FB94, 0x0001FBCA}, -}; - -const std::vector> unicode_ranges_control = { -{0x00000000, 0x0000001F}, {0x0000007F, 0x0000009F}, {0x000000AD, 0x000000AD}, {0x00000378, 0x00000379}, -{0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, {0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2}, -{0x00000530, 0x00000530}, {0x00000557, 0x00000558}, {0x0000058B, 0x0000058C}, {0x00000590, 0x00000590}, -{0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, {0x000005F5, 0x00000605}, {0x0000061C, 0x0000061C}, -{0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, {0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF}, -{0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, {0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D}, -{0x0000085F, 0x0000085F}, {0x0000086B, 0x0000086F}, {0x0000088F, 0x00000897}, {0x000008E2, 0x000008E2}, -{0x00000984, 0x00000984}, {0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9}, -{0x000009B1, 0x000009B1}, {0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6}, -{0x000009C9, 0x000009CA}, {0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE}, -{0x000009E4, 0x000009E5}, {0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E}, -{0x00000A11, 0x00000A12}, {0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34}, -{0x00000A37, 0x00000A37}, {0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46}, -{0x00000A49, 0x00000A4A}, {0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D}, -{0x00000A5F, 0x00000A65}, {0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E}, -{0x00000A92, 0x00000A92}, {0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4}, -{0x00000ABA, 0x00000ABB}, {0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF}, -{0x00000AD1, 0x00000ADF}, {0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00}, -{0x00000B04, 0x00000B04}, {0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29}, -{0x00000B31, 0x00000B31}, {0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46}, -{0x00000B49, 0x00000B4A}, {0x00000B4E, 0x00000B54}, {0x00000B58, 0x00000B5B}, {0x00000B5E, 0x00000B5E}, -{0x00000B64, 0x00000B65}, {0x00000B78, 0x00000B81}, {0x00000B84, 0x00000B84}, {0x00000B8B, 0x00000B8D}, -{0x00000B91, 0x00000B91}, {0x00000B96, 0x00000B98}, {0x00000B9B, 0x00000B9B}, {0x00000B9D, 0x00000B9D}, -{0x00000BA0, 0x00000BA2}, {0x00000BA5, 0x00000BA7}, {0x00000BAB, 0x00000BAD}, {0x00000BBA, 0x00000BBD}, -{0x00000BC3, 0x00000BC5}, {0x00000BC9, 0x00000BC9}, {0x00000BCE, 0x00000BCF}, {0x00000BD1, 0x00000BD6}, -{0x00000BD8, 0x00000BE5}, {0x00000BFB, 0x00000BFF}, {0x00000C0D, 0x00000C0D}, {0x00000C11, 0x00000C11}, -{0x00000C29, 0x00000C29}, {0x00000C3A, 0x00000C3B}, {0x00000C45, 0x00000C45}, {0x00000C49, 0x00000C49}, -{0x00000C4E, 0x00000C54}, {0x00000C57, 0x00000C57}, {0x00000C5B, 0x00000C5C}, {0x00000C5E, 0x00000C5F}, -{0x00000C64, 0x00000C65}, {0x00000C70, 0x00000C76}, {0x00000C8D, 0x00000C8D}, {0x00000C91, 0x00000C91}, -{0x00000CA9, 0x00000CA9}, {0x00000CB4, 0x00000CB4}, {0x00000CBA, 0x00000CBB}, {0x00000CC5, 0x00000CC5}, -{0x00000CC9, 0x00000CC9}, {0x00000CCE, 0x00000CD4}, {0x00000CD7, 0x00000CDC}, {0x00000CDF, 0x00000CDF}, -{0x00000CE4, 0x00000CE5}, {0x00000CF0, 0x00000CF0}, {0x00000CF4, 0x00000CFF}, {0x00000D0D, 0x00000D0D}, -{0x00000D11, 0x00000D11}, {0x00000D45, 0x00000D45}, {0x00000D49, 0x00000D49}, {0x00000D50, 0x00000D53}, -{0x00000D64, 0x00000D65}, {0x00000D80, 0x00000D80}, {0x00000D84, 0x00000D84}, {0x00000D97, 0x00000D99}, -{0x00000DB2, 0x00000DB2}, {0x00000DBC, 0x00000DBC}, {0x00000DBE, 0x00000DBF}, {0x00000DC7, 0x00000DC9}, -{0x00000DCB, 0x00000DCE}, {0x00000DD5, 0x00000DD5}, {0x00000DD7, 0x00000DD7}, {0x00000DE0, 0x00000DE5}, -{0x00000DF0, 0x00000DF1}, {0x00000DF5, 0x00000E00}, {0x00000E3B, 0x00000E3E}, {0x00000E5C, 0x00000E80}, -{0x00000E83, 0x00000E83}, {0x00000E85, 0x00000E85}, {0x00000E8B, 0x00000E8B}, {0x00000EA4, 0x00000EA4}, -{0x00000EA6, 0x00000EA6}, {0x00000EBE, 0x00000EBF}, {0x00000EC5, 0x00000EC5}, {0x00000EC7, 0x00000EC7}, -{0x00000ECF, 0x00000ECF}, {0x00000EDA, 0x00000EDB}, {0x00000EE0, 0x00000EFF}, {0x00000F48, 0x00000F48}, -{0x00000F6D, 0x00000F70}, {0x00000F98, 0x00000F98}, {0x00000FBD, 0x00000FBD}, {0x00000FCD, 0x00000FCD}, -{0x00000FDB, 0x00000FFF}, {0x000010C6, 0x000010C6}, {0x000010C8, 0x000010CC}, {0x000010CE, 0x000010CF}, -{0x00001249, 0x00001249}, {0x0000124E, 0x0000124F}, {0x00001257, 0x00001257}, {0x00001259, 0x00001259}, -{0x0000125E, 0x0000125F}, {0x00001289, 0x00001289}, {0x0000128E, 0x0000128F}, {0x000012B1, 0x000012B1}, -{0x000012B6, 0x000012B7}, {0x000012BF, 0x000012BF}, {0x000012C1, 0x000012C1}, {0x000012C6, 0x000012C7}, -{0x000012D7, 0x000012D7}, {0x00001311, 0x00001311}, {0x00001316, 0x00001317}, {0x0000135B, 0x0000135C}, -{0x0000137D, 0x0000137F}, {0x0000139A, 0x0000139F}, {0x000013F6, 0x000013F7}, {0x000013FE, 0x000013FF}, -{0x0000169D, 0x0000169F}, {0x000016F9, 0x000016FF}, {0x00001716, 0x0000171E}, {0x00001737, 0x0000173F}, -{0x00001754, 0x0000175F}, {0x0000176D, 0x0000176D}, {0x00001771, 0x00001771}, {0x00001774, 0x0000177F}, -{0x000017DE, 0x000017DF}, {0x000017EA, 0x000017EF}, {0x000017FA, 0x000017FF}, {0x0000180E, 0x0000180E}, -{0x0000181A, 0x0000181F}, {0x00001879, 0x0000187F}, {0x000018AB, 0x000018AF}, {0x000018F6, 0x000018FF}, -{0x0000191F, 0x0000191F}, {0x0000192C, 0x0000192F}, {0x0000193C, 0x0000193F}, {0x00001941, 0x00001943}, -{0x0000196E, 0x0000196F}, {0x00001975, 0x0000197F}, {0x000019AC, 0x000019AF}, {0x000019CA, 0x000019CF}, -{0x000019DB, 0x000019DD}, {0x00001A1C, 0x00001A1D}, {0x00001A5F, 0x00001A5F}, {0x00001A7D, 0x00001A7E}, -{0x00001A8A, 0x00001A8F}, {0x00001A9A, 0x00001A9F}, {0x00001AAE, 0x00001AAF}, {0x00001ACF, 0x00001AFF}, -{0x00001B4D, 0x00001B4F}, {0x00001B7F, 0x00001B7F}, {0x00001BF4, 0x00001BFB}, {0x00001C38, 0x00001C3A}, -{0x00001C4A, 0x00001C4C}, {0x00001C89, 0x00001C8F}, {0x00001CBB, 0x00001CBC}, {0x00001CC8, 0x00001CCF}, -{0x00001CFB, 0x00001CFF}, {0x00001F16, 0x00001F17}, {0x00001F1E, 0x00001F1F}, {0x00001F46, 0x00001F47}, -{0x00001F4E, 0x00001F4F}, {0x00001F58, 0x00001F58}, {0x00001F5A, 0x00001F5A}, {0x00001F5C, 0x00001F5C}, -{0x00001F5E, 0x00001F5E}, {0x00001F7E, 0x00001F7F}, {0x00001FB5, 0x00001FB5}, {0x00001FC5, 0x00001FC5}, -{0x00001FD4, 0x00001FD5}, {0x00001FDC, 0x00001FDC}, {0x00001FF0, 0x00001FF1}, {0x00001FF5, 0x00001FF5}, -{0x00001FFF, 0x00001FFF}, {0x0000200B, 0x0000200F}, {0x0000202A, 0x0000202E}, {0x00002060, 0x0000206F}, -{0x00002072, 0x00002073}, {0x0000208F, 0x0000208F}, {0x0000209D, 0x0000209F}, {0x000020C1, 0x000020CF}, -{0x000020F1, 0x000020FF}, {0x0000218C, 0x0000218F}, {0x00002427, 0x0000243F}, {0x0000244B, 0x0000245F}, -{0x00002B74, 0x00002B75}, {0x00002B96, 0x00002B96}, {0x00002CF4, 0x00002CF8}, {0x00002D26, 0x00002D26}, -{0x00002D28, 0x00002D2C}, {0x00002D2E, 0x00002D2F}, {0x00002D68, 0x00002D6E}, {0x00002D71, 0x00002D7E}, -{0x00002D97, 0x00002D9F}, {0x00002DA7, 0x00002DA7}, {0x00002DAF, 0x00002DAF}, {0x00002DB7, 0x00002DB7}, -{0x00002DBF, 0x00002DBF}, {0x00002DC7, 0x00002DC7}, {0x00002DCF, 0x00002DCF}, {0x00002DD7, 0x00002DD7}, -{0x00002DDF, 0x00002DDF}, {0x00002E5E, 0x00002E7F}, {0x00002E9A, 0x00002E9A}, {0x00002EF4, 0x00002EFF}, -{0x00002FD6, 0x00002FEF}, {0x00003040, 0x00003040}, {0x00003097, 0x00003098}, {0x00003100, 0x00003104}, -{0x00003130, 0x00003130}, {0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EE}, {0x0000321F, 0x0000321F}, -{0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, {0x0000A6F8, 0x0000A6FF}, -{0x0000A7CB, 0x0000A7CF}, {0x0000A7D2, 0x0000A7D2}, {0x0000A7D4, 0x0000A7D4}, {0x0000A7DA, 0x0000A7F1}, -{0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, {0x0000A878, 0x0000A87F}, {0x0000A8C6, 0x0000A8CD}, -{0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, {0x0000A97D, 0x0000A97F}, {0x0000A9CE, 0x0000A9CE}, -{0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, {0x0000AA37, 0x0000AA3F}, {0x0000AA4E, 0x0000AA4F}, -{0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, {0x0000AAF7, 0x0000AB00}, {0x0000AB07, 0x0000AB08}, -{0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, {0x0000AB27, 0x0000AB27}, {0x0000AB2F, 0x0000AB2F}, -{0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, {0x0000ABFA, 