Merge branch 'master' into fix-bug-in-minicpm-v-code
This commit is contained in:
tc-mb
GitHub
commit
ef4222e9f4
120 changed files with 9467 additions and 1516 deletions
|
|
@ -40,3 +40,11 @@ indent_style = tab
|
|||
[examples/cvector-generator/*.txt]
|
||||
trim_trailing_whitespace = unset
|
||||
insert_final_newline = unset
|
||||
|
||||
[models/templates/*.jinja]
|
||||
indent_style = unset
|
||||
indent_size = unset
|
||||
end_of_line = unset
|
||||
charset = unset
|
||||
trim_trailing_whitespace = unset
|
||||
insert_final_newline = unset
|
||||
|
|
|
|||
256
.github/workflows/build.yml
vendored
256
.github/workflows/build.yml
vendored
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@ -43,6 +43,12 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: macOS-latest-cmake-arm64
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
continue-on-error: true
|
||||
|
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@ -53,16 +59,14 @@ jobs:
|
|||
id: cmake_build
|
||||
run: |
|
||||
sysctl -a
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DCMAKE_BUILD_RPATH="@loader_path" \
|
||||
-DLLAMA_FATAL_WARNINGS=ON \
|
||||
-DLLAMA_CURL=ON \
|
||||
-DGGML_METAL_USE_BF16=ON \
|
||||
-DGGML_METAL_EMBED_LIBRARY=ON \
|
||||
-DGGML_RPC=ON
|
||||
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
|
||||
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
|
||||
|
||||
- name: Test
|
||||
id: cmake_test
|
||||
|
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@ -108,6 +112,12 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: macOS-latest-cmake-x64
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
continue-on-error: true
|
||||
|
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@ -172,6 +182,12 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-cpu-cmake
|
||||
evict-old-files: 1d
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||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
run: |
|
||||
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@ -181,13 +197,11 @@ jobs:
|
|||
- name: Build
|
||||
id: cmake_build
|
||||
run: |
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DLLAMA_FATAL_WARNINGS=ON \
|
||||
-DLLAMA_CURL=ON \
|
||||
-DGGML_RPC=ON
|
||||
cmake --build . --config Release -j $(nproc)
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
|
||||
- name: Test
|
||||
id: cmake_test
|
||||
|
|
@ -249,6 +263,12 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }}
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
run: |
|
||||
|
|
@ -259,26 +279,52 @@ jobs:
|
|||
id: cmake_build
|
||||
if: ${{ matrix.sanitizer != 'THREAD' }}
|
||||
run: |
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DLLAMA_FATAL_WARNINGS=ON \
|
||||
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
|
||||
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
|
||||
cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
|
||||
|
||||
- name: Build (no OpenMP)
|
||||
id: cmake_build_no_openmp
|
||||
if: ${{ matrix.sanitizer == 'THREAD' }}
|
||||
run: |
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DLLAMA_FATAL_WARNINGS=ON \
|
||||
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
|
||||
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
|
||||
-DGGML_OPENMP=OFF
|
||||
cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
|
||||
cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
|
||||
|
||||
- name: Test
|
||||
id: cmake_test
|
||||
run: |
|
||||
cd build
|
||||
ctest -L main --verbose --timeout 900
|
||||
|
||||
ubuntu-latest-llguidance:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- name: Clone
|
||||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
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_LLGUIDANCE=ON
|
||||
cmake --build . --config Release -j $(nproc)
|
||||
|
||||
- name: Test
|
||||
id: cmake_test
|
||||
|
|
@ -296,6 +342,12 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-latest-cmake-rpc
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
run: |
|
||||
|
|
@ -305,11 +357,9 @@ jobs:
|
|||
- name: Build
|
||||
id: cmake_build
|
||||
run: |
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DGGML_RPC=ON
|
||||
cmake --build . --config Release -j $(nproc)
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
|
||||
- name: Test
|
||||
id: cmake_test
|
||||
|
|
@ -325,6 +375,12 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-22-cmake-vulkan
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
run: |
|
||||
|
|
@ -336,11 +392,9 @@ jobs:
|
|||
- name: Build
|
||||
id: cmake_build
|
||||
run: |
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DGGML_VULKAN=ON
|
||||
cmake --build . --config Release -j $(nproc)
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
|
||||
- name: Test
|
||||
id: cmake_test
|
||||
|
|
@ -364,6 +418,12 @@ jobs:
|
|||
sudo apt-get update
|
||||
sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-22-cmake-hip
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Build with native CMake HIP support
|
||||
id: cmake_build
|
||||
run: |
|
||||
|
|
@ -396,6 +456,12 @@ jobs:
|
|||
apt-get update
|
||||
apt-get install -y build-essential git cmake libcurl4-openssl-dev
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-22-cmake-musa
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Build with native CMake MUSA support
|
||||
id: cmake_build
|
||||
run: |
|
||||
|
|
@ -435,17 +501,21 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-22-cmake-sycl
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Build
|
||||
id: cmake_build
|
||||
run: |
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DGGML_SYCL=ON \
|
||||
-DCMAKE_C_COMPILER=icx \
|
||||
-DCMAKE_CXX_COMPILER=icpx
|
||||
cmake --build . --config Release -j $(nproc)
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
|
||||
ubuntu-22-cmake-sycl-fp16:
|
||||
runs-on: ubuntu-22.04
|
||||
|
|
@ -479,18 +549,22 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-22-cmake-sycl-fp16
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Build
|
||||
id: cmake_build
|
||||
run: |
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. \
|
||||
cmake -B build \
|
||||
-DGGML_SYCL=ON \
|
||||
-DCMAKE_C_COMPILER=icx \
|
||||
-DCMAKE_CXX_COMPILER=icpx \
|
||||
-DGGML_SYCL_F16=ON
|
||||
cmake --build . --config Release -j $(nproc)
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
|
||||
macOS-latest-cmake-ios:
|
||||
runs-on: macos-latest
|
||||
|
|
@ -500,6 +574,12 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: macOS-latest-cmake-ios
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
continue-on-error: true
|
||||
|
|
@ -510,9 +590,7 @@ jobs:
|
|||
id: cmake_build
|
||||
run: |
|
||||
sysctl -a
|
||||
mkdir build
|
||||
cd build
|
||||
cmake -G Xcode .. \
|
||||
cmake -B build -G Xcode \
|
||||
-DGGML_METAL_USE_BF16=ON \
|
||||
-DGGML_METAL_EMBED_LIBRARY=ON \
|
||||
-DLLAMA_BUILD_EXAMPLES=OFF \
|
||||
|
|
@ -521,7 +599,7 @@ jobs:
|
|||
-DCMAKE_SYSTEM_NAME=iOS \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
|
||||
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
|
||||
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
|
||||
|
||||
macOS-latest-cmake-tvos:
|
||||
runs-on: macos-latest
|
||||
|
|
@ -531,6 +609,12 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: macOS-latest-cmake-tvos
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
continue-on-error: true
|
||||
|
|
@ -541,9 +625,7 @@ jobs:
|
|||
id: cmake_build
|
||||
run: |
|
||||
sysctl -a
|
||||
mkdir build
|
||||
cd build
|
||||
cmake -G Xcode .. \
|
||||
cmake -B build -G Xcode \
|
||||
-DGGML_METAL_USE_BF16=ON \
|
||||
-DGGML_METAL_EMBED_LIBRARY=ON \
|
||||
-DLLAMA_BUILD_EXAMPLES=OFF \
|
||||
|
|
@ -552,7 +634,7 @@ jobs:
|
|||
-DCMAKE_SYSTEM_NAME=tvOS \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
|
||||
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
|
||||
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
|
||||
|
||||
macOS-latest-swift:
|
||||
runs-on: macos-latest
|
||||
|
|
@ -566,6 +648,12 @@ jobs:
|
|||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: macOS-latest-swift
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Dependencies
|
||||
id: depends
|
||||
continue-on-error: true
|
||||
|
|
@ -576,17 +664,15 @@ jobs:
|
|||
id: cmake_build
|
||||
run: |
|
||||
sysctl -a
|
||||
mkdir build
|
||||
cd build
|
||||
cmake -G Xcode .. \
|
||||
cmake -B build -G Xcode \
|
||||
-DGGML_METAL_USE_BF16=ON \
|
||||
-DGGML_METAL_EMBED_LIBRARY=ON \
|
||||
-DLLAMA_BUILD_EXAMPLES=OFF \
|
||||
-DLLAMA_BUILD_TESTS=OFF \
|
||||
-DLLAMA_BUILD_SERVER=OFF \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
|
||||
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
|
||||
sudo cmake --install . --config Release
|
||||
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
|
||||
sudo cmake --install build --config Release
|
||||
|
||||
- name: xcodebuild for swift package
|
||||
id: xcodebuild
|
||||
|
|
@ -607,6 +693,13 @@ jobs:
|
|||
- name: Clone
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: windows-msys2
|
||||
variant: sccache
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Setup ${{ matrix.sys }}
|
||||
uses: msys2/setup-msys2@v2
|
||||
with:
|
||||
|
|
@ -675,6 +768,13 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: windows-latest-cmake-${{ matrix.build }}
|
||||
variant: sccache
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Clone Kompute submodule
|
||||
id: clone_kompute
|
||||
if: ${{ matrix.build == 'kompute-x64' }}
|
||||
|
|
@ -714,21 +814,19 @@ jobs:
|
|||
run: |
|
||||
git clone https://github.com/KhronosGroup/OpenCL-Headers
|
||||
cd OpenCL-Headers
|
||||
mkdir build && cd build
|
||||
cmake .. `
|
||||
cmake -B build `
|
||||
-DBUILD_TESTING=OFF `
|
||||
-DOPENCL_HEADERS_BUILD_TESTING=OFF `
|
||||
-DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
|
||||
-DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
|
||||
cmake --build . --target install
|
||||
cmake --build build --target install
|
||||
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
|
||||
cd OpenCL-ICD-Loader
|
||||
mkdir build-arm64-release && cd build-arm64-release
|
||||
cmake .. `
|
||||
cmake -B build-arm64-release `
|
||||
-A arm64 `
|
||||
-DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" `
|
||||
-DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
|
||||
cmake --build . --target install --config release
|
||||
cmake --build build-arm64-release --target install --config release
|
||||
|
||||
- name: Build
|
||||
id: cmake_build
|
||||
|
|
@ -813,6 +911,8 @@ jobs:
|
|||
- name: Clone
|
||||
id: checkout
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Install dependencies
|
||||
env:
|
||||
|
|
@ -821,6 +921,12 @@ jobs:
|
|||
apt update
|
||||
apt install -y cmake build-essential ninja-build libgomp1 git
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ubuntu-latest-cmake-cuda
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Build with CMake
|
||||
run: |
|
||||
cmake -S . -B build -G Ninja \
|
||||
|
|
@ -847,6 +953,13 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Install ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
|
||||
variant: sccache
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Install Cuda Toolkit 11.7
|
||||
if: ${{ matrix.cuda == '11.7' }}
|
||||
run: |
|
||||
|
|
@ -903,11 +1016,6 @@ jobs:
|
|||
echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
|
||||
echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
|
||||
|
||||
- name: Install ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2
|
||||
with:
|
||||
key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }}
|
||||
|
||||
- name: Install Ninja
|
||||
id: install_ninja
|
||||
run: |
|
||||
|
|
@ -987,6 +1095,13 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: windows-latest-cmake-sycl
|
||||
variant: sccache
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Install
|
||||
run: |
|
||||
scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
|
||||
|
|
@ -1066,9 +1181,10 @@ jobs:
|
|||
& 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
|
||||
|
||||
- name: Install ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: ${{ github.job }}
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Build
|
||||
id: cmake_build
|
||||
|
|
@ -1098,6 +1214,12 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: windows-latest-cmake-hip-release
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Install
|
||||
id: depends
|
||||
run: |
|
||||
|
|
@ -1166,9 +1288,7 @@ jobs:
|
|||
id: cmake_build
|
||||
run: |
|
||||
sysctl -a
|
||||
mkdir build
|
||||
cd build
|
||||
cmake -G Xcode .. \
|
||||
cmake -B build -G Xcode \
|
||||
-DGGML_METAL_USE_BF16=ON \
|
||||
-DGGML_METAL_EMBED_LIBRARY=ON \
|
||||
-DLLAMA_BUILD_EXAMPLES=OFF \
|
||||
|
|
@ -1177,8 +1297,8 @@ jobs:
|
|||
-DCMAKE_SYSTEM_NAME=iOS \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
|
||||
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
|
||||
sudo cmake --install . --config Release
|
||||
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
|
||||
sudo cmake --install build --config Release
|
||||
|
||||
- name: xcodebuild for swift package
|
||||
id: xcodebuild
|
||||
|
|
@ -1195,6 +1315,12 @@ jobs:
|
|||
- name: Clone
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: android-build
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Set up JDK
|
||||
uses: actions/setup-java@v3
|
||||
with:
|
||||
|
|
@ -1232,6 +1358,12 @@ jobs:
|
|||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: ccache
|
||||
uses: hendrikmuhs/ccache-action@v1.2.16
|
||||
with:
|
||||
key: release
|
||||
evict-old-files: 1d
|
||||
|
||||
- name: Determine tag name
|
||||
id: tag
|
||||
shell: bash
|
||||
|
|
|
|||
2
.github/workflows/close-issue.yml
vendored
2
.github/workflows/close-issue.yml
vendored
|
|
@ -17,7 +17,7 @@ jobs:
|
|||
steps:
|
||||
- uses: actions/stale@v5
|
||||
with:
|
||||
exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
|
||||
exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap"
|
||||
days-before-issue-stale: 30
|
||||
days-before-issue-close: 14
|
||||
stale-issue-label: "stale"
|
||||
|
|
|
|||
2
.github/workflows/server.yml
vendored
2
.github/workflows/server.yml
vendored
|
|
@ -205,7 +205,7 @@ jobs:
|
|||
run: |
|
||||
cd examples/server/tests
|
||||
$env:PYTHONIOENCODING = ":replace"
|
||||
pytest -v -x
|
||||
pytest -v -x -m "not slow"
|
||||
|
||||
- name: Slow tests
|
||||
id: server_integration_tests_slow
|
||||
|
|
|
|||
83
AUTHORS
83
AUTHORS
|
|
@ -1,4 +1,4 @@
|
|||
# date: Thu Nov 28 20:46:15 EET 2024
|
||||
# date: Tue Feb 4 13:04:05 EET 2025
|
||||
# this file is auto-generated by scripts/gen-authors.sh
|
||||
|
||||
0cc4m <picard12@live.de>
|
||||
|
|
@ -20,6 +20,8 @@ Adithya Balaji <adithya.b94@gmail.com>
|
|||
AdithyanI <adithyan.i4internet@gmail.com>
|
||||
Adrian <smith.adriane@gmail.com>
|
||||
Adrian Hesketh <a-h@users.noreply.github.com>
|
||||
Adrien Gallouët <adrien@gallouet.fr>
|
||||
Adrien Gallouët <angt@huggingface.co>
|
||||
Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com>
|
||||
Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr>
|
||||
AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
|
||||
|
|
@ -55,6 +57,7 @@ Ananta Bastola <anantarajbastola@gmail.com>
|
|||
Anas Ahouzi <112881240+aahouzi@users.noreply.github.com>
|
||||
András Salamon <ott2@users.noreply.github.com>
|
||||
Andreas (Andi) Kunar <andreask@msn.com>
|
||||