0x0000ABFF}, {0x0000D7A4, 0x0000D7AF}, -{0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000F8FF}, {0x0000FA6E, 0x0000FA6F}, {0x0000FADA, 0x0000FAFF}, -{0x0000FB07, 0x0000FB12}, {0x0000FB18, 0x0000FB1C}, {0x0000FB37, 0x0000FB37}, {0x0000FB3D, 0x0000FB3D}, -{0x0000FB3F, 0x0000FB3F}, {0x0000FB42, 0x0000FB42}, {0x0000FB45, 0x0000FB45}, {0x0000FBC3, 0x0000FBD2}, -{0x0000FD90, 0x0000FD91}, {0x0000FDC8, 0x0000FDCE}, {0x0000FDD0, 0x0000FDEF}, {0x0000FE1A, 0x0000FE1F}, -{0x0000FE53, 0x0000FE53}, {0x0000FE67, 0x0000FE67}, {0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75}, -{0x0000FEFD, 0x0000FF00}, {0x0000FFBF, 0x0000FFC1}, {0x0000FFC8, 0x0000FFC9}, {0x0000FFD0, 0x0000FFD1}, -{0x0000FFD8, 0x0000FFD9}, {0x0000FFDD, 0x0000FFDF}, {0x0000FFE7, 0x0000FFE7}, {0x0000FFEF, 0x0000FFFB}, -{0x0000FFFE, 0x0000FFFF}, {0x0001000C, 0x0001000C}, {0x00010027, 0x00010027}, {0x0001003B, 0x0001003B}, -{0x0001003E, 0x0001003E}, {0x0001004E, 0x0001004F}, {0x0001005E, 0x0001007F}, {0x000100FB, 0x000100FF}, -{0x00010103, 0x00010106}, {0x00010134, 0x00010136}, {0x0001018F, 0x0001018F}, {0x0001019D, 0x0001019F}, -{0x000101A1, 0x000101CF}, {0x000101FE, 0x0001027F}, {0x0001029D, 0x0001029F}, {0x000102D1, 0x000102DF}, -{0x000102FC, 0x000102FF}, {0x00010324, 0x0001032C}, {0x0001034B, 0x0001034F}, {0x0001037B, 0x0001037F}, -{0x0001039E, 0x0001039E}, {0x000103C4, 0x000103C7}, {0x000103D6, 0x000103FF}, {0x0001049E, 0x0001049F}, -{0x000104AA, 0x000104AF}, {0x000104D4, 0x000104D7}, {0x000104FC, 0x000104FF}, {0x00010528, 0x0001052F}, -{0x00010564, 0x0001056E}, {0x0001057B, 0x0001057B}, {0x0001058B, 0x0001058B}, {0x00010593, 0x00010593}, -{0x00010596, 0x00010596}, {0x000105A2, 0x000105A2}, {0x000105B2, 0x000105B2}, {0x000105BA, 0x000105BA}, -{0x000105BD, 0x000105FF}, {0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x0001077F}, -{0x00010786, 0x00010786}, {0x000107B1, 0x000107B1}, {0x000107BB, 0x000107FF}, {0x00010806, 0x00010807}, -{0x00010809, 0x00010809}, {0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, {0x0001083D, 0x0001083E}, -{0x00010856, 0x00010856}, {0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, {0x000108F3, 0x000108F3}, -{0x000108F6, 0x000108FA}, {0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, {0x00010940, 0x0001097F}, -{0x000109B8, 0x000109BB}, {0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, {0x00010A07, 0x00010A0B}, -{0x00010A14, 0x00010A14}, {0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, {0x00010A3B, 0x00010A3E}, -{0x00010A49, 0x00010A4F}, {0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, {0x00010AE7, 0x00010AEA}, -{0x00010AF7, 0x00010AFF}, {0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, {0x00010B73, 0x00010B77}, -{0x00010B92, 0x00010B98}, {0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, {0x00010C49, 0x00010C7F}, -{0x00010CB3, 0x00010CBF}, {0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, {0x00010D3A, 0x00010E5F}, -{0x00010E7F, 0x00010E7F}, {0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, {0x00010EB2, 0x00010EFC}, -{0x00010F28, 0x00010F2F}, {0x00010F5A, 0x00010F6F}, {0x00010F8A, 0x00010FAF}, {0x00010FCC, 0x00010FDF}, -{0x00010FF7, 0x00010FFF}, {0x0001104E, 0x00011051}, {0x00011076, 0x0001107E}, {0x000110BD, 0x000110BD}, -{0x000110C3, 0x000110CF}, {0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135}, -{0x00011148, 0x0001114F}, {0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF}, -{0x00011212, 0x00011212}, {0x00011242, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289}, -{0x0001128E, 0x0001128E}, {0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF}, -{0x000112FA, 0x000112FF}, {0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312}, -{0x00011329, 0x00011329}, {0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A}, -{0x00011345, 0x00011346}, {0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356}, -{0x00011358, 0x0001135C}, {0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF}, -{0x0001145C, 0x0001145C}, {0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F}, -{0x000115B6, 0x000115B7}, {0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F}, -{0x0001166D, 0x0001167F}, {0x000116BA, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C}, -{0x0001172C, 0x0001172F}, {0x00011747, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE}, -{0x00011907, 0x00011908}, {0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917}, -{0x00011936, 0x00011936}, {0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F}, -{0x000119A8, 0x000119A9}, {0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F}, -{0x00011AA3, 0x00011AAF}, {0x00011AF9, 0x00011AFF}, {0x00011B0A, 0x00011BFF}, {0x00011C09, 0x00011C09}, -{0x00011C37, 0x00011C37}, {0x00011C46, 0x00011C4F}, {0x00011C6D, 0x00011C6F}, {0x00011C90, 0x00011C91}, -{0x00011CA8, 0x00011CA8}, {0x00011CB7, 0x00011CFF}, {0x00011D07, 0x00011D07}, {0x00011D0A, 0x00011D0A}, -{0x00011D37, 0x00011D39}, {0x00011D3B, 0x00011D3B}, {0x00011D3E, 0x00011D3E}, {0x00011D48, 0x00011D4F}, -{0x00011D5A, 0x00011D5F}, {0x00011D66, 0x00011D66}, {0x00011D69, 0x00011D69}, {0x00011D8F, 0x00011D8F}, -{0x00011D92, 0x00011D92}, {0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011EFF}, -{0x00011F11, 0x00011F11}, {0x00011F3B, 0x00011F3D}, {0x00011F5A, 0x00011FAF}, {0x00011FB1, 0x00011FBF}, -{0x00011FF2, 0x00011FFE}, {0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F}, -{0x00012544, 0x00012F8F}, {0x00012FF3, 0x00012FFF}, {0x00013430, 0x0001343F}, {0x00013456, 0x000143FF}, -{0x00014647, 0x000167FF}, {0x00016A39, 0x00016A3F}, {0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D}, -{0x00016ABF, 0x00016ABF}, {0x00016ACA, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, {0x00016AF6, 0x00016AFF}, -{0x00016B46, 0x00016B4F}, {0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, {0x00016B78, 0x00016B7C}, -{0x00016B90, 0x00016E3F}, {0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, {0x00016F88, 0x00016F8E}, -{0x00016FA0, 0x00016FDF}, {0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, {0x000187F8, 0x000187FF}, -{0x00018CD6, 0x00018CFF}, {0x00018D09, 0x0001AFEF}, {0x0001AFF4, 0x0001AFF4}, {0x0001AFFC, 0x0001AFFC}, -{0x0001AFFF, 0x0001AFFF}, {0x0001B123, 0x0001B131}, {0x0001B133, 0x0001B14F}, {0x0001B153, 0x0001B154}, -{0x0001B156, 0x0001B163}, {0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F}, -{0x0001BC7D, 0x0001BC7F}, {0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CEFF}, -{0x0001CF2E, 0x0001CF2F}, {0x0001CF47, 0x0001CF4F}, {0x0001CFC4, 0x0001CFFF}, {0x0001D0F6, 0x0001D0FF}, -{0x0001D127, 0x0001D128}, {0x0001D173, 0x0001D17A}, {0x0001D1EB, 0x0001D1FF}, {0x0001D246, 0x0001D2BF}, -{0x0001D2D4, 0x0001D2DF}, {0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, -{0x0001D455, 0x0001D455}, {0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, -{0x0001D4A7, 0x0001D4A8}, {0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, -{0x0001D4C4, 0x0001D4C4}, {0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, -{0x0001D51D, 0x0001D51D}, {0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, -{0x0001D547, 0x0001D549}, {0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, -{0x0001DA8C, 0x0001DA9A}, {0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DEFF}, {0x0001DF1F, 0x0001DF24}, -{0x0001DF2B, 0x0001DFFF}, {0x0001E007, 0x0001E007}, {0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, -{0x0001E025, 0x0001E025}, {0x0001E02B, 0x0001E02F}, {0x0001E06E, 0x0001E08E}, {0x0001E090, 0x0001E0FF}, -{0x0001E12D, 0x0001E12F}, {0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E28F}, -{0x0001E2AF, 0x0001E2BF}, {0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E4CF}, {0x0001E4FA, 0x0001E7DF}, -{0x0001E7E7, 0x0001E7E7}, {0x0001E7EC, 0x0001E7EC}, {0x0001E7EF, 0x0001E7EF}, {0x0001E7FF, 