Andreas Kieslinger <47689530+aendk@users.noreply.github.com>
|
||||
Andrei <abetlen@gmail.com>
|
||||
Andrew Canis <andrew.canis@gmail.com>
|
||||
Andrew Downing <andrew2085@gmail.com>
|
||||
|
|
@ -91,13 +94,17 @@ Ben Siraphob <bensiraphob@gmail.com>
|
|||
Ben Williams <ben@719ben.com>
|
||||
Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com>
|
||||
Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com>
|
||||
Benson Wong <mostlygeek@gmail.com>
|
||||
Bernat Vadell <hounter.caza@gmail.com>
|
||||
Bernhard M. Wiedemann <githubbmwprimary@lsmod.de>
|
||||
Bert Wagner <github@bertwagner.com>
|
||||
Billel Mokeddem <billel.mokeddem.ml@gmail.com>
|
||||
Bingan <70050083+binganao@users.noreply.github.com>
|
||||
Bjarke Viksøe <164612031+bviksoe@users.noreply.github.com>
|
||||
Bodo Graumann <mail@bodograumann.de>
|
||||
Bono Lv <lvscar@users.noreply.github.com>
|
||||
Borislav Stanimirov <b.stanimirov@abv.bg>
|
||||
Borislav Stanimirov <b@ibob.bg>
|
||||
Branden Butler <bwtbutler@hotmail.com>
|
||||
Brandon Squizzato <35474886+bsquizz@users.noreply.github.com>
|
||||
Brian <mofosyne@gmail.com>
|
||||
|
|
@ -117,6 +124,7 @@ Casey Primozic <casey@cprimozic.net>
|
|||
Casey Primozic <me@ameo.link>
|
||||
CausalLM <148736309+CausalLM@users.noreply.github.com>
|
||||
Cebtenzzre <cebtenzzre@gmail.com>
|
||||
CentricStorm <CentricStorm@users.noreply.github.com>
|
||||
Chad Brewbaker <crb002@gmail.com>
|
||||
Changyeon Kim <cyzero.kim@samsung.com>
|
||||
Chao Jiang <jc19chaoj@zoho.com>
|
||||
|
|
@ -131,12 +139,15 @@ Chris Kuehl <ckuehl@ckuehl.me>
|
|||
Christian Demsar <christian@github.email.demsar.us>
|
||||
Christian Demsar <crasm@git.vczf.us>
|
||||
Christian Falch <875252+chrfalch@users.noreply.github.com>
|
||||
Christian Kastner <ckk@kvr.at>
|
||||
Christian Kögler <ck3d@gmx.de>
|
||||
Christian Köhnenkamp <cvk5@me.com>
|
||||
Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com>
|
||||
Christopher Nielsen <62156882+mascguy@users.noreply.github.com>
|
||||
Clark Saben <76020733+csaben@users.noreply.github.com>
|
||||
Clint Herron <hanclinto@gmail.com>
|
||||
Conrad Kramer <conrad@conradkramer.com>
|
||||
Corentin REGAL <corentin.regal@gmail.com>
|
||||
CrispStrobe <154636388+CrispStrobe@users.noreply.github.com>
|
||||
Csaba Kecskemeti <csaba.kecskemeti@gmail.com>
|
||||
Cuong Trinh Manh <nguoithichkhampha@gmail.com>
|
||||
|
|
@ -176,6 +187,7 @@ Dibakar Gope <dibakar.gope@arm.com>
|
|||
Didzis Gosko <didzis@users.noreply.github.com>
|
||||
Diego Devesa <slarengh@gmail.com>
|
||||
Diogo Teles Sant'Anna <diogoteles@google.com>
|
||||
Djip007 <3705339+Djip007@users.noreply.github.com>
|
||||
Djip007 <djip.perois@free.fr>
|
||||
Don Mahurin <dmahurin@users.noreply.github.com>
|
||||
DooWoong Lee (David) <manics99@naver.com>
|
||||
|
|
@ -193,6 +205,7 @@ Edward Taylor <edeetee@gmail.com>
|
|||
Elaine <elaine.zosa@gmail.com>
|
||||
Elbios <141279586+Elbios@users.noreply.github.com>
|
||||
Elton Kola <eltonkola@gmail.com>
|
||||
Emreerdog <34742675+Emreerdog@users.noreply.github.com>
|
||||
Engininja2 <139037756+Engininja2@users.noreply.github.com>
|
||||
Equim <sayaka@ekyu.moe>
|
||||
Eric Curtin <ecurtin@redhat.com>
|
||||
|
|
@ -233,6 +246,7 @@ Fred Douglas <43351173+fredlas@users.noreply.github.com>
|
|||
Frederik Vogel <Schaltfehler@users.noreply.github.com>
|
||||
Gabe Goodhart <gabe.l.hart@gmail.com>
|
||||
Gabe Goodhart <ghart@us.ibm.com>
|
||||
Gaetan Bisson <gaetan@fenua.org>
|
||||
GainLee <perfecter.gen@gmail.com>
|
||||
Galunid <karolek1231456@gmail.com>
|
||||
Gary Linscott <glinscott@gmail.com>
|
||||
|
|
@ -249,6 +263,7 @@ Guillaume "Vermeille" Sanchez <Guillaume.V.Sanchez@gmail.com>
|
|||
Guillaume Wenzek <gwenzek@users.noreply.github.com>
|
||||
Guoliang Hua <32868157+nbcsm@users.noreply.github.com>
|
||||
Guoteng <32697156+SolenoidWGT@users.noreply.github.com>
|
||||
Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com>
|
||||
Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
|
||||
Haggai Nuchi <h.nuchi@gmail.com>
|
||||
Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
|
||||
|
|
@ -259,11 +274,13 @@ Haoxiang Fei <tonyfettes@tonyfettes.com>
|
|||
Harald Fernengel <harald.fernengel@here.com>
|
||||
Hatsune Miku <129688334+at8u@users.noreply.github.com>
|
||||
HatsuneMikuUwU33 <173229399+HatsuneMikuUwU33@users.noreply.github.com>
|
||||
Haus1 <haus.xda@gmail.com>
|
||||
Henk Poley <HenkPoley@gmail.com>
|
||||
Henri Vasserman <henv@hot.ee>
|
||||
Henrik Forstén <henrik.forsten@gmail.com>
|
||||
Herman Semenov <GermanAizek@yandex.ru>
|
||||
Hesen Peng <hesen.peng@gmail.com>
|
||||
HimariO <dsfhe49854@gmail.com>
|
||||
Hoang Nguyen <hugo53@users.noreply.github.com>
|
||||
Hong Bo PENG <penghb@cn.ibm.com>
|
||||
Hongyu Ouyang <96765450+casavaca@users.noreply.github.com>
|
||||
|
|
@ -280,6 +297,7 @@ Icecream95 <the.real.icecream95@gmail.com>
|
|||
Ido S <ido.pluto@gmail.com>
|
||||
IgnacioFDM <ignaciofdm@gmail.com>
|
||||
Igor Okulist <okigan@gmail.com>
|
||||
Ihar Hrachyshka <ihrachys@redhat.com>
|
||||
Ikko Eltociear Ashimine <eltociear@gmail.com>
|
||||
Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
|
||||
Ionoclast Laboratories <brigham@ionoclast.com>
|
||||
|
|
@ -289,12 +307,14 @@ Ivan <nekotekina@gmail.com>
|
|||
Ivan Filipov <159561759+vanaka11@users.noreply.github.com>
|
||||
Ivan Komarov <Ivan.Komarov@dfyz.info>
|
||||
Ivan Stepanov <ivanstepanovftw@gmail.com>
|
||||
JFLFY2255 <JFLFY2255@163.com>
|
||||
JH23X <165871467+JH23X@users.noreply.github.com>
|
||||
Jack Mousseau <jack@software.inc>
|
||||
Jack Mousseau <jmousseau@users.noreply.github.com>
|
||||
JackJollimore <130917767+JackJollimore@users.noreply.github.com>
|
||||
Jaeden Amero <jaeden@patater.com>
|
||||
Jaemin Son <woalsdnd@gmail.com>
|
||||
Jafar Uruç <jafar.uruc@gmail.com>
|
||||
Jag Chadha <jagtesh@gmail.com>
|
||||
Jakub N <jakubniemczyk97@gmail.com>
|
||||
James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
|
||||
|
|
@ -315,6 +335,7 @@ Jeffrey Morgan <jmorganca@gmail.com>
|
|||
Jeffrey Quesnelle <emozilla@nousresearch.com>
|
||||
Jeroen Mostert <jeroen.mostert@cm.com>
|
||||
Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
|
||||
Jett Janiak <jettjaniak@gmail.com>
|
||||
Jeximo <jeximo@gmail.com>
|
||||
Jhen-Jie Hong <iainst0409@gmail.com>
|
||||
Jiahao Li <liplus17@163.com>
|
||||
|
|
@ -343,6 +364,7 @@ Josh Ramer <josh.ramer@icloud.com>
|
|||
Joyce <joycebrum@google.com>
|
||||
Juan Calderon-Perez <835733+gaby@users.noreply.github.com>
|
||||
Judd <foldl@users.noreply.github.com>
|
||||
Juk Armstrong <69222624+jukofyork@users.noreply.github.com>
|
||||
Julius Arkenberg <arki05@users.noreply.github.com>
|
||||
Jun Hee Yoo <contact.jhyoo@gmail.com>
|
||||
Jun Jie <71215065+junnjiee16@users.noreply.github.com>
|
||||
|
|
@ -357,6 +379,7 @@ Justine Tunney <jtunney@mozilla.com>
|
|||
Juuso Alasuutari <juuso.alasuutari@gmail.com>
|
||||
KASR <karim.asrih@gmail.com>
|
||||
Kamil Tomšík <info@tomsik.cz>
|
||||
Karol Kontny <82021046+kkontny@users.noreply.github.com>
|
||||
Karsten Weiss <knweiss@gmail.com>
|
||||
Karthick <j.karthic2004@gmail.com>
|
||||
Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com>
|
||||
|
|
@ -376,6 +399,7 @@ Kolen Cheung <ickc@users.noreply.github.com>
|
|||
Konstantin Herud <konstantin.herud@denkbares.com>
|
||||
Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
|
||||
Kunshang Ji <kunshang.ji@intel.com>
|
||||
Kyle Bruene <KyleBruene@users.noreply.github.com>
|
||||
Kyle Liang <liangmanlai@gmail.com>
|
||||
Kyle Mistele <kyle@mistele.com>
|
||||
Kylin <56434533+KyL0N@users.noreply.github.com>
|
||||
|
|
@ -394,6 +418,7 @@ Liu Jia <jia3.liu@intel.com>
|
|||
LoganDark <github@logandark.mozmail.com>
|
||||
Loïc Carrère <loic.carrere@gmail.com>
|
||||
LostRuins <39025047+LostRuins@users.noreply.github.com>
|
||||
LostRuins Concedo <39025047+LostRuins@users.noreply.github.com>
|
||||
Luciano <lucianostrika44@gmail.com>
|
||||
Luo Tian <lt@basecity.com>
|
||||
Lyle Dean <dean@lyle.dev>
|
||||
|
|
@ -423,6 +448,7 @@ MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com>
|
|||
Mateusz Charytoniuk <mateusz.charytoniuk@protonmail.com>
|
||||
Matheus C. França <matheus-catarino@hotmail.com>
|
||||
Matheus Gabriel Alves Silva <matheusgasource@gmail.com>
|
||||
Mathieu Baudier <mbaudier@argeo.org>
|
||||
Mathieu Geli <mathieu.geli@gmail.com>
|
||||
Mathieu Nayrolles <MathieuNls@users.noreply.github.com>
|
||||
Mathijs Henquet <mathijs.henquet@gmail.com>
|
||||
|
|
@ -444,6 +470,7 @@ Meng, Hengyu <hengyu.meng@intel.com>
|
|||
Mengqing Cao <cmq0113@163.com>
|
||||
Merrick Christensen <merrick.christensen@gmail.com>
|
||||
Michael Coppola <m18coppola@gmail.com>
|
||||
Michael Engel <mengel@redhat.com>
|
||||
Michael Francis <edude03@gmail.com>
|
||||
Michael Hueschen <m@mhueschen.dev>
|
||||
Michael Kesper <mkesper@schokokeks.org>
|
||||
|
|
@ -452,7 +479,9 @@ Michael Podvitskiy <podvitskiymichael@gmail.com>
|
|||
Michael Potter <NanoTekGuy@Gmail.com>
|
||||
Michael de Gans <michael.john.degans@gmail.com>
|
||||
Michaël de Vries <vriesdemichael@gmail.com>
|
||||
Michał Moskal <michal@moskal.me>
|
||||
Michał Tuszyński <srgtuszy@gmail.com>
|
||||
Michelle Tan <41475767+MichelleTanPY@users.noreply.github.com>
|
||||
Mihai <mihai.chirculescu@yahoo.com>
|
||||
Mike <ytianhui2004@gmail.com>
|
||||
Mikko Juola <mikjuo@gmail.com>
|
||||
|
|
@ -477,6 +506,7 @@ Neo Zhang <14088817+arthw@users.noreply.github.com>
|
|||
Neo Zhang <zhang.jianyu@outlook.com>
|
||||
Neo Zhang Jianyu <jianyu.zhang@intel.com>
|
||||
Neuman Vong <neuman.vong@gmail.com>
|
||||
NeverLucky <92274250+nvrxq@users.noreply.github.com>
|
||||
Nexes the Old <124105151+Nexesenex@users.noreply.github.com>
|
||||
Nexesenex <124105151+Nexesenex@users.noreply.github.com>
|
||||
Niall Coates <1349685+Niall-@users.noreply.github.com>
|
||||
|
|
@ -484,11 +514,15 @@ Nicholai Tukanov <nicholaitukanov@gmail.com>
|
|||
Nico Bosshard <nico@bosshome.ch>
|
||||
Nicolai Weitkemper <kontakt@nicolaiweitkemper.de>
|
||||
Nicolás Pérez <nicolas_perez@brown.edu>
|
||||
Nicolò Scipione <nicolo.scipione@codeplay.com>
|
||||
Nigel Bosch <pnigelb@gmail.com>
|
||||
Nikita Sarychev <42014488+sARY77@users.noreply.github.com>
|
||||
Niklas Korz <niklas@niklaskorz.de>
|
||||
NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com>
|
||||
Nikolaos Pothitos <pothitos@di.uoa.gr>
|
||||
Nikolas <127742645+nneubacher@users.noreply.github.com>
|
||||
Nindaleth <Nindaleth@users.noreply.github.com>
|
||||
Nuno <rare-magma@posteo.eu>
|
||||
OSecret <135510162+OLSecret@users.noreply.github.com>
|
||||
Oleksandr Nikitin <oleksandr@tvori.info>
|
||||
Oleksii Maryshchenko <oleksii.maryshchenko@gmail.com>
|
||||
|
|
@ -504,6 +538,7 @@ Pavel Zloi <github.com@drteam.rocks>
|
|||
Pavol Rusnak <pavol@rusnak.io>
|
||||
Paweł Wodnicki <151604+32bitmicro@users.noreply.github.com>
|
||||
Pedro Cuenca <pedro@huggingface.co>
|
||||
Peter <peter277@users.noreply.github.com>
|
||||
Peter Sugihara <peter@campsh.com>
|
||||
Phil H <5756783+phiharri@users.noreply.github.com>
|
||||
Philip Taron <philip.taron@gmail.com>
|
||||
|
|
@ -529,9 +564,12 @@ Rand Xie <randxiexyy29@gmail.com>
|
|||
Randall Fitzgerald <randall@dasaku.net>
|
||||
Random Fly <renfei8@live.cn>
|
||||
Reinforce-II <fate@eastal.com>
|
||||
Rémy Oudompheng <oudomphe@phare.normalesup.org>
|
||||
Ren Xuancheng <jklj077@users.noreply.github.com>
|
||||
Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com>
|
||||
Reza Kakhki <rezakakhki.de@gmail.com>
|
||||
RhinoDevel <RhinoDevel@users.noreply.github.com>
|
||||
Riccardo Orlando <Riccorl@users.noreply.github.com>
|
||||
Riceball LEE <snowyu.lee@gmail.com>
|
||||
Rich Dougherty <rich@rd.nz>
|
||||
Richard Kiss <him@richardkiss.com>
|
||||
|
|
@ -544,6 +582,8 @@ Riley Stewart <ristew@users.noreply.github.com>
|
|||
Rinne <AsakusaRinne@gmail.com>
|
||||
Rinne <liu_yaohui1998@126.com>
|
||||
Robert Brisita <986796+rbrisita@users.noreply.github.com>
|
||||
Robert Collins <roberto.tomas.cuentas@gmail.com>
|
||||
Robert Ormandi <52251610+ormandi@users.noreply.github.com>
|
||||
Robert Sung-wook Shin <edp1096@users.noreply.github.com>
|
||||
Robey Holderith <robey@flaminglunchbox.net>
|
||||
Robyn <robyngraf@users.noreply.github.com>
|
||||
|
|
@ -559,7 +599,9 @@ Roni <sulpher@gmx.net>
|
|||
Ronny Brendel <ronnybrendel@gmail.com>
|
||||
Ronsor <ronsor@ronsor.pw>
|
||||
Rowan Hart <rowanbhart@gmail.com>
|
||||
Ruan <47767371+ruanych@users.noreply.github.com>
|
||||
Ruchira Hasaranga <ruchira66@gmail.com>
|
||||
Rudi Servo <rudiservo@gmail.com>
|
||||
Ruixin Huang <18860020911@163.com>
|
||||
Rune <43761327+Rune-AI@users.noreply.github.com>
|
||||
RunningLeon <maningsheng@sensetime.com>
|
||||
|
|
@ -623,12 +665,14 @@ Steven Roussey <sroussey@gmail.com>
|
|||
Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
|
||||
StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com>
|
||||
Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
|
||||
Sukriti Sharma <Ssukriti@users.noreply.github.com>
|
||||
SuperUserNameMan <yoann@terminajones.com>
|
||||
Sutou Kouhei <kou@cozmixng.org>
|
||||
Tai Duc Nguyen <taiducnguyen.drexel@gmail.com>
|
||||
Taikono-Himazin <kazu@po.harenet.ne.jp>
|
||||
Tameem <113388789+AhmadTameem@users.noreply.github.com>
|
||||
Tamotsu Takahashi <ttakah+github@gmail.com>
|
||||
Tei Home <taiteitonghome@proton.me>
|
||||
Thái Hoàng Tâm <75922889+RoyalHeart@users.noreply.github.com>
|
||||
Thatcher Chamberlin <j.thatcher.c@gmail.com>
|
||||
Theia Vogel <theia@vgel.me>
|
||||
|
|
@ -640,6 +684,7 @@ Tim Miller <drasticactions@users.noreply.github.com>
|
|||
Tim Wang <overocean@gmail.com>
|
||||
Timmy Knight <r2d2fish@gmail.com>
|
||||
Timothy Cronin <40186632+4imothy@users.noreply.github.com>
|
||||
Ting Lou <louting@189.cn>
|
||||
Ting Lou <ting.lou@gmail.com>
|
||||
Ting Sun <suntcrick@gmail.com>
|
||||
Tobias Lütke <tobi@shopify.com>
|
||||
|
|
@ -661,6 +706,7 @@ Uzo Nweke <uzoechi@gmail.com>
|
|||
Vaibhav Srivastav <vaibhavs10@gmail.com>
|
||||
Val Kharitonov <mail@kharvd.com>
|
||||
Valentin Konovalov <valle.ketsujin@gmail.com>
|
||||
Valentin Mamedov <45292985+Inf1delis@users.noreply.github.com>
|
||||
Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com>
|
||||
Vali Malinoiu <0x4139@gmail.com>
|
||||
Victor Nogueira <felladrin@gmail.com>
|
||||
|
|
@ -673,13 +719,17 @@ Vladimir Malyutin <first-leon@yandex.ru>
|
|||
Vladimir Zorin <vladimir@deviant.guru>
|
||||
VoidIsVoid <343750470@qq.com>
|
||||
Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com>
|
||||
Wang Qin <37098874+wangqin0@users.noreply.github.com>
|
||||
Wang Ran (汪然) <wangr@smail.nju.edu.cn>
|
||||
WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com>
|
||||
Weird Constructor <weirdconstructor@gmail.com>
|
||||
Welby Seely <welbyseely@gmail.com>
|
||||
Wentai Zhang <rchardx@gmail.com>
|
||||
WillCorticesAI <150854901+WillCorticesAI@users.noreply.github.com>
|
||||
William Tambellini <william.tambellini@gmail.com>
|
||||
William Tambellini <wtambellini@sdl.com>
|
||||
Willy Tarreau <w@1wt.eu>
|
||||
Woof Dog <197125663+woof-dog@users.noreply.github.com>
|
||||
Wouter <9594229+DifferentialityDevelopment@users.noreply.github.com>
|
||||
Wu Jian Ping <wujjpp@hotmail.com>
|
||||
Wu Jian Ping <wujp@greatld.com>
|
||||
|
|
@ -692,6 +742,7 @@ Xie Yanbo <xieyanbo@gmail.com>
|
|||
Xingchen Song(宋星辰) <xingchensong1996@163.com>
|
||||
Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com>
|
||||
Xuan Son Nguyen <thichthat@gmail.com>
|
||||