0x0001E7FF}, -{0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF}, {0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, -{0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00}, {0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, -{0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23}, {0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, -{0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38}, {0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, -{0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48}, {0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, -{0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53}, {0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, -{0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C}, {0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, -{0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66}, {0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, -{0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D}, {0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, -{0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4}, {0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, -{0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F}, {0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, -{0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0}, {0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, -{0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F}, {0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, -{0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DB}, {0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, -{0x0001F777, 0x0001F77A}, {0x0001F7DA, 0x0001F7DF}, {0x0001F7EC, 0x0001F7EF}, {0x0001F7F1, 0x0001F7FF}, -{0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, {0x0001F888, 0x0001F88F}, -{0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, -{0x0001FA7D, 0x0001FA7F}, {0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD}, -{0x0001FADC, 0x0001FADF}, {0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, -{0x0001FBCB, 0x0001FBEF}, {0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F}, -{0x0002B81E, 0x0002B81F}, {0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002EBEF}, {0x0002EE5E, 0x0002F7FF}, -{0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, {0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF}, -}; - -const std::map unicode_map_lowercase = { -{0x00000041, 0x00000061}, {0x00000042, 0x00000062}, {0x00000043, 0x00000063}, {0x00000044, 0x00000064}, -{0x00000045, 0x00000065}, {0x00000046, 0x00000066}, {0x00000047, 0x00000067}, {0x00000048, 0x00000068}, -{0x00000049, 0x00000069}, {0x0000004A, 0x0000006A}, {0x0000004B, 0x0000006B}, {0x0000004C, 0x0000006C}, -{0x0000004D, 0x0000006D}, {0x0000004E, 0x0000006E}, {0x0000004F, 0x0000006F}, {0x00000050, 0x00000070}, -{0x00000051, 0x00000071}, {0x00000052, 0x00000072}, {0x00000053, 0x00000073}, {0x00000054, 0x00000074}, -{0x00000055, 0x00000075}, {0x00000056, 0x00000076}, {0x00000057, 0x00000077}, {0x00000058, 0x00000078}, -{0x00000059, 0x00000079}, {0x0000005A, 0x0000007A}, {0x000000C0, 0x000000E0}, {0x000000C1, 0x000000E1}, -{0x000000C2, 0x000000E2}, {0x000000C3, 0x000000E3}, {0x000000C4, 0x000000E4}, {0x000000C5, 0x000000E5}, -{0x000000C6, 0x000000E6}, {0x000000C7, 0x000000E7}, {0x000000C8, 0x000000E8}, {0x000000C9, 0x000000E9}, -{0x000000CA, 0x000000EA}, {0x000000CB, 0x000000EB}, {0x000000CC, 0x000000EC}, {0x000000CD, 0x000000ED}, -{0x000000CE, 0x000000EE}, {0x000000CF, 0x000000EF}, {0x000000D0, 0x000000F0}, {0x000000D1, 0x000000F1}, -{0x000000D2, 0x000000F2}, {0x000000D3, 0x000000F3}, {0x000000D4, 0x000000F4}, {0x000000D5, 0x000000F5}, -{0x000000D6, 0x000000F6}, {0x000000D8, 0x000000F8}, {0x000000D9, 0x000000F9}, {0x000000DA, 0x000000FA}, -{0x000000DB, 0x000000FB}, {0x000000DC, 0x000000FC}, {0x000000DD, 0x000000FD}, {0x000000DE, 0x000000FE}, -{0x00000100, 0x00000101}, {0x00000102, 0x00000103}, {0x00000104, 0x00000105}, {0x00000106, 0x00000107}, -{0x00000108, 0x00000109}, {0x0000010A, 0x0000010B}, {0x0000010C, 0x0000010D}, {0x0000010E, 0x0000010F}, -{0x00000110, 0x00000111}, {0x00000112, 0x00000113}, {0x00000114, 0x00000115}, {0x00000116, 0x00000117}, -{0x00000118, 0x00000119}, {0x0000011A, 0x0000011B}, {0x0000011C, 0x0000011D}, {0x0000011E, 0x0000011F}, -{0x00000120, 0x00000121}, {0x00000122, 0x00000123}, {0x00000124, 0x00000125}, {0x00000126, 0x00000127}, -{0x00000128, 0x00000129}, {0x0000012A, 0x0000012B}, {0x0000012C, 0x0000012D}, {0x0000012E, 0x0000012F}, -{0x00000130, 0x00000069}, {0x00000132, 0x00000133}, {0x00000134, 0x00000135}, {0x00000136, 0x00000137}, -{0x00000139, 0x0000013A}, {0x0000013B, 0x0000013C}, {0x0000013D, 0x0000013E}, {0x0000013F, 0x00000140}, -{0x00000141, 0x00000142}, {0x00000143, 0x00000144}, {0x00000145, 0x00000146}, {0x00000147, 0x00000148}, -{0x0000014A, 0x0000014B}, {0x0000014C, 0x0000014D}, {0x0000014E, 0x0000014F}, {0x00000150, 0x00000151}, -{0x00000152, 0x00000153}, {0x00000154, 0x00000155}, {0x00000156, 0x00000157}, {0x00000158, 0x00000159}, -{0x0000015A, 0x0000015B}, {0x0000015C, 0x0000015D}, {0x0000015E, 0x0000015F}, {0x00000160, 0x00000161}, -{0x00000162, 0x00000163}, {0x00000164, 0x00000165}, {0x00000166, 0x00000167}, {0x00000168, 0x00000169}, -{0x0000016A, 0x0000016B}, {0x0000016C, 0x0000016D}, {0x0000016E, 0x0000016F}, {0x00000170, 0x00000171}, -{0x00000172, 0x00000173}, {0x00000174, 0x00000175}, {0x00000176, 0x00000177}, {0x00000178, 0x000000FF}, -{0x00000179, 0x0000017A}, {0x0000017B, 0x0000017C}, {0x0000017D, 0x0000017E}, {0x00000181, 0x00000253}, -{0x00000182, 0x00000183}, {0x00000184, 0x00000185}, {0x00000186, 0x00000254}, {0x00000187, 0x00000188}, -{0x00000189, 0x00000256}, {0x0000018A, 0x00000257}, {0x0000018B, 0x0000018C}, {0x0000018E, 0x000001DD}, -{0x0000018F, 0x00000259}, {0x00000190, 0x0000025B}, {0x00000191, 0x00000192}, {0x00000193, 0x00000260}, -{0x00000194, 0x00000263}, {0x00000196, 0x00000269}, {0x00000197, 0x00000268}, {0x00000198, 0x00000199}, -{0x0000019C, 0x0000026F}, {0x0000019D, 0x00000272}, {0x0000019F, 0x00000275}, {0x000001A0, 0x000001A1}, -{0x000001A2, 0x000001A3}, {0x000001A4, 0x000001A5}, {0x000001A6, 0x00000280}, {0x000001A7, 0x000001A8}, -{0x000001A9, 0x00000283}, {0x000001AC, 0x000001AD}, {0x000001AE, 0x00000288}, {0x000001AF, 0x000001B0}, -{0x000001B1, 0x0000028A}, {0x000001B2, 0x0000028B}, {0x000001B3, 0x000001B4}, {0x000001B5, 0x000001B6}, -{0x000001B7, 0x00000292}, {0x000001B8, 0x000001B9}, {0x000001BC, 0x000001BD}, {0x000001C4, 0x000001C6}, -{0x000001C5, 0x000001C6}, {0x000001C7, 0x000001C9}, {0x000001C8, 0x000001C9}, {0x000001CA, 0x000001CC}, -{0x000001CB, 0x000001CC}, {0x000001CD, 0x000001CE}, {0x000001CF, 0x000001D0}, {0x000001D1, 0x000001D2}, -{0x000001D3, 0x000001D4}, {0x000001D5, 0x000001D6}, {0x000001D7, 0x000001D8}, {0x000001D9, 0x000001DA}, -{0x000001DB, 0x000001DC}, {0x000001DE, 0x000001DF}, {0x000001E0, 0x000001E1}, {0x000001E2, 0x000001E3}, -{0x000001E4, 0x000001E5}, {0x000001E6, 0x000001E7}, {0x000001E8, 0x000001E9}, {0x000001EA, 0x000001EB}, -{0x000001EC, 0x000001ED}, {0x000001EE, 0x000001EF}, {0x000001F1, 0x000001F3}, {0x000001F2, 0x000001F3}, -{0x000001F4, 0x000001F5}, {0x000001F6, 0x00000195}, {0x000001F7, 0x000001BF}, {0x000001F8, 0x000001F9}, -{0x000001FA, 0x000001FB}, {0x000001FC, 0x000001FD}, {0x000001FE, 0x000001FF}, {0x00000200, 0x00000201}, -{0x00000202, 0x00000203}, {0x00000204, 0x00000205}, {0x00000206, 0x00000207}, {0x00000208, 0x00000209}, -{0x0000020A, 0x0000020B}, {0x0000020C, 0x0000020D}, {0x0000020E, 0x0000020F}, {0x00000210, 0x00000211}, -{0x00000212, 0x00000213}, {0x00000214, 0x00000215}, {0x00000216, 0x00000217}, {0x00000218, 0x00000219}, -{0x0000021A, 0x0000021B}, {0x0000021C, 0x0000021D}, {0x0000021E, 0x0000021F}, {0x00000220, 0x0000019E}, -{0x00000222, 0x00000223}, {0x00000224, 0x00000225}, {0x00000226, 0x00000227}, {0x00000228, 0x00000229}, -{0x0000022A, 0x0000022B}, {0x0000022C, 0x0000022D}, {0x0000022E, 0x0000022F}, {0x00000230, 0x00000231}, -{0x00000232, 0x00000233}, {0x0000023A, 0x00002C65}, {0x0000023B, 0x0000023C}, {0x0000023D, 0x0000019A}, -{0x0000023E, 0x00002C66}, {0x00000241, 0x00000242}, {0x00000243, 0x00000180}, {0x00000244, 0x00000289}, -{0x00000245, 0x0000028C}, {0x00000246, 0x00000247}, {0x00000248, 0x00000249}, {0x0000024A, 0x0000024B}, -{0x0000024C, 0x0000024D}, {0x0000024E, 0x0000024F}, {0x00000370, 0x00000371}, {0x00000372, 0x00000373}, -{0x00000376, 0x00000377}, {0x0000037F, 