Xuan-Son Nguyen <thichthat@gmail.com>
|
||||
Yaiko <elyaiko@hotmail.com>
|
||||
Yann Follet <131855179+YannFollet@users.noreply.github.com>
|
||||
Yaroslav <yaroslav.yashin@me.com>
|
||||
|
|
@ -702,7 +753,9 @@ Yoshi Suhara <y.suhara@gmail.com>
|
|||
Yoshi Suhara <ysuhara@nvidia.com>
|
||||
Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
|
||||
Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com>
|
||||
Yüg <eugeniosegalaweb@gmail.com>
|
||||
Yui <dev@sleepyyui.com>
|
||||
Yun Dou <dixyes@gmail.com>
|
||||
Yuri Khrustalev <ykhrustalev@users.noreply.github.com>
|
||||
Yusuf Kağan Hanoğlu <hanoglu@yahoo.com>
|
||||
Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com>
|
||||
|
|
@ -714,18 +767,23 @@ Zhang Peiyuan <a1286225768@gmail.com>
|
|||
Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com>
|
||||
Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com>
|
||||
Zhiyuan Li <lizhiyuan@uniartisan.com>
|
||||
Zhiyuan Li <uniartisan2017@gmail.com>
|
||||
ZhouYuChen <zhouyuchen@naver.com>
|
||||
Ziad Ben Hadj-Alouane <zied.benhadjalouane@gmail.com>
|
||||
Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com>
|
||||
Zsapi <martin1.zsapka@gmail.com>
|
||||
a-n-n-a-l-e-e <150648636+a-n-n-a-l-e-e@users.noreply.github.com>
|
||||
a3sh <38979186+A3shTnT@users.noreply.github.com>
|
||||
adel boussaken <netdur@gmail.com>
|
||||
afrideva <95653597+afrideva@users.noreply.github.com>
|
||||
ag2s20150909 <19373730+ag2s20150909@users.noreply.github.com>
|
||||
agray3 <agray3@users.noreply.github.com>
|
||||
akawrykow <142945436+akawrykow@users.noreply.github.com>
|
||||
alek3y <44779186+alek3y@users.noreply.github.com>
|
||||
alexpinel <93524949+alexpinel@users.noreply.github.com>
|
||||
alonfaraj <alonfaraj@gmail.com>
|
||||
alwqx <kenan3015@gmail.com>
|
||||
amd-dwang <dong.wang@amd.com>
|
||||
amd-lalithnc <lalithnc@amd.com>
|
||||
amritahs-ibm <amritahs@linux.vnet.ibm.com>
|
||||
andrijdavid <david@geek.mg>
|
||||
|
|
@ -737,6 +795,7 @@ arch-btw <57669023+arch-btw@users.noreply.github.com>
|
|||
arcrank <arcrank@gmail.com>
|
||||
ardfork <134447697+ardfork@users.noreply.github.com>
|
||||
arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
|
||||
aryantandon01 <80969509+aryantandon01@users.noreply.github.com>
|
||||
at8u <129688334+at8u@users.noreply.github.com>
|
||||
automaticcat <daogiatuank54@gmail.com>
|
||||
awatuna <23447591+awatuna@users.noreply.github.com>
|
||||
|
|
@ -751,12 +810,14 @@ bryanSwk <93190252+bryanSwk@users.noreply.github.com>
|
|||
bsilvereagle <bsilvereagle@users.noreply.github.com>
|
||||
bssrdf <merlintiger@hotmail.com>
|
||||
byte-6174 <88070277+byte-6174@users.noreply.github.com>
|
||||
cduk <19917266+cduk@users.noreply.github.com>
|
||||
cebtenzzre <cebtenzzre@gmail.com>
|
||||
chaihahaha <chai836275709@gmail.com>
|
||||
chiranko <96988916+chiranko@users.noreply.github.com>
|
||||
clibdev <52199778+clibdev@users.noreply.github.com>
|
||||
clyang <clyang@clyang.net>
|
||||
cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com>
|
||||
codezjx <code.zjx@gmail.com>
|
||||
coezbek <c.oezbek@gmail.com>
|
||||
comex <comexk@gmail.com>
|
||||
compilade <113953597+compilade@users.noreply.github.com>
|
||||
|
|
@ -780,14 +841,17 @@ drbh <david.richard.holtz@gmail.com>
|
|||
ds5t5 <145942675+ds5t5@users.noreply.github.com>
|
||||
dylan <canardleteer@users.noreply.github.com>
|
||||
eastriver <lee@eastriver.dev>
|
||||
ebraminio <ebrahim@gnu.org>
|
||||
ebraminio <ebraminio@gmail.com>
|
||||
eiery <19350831+eiery@users.noreply.github.com>
|
||||
eric8607242 <e0928021388@gmail.com>
|
||||
fairydreaming <166155368+fairydreaming@users.noreply.github.com>
|
||||
fengerhu1 <2748250768@qq.com>
|
||||
fj-y-saito <85871716+fj-y-saito@users.noreply.github.com>
|
||||
fraxy-v <65565042+fraxy-v@users.noreply.github.com>
|
||||
github-actions[bot] <github-actions[bot]@users.noreply.github.com>
|
||||
gliptic <gliptic@users.noreply.github.com>
|
||||
gn64 <yukikaze.jp@gmail.com>
|
||||
goerch <jhr.walter@t-online.de>
|
||||
grahameth <96447521+grahameth@users.noreply.github.com>
|
||||
gtygo <gtydoit@gmail.com>
|
||||
|
|
@ -812,10 +876,12 @@ icppWorld <124377669+icppWorld@users.noreply.github.com>
|
|||
igarnier <igarnier@protonmail.com>
|
||||
intelmatt <61025942+intelmatt@users.noreply.github.com>
|
||||
iohub <rickyang.pro@gmail.com>
|
||||
issixx <46835150+issixx@users.noreply.github.com>
|
||||
jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com>
|
||||
jaime-m-p <167997752+jaime-m-p@users.noreply.github.com>
|
||||
jameswu2014 <545426914@qq.com>
|
||||
jdomke <28772296+jdomke@users.noreply.github.com>
|
||||
jiahao su <damow890@gmail.com>
|
||||
jiez <373447296@qq.com>
|
||||
jneem <joeneeman@gmail.com>
|
||||
joecryptotoo <80373433+joecryptotoo@users.noreply.github.com>
|
||||
|
|
@ -828,6 +894,7 @@ junchao-loongson <68935141+junchao-loongson@users.noreply.github.com>
|
|||
jwj7140 <32943891+jwj7140@users.noreply.github.com>
|
||||
k.h.lai <adrian.k.h.lai@outlook.com>
|
||||
kaizau <kaizau@users.noreply.github.com>
|
||||
kallewoof <kalle.alm@gmail.com>
|
||||
kalomaze <66376113+kalomaze@users.noreply.github.com>
|
||||
kang <tpdns9032100@gmail.com>
|
||||
katsu560 <118887472+katsu560@users.noreply.github.com>
|
||||
|
|
@ -835,6 +902,7 @@ kchro3 <62481661+kchro3@users.noreply.github.com>
|
|||
khimaros <me@khimaros.com>
|
||||
kiltyj <kiltyj@gmail.com>
|
||||
klosax <131523366+klosax@users.noreply.github.com>
|
||||
krystiancha <krystian@krystianch.com>
|
||||
kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
|
||||
kunnis <kunnis@users.noreply.github.com>
|
||||
kuronekosaiko <EvanChanJ@163.com>
|
||||
|
|
@ -847,6 +915,8 @@ ldwang <ftgreat@163.com>
|
|||
le.chang <cljs118@126.com>
|
||||
leejet <leejet714@gmail.com>
|
||||
leo-pony <nengjunma@outlook.com>
|
||||
lexasub <lexakopp2212@gmail.com>
|
||||
lhez <quic_lih@quicinc.com>
|
||||
limitedAtonement <limitedAtonement@users.noreply.github.com>
|
||||
liuwei-git <14815172+liuwei-git@users.noreply.github.com>
|
||||
lon <114724657+longregen@users.noreply.github.com>
|
||||
|
|
@ -855,10 +925,13 @@ ltoniazzi <61414566+ltoniazzi@users.noreply.github.com>
|
|||
luoyu-intel <yu.luo@intel.com>
|
||||
m3ndax <adrian.goessl@outlook.com>
|
||||
maddes8cht <55592906+maddes8cht@users.noreply.github.com>
|
||||
mahorozte <41834471+mahorozte@users.noreply.github.com>
|
||||
makomk <makosoft@googlemail.com>
|
||||
manikbhandari <mbbhandarimanik2@gmail.com>
|
||||
maor-ps <154728172+maor-ps@users.noreply.github.com>
|
||||
mashdragon <122402293+mashdragon@users.noreply.github.com>
|
||||
matiaslin <45382001+matiaslin@users.noreply.github.com>
|
||||
matt23654 <matthew.webber@protonmail.com>
|
||||
matteo <matteogeniaccio@yahoo.it>
|
||||
mdrokz <mohammadmunshi@gmail.com>
|
||||
mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
|
||||
|
|
@ -868,6 +941,7 @@ mmyjona <jonathan.gonse@gmail.com>
|
|||
momonga <115213907+mmnga@users.noreply.github.com>
|
||||
momonga <146910567+mmngays@users.noreply.github.com>
|
||||
moritzbrantner <31051084+moritzbrantner@users.noreply.github.com>
|
||||
musoles <135031143+musoles@users.noreply.github.com>
|
||||
mzcu <milos.cubrilo@gmail.com>
|
||||
nanahi <130121847+na-na-hi@users.noreply.github.com>
|
||||
ngc92 <7938269+ngc92@users.noreply.github.com>
|
||||
|
|
@ -885,6 +959,7 @@ oobabooga <112222186+oobabooga@users.noreply.github.com>
|
|||
opparco <parco.opaai@gmail.com>
|
||||
ostix360 <55257054+ostix360@users.noreply.github.com>
|
||||
pculliton <phillipculliton@gmail.com>
|
||||
peidaqi <peidaqi@gmail.com>
|
||||
pengxin99 <pengxin.yuan@intel.com>
|
||||
perserk <perserk@gmail.com>
|
||||
piDack <104877312+piDack@users.noreply.github.com>
|
||||
|
|
@ -892,10 +967,12 @@ pmysl <piotr.myslinski@outlook.com>
|
|||
postmasters <namnguyen@google.com>
|
||||
pudepiedj <pudepiedj@gmail.com>
|
||||
qingfengfenga <41416092+qingfengfenga@users.noreply.github.com>
|
||||
qingy1337 <qxli2@students.everettcc.edu>
|
||||
qouoq <qouoq@fastmail.com>
|
||||
qunash <anzoria@gmail.com>
|
||||
rabidcopy <rabidcopy@yahoo.com>
|
||||
rankaiyx <rankaiyx@rankaiyx.com>
|
||||
redbeard <bharrington@alticon.net>
|
||||
rhjdvsgsgks <26178113+rhjdvsgsgks@users.noreply.github.com>
|
||||
rhuddleston <ryan.huddleston@percona.com>
|
||||
rimoliga <53384203+rimoliga@users.noreply.github.com>
|
||||
|
|
@ -912,6 +989,7 @@ sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com>
|
|||
slaren <2141330+slaren@users.noreply.github.com>
|
||||
slaren <slarengh@gmail.com>
|
||||
snadampal <87143774+snadampal@users.noreply.github.com>
|
||||
someone13574 <81528246+someone13574@users.noreply.github.com>
|
||||
standby24x7 <standby24x7@gmail.com>
|
||||
staviq <staviq@gmail.com>
|
||||
stduhpf <stephduh@live.fr>
|
||||
|
|
@ -931,6 +1009,7 @@ uint256_t <konndennsa@gmail.com>
|
|||
uint256_t <maekawatoshiki1017@gmail.com>
|
||||
unbounded <haakon@likedan.net>
|
||||
uvos <devnull@uvos.xyz>
|
||||
uvos <philipp@uvos.xyz>
|
||||
valiray <133289098+valiray@users.noreply.github.com>
|
||||
vb <vaibhavs10@gmail.com>
|
||||
vik <vikhyatk@gmail.com>
|
||||
|
|
@ -951,6 +1030,7 @@ xaedes <xaedes@googlemail.com>
|
|||
xctan <axunlei@gmail.com>
|
||||
xloem <0xloem@gmail.com>
|
||||
yangli2 <yangli2@gmail.com>
|
||||
ymcki <84055651+ymcki@users.noreply.github.com>
|
||||
yuiseki <yuiseki@gmail.com>
|
||||
yuri@FreeBSD <yurivict@users.noreply.github.com>
|
||||
zakkor <edward.partenie@gmail.com>
|
||||
|
|
@ -963,4 +1043,5 @@ zrm <trustiosity.zrm@gmail.com>
|
|||
杨朱 · Kiki <baofa.fan@daocloud.io>
|
||||
源文雨 <41315874+fumiama@users.noreply.github.com>
|
||||
蕭澧邦 <45505768+shou692199@users.noreply.github.com>
|
||||
谢乃闻 <sienaiwun@users.noreply.github.com>
|
||||
Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>
|
||||
|
|
|
|||
|
|
@ -80,6 +80,7 @@ option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE})
|
|||
|
||||
# 3rd party libs
|
||||
option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
|
||||
option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
|
||||
|
||||
# Required for relocatable CMake package
|
||||
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
|
||||
|
|
|
|||
11
Makefile
11
Makefile
|
|
@ -52,6 +52,7 @@ TEST_TARGETS = \
|
|||
tests/test-arg-parser \
|
||||
tests/test-autorelease \
|
||||
tests/test-backend-ops \
|
||||
tests/test-chat \
|
||||
tests/test-chat-template \
|
||||
tests/test-double-float \
|
||||
tests/test-grammar-integration \
|
||||
|
|
@ -595,7 +596,7 @@ ifdef GGML_RPC
|
|||
OBJ_GGML_EXT += ggml/src/ggml-rpc.o
|
||||
endif # GGML_RPC
|
||||
|
||||
OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu))
|
||||
OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu))
|
||||
OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu))
|
||||
|
||||
ifdef GGML_CUDA_FA_ALL_QUANTS
|
||||
|
|
@ -983,6 +984,7 @@ OBJ_COMMON = \
|
|||
$(DIR_COMMON)/ngram-cache.o \
|
||||
$(DIR_COMMON)/sampling.o \
|
||||
$(DIR_COMMON)/speculative.o \
|
||||
$(DIR_COMMON)/chat.o \
|
||||
$(DIR_COMMON)/build-info.o \
|
||||
$(DIR_COMMON)/json-schema-to-grammar.o
|
||||
|
||||
|
|
@ -1361,6 +1363,8 @@ llama-server: \
|
|||
examples/server/httplib.h \
|
||||
examples/server/index.html.hpp \
|
||||
examples/server/loading.html.hpp \
|
||||
common/chat.cpp \
|
||||
common/chat.hpp \
|
||||
common/chat-template.hpp \
|
||||
common/json.hpp \
|
||||
common/minja.hpp \
|
||||
|
|
@ -1471,6 +1475,11 @@ tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
|
|||
$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-chat: tests/test-chat.cpp \
|
||||
$(OBJ_ALL)
|
||||
$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
|
||||
|
||||
tests/test-opt: tests/test-opt.cpp \
|
||||
$(OBJ_GGML)
|
||||
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
|
|||
|
||||
- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggerganov/llama.cpp/pull/11427
|
||||
- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
|
||||
- Universal tool call support in `llama-server`: https://github.com/ggerganov/llama.cpp/pull/9639
|
||||
- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
|
||||
- Introducing GGUF-my-LoRA https://github.com/ggerganov/llama.cpp/discussions/10123
|
||||
- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669
|
||||
|
|
@ -95,7 +96,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
|
|||
- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
|
||||
- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
|
||||
- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
|
||||
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
|
||||
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
|
||||
- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
|
||||
- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
|
||||
- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
|
||||
|
|
@ -116,6 +117,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
|
|||
- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
|
||||
- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
|
||||
- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
|
||||
- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
|
||||
- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
|
||||
|
||||
</details>
|
||||
|
|
@ -134,6 +136,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
|
|||
- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
|
||||
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
|
||||
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
|
||||
- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
|
||||
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
|
||||
- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
|
||||
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
|
||||
|
|
|
|||
|
|
@ -56,6 +56,8 @@ add_library(${TARGET} STATIC
|
|||
arg.cpp
|
||||
arg.h
|
||||
base64.hpp
|
||||
chat.cpp
|
||||
chat.hpp
|
||||
chat-template.hpp
|
||||
common.cpp
|
||||
common.h
|
||||
|
|
@ -63,6 +65,7 @@ add_library(${TARGET} STATIC
|
|||
console.h
|
||||
json-schema-to-grammar.cpp
|
||||
json.hpp
|
||||
llguidance.cpp
|
||||
log.cpp
|
||||
log.h
|
||||
minja.hpp
|
||||
|
|
@ -89,6 +92,33 @@ if (LLAMA_CURL)
|
|||
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
|
||||
endif ()
|
||||
|
||||
if (LLAMA_LLGUIDANCE)
|
||||
include(ExternalProject)
|
||||
set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source)
|
||||
set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release)
|
||||
ExternalProject_Add(llguidance_ext
|
||||
GIT_REPOSITORY https://github.com/guidance-ai/llguidance
|
||||
# v0.6.12:
|
||||
GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09
|
||||
PREFIX ${CMAKE_BINARY_DIR}/llguidance
|
||||
SOURCE_DIR ${LLGUIDANCE_SRC}
|
||||
BUILD_IN_SOURCE TRUE
|
||||
CONFIGURE_COMMAND ""
|
||||
BUILD_COMMAND cargo build --release
|
||||
INSTALL_COMMAND ""
|
||||
BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/libllguidance.a ${LLGUIDANCE_PATH}/llguidance.h
|
||||
UPDATE_COMMAND ""
|
||||
)
|
||||
target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE)
|
||||
|
||||
add_library(llguidance STATIC IMPORTED)
|
||||
set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/libllguidance.a)
|
||||
add_dependencies(llguidance llguidance_ext)
|
||||
|
||||
target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH})
|
||||
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance)
|
||||
endif ()
|
||||
|
||||
target_include_directories(${TARGET} PUBLIC .)