0x000003F3}, {0x00000386, 0x000003AC}, {0x00000388, 0x000003AD}, -{0x00000389, 0x000003AE}, {0x0000038A, 0x000003AF}, {0x0000038C, 0x000003CC}, {0x0000038E, 0x000003CD}, -{0x0000038F, 0x000003CE}, {0x00000391, 0x000003B1}, {0x00000392, 0x000003B2}, {0x00000393, 0x000003B3}, -{0x00000394, 0x000003B4}, {0x00000395, 0x000003B5}, {0x00000396, 0x000003B6}, {0x00000397, 0x000003B7}, -{0x00000398, 0x000003B8}, {0x00000399, 0x000003B9}, {0x0000039A, 0x000003BA}, {0x0000039B, 0x000003BB}, -{0x0000039C, 0x000003BC}, {0x0000039D, 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0x00010CDF}, -{0x00010CA0, 0x00010CE0}, {0x00010CA1, 0x00010CE1}, {0x00010CA2, 0x00010CE2}, {0x00010CA3, 0x00010CE3}, -{0x00010CA4, 0x00010CE4}, {0x00010CA5, 0x00010CE5}, {0x00010CA6, 0x00010CE6}, {0x00010CA7, 0x00010CE7}, -{0x00010CA8, 0x00010CE8}, {0x00010CA9, 0x00010CE9}, {0x00010CAA, 0x00010CEA}, {0x00010CAB, 0x00010CEB}, -{0x00010CAC, 0x00010CEC}, {0x00010CAD, 0x00010CED}, {0x00010CAE, 0x00010CEE}, {0x00010CAF, 0x00010CEF}, -{0x00010CB0, 0x00010CF0}, {0x00010CB1, 0x00010CF1}, {0x00010CB2, 0x00010CF2}, {0x000118A0, 0x000118C0}, -{0x000118A1, 0x000118C1}, {0x000118A2, 0x000118C2}, {0x000118A3, 0x000118C3}, {0x000118A4, 0x000118C4}, -{0x000118A5, 0x000118C5}, {0x000118A6, 0x000118C6}, {0x000118A7, 0x000118C7}, {0x000118A8, 0x000118C8}, -{0x000118A9, 0x000118C9}, {0x000118AA, 0x000118CA}, {0x000118AB, 0x000118CB}, {0x000118AC, 0x000118CC}, -{0x000118AD, 0x000118CD}, {0x000118AE, 0x000118CE}, {0x000118AF, 0x000118CF}, {0x000118B0, 0x000118D0}, -{0x000118B1, 0x000118D1}, {0x000118B2, 0x000118D2}, {0x000118B3, 0x000118D3}, {0x000118B4, 0x000118D4}, -{0x000118B5, 0x000118D5}, {0x000118B6, 0x000118D6}, {0x000118B7, 0x000118D7}, {0x000118B8, 0x000118D8}, -{0x000118B9, 0x000118D9}, {0x000118BA, 0x000118DA}, {0x000118BB, 0x000118DB}, {0x000118BC, 0x000118DC}, -{0x000118BD, 0x000118DD}, {0x000118BE, 0x000118DE}, {0x000118BF, 0x000118DF}, {0x00016E40, 0x00016E60}, -{0x00016E41, 0x00016E61}, {0x00016E42, 0x00016E62}, {0x00016E43, 0x00016E63}, {0x00016E44, 0x00016E64}, -{0x00016E45, 0x00016E65}, {0x00016E46, 0x00016E66}, {0x00016E47, 0x00016E67}, {0x00016E48, 0x00016E68}, -{0x00016E49, 0x00016E69}, {0x00016E4A, 0x00016E6A}, {0x00016E4B, 0x00016E6B}, {0x00016E4C, 0x00016E6C}, -{0x00016E4D, 0x00016E6D}, {0x00016E4E, 0x00016E6E}, {0x00016E4F, 0x00016E6F}, {0x00016E50, 0x00016E70}, -{0x00016E51, 0x00016E71}, {0x00016E52, 0x00016E72}, {0x00016E53, 0x00016E73}, {0x00016E54, 0x00016E74}, -{0x00016E55, 0x00016E75}, {0x00016E56, 0x00016E76}, {0x00016E57, 0x00016E77}, {0x00016E58, 0x00016E78}, -{0x00016E59, 0x00016E79}, {0x00016E5A, 0x00016E7A}, {0x00016E5B, 0x00016E7B}, {0x00016E5C, 0x00016E7C}, -{0x00016E5D, 0x00016E7D}, {0x00016E5E, 0x00016E7E}, {0x00016E5F, 0x00016E7F}, {0x0001E900, 0x0001E922}, -{0x0001E901, 0x0001E923}, {0x0001E902, 0x0001E924}, {0x0001E903, 0x0001E925}, {0x0001E904, 0x0001E926}, -{0x0001E905, 0x0001E927}, {0x0001E906, 0x0001E928}, {0x0001E907, 0x0001E929}, {0x0001E908, 0x0001E92A}, -{0x0001E909, 0x0001E92B}, {0x0001E90A, 0x0001E92C}, {0x0001E90B, 0x0001E92D}, {0x0001E90C, 0x0001E92E}, -{0x0001E90D, 0x0001E92F}, {0x0001E90E, 0x0001E930}, {0x0001E90F, 0x0001E931}, {0x0001E910, 0x0001E932}, -{0x0001E911, 0x0001E933}, {0x0001E912, 0x0001E934}, {0x0001E913, 0x0001E935}, {0x0001E914, 0x0001E936}, -{0x0001E915, 0x0001E937}, {0x0001E916, 0x0001E938}, {0x0001E917, 0x0001E939}, {0x0001E918, 0x0001E93A}, -{0x0001E919, 0x0001E93B}, {0x0001E91A, 0x0001E93C}, {0x0001E91B, 0x0001E93D}, {0x0001E91C, 0x0001E93E}, -{0x0001E91D, 0x0001E93F}, {0x0001E91E, 0x0001E940}, {0x0001E91F, 0x0001E941}, {0x0001E920, 0x0001E942}, -{0x0001E921, 0x0001E943}, -}; - -const std::map unicode_map_uppercase = { -{0x00000061, 0x00000041}, {0x00000062, 0x00000042}, {0x00000063, 0x00000043}, {0x00000064, 0x00000044}, -{0x00000065, 0x00000045}, {0x00000066, 0x00000046}, {0x00000067, 0x00000047}, {0x00000068, 0x00000048}, -{0x00000069, 0x00000049}, {0x0000006A, 0x0000004A}, {0x0000006B, 0x0000004B}, {0x0000006C, 0x0000004C}, -{0x0000006D, 0x0000004D}, {0x0000006E, 0x0000004E}, {0x0000006F, 0x0000004F}, {0x00000070, 0x00000050}, -{0x00000071, 0x00000051}, {0x00000072, 0x00000052}, {0x00000073, 0x00000053}, {0x00000074, 0x00000054}, -{0x00000075, 0x00000055}, {0x00000076, 0x00000056}, {0x00000077, 0x00000057}, {0x00000078, 0x00000058}, -{0x00000079, 0x00000059}, {0x0000007A, 0x0000005A}, {0x000000B5, 0x0000039C}, {0x000000DF, 0x00000053}, -{0x000000E0, 0x000000C0}, {0x000000E1, 0x000000C1}, {0x000000E2, 0x000000C2}, {0x000000E3, 0x000000C3}, -{0x000000E4, 0x000000C4}, {0x000000E5, 0x000000C5}, {0x000000E6, 0x000000C6}, {0x000000E7, 0x000000C7}, -{0x000000E8, 0x000000C8}, {0x000000E9, 0x000000C9}, {0x000000EA, 0x000000CA}, {0x000000EB, 0x000000CB}, -{0x000000EC, 0x000000CC}, {0x000000ED, 0x000000CD}, {0x000000EE, 0x000000CE}, {0x000000EF, 0x000000CF}, -{0x000000F0, 0x000000D0}, {0x000000F1, 0x000000D1}, {0x000000F2, 0x000000D2}, {0x000000F3, 0x000000D3}, -{0x000000F4, 0x000000D4}, {0x000000F5, 0x000000D5}, {0x000000F6, 0x000000D6}, {0x000000F8, 0x000000D8}, -{0x000000F9, 0x000000D9}, {0x000000FA, 0x000000DA}, {0x000000FB, 0x000000DB}, {0x000000FC, 0x000000DC}, -{0x000000FD, 0x000000DD}, {0x000000FE, 0x000000DE}, {0x000000FF, 0x00000178}, {0x00000101, 0x00000100}, -{0x00000103, 0x00000102}, {0x00000105, 0x00000104}, {0x00000107, 0x00000106}, {0x00000109, 0x00000108}, -{0x0000010B, 0x0000010A}, {0x0000010D, 0x0000010C}, {0x0000010F, 0x0000010E}, {0x00000111, 0x00000110}, -{0x00000113, 0x00000112}, {0x00000115, 0x00000114}, {0x00000117, 0x00000116}, {0x00000119, 0x00000118}, -{0x0000011B, 0x0000011A}, {0x0000011D, 0x0000011C}, {0x0000011F, 0x0000011E}, {0x00000121, 0x00000120}, -{0x00000123, 0x00000122}, {0x00000125, 0x00000124}, {0x00000127, 0x00000126}, {0x00000129, 0x00000128}, -{0x0000012B, 0x0000012A}, {0x0000012D, 0x0000012C}, {0x0000012F, 0x0000012E}, {0x00000131, 0x00000049}, -{0x00000133, 0x00000132}, {0x00000135, 0x00000134}, {0x00000137, 0x00000136}, {0x0000013A, 0x00000139}, -{0x0000013C, 0x0000013B}, {0x0000013E, 0x0000013D}, {0x00000140, 0x0000013F}, {0x00000142, 0x00000141}, -{0x00000144, 0x00000143}, {0x00000146, 0x00000145}, {0x00000148, 0x00000147}, {0x00000149, 0x000002BC}, -{0x0000014B, 0x0000014A}, {0x0000014D, 0x0000014C}, {0x0000014F, 0x0000014E}, {0x00000151, 0x00000150}, -{0x00000153, 0x00000152}, {0x00000155, 0x00000154}, {0x00000157, 0x00000156}, {0x00000159, 0x00000158}, -{0x0000015B, 0x0000015A}, {0x0000015D, 0x0000015C}, {0x0000015F, 0x0000015E}, {0x00000161, 0x00000160}, -{0x00000163, 0x00000162}, {0x00000165, 0x00000164}, {0x00000167, 0x00000166}, {0x00000169, 0x00000168}, -{0x0000016B, 0x0000016A}, {0x0000016D, 0x0000016C}, {0x0000016F, 0x0000016E}, {0x00000171, 0x00000170}, -{0x00000173, 0x00000172}, {0x00000175, 0x00000174}, {0x00000177, 0x00000176}, {0x0000017A, 0x00000179}, -{0x0000017C, 0x0000017B}, {0x0000017E, 0x0000017D}, {0x0000017F, 0x00000053}, {0x00000180, 0x00000243}, -{0x00000183, 0x00000182}, {0x00000185, 0x00000184}, {0x00000188, 0x00000187}, {0x0000018C, 0x0000018B}, -{0x00000192, 0x00000191}, {0x00000195, 0x000001F6}, {0x00000199, 0x00000198}, {0x0000019A, 0x0000023D}, -{0x0000019E, 0x00000220}, {0x000001A1, 0x000001A0}, {0x000001A3, 0x000001A2}, {0x000001A5, 0x000001A4}, -{0x000001A8, 0x000001A7}, {0x000001AD, 0x000001AC}, {0x000001B0, 0x000001AF}, {0x000001B4, 0x000001B3}, -{0x000001B6, 0x000001B5}, {0x000001B9, 0x000001B8}, {0x000001BD, 0x000001BC}, {0x000001BF, 0x000001F7}, -{0x000001C5, 0x000001C4}, {0x000001C6, 0x000001C4}, {0x000001C8, 0x000001C7}, {0x000001C9, 0x000001C7}, -{0x000001CB, 0x000001CA}, {0x000001CC, 0x000001CA}, {0x000001CE, 0x000001CD}, {0x000001D0, 0x000001CF}, -{0x000001D2, 0x000001D1}, {0x000001D4, 0x000001D3}, {0x000001D6, 0x000001D5}, {0x000001D8, 0x000001D7}, -{0x000001DA, 0x000001D9}, {0x000001DC, 0x000001DB}, {0x000001DD, 0x0000018E}, {0x000001DF, 0x000001DE}, -{0x000001E1, 0x000001E0}, {0x000001E3, 0x000001E2}, {0x000001E5, 0x000001E4}, {0x000001E7, 0x000001E6}, -{0x000001E9, 0x000001E8}, {0x000001EB, 0x000001EA}, {0x000001ED, 0x000001EC}, {0x000001EF, 