|
||||
target_compile_features (${TARGET} PUBLIC cxx_std_17)
|
||||
target_link_libraries (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
|
||||
|
|
|
|||
|
|
@ -1465,15 +1465,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
|
|||
{"--list-devices"},
|
||||
"print list of available devices and exit",
|
||||
[](common_params &) {
|
||||
printf("Available devices:\n");
|
||||
std::vector<ggml_backend_dev_t> rpc_devices;
|
||||
std::vector<ggml_backend_dev_t> all_devices;
|
||||
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
|
||||
auto * dev = ggml_backend_dev_get(i);
|
||||
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
|
||||
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
|
||||
if (ggml_backend_reg_name(reg) == std::string("RPC")) {
|
||||
rpc_devices.push_back(dev);
|
||||
} else {
|
||||
all_devices.push_back(dev);
|
||||
}
|
||||
}
|
||||
}
|
||||
// insert RPC devices in front
|
||||
all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end());
|
||||
printf("Available devices:\n");
|
||||
for (size_t i = 0; i < all_devices.size(); ++i) {
|
||||
auto * dev = all_devices[i];
|
||||
size_t free, total;
|
||||
ggml_backend_dev_memory(dev, &free, &total);
|
||||
printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
|
||||
}
|
||||
}
|
||||
exit(0);
|
||||
}
|
||||
));
|
||||
|
|
|
|||
|
|
@ -33,6 +33,29 @@ struct chat_template_caps {
|
|||
bool requires_typed_content = false;
|
||||
};
|
||||
|
||||
struct chat_template_inputs {
|
||||
nlohmann::ordered_json messages;
|
||||
nlohmann::ordered_json tools;
|
||||
bool add_generation_prompt = true;
|
||||
nlohmann::ordered_json extra_context;
|
||||
std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
|
||||
};
|
||||
|
||||
struct chat_template_options {
|
||||
bool apply_polyfills = true;
|
||||
bool use_bos_token = true;
|
||||
bool use_eos_token = true;
|
||||
bool define_strftime_now = true;
|
||||
|
||||
bool polyfill_tools = true;
|
||||
bool polyfill_tool_call_examples = true;
|
||||
bool polyfill_tool_calls = true;
|
||||
bool polyfill_tool_responses = true;
|
||||
bool polyfill_system_role = true;
|
||||
bool polyfill_object_arguments = true;
|
||||
bool polyfill_typed_content = true;
|
||||
};
|
||||
|
||||
class chat_template {
|
||||
|
||||
private:
|
||||
|
|
@ -41,6 +64,7 @@ class chat_template {
|
|||
std::string bos_token_;
|
||||
std::string eos_token_;
|
||||
std::shared_ptr<minja::TemplateNode> template_root_;
|
||||
std::string tool_call_example_;
|
||||
|
||||
std::string try_raw_render(
|
||||
const nlohmann::ordered_json & messages,
|
||||
|
|
@ -49,7 +73,18 @@ class chat_template {
|
|||
const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
|
||||
{
|
||||
try {
|
||||
auto prompt = apply(messages, tools, add_generation_prompt, extra_context, /* adjust_inputs= */ false);
|
||||
chat_template_inputs inputs;
|
||||
inputs.messages = messages;
|
||||
inputs.tools = tools;
|
||||
inputs.add_generation_prompt = add_generation_prompt;
|
||||
inputs.extra_context = extra_context;
|
||||
// Use fixed date for tests
|
||||
inputs.now = std::chrono::system_clock::from_time_t(0);
|
||||
|
||||
chat_template_options opts;
|
||||
opts.apply_polyfills = false;
|
||||
|
||||
auto prompt = apply(inputs, opts);
|
||||
// fprintf(stderr, "try_raw_render: %s\n", prompt.c_str());
|
||||
return prompt;
|
||||
} catch (const std::exception & e) {
|
||||
|
|
@ -176,6 +211,58 @@ class chat_template {
|
|||
caps_.supports_tool_responses = contains(out, "Some response!");
|
||||
caps_.supports_tool_call_id = contains(out, "call_911_");
|
||||
}
|
||||
|
||||
try {
|
||||
if (!caps_.supports_tools) {
|
||||
const json user_msg {
|
||||
{"role", "user"},
|
||||
{"content", "Hey"},
|
||||
};
|
||||
const json args {
|
||||
{"arg1", "some_value"},
|
||||
};
|
||||
const json tool_call_msg {
|
||||
{"role", "assistant"},
|
||||
{"content", nullptr},
|
||||
{"tool_calls", json::array({
|
||||
{
|
||||
// TODO: detect if requires numerical id or fixed length == 6 like Nemo
|
||||
{"id", "call_1___"},
|
||||
{"type", "function"},
|
||||
{"function", {
|
||||
{"name", "tool_name"},
|
||||
{"arguments", (caps_.requires_object_arguments ? args : json(minja::Value(args).dump(-1, /* to_json= */ true)))},
|
||||
}},
|
||||
},
|
||||
})},
|
||||
};
|
||||
std::string prefix, full;
|
||||
{
|
||||
chat_template_inputs inputs;
|
||||
inputs.messages = json::array({user_msg});
|
||||
inputs.add_generation_prompt = true;
|
||||
prefix = apply(inputs);
|
||||
}
|
||||
{
|
||||
chat_template_inputs inputs;
|
||||
inputs.messages = json::array({user_msg, tool_call_msg});
|
||||
inputs.add_generation_prompt = false;
|
||||
full = apply(inputs);
|
||||
}
|
||||
|
||||
if (full.find(prefix) != 0) {
|
||||
if (prefix.rfind(eos_token_) == prefix.size() - eos_token_.size()) {
|
||||
prefix = prefix.substr(0, prefix.size() - eos_token_.size());
|
||||
}
|
||||
}
|
||||
if (full.find(prefix) != 0) {
|
||||
fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
|
||||
}
|
||||
tool_call_example_ = full.substr(prefix.size());
|
||||
}
|
||||
} catch (const std::exception & e) {
|
||||
fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
|
||||
}
|
||||
}
|
||||
|
||||
const std::string & source() const { return source_; }
|
||||
|
|
@ -183,28 +270,72 @@ class chat_template {
|
|||
const std::string & eos_token() const { return eos_token_; }
|
||||
const chat_template_caps & original_caps() const { return caps_; }
|
||||
|
||||
// Deprecated, please use the form with chat_template_inputs and chat_template_options
|
||||
std::string apply(
|
||||
const nlohmann::ordered_json & messages,
|
||||
const nlohmann::ordered_json & tools,
|
||||
bool add_generation_prompt,
|
||||
const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(),
|
||||
bool adjust_inputs = true) const
|
||||
bool apply_polyfills = true)
|
||||
{
|
||||
fprintf(stderr, "[%s] Deprecated!\n", __func__);
|
||||
chat_template_inputs inputs;
|
||||
inputs.messages = messages;
|
||||
inputs.tools = tools;
|
||||
inputs.add_generation_prompt = add_generation_prompt;
|
||||
inputs.extra_context = extra_context;
|
||||
inputs.now = std::chrono::system_clock::now();
|
||||
|
||||
chat_template_options opts;
|
||||
opts.apply_polyfills = apply_polyfills;
|
||||
|
||||
return apply(inputs, opts);
|
||||
}
|
||||
|
||||
std::string apply(
|
||||
const chat_template_inputs & inputs,
|
||||
const chat_template_options & opts = chat_template_options()) const
|
||||
{
|
||||
json actual_messages;
|
||||
|
||||
auto needs_adjustments = adjust_inputs && (false
|
||||
|| !caps_.supports_system_role
|
||||
|| !caps_.supports_tools
|
||||
|| !caps_.supports_tool_responses
|
||||
|| !caps_.supports_tool_calls
|
||||
|| caps_.requires_object_arguments
|
||||
|| caps_.requires_typed_content
|
||||
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
|
||||
auto has_tool_calls = false;
|
||||
auto has_tool_responses = false;
|
||||
auto has_string_content = false;
|
||||
for (const auto & message : inputs.messages) {
|
||||
if (message.contains("tool_calls") && !message["tool_calls"].is_null()) {
|
||||
has_tool_calls = true;
|
||||
}
|
||||
if (message.contains("role") && message["role"] == "tool") {
|
||||
has_tool_responses = true;
|
||||
}
|
||||
if (message.contains("content") && message["content"].is_string()) {
|
||||
has_string_content = true;
|
||||
}
|
||||
}
|
||||
|
||||
auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role;
|
||||
auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools;
|
||||
auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples;
|
||||
auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls;
|
||||
auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses;
|
||||
auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments;
|
||||
auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content;
|
||||
|
||||
auto needs_polyfills = opts.apply_polyfills && (false
|
||||
|| polyfill_system_role
|
||||
|| polyfill_tools
|
||||
|| polyfill_tool_calls
|
||||
|| polyfill_tool_responses
|
||||
|| polyfill_object_arguments
|
||||
|| polyfill_typed_content
|
||||
);
|
||||
if (needs_adjustments) {
|
||||
|
||||
if (needs_polyfills) {
|
||||
actual_messages = json::array();
|
||||
|
||||
auto add_message = [&](const json & msg) {
|
||||
if (caps_.requires_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
|
||||
if (polyfill_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
|
||||
actual_messages.push_back({
|
||||
{"role", msg.at("role")},
|
||||
{"content", {{
|
||||
|
|
@ -227,9 +358,17 @@ class chat_template {
|
|||
pending_system.clear();
|
||||
}
|
||||
};
|
||||
auto needs_tools_in_system = !tools.is_null() && tools.size() > 0 && !caps_.supports_tools;