0x000001EE}, -{0x000001F0, 0x0000004A}, {0x000001F2, 0x000001F1}, {0x000001F3, 0x000001F1}, {0x000001F5, 0x000001F4}, -{0x000001F9, 0x000001F8}, {0x000001FB, 0x000001FA}, {0x000001FD, 0x000001FC}, {0x000001FF, 0x000001FE}, -{0x00000201, 0x00000200}, {0x00000203, 0x00000202}, {0x00000205, 0x00000204}, {0x00000207, 0x00000206}, -{0x00000209, 0x00000208}, {0x0000020B, 0x0000020A}, {0x0000020D, 0x0000020C}, {0x0000020F, 0x0000020E}, -{0x00000211, 0x00000210}, {0x00000213, 0x00000212}, {0x00000215, 0x00000214}, {0x00000217, 0x00000216}, -{0x00000219, 0x00000218}, {0x0000021B, 0x0000021A}, {0x0000021D, 0x0000021C}, {0x0000021F, 0x0000021E}, -{0x00000223, 0x00000222}, {0x00000225, 0x00000224}, {0x00000227, 0x00000226}, {0x00000229, 0x00000228}, -{0x0000022B, 0x0000022A}, {0x0000022D, 0x0000022C}, {0x0000022F, 0x0000022E}, {0x00000231, 0x00000230}, -{0x00000233, 0x00000232}, {0x0000023C, 0x0000023B}, {0x0000023F, 0x00002C7E}, {0x00000240, 0x00002C7F}, -{0x00000242, 0x00000241}, {0x00000247, 0x00000246}, {0x00000249, 0x00000248}, {0x0000024B, 0x0000024A}, -{0x0000024D, 0x0000024C}, {0x0000024F, 0x0000024E}, {0x00000250, 0x00002C6F}, {0x00000251, 0x00002C6D}, -{0x00000252, 0x00002C70}, {0x00000253, 0x00000181}, {0x00000254, 0x00000186}, {0x00000256, 0x00000189}, -{0x00000257, 0x0000018A}, {0x00000259, 0x0000018F}, {0x0000025B, 0x00000190}, {0x0000025C, 0x0000A7AB}, -{0x00000260, 0x00000193}, {0x00000261, 0x0000A7AC}, {0x00000263, 0x00000194}, {0x00000265, 0x0000A78D}, -{0x00000266, 0x0000A7AA}, {0x00000268, 0x00000197}, {0x00000269, 0x00000196}, {0x0000026A, 0x0000A7AE}, -{0x0000026B, 0x00002C62}, {0x0000026C, 0x0000A7AD}, {0x0000026F, 0x0000019C}, {0x00000271, 0x00002C6E}, -{0x00000272, 0x0000019D}, {0x00000275, 0x0000019F}, {0x0000027D, 0x00002C64}, {0x00000280, 0x000001A6}, -{0x00000282, 0x0000A7C5}, {0x00000283, 0x000001A9}, {0x00000287, 0x0000A7B1}, {0x00000288, 0x000001AE}, -{0x00000289, 0x00000244}, {0x0000028A, 0x000001B1}, {0x0000028B, 0x000001B2}, {0x0000028C, 0x00000245}, -{0x00000292, 0x000001B7}, {0x0000029D, 0x0000A7B2}, {0x0000029E, 0x0000A7B0}, {0x00000345, 0x00000399}, -{0x00000371, 0x00000370}, {0x00000373, 0x00000372}, {0x00000377, 0x00000376}, {0x0000037B, 0x000003FD}, -{0x0000037C, 0x000003FE}, {0x0000037D, 0x000003FF}, {0x00000390, 0x00000399}, {0x000003AC, 0x00000386}, -{0x000003AD, 0x00000388}, {0x000003AE, 0x00000389}, {0x000003AF, 0x0000038A}, {0x000003B0, 0x000003A5}, -{0x000003B1, 0x00000391}, {0x000003B2, 0x00000392}, {0x000003B3, 0x00000393}, {0x000003B4, 0x00000394}, -{0x000003B5, 0x00000395}, {0x000003B6, 0x00000396}, {0x000003B7, 0x00000397}, {0x000003B8, 0x00000398}, -{0x000003B9, 0x00000399}, {0x000003BA, 0x0000039A}, {0x000003BB, 0x0000039B}, {0x000003BC, 0x0000039C}, -{0x000003BD, 0x0000039D}, {0x000003BE, 0x0000039E}, {0x000003BF, 0x0000039F}, {0x000003C0, 0x000003A0}, -{0x000003C1, 0x000003A1}, {0x000003C2, 0x000003A3}, {0x000003C3, 0x000003A3}, {0x000003C4, 0x000003A4}, -{0x000003C5, 0x000003A5}, {0x000003C6, 0x000003A6}, {0x000003C7, 0x000003A7}, {0x000003C8, 0x000003A8}, -{0x000003C9, 0x000003A9}, {0x000003CA, 0x000003AA}, {0x000003CB, 0x000003AB}, {0x000003CC, 0x0000038C}, -{0x000003CD, 0x0000038E}, {0x000003CE, 0x0000038F}, {0x000003D0, 0x00000392}, {0x000003D1, 0x00000398}, -{0x000003D5, 0x000003A6}, {0x000003D6, 0x000003A0}, {0x000003D7, 0x000003CF}, {0x000003D9, 0x000003D8}, -{0x000003DB, 0x000003DA}, {0x000003DD, 0x000003DC}, {0x000003DF, 0x000003DE}, {0x000003E1, 0x000003E0}, -{0x000003E3, 0x000003E2}, {0x000003E5, 0x000003E4}, {0x000003E7, 0x000003E6}, {0x000003E9, 0x000003E8}, -{0x000003EB, 0x000003EA}, {0x000003ED, 0x000003EC}, {0x000003EF, 0x000003EE}, {0x000003F0, 0x0000039A}, -{0x000003F1, 0x000003A1}, {0x000003F2, 0x000003F9}, {0x000003F3, 0x0000037F}, {0x000003F5, 0x00000395}, -{0x000003F8, 0x000003F7}, {0x000003FB, 0x000003FA}, {0x00000430, 0x00000410}, {0x00000431, 0x00000411}, -{0x00000432, 0x00000412}, {0x00000433, 0x00000413}, {0x00000434, 0x00000414}, {0x00000435, 0x00000415}, -{0x00000436, 0x00000416}, {0x00000437, 0x00000417}, {0x00000438, 0x00000418}, {0x00000439, 0x00000419}, -{0x0000043A, 0x0000041A}, {0x0000043B, 0x0000041B}, {0x0000043C, 0x0000041C}, {0x0000043D, 0x0000041D}, -{0x0000043E, 0x0000041E}, {0x0000043F, 0x0000041F}, {0x00000440, 0x00000420}, {0x00000441, 0x00000421}, -{0x00000442, 0x00000422}, {0x00000443, 0x00000423}, {0x00000444, 0x00000424}, {0x00000445, 0x00000425}, -{0x00000446, 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0x00010C9A}, {0x00010CDB, 0x00010C9B}, -{0x00010CDC, 0x00010C9C}, {0x00010CDD, 0x00010C9D}, {0x00010CDE, 0x00010C9E}, {0x00010CDF, 0x00010C9F}, -{0x00010CE0, 0x00010CA0}, {0x00010CE1, 0x00010CA1}, {0x00010CE2, 0x00010CA2}, {0x00010CE3, 0x00010CA3}, -{0x00010CE4, 0x00010CA4}, {0x00010CE5, 0x00010CA5}, {0x00010CE6, 0x00010CA6}, {0x00010CE7, 0x00010CA7}, -{0x00010CE8, 0x00010CA8}, {0x00010CE9, 0x00010CA9}, {0x00010CEA, 0x00010CAA}, {0x00010CEB, 0x00010CAB}, -{0x00010CEC, 0x00010CAC}, {0x00010CED, 0x00010CAD}, {0x00010CEE, 0x00010CAE}, {0x00010CEF, 0x00010CAF}, -{0x00010CF0, 0x00010CB0}, {0x00010CF1, 0x00010CB1}, {0x00010CF2, 0x00010CB2}, {0x000118C0, 0x000118A0}, -{0x000118C1, 0x000118A1}, {0x000118C2, 0x000118A2}, {0x000118C3, 0x000118A3}, {0x000118C4, 0x000118A4}, -{0x000118C5, 0x000118A5}, {0x000118C6, 0x000118A6}, {0x000118C7, 0x000118A7}, {0x000118C8, 0x000118A8}, -{0x000118C9, 0x000118A9}, {0x000118CA, 0x000118AA}, {0x000118CB, 0x000118AB}, {0x000118CC, 0x000118AC}, -{0x000118CD, 0x000118AD}, {0x000118CE, 0x000118AE}, {0x000118CF, 0x000118AF}, {0x000118D0, 0x000118B0}, -{0x000118D1, 0x000118B1}, {0x000118D2, 0x000118B2}, {0x000118D3, 0x000118B3}, {0x000118D4, 0x000118B4}, -{0x000118D5, 0x000118B5}, {0x000118D6, 0x000118B6}, {0x000118D7, 0x000118B7}, {0x000118D8, 0x000118B8}, -{0x000118D9, 0x000118B9}, {0x000118DA, 0x000118BA}, {0x000118DB, 0x000118BB}, {0x000118DC, 0x000118BC}, -{0x000118DD, 0x000118BD}, {0x000118DE, 0x000118BE}, {0x000118DF, 0x000118BF}, {0x00016E60, 0x00016E40}, -{0x00016E61, 0x00016E41}, {0x00016E62, 0x00016E42}, {0x00016E63, 0x00016E43}, {0x00016E64, 0x00016E44}, -{0x00016E65, 0x00016E45}, {0x00016E66, 0x00016E46}, {0x00016E67, 0x00016E47}, {0x00016E68, 0x00016E48}, -{0x00016E69, 0x00016E49}, {0x00016E6A, 0x00016E4A}, {0x00016E6B, 0x00016E4B}, {0x00016E6C, 0x00016E4C}, -{0x00016E6D, 0x00016E4D}, {0x00016E6E, 0x00016E4E}, {0x00016E6F, 0x00016E4F}, {0x00016E70, 0x00016E50}, -{0x00016E71, 0x00016E51}, {0x00016E72, 0x00016E52}, {0x00016E73, 0x00016E53}, {0x00016E74, 0x00016E54}, -{0x00016E75, 0x00016E55}, {0x00016E76, 0x00016E56}, {0x00016E77, 0x00016E57}, {0x00016E78, 0x00016E58}, -{0x00016E79, 0x00016E59}, {0x00016E7A, 0x00016E5A}, {0x00016E7B, 0x00016E5B}, {0x00016E7C, 0x00016E5C}, -{0x00016E7D, 0x00016E5D}, {0x00016E7E, 0x00016E5E}, {0x00016E7F, 0x00016E5F}, {0x0001E922, 0x0001E900}, -{0x0001E923, 0x0001E901}, {0x0001E924, 0x0001E902}, {0x0001E925, 0x0001E903}, {0x0001E926, 0x0001E904}, -{0x0001E927, 0x0001E905}, {0x0001E928, 0x0001E906}, {0x0001E929, 0x0001E907}, {0x0001E92A, 0x0001E908}, -{0x0001E92B, 0x0001E909}, {0x0001E92C, 0x0001E90A}, {0x0001E92D, 0x0001E90B}, {0x0001E92E, 0x0001E90C}, -{0x0001E92F, 0x0001E90D}, {0x0001E930, 0x0001E90E}, {0x0001E931, 0x0001E90F}, {0x0001E932, 0x0001E910}, -{0x0001E933, 0x0001E911}, {0x0001E934, 0x0001E912}, {0x0001E935, 0x0001E913}, {0x0001E936, 0x0001E914}, -{0x0001E937, 0x0001E915}, {0x0001E938, 0x0001E916}, {0x0001E939, 0x0001E917}, {0x0001E93A, 0x0001E918}, -{0x0001E93B, 0x0001E919}, {0x0001E93C, 0x0001E91A}, {0x0001E93D, 0x0001E91B}, {0x0001E93E, 0x0001E91C}, -{0x0001E93F, 0x0001E91D}, {0x0001E940, 0x0001E91E}, {0x0001E941, 0x0001E91F}, {0x0001E942, 0x0001E920}, -{0x0001E943, 0x0001E921}, -}; - -const std::multimap unicode_map_nfd = { -{0x000000C0, 0x00000041}, {0x000000C0, 0x00000300}, {0x000000C1, 0x00000041}, {0x000000C1, 0x00000301}, -{0x000000C2, 0x00000041}, {0x000000C2, 0x00000302}, {0x000000C3, 0x00000041}, {0x000000C3, 0x00000303}, -{0x000000C4, 0x00000041}, {0x000000C4, 0x00000308}, {0x000000C5, 0x00000041}, {0x000000C5, 0x0000030A}, -{0x000000C7, 0x00000043}, {0x000000C7, 0x00000327}, {0x000000C8, 0x00000045}, {0x000000C8, 0x00000300}, -{0x000000C9, 0x00000045}, {0x000000C9, 0x00000301}, {0x000000CA, 0x00000045}, {0x000000CA, 0x00000302}, -{0x000000CB, 0x00000045}, {0x000000CB, 0x00000308}, {0x000000CC, 0x00000049}, {0x000000CC, 0x00000300}, -{0x000000CD, 0x00000049}, {0x000000CD, 0x00000301}, {0x000000CE, 0x00000049}, {0x000000CE, 0x00000302}, -{0x000000CF, 0x00000049}, {0x000000CF, 0x00000308}, {0x000000D1, 0x0000004E}, {0x000000D1, 0x00000303}, -{0x000000D2, 0x0000004F}, {0x000000D2, 0x00000300}, {0x000000D3, 0x0000004F}, {0x000000D3, 0x00000301}, -{0x000000D4, 0x0000004F}, {0x000000D4, 0x00000302}, {0x000000D5, 0x0000004F}, {0x000000D5, 0x00000303}, -{0x000000D6, 0x0000004F}, {0x000000D6, 0x00000308}, {0x000000D9, 0x00000055}, {0x000000D9, 0x00000300}, -{0x000000DA, 0x00000055}, {0x000000DA, 0x00000301}, {0x000000DB, 0x00000055}, {0x000000DB, 0x00000302}, -{0x000000DC, 0x00000055}, {0x000000DC, 0x00000308}, {0x000000DD, 0x00000059}, {0x000000DD, 0x00000301}, -{0x000000E0, 0x00000061}, {0x000000E0, 0x00000300}, {0x000000E1, 0x00000061}, {0x000000E1, 0x00000301}, -{0x000000E2, 0x00000061}, {0x000000E2, 0x00000302}, {0x000000E3, 0x00000061}, {0x000000E3, 0x00000303}, -{0x000000E4, 0x00000061}, {0x000000E4, 0x00000308}, {0x000000E5, 0x00000061}, {0x000000E5, 0x0000030A}, -{0x000000E7, 0x00000063}, {0x000000E7, 0x00000327}, {0x000000E8, 0x00000065}, {0x000000E8, 0x00000300}, -{0x000000E9, 0x00000065}, {0x000000E9, 0x00000301}, {0x000000EA, 0x00000065}, {0x000000EA, 0x00000302}, -{0x000000EB, 0x00000065}, {0x000000EB, 0x00000308}, {0x000000EC, 0x00000069}, {0x000000EC, 0x00000300}, -{0x000000ED, 0x00000069}, {0x000000ED, 0x00000301}, {0x000000EE, 0x00000069}, {0x000000EE, 0x00000302}, -{0x000000EF, 0x00000069}, {0x000000EF, 0x00000308}, {0x000000F1, 0x0000006E}, {0x000000F1, 0x00000303}, -{0x000000F2, 0x0000006F}, {0x000000F2, 0x00000300}, {0x000000F3, 0x0000006F}, {0x000000F3, 0x00000301}, -{0x000000F4, 0x0000006F}, {0x000000F4, 0x00000302}, {0x000000F5, 0x0000006F}, {0x000000F5, 0x00000303}, -{0x000000F6, 0x0000006F}, {0x000000F6, 0x00000308}, {0x000000F9, 0x00000075}, {0x000000F9, 0x00000300}, -{0x000000FA, 0x00000075}, {0x000000FA, 0x00000301}, {0x000000FB, 0x00000075}, {0x000000FB, 0x00000302}, -{0x000000FC, 0x00000075}, {0x000000FC, 0x00000308}, {0x000000FD, 0x00000079}, {0x000000FD, 0x00000301}, -{0x000000FF, 0x00000079}, {0x000000FF, 0x00000308}, {0x00000100, 0x00000041}, {0x00000100, 0x00000304}, -{0x00000101, 0x00000061}, {0x00000101, 0x00000304}, {0x00000102, 0x00000041}, {0x00000102, 0x00000306}, -{0x00000103, 0x00000061}, {0x00000103, 0x00000306}, {0x00000104, 0x00000041}, {0x00000104, 0x00000328}, -{0x00000105, 0x00000061}, {0x00000105, 0x00000328}, {0x00000106, 0x00000043}, {0x00000106, 0x00000301}, -{0x00000107, 0x00000063}, {0x00000107, 0x00000301}, {0x00000108, 0x00000043}, {0x00000108, 0x00000302}, -{0x00000109, 0x00000063}, {0x00000109, 0x00000302}, {0x0000010A, 0x00000043}, {0x0000010A, 0x00000307}, -{0x0000010B, 0x00000063}, {0x0000010B, 0x00000307}, {0x0000010C, 0x00000043}, {0x0000010C, 0x0000030C}, -{0x0000010D, 0x00000063}, {0x0000010D, 0x0000030C}, {0x0000010E, 0x00000044}, {0x0000010E, 0x0000030C}, -{0x0000010F, 0x00000064}, {0x0000010F, 0x0000030C}, {0x00000112, 0x00000045}, {0x00000112, 0x00000304}, -{0x00000113, 0x00000065}, {0x00000113, 0x00000304}, {0x00000114, 0x00000045}, {0x00000114, 0x00000306}, -{0x00000115, 0x00000065}, {0x00000115, 0x00000306}, {0x00000116, 0x00000045}, {0x00000116, 0x00000307}, -{0x00000117, 0x00000065}, {0x00000117, 0x00000307}, {0x00000118, 0x00000045}, {0x00000118, 0x00000328}, -{0x00000119, 0x00000065}, {0x00000119, 0x00000328}, {0x0000011A, 0x00000045}, {0x0000011A, 0x0000030C}, -{0x0000011B, 0x00000065}, {0x0000011B, 0x0000030C}, {0x0000011C, 0x00000047}, {0x0000011C, 0x00000302}, -{0x0000011D, 0x00000067}, {0x0000011D, 0x00000302}, {0x0000011E, 0x00000047}, {0x0000011E, 0x00000306}, -{0x0000011F, 0x00000067}, {0x0000011F, 0x00000306}, {0x00000120, 0x00000047}, {0x00000120, 0x00000307}, -{0x00000121, 0x00000067}, {0x00000121, 0x00000307}, {0x00000122, 0x00000047}, {0x00000122, 0x00000327}, -{0x00000123, 0x00000067}, {0x00000123, 0x00000327}, {0x00000124, 0x00000048}, {0x00000124, 0x00000302}, -{0x00000125, 0x00000068}, {0x00000125, 0x00000302}, {0x00000128, 0x00000049}, {0x00000128, 0x00000303}, -{0x00000129, 0x00000069}, {0x00000129, 0x00000303}, {0x0000012A, 0x00000049}, {0x0000012A, 0x00000304}, -{0x0000012B, 0x00000069}, {0x0000012B, 0x00000304}, {0x0000012C, 0x00000049}, {0x0000012C, 0x00000306}, -{0x0000012D, 0x00000069}, {0x0000012D, 0x00000306}, {0x0000012E, 0x00000049}, {0x0000012E, 0x00000328}, -{0x0000012F, 0x00000069}, {0x0000012F, 0x00000328}, {0x00000130, 0x00000049}, {0x00000130, 0x00000307}, -{0x00000134, 0x0000004A}, {0x00000134, 0x00000302}, {0x00000135, 0x0000006A}, {0x00000135, 0x00000302}, -{0x00000136, 0x0000004B}, {0x00000136, 0x00000327}, {0x00000137, 0x0000006B}, {0x00000137, 0x00000327}, -{0x00000139, 0x0000004C}, {0x00000139, 0x00000301}, {0x0000013A, 0x0000006C}, {0x0000013A, 0x00000301}, -{0x0000013B, 0x0000004C}, {0x0000013B, 0x00000327}, {0x0000013C, 0x0000006C}, {0x0000013C, 0x00000327}, -{0x0000013D, 0x0000004C}, {0x0000013D, 0x0000030C}, {0x0000013E, 0x0000006C}, {0x0000013E, 0x0000030C}, -{0x00000143, 0x0000004E}, {0x00000143, 0x00000301}, {0x00000144, 0x0000006E}, {0x00000144, 0x00000301}, -{0x00000145, 0x0000004E}, {0x00000145, 0x00000327}, {0x00000146, 0x0000006E}, {0x00000146, 0x00000327}, -{0x00000147, 0x0000004E}, {0x00000147, 0x0000030C}, {0x00000148, 0x0000006E}, {0x00000148, 0x0000030C}, -{0x0000014C, 0x0000004F}, {0x0000014C, 0x00000304}, {0x0000014D, 0x0000006F}, {0x0000014D, 0x00000304}, -{0x0000014E, 0x0000004F}, {0x0000014E, 0x00000306}, {0x0000014F, 0x0000006F}, {0x0000014F, 0x00000306}, -{0x00000150, 0x0000004F}, {0x00000150, 0x0000030B}, {0x00000151, 0x0000006F}, {0x00000151, 0x0000030B}, -{0x00000154, 0x00000052}, {0x00000154, 0x00000301}, {0x00000155, 0x00000072}, {0x00000155, 0x00000301}, -{0x00000156, 0x00000052}, {0x00000156, 0x00000327}, {0x00000157, 0x00000072}, {0x00000157, 0x00000327}, -{0x00000158, 0x00000052}, {0x00000158, 0x0000030C}, {0x00000159, 0x00000072}, {0x00000159, 0x0000030C}, -{0x0000015A, 0x00000053}, {0x0000015A, 0x00000301}, {0x0000015B, 0x00000073}, {0x0000015B, 0x00000301}, -{0x0000015C, 0x00000053}, {0x0000015C, 0x00000302}, {0x0000015D, 0x00000073}, {0x0000015D, 0x00000302}, -{0x0000015E, 0x00000053}, {0x0000015E, 0x00000327}, {0x0000015F, 0x00000073}, {0x0000015F, 0x00000327}, -{0x00000160, 0x00000053}, {0x00000160, 0x0000030C}, {0x00000161, 0x00000073}, {0x00000161, 0x0000030C}, -{0x00000162, 0x00000054}, {0x00000162, 0x00000327}, {0x00000163, 0x00000074}, {0x00000163, 0x00000327}, -{0x00000164, 0x00000054}, {0x00000164, 0x0000030C}, {0x00000165, 0x00000074}, {0x00000165, 0x0000030C}, -{0x00000168, 0x00000055}, {0x00000168, 0x00000303}, {0x00000169, 0x00000075}, {0x00000169, 0x00000303}, -{0x0000016A, 0x00000055}, {0x0000016A, 0x00000304}, {0x0000016B, 0x00000075}, {0x0000016B, 0x00000304}, -{0x0000016C, 0x00000055}, {0x0000016C, 0x00000306}, {0x0000016D, 0x00000075}, {0x0000016D, 0x00000306}, -{0x0000016E, 0x00000055}, {0x0000016E, 0x0000030A}, {0x0000016F, 0x00000075}, {0x0000016F, 0x0000030A}, -{0x00000170, 0x00000055}, {0x00000170, 0x0000030B}, {0x00000171, 0x00000075}, {0x00000171, 0x0000030B}, -{0x00000172, 0x00000055}, {0x00000172, 0x00000328}, {0x00000173, 0x00000075}, {0x00000173, 0x00000328}, -{0x00000174, 0x00000057}, {0x00000174, 0x00000302}, {0x00000175, 0x00000077}, {0x00000175, 0x00000302}, -{0x00000176, 0x00000059}, {0x00000176, 0x00000302}, {0x00000177, 0x00000079}, {0x00000177, 0x00000302}, -{0x00000178, 0x00000059}, {0x00000178, 0x00000308}, {0x00000179, 0x0000005A}, {0x00000179, 0x00000301}, -{0x0000017A, 0x0000007A}, {0x0000017A, 0x00000301}, {0x0000017B, 0x0000005A}, {0x0000017B, 0x00000307}, -{0x0000017C, 0x0000007A}, {0x0000017C, 0x00000307}, {0x0000017D, 0x0000005A}, {0x0000017D, 0x0000030C}, -{0x0000017E, 0x0000007A}, {0x0000017E, 0x0000030C}, {0x000001A0, 0x0000004F}, {0x000001A0, 0x0000031B}, -{0x000001A1, 0x0000006F}, {0x000001A1, 0x0000031B}, {0x000001AF, 0x00000055}, {0x000001AF, 0x0000031B}, -{0x000001B0, 0x00000075}, {0x000001B0, 0x0000031B}, {0x000001CD, 0x00000041}, {0x000001CD, 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0x00008D77}, {0x0002F9D8, 0x00027F2F}, {0x0002F9D9, 0x00020804}, -{0x0002F9DA, 0x00008DCB}, {0x0002F9DB, 0x00008DBC}, {0x0002F9DC, 0x00008DF0}, {0x0002F9DD, 0x000208DE}, -{0x0002F9DE, 0x00008ED4}, {0x0002F9DF, 0x00008F38}, {0x0002F9E0, 0x000285D2}, {0x0002F9E1, 0x000285ED}, -{0x0002F9E2, 0x00009094}, {0x0002F9E3, 0x000090F1}, {0x0002F9E4, 0x00009111}, {0x0002F9E5, 0x0002872E}, -{0x0002F9E6, 0x0000911B}, {0x0002F9E7, 0x00009238}, {0x0002F9E8, 0x000092D7}, {0x0002F9E9, 0x000092D8}, -{0x0002F9EA, 0x0000927C}, {0x0002F9EB, 0x000093F9}, {0x0002F9EC, 0x00009415}, {0x0002F9ED, 0x00028BFA}, -{0x0002F9EE, 0x0000958B}, {0x0002F9EF, 0x00004995}, {0x0002F9F0, 0x000095B7}, {0x0002F9F1, 0x00028D77}, -{0x0002F9F2, 0x000049E6}, {0x0002F9F3, 0x000096C3}, {0x0002F9F4, 0x00005DB2}, {0x0002F9F5, 0x00009723}, -{0x0002F9F6, 0x00029145}, {0x0002F9F7, 0x0002921A}, {0x0002F9F8, 0x00004A6E}, {0x0002F9F9, 0x00004A76}, -{0x0002F9FA, 0x000097E0}, {0x0002F9FB, 0x0002940A}, {0x0002F9FC, 0x00004AB2}, {0x0002F9FD, 0x00029496}, -{0x0002F9FE, 0x0000980B}, {0x0002F9FF, 0x0000980B}, {0x0002FA00, 0x00009829}, {0x0002FA01, 0x000295B6}, -{0x0002FA02, 0x000098E2}, {0x0002FA03, 0x00004B33}, {0x0002FA04, 0x00009929}, {0x0002FA05, 0x000099A7}, -{0x0002FA06, 0x000099C2}, {0x0002FA07, 0x000099FE}, {0x0002FA08, 0x00004BCE}, {0x0002FA09, 0x00029B30}, -{0x0002FA0A, 0x00009B12}, {0x0002FA0B, 0x00009C40}, {0x0002FA0C, 0x00009CFD}, {0x0002FA0D, 0x00004CCE}, -{0x0002FA0E, 0x00004CED}, {0x0002FA0F, 0x00009D67}, {0x0002FA10, 0x0002A0CE}, {0x0002FA11, 0x00004CF8}, -{0x0002FA12, 0x0002A105}, {0x0002FA13, 0x0002A20E}, {0x0002FA14, 0x0002A291}, {0x0002FA15, 0x00009EBB}, -{0x0002FA16, 0x00004D56}, {0x0002FA17, 0x00009EF9}, {0x0002FA18, 0x00009EFE}, {0x0002FA19, 0x00009F05}, -{0x0002FA1A, 0x00009F0F}, {0x0002FA1B, 0x00009F16}, {0x0002FA1D, 0x0002A600}, -}; diff --git a/unicode-data.h b/unicode-data.h index 3cccf2068..e27fe1770 100644 --- a/unicode-data.h +++ b/unicode-data.h @@ -1,17 +1,20 @@ #pragma once #include -#include -#include #include +#include +#include -extern const std::vector> unicode_ranges_number; -extern const std::vector> unicode_ranges_letter; -extern const std::vector> unicode_ranges_separator; -extern const std::vector> unicode_ranges_whitespace; -extern const std::vector> unicode_ranges_accent_mark; -extern const std::vector> unicode_ranges_punctuation; -extern const std::vector> unicode_ranges_symbol; -extern const std::vector> unicode_ranges_control; -extern const std::multimap unicode_map_nfd; -extern const std::map unicode_map_lowercase; +struct range_nfd { + uint32_t first; + uint32_t last; + uint32_t nfd; +}; + +static const uint32_t MAX_CODEPOINTS = 0x110000; + +extern const std::vector> unicode_ranges_flags; +extern const std::unordered_set unicode_set_whitespace; +extern const std::unordered_map unicode_map_lowercase; +extern const std::unordered_map unicode_map_uppercase; +extern const std::vector unicode_ranges_nfd; diff --git a/unicode.cpp b/unicode.cpp index ca03c49d3..056a4c741 100644 --- a/unicode.cpp +++ b/unicode.cpp @@ -1,4 +1,4 @@ -#include "unicode.h" +#include "unicode.h" #include "unicode-data.h" #include @@ -109,57 +109,49 @@ static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) // return result; //} -static std::unordered_map unicode_cpt_type_map() { - std::unordered_map cpt_types; - for (auto p : unicode_ranges_number) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_NUMBER; +static std::vector unicode_cpt_flags_array() { + std::vector cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED); + + assert (unicode_ranges_flags.front().first == 0); + assert (unicode_ranges_flags.back().first == MAX_CODEPOINTS); + for (size_t i = 1; i < unicode_ranges_flags.size(); ++i) { + const auto range_ini = unicode_ranges_flags[i-1]; // codepoint_ini, flags + const auto range_end = unicode_ranges_flags[i]; // codepoint_end, flags + for (uint32_t cpt = range_ini.first; cpt < range_end.first; ++cpt) { + cpt_flags[cpt] = range_ini.second; } } - for (auto p : unicode_ranges_letter) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_LETTER; - } + + for (auto cpt : unicode_set_whitespace) { + cpt_flags[cpt].is_whitespace = true; } - for (auto p : unicode_ranges_separator) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_SEPARATOR; - } + + for (auto p : unicode_map_lowercase) { + cpt_flags[p.second].is_lowercase = true; } - for (auto p : unicode_ranges_accent_mark) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK; - } + + for (auto p : unicode_map_uppercase) { + cpt_flags[p.second].is_uppercase = true; } - for (auto p : unicode_ranges_punctuation) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION; - } + + for (auto &range : unicode_ranges_nfd) { // start, last, nfd + cpt_flags[range.nfd].is_nfd = true; } - for (auto p : unicode_ranges_symbol) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_SYMBOL; - } - } - for (auto p : unicode_ranges_control) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_CONTROL; - } - } - return cpt_types; + + return cpt_flags; } static std::unordered_map unicode_byte_to_utf8_map() { std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { + for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { + for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } @@ -175,15 +167,15 @@ static std::unordered_map unicode_byte_to_utf8_map() { static std::unordered_map unicode_utf8_to_byte_map() { std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { + for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { + for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } @@ -238,8 +230,9 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; }; - auto _get_cpt_type = [&] (const size_t pos) -> int { - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + auto _get_flags = [&] (const size_t pos) -> codepoint_flags { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; }; size_t _prev_end = offset_ini; @@ -261,7 +254,7 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { const char32_t cpt = _get_cpt(pos); - const int cpt_type = _get_cpt_type(pos); + const auto flags = _get_flags(pos); // regex: 's|'t|'re|'ve|'m|'ll|'d if (cpt == '\'' && pos+1 < offset_end) { @@ -281,39 +274,37 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t } } - char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); - int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); + auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags); // regex: ?\p{L}+ - if (cpt2_type == CODEPOINT_TYPE_LETTER) { + if (flags2.is_letter) { pos += (cpt == ' '); - while (cpt2_type == CODEPOINT_TYPE_LETTER) { - cpt2_type = _get_cpt_type(++pos); + while (flags2.is_letter) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } // regex: ?\p{N}+ - if (cpt2_type == CODEPOINT_TYPE_NUMBER) { + if (flags2.is_number) { pos += (cpt == ' '); - while (cpt2_type == CODEPOINT_TYPE_NUMBER) { - cpt2_type = _get_cpt_type(++pos); + while (flags2.is_number) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } // regex: ?[^\s\p{L}\p{N}]+ - if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { pos += (cpt == ' '); - while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { - cpt2_type = _get_cpt_type(++pos); - cpt2 = _get_cpt(pos); + while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } size_t num_whitespaces = 0; - while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + while (_get_flags(pos+num_whitespaces).is_whitespace) { num_whitespaces++; } @@ -357,8 +348,9 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; }; - auto _get_cpt_type = [&] (const size_t pos) -> int { - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + auto _get_flags = [&] (const size_t pos) -> codepoint_flags { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; }; size_t _prev_end = offset_ini; @@ -380,7 +372,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { const char32_t cpt = _get_cpt(pos); - const int cpt_type = _get_cpt_type(pos); + const auto flags = _get_flags(pos); // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive if (cpt == '\'' && pos+1 < offset_end) { @@ -401,10 +393,10 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: [^\r\n\p{L}\p{N}]?\p{L}+ //####FIXME: the first \p{L} is correct? - if (cpt != '\r' && cpt != '\n' && /*cpt_type != CODEPOINT_TYPE_LETTER &&*/ cpt_type != CODEPOINT_TYPE_NUMBER) { - if (cpt_type == CODEPOINT_TYPE_LETTER || _get_cpt_type(pos+1) == CODEPOINT_TYPE_LETTER) { // one or more letters + if (!(cpt == '\r' || cpt == '\n' || /*flags.is_letter |*/ flags.is_number)) { + if (flags.is_letter || _get_flags(pos+1).is_letter) { // one or more letters pos++; - while (_get_cpt_type(pos) == CODEPOINT_TYPE_LETTER) { + while (_get_flags(pos).is_letter) { pos++; } _add_token(pos); @@ -413,9 +405,9 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: \p{N}{1,3} - if (cpt_type == CODEPOINT_TYPE_NUMBER) { + if (flags.is_number) { size_t ini = pos; - while (_get_cpt_type(pos) == CODEPOINT_TYPE_NUMBER) { + while (_get_flags(pos).is_number) { if (++pos - ini >= 3 ) { _add_token(pos); ini = pos; @@ -426,14 +418,13 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: ?[^\s\p{L}\p{N}]+[\r\n]* - char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); - int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); - if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags); + if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { pos += (cpt == ' '); - while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { - cpt2_type = _get_cpt_type(++pos); - cpt2 = _get_cpt(pos); + while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + flags2 = _get_flags(++pos); } + char32_t cpt2 = _get_cpt(pos); while (cpt2 == '\r' || cpt2 == '\n') { cpt2 = _get_cpt(++pos); } @@ -443,7 +434,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & size_t num_whitespaces = 0; size_t last_end_r_or_n = 0; - while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + while (_get_flags(pos+num_whitespaces).is_whitespace) { char32_t cpt2 = _get_cpt(pos+num_whitespaces); if (cpt2 == '\r' || cpt2 == '\n') { last_end_r_or_n = pos + num_whitespaces + 1; @@ -589,15 +580,14 @@ std::string unicode_cpt_to_utf8(uint32_t cp) { } std::vector unicode_cpts_normalize_nfd(const std::vector & cpts) { - std::vector result; - result.reserve(cpts.size()); + auto comp = [] (const uint32_t cpt, const range_nfd & range) { + return cpt < range.first; + }; + std::vector result(cpts.size()); for (size_t i = 0; i < cpts.size(); ++i) { - auto it = unicode_map_nfd.find(cpts[i]); - if (it == unicode_map_nfd.end()) { - result.push_back(cpts[i]); - } else { - result.push_back(it->second); - } + const uint32_t cpt = cpts[i]; + auto it = std::upper_bound(unicode_ranges_nfd.cbegin(), unicode_ranges_nfd.cend(), cpt, comp) - 1; + result[i] = (it->first <= cpt && cpt <= it->last) ? it->nfd : cpt; } return result; } @@ -611,31 +601,19 @@ std::vector unicode_cpts_from_utf8(const std::string & utf8) { return result; } -int unicode_cpt_type(uint32_t cp) { - static std::unordered_map cpt_types = unicode_cpt_type_map(); - const auto it = cpt_types.find(cp); - return it == cpt_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second; +codepoint_flags unicode_cpt_flags(const uint32_t cp) { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + static const auto cpt_flags = unicode_cpt_flags_array(); + return cp < cpt_flags.size() ? cpt_flags[cp] : undef; } -int unicode_cpt_type(const std::string & utf8) { - if (utf8.length() == 0) { - return CODEPOINT_TYPE_UNIDENTIFIED; +codepoint_flags unicode_cpt_flags(const std::string & utf8) { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + if (utf8.empty()) { + return undef; // undefined } size_t offset = 0; - return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset)); -} - -bool unicode_cpt_is_whitespace(uint32_t cp) { - static const std::unordered_set is_whitespace = [] { - std::unordered_set is_whitespace; - for (auto p : unicode_ranges_whitespace) { - for (auto i = p.first; i <= p.second; ++i) { - is_whitespace.insert(i); - } - } - return is_whitespace; - }(); - return (bool)is_whitespace.count(cp); + return unicode_cpt_flags(unicode_cpt_from_utf8(utf8, offset)); } std::string unicode_byte_to_utf8(uint8_t byte) { @@ -656,21 +634,21 @@ char32_t unicode_tolower(char32_t cp) { std::vector unicode_regex_split(const std::string & text, const std::vector & regex_exprs) { // unicode categories static const std::map k_ucat_enum = { - { "\\p{N}", CODEPOINT_TYPE_NUMBER }, - { "\\p{L}", CODEPOINT_TYPE_LETTER }, - { "\\p{P}", CODEPOINT_TYPE_PUNCTUATION }, + { "\\p{N}", codepoint_flags::NUMBER }, + { "\\p{L}", codepoint_flags::LETTER }, + { "\\p{P}", codepoint_flags::PUNCTUATION }, }; static const std::map k_ucat_cpt = { - { CODEPOINT_TYPE_NUMBER, 0xD1 }, - { CODEPOINT_TYPE_LETTER, 0xD2 }, - { CODEPOINT_TYPE_PUNCTUATION, 0xD3 }, + { codepoint_flags::NUMBER, 0xD1 }, + { codepoint_flags::LETTER, 0xD2 }, + { codepoint_flags::PUNCTUATION, 0xD3 }, }; static const std::map k_ucat_map = { - { CODEPOINT_TYPE_NUMBER, "\x30-\x39" }, // 0-9 - { CODEPOINT_TYPE_LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z - { CODEPOINT_TYPE_PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} + { codepoint_flags::NUMBER, "\x30-\x39" }, // 0-9 + { codepoint_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z + { codepoint_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} }; // compute collapsed codepoints only if needed by at least one regex @@ -701,10 +679,10 @@ std::vector unicode_regex_split(const std::string & text, const std continue; } - const int cpt_type = unicode_cpt_type(cpts[i]); + const int cpt_flag = unicode_cpt_flags(cpts[i]).category_flag(); - if (k_ucat_cpt.find(cpt_type) != k_ucat_cpt.end()) { - text_collapsed[i] = k_ucat_cpt.at(cpt_type); + if (k_ucat_cpt.find(cpt_flag) != k_ucat_cpt.end()) { + text_collapsed[i] = k_ucat_cpt.at(cpt_flag); } else { text_collapsed[i] = (char) 0xD0; // fallback } diff --git a/unicode.h b/unicode.h index d6a14d470..7513be4ad 100644 --- a/unicode.h +++ b/unicode.h @@ -4,24 +4,56 @@ #include #include -#define CODEPOINT_TYPE_UNIDENTIFIED 0 -#define CODEPOINT_TYPE_NUMBER 1 -#define CODEPOINT_TYPE_LETTER 2 -#define CODEPOINT_TYPE_SEPARATOR 3 -#define CODEPOINT_TYPE_ACCENT_MARK 4 -#define CODEPOINT_TYPE_PUNCTUATION 5 -#define CODEPOINT_TYPE_SYMBOL 6 -#define CODEPOINT_TYPE_CONTROL 7 +struct codepoint_flags { + enum { + UNDEFINED = 0x0001, + NUMBER = 0x0002, // regex: \p{N} + LETTER = 0x0004, // regex: \p{L} + SEPARATOR = 0x0008, // regex: \p{Z} + ACCENT_MARK = 0x0010, // regex: \p{M} + PUNCTUATION = 0x0020, // regex: \p{P} + SYMBOL = 0x0040, // regex: \p{S} + CONTROL = 0x0080, // regex: \p{C} + MASK_CATEGORIES = 0x00FF, + }; + + // codepoint type + uint16_t is_undefined : 1; + uint16_t is_number : 1; // regex: \p{N} + uint16_t is_letter : 1; // regex: \p{L} + uint16_t is_separator : 1; // regex: \p{Z} + uint16_t is_accent_mark : 1; // regex: \p{M} + uint16_t is_punctuation : 1; // regex: \p{P} + uint16_t is_symbol : 1; // regex: \p{S} + uint16_t is_control : 1; // regex: \p{C} + // helper flags + uint16_t is_whitespace : 1; // regex: \s + uint16_t is_lowercase : 1; + uint16_t is_uppercase : 1; + uint16_t is_nfd : 1; + + // decode from uint16 + inline codepoint_flags(const uint16_t flags=0) { + *reinterpret_cast(this) = flags; + } + + inline uint16_t as_uint() const { + return *reinterpret_cast(this); + } + + inline uint16_t category_flag() const { + return this->as_uint() & MASK_CATEGORIES; + } +}; + std::string unicode_cpt_to_utf8(uint32_t cp); std::vector unicode_cpts_from_utf8(const std::string & utf8); std::vector unicode_cpts_normalize_nfd(const std::vector & cpts); -int unicode_cpt_type(uint32_t cp); -int unicode_cpt_type(const std::string & utf8); - -bool unicode_cpt_is_whitespace(uint32_t cp); +codepoint_flags unicode_cpt_flags(const uint32_t cp); +codepoint_flags unicode_cpt_flags(const std::string & utf8); std::string unicode_byte_to_utf8(uint8_t byte); uint8_t unicode_utf8_to_byte(const std::string & utf8);