|
||||
|
||||
for (const auto & message_ : needs_tools_in_system ? add_system(messages, "Available tools: " + tools.dump(2)) : messages) {
|
||||
json adjusted_messages;
|
||||
if (polyfill_tools) {
|
||||
adjusted_messages = add_system(inputs.messages,
|
||||
"You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
|
||||
(!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_));
|
||||
} else {
|
||||
adjusted_messages = inputs.messages;
|
||||
}
|
||||
|
||||
for (const auto & message_ : adjusted_messages) {
|
||||
auto message = message_;
|
||||
if (!message.contains("role") || !message.contains("content")) {
|
||||
throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
|
||||
|
|
@ -237,7 +376,7 @@ class chat_template {
|
|||
std::string role = message.at("role");
|
||||
|
||||
if (message.contains("tool_calls")) {
|
||||
if (caps_.requires_object_arguments || !caps_.supports_tool_calls) {
|
||||
if (polyfill_object_arguments || polyfill_tool_calls) {
|
||||
for (auto & tool_call : message.at("tool_calls")) {
|
||||
if (tool_call["type"] == "function") {
|
||||
auto & function = tool_call.at("function");
|
||||
|
|
@ -252,7 +391,7 @@ class chat_template {
|
|||
}
|
||||
}
|
||||
}
|
||||
if (!caps_.supports_tool_calls) {
|
||||
if (polyfill_tool_calls) {
|
||||
auto content = message.at("content");
|
||||
auto tool_calls = json::array();
|
||||
for (const auto & tool_call : message.at("tool_calls")) {
|
||||
|
|
@ -279,14 +418,16 @@ class chat_template {
|
|||
message.erase("tool_calls");
|
||||
}
|
||||
}
|
||||
if (!caps_.supports_tool_responses && role == "tool") {
|
||||
if (polyfill_tool_responses && role == "tool") {
|
||||
message["role"] = "user";
|
||||
auto obj = json {
|
||||
{"tool_response", {
|
||||
{"tool", message.at("name")},
|
||||
{"content", message.at("content")},
|
||||
}},
|
||||
};
|
||||
if (message.contains("name")) {
|
||||
obj["tool_response"]["name"] = message.at("name");
|
||||
}
|
||||
if (message.contains("tool_call_id")) {
|
||||
obj["tool_response"]["tool_call_id"] = message.at("tool_call_id");
|
||||
}
|
||||
|
|
@ -294,7 +435,7 @@ class chat_template {
|
|||
message.erase("name");
|
||||
}
|
||||
|
||||
if (!message["content"].is_null() && !caps_.supports_system_role) {
|
||||
if (!message["content"].is_null() && polyfill_system_role) {
|
||||
std::string content = message.at("content");
|
||||
if (role == "system") {
|
||||
if (!pending_system.empty()) pending_system += "\n";
|
||||
|
|
@ -313,28 +454,36 @@ class chat_template {
|
|||
}
|
||||
add_message(message);
|
||||
}
|
||||
if (!caps_.supports_system_role) {
|
||||
flush_sys();
|
||||
}
|
||||
} else {
|
||||
actual_messages = messages;
|
||||
actual_messages = inputs.messages;
|
||||
}
|
||||
|
||||
auto context = minja::Context::make(json({
|
||||
{"messages", actual_messages},
|
||||
{"add_generation_prompt", add_generation_prompt},
|
||||
{"bos_token", bos_token_},
|
||||
{"eos_token", eos_token_},
|
||||
{"add_generation_prompt", inputs.add_generation_prompt},
|
||||
}));
|
||||
context->set("bos_token", opts.use_bos_token ? bos_token_ : "");
|
||||
context->set("eos_token", opts.use_eos_token ? eos_token_ : "");
|
||||
if (opts.define_strftime_now) {
|
||||
auto now = inputs.now;
|
||||
context->set("strftime_now", Value::callable([now](const std::shared_ptr<minja::Context> &, minja::ArgumentsValue & args) {
|
||||
args.expectArgs("strftime_now", {1, 1}, {0, 0});
|
||||
auto format = args.args[0].get<std::string>();
|
||||
|
||||
if (!tools.is_null()) {
|
||||
auto tools_val = minja::Value(tools);
|
||||
context->set("tools", tools_val);
|
||||
auto time = std::chrono::system_clock::to_time_t(now);
|
||||
auto local_time = *std::localtime(&time);
|
||||
std::ostringstream ss;
|
||||
ss << std::put_time(&local_time, format.c_str());
|
||||
return ss.str();
|
||||
}));
|
||||
}
|
||||
if (!extra_context.is_null()) {
|
||||
for (auto & kv : extra_context.items()) {
|
||||
minja::Value val(kv.value());
|
||||
context->set(kv.key(), val);
|
||||
if (!inputs.tools.is_null()) {
|
||||
context->set("tools", minja::Value(inputs.tools));
|
||||
}
|
||||
if (!inputs.extra_context.is_null()) {
|
||||
for (auto & kv : inputs.extra_context.items()) {
|
||||
context->set(kv.key(), minja::Value(kv.value()));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -351,7 +500,7 @@ class chat_template {
|
|||
std::string existing_system = messages_with_system.at(0).at("content");
|
||||
messages_with_system[0] = json {
|
||||
{"role", "system"},
|
||||
{"content", existing_system + "\n" + system_prompt},
|
||||
{"content", existing_system + "\n\n" + system_prompt},
|
||||
};
|
||||
} else {
|
||||
messages_with_system.insert(messages_with_system.begin(), json {
|
||||
|
|
|
|||
966
common/chat.cpp
Normal file
966
common/chat.cpp
Normal file
|
|
@ -0,0 +1,966 @@
|
|||
#include "chat.hpp"
|
||||
#include "chat-template.hpp"
|
||||
#include "json-schema-to-grammar.h"
|
||||
#include "log.h"
|
||||
#include "minja.hpp"
|
||||
|
||||
std::string common_chat_format_name(common_chat_format format) {
|
||||
switch (format) {
|
||||
case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only";
|
||||
case COMMON_CHAT_FORMAT_GENERIC: return "Generic";
|
||||
case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo";
|
||||
case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x";
|
||||
case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools";
|
||||
case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1";
|
||||
case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2";
|
||||
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
|
||||
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
|
||||
case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
|
||||
case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
|
||||
default:
|
||||
throw std::runtime_error("Unknown chat format");
|
||||
}
|
||||
}
|
||||
|
||||
const common_grammar_options grammar_options {
|
||||
/* .dotall = */ false,
|
||||
/* .compact_spaces = */ false,
|
||||
// /* .compact_spaces = */ true,
|
||||
};
|
||||
|
||||
static bool parse_json(std::string::const_iterator & it, const std::string::const_iterator & end, json & out) {
|
||||
// // https://json.nlohmann.me/features/parsing/sax_interface/
|
||||
struct json_error_locator : public nlohmann::json_sax<json> {
|
||||
std::size_t position;
|
||||
bool found_error;
|
||||
|
||||
json_error_locator() : position(0), found_error(false) {}
|
||||
|
||||
bool parse_error(std::size_t position, const std::string &, const json::exception &) override {
|
||||
this->position = position - 1;
|
||||
this->found_error = true;
|
||||
return false;
|
||||
}
|
||||
bool null() override { return true; }
|
||||
bool boolean(bool) override { return true; }
|
||||
bool number_integer(number_integer_t) override { return true; }
|
||||
bool number_unsigned(number_unsigned_t) override { return true; }
|
||||
bool number_float(number_float_t, const string_t &) override { return true; }
|
||||
bool string(string_t &) override { return true; }
|
||||
bool binary(binary_t &) override { return true; }
|
||||
bool start_object(std::size_t) override { return true; }
|
||||
bool key(string_t &) override { return true; }
|
||||
bool end_object() override { return true; }
|
||||
bool start_array(std::size_t) override { return true; }
|
||||
bool end_array() override { return true; }
|
||||
};
|
||||
json_error_locator err_loc;
|
||||
json::sax_parse(it, end, &err_loc);
|
||||
|
||||
std::string::const_iterator temptative_end;
|
||||
if (err_loc.found_error) {
|
||||
temptative_end = it + err_loc.position;
|
||||
} else {
|
||||
temptative_end = end;
|
||||
}
|
||||
std::string json_sub {it, temptative_end};
|
||||
try {
|
||||
out = json::parse(json_sub);
|
||||
it = temptative_end;
|
||||
return true;
|
||||
} catch (const std::exception &) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between.
|
||||
* Aggregates the prefix, suffix and in-between text into the content.
|
||||
*/
|
||||
static common_chat_msg parse_json_tool_calls(
|
||||
const std::string& input,
|
||||
const std::optional<std::regex> & trigger_opt,
|
||||
const std::regex & function_regex,
|
||||
const std::regex & close_regex) {
|
||||
std::smatch match;
|
||||
|
||||
common_chat_msg result;
|
||||
result.role = "assistant";
|
||||
|
||||
|
||||
auto end = input.end();
|
||||
auto it = input.begin();
|
||||
|
||||
if (trigger_opt) {
|
||||
if (!std::regex_search(it, end, match, *trigger_opt)) {
|
||||
result.content = input;
|
||||
return result;
|
||||
}
|
||||
result.content = match.prefix().str();
|
||||
it = match.suffix().first;
|
||||
}
|
||||
|
||||
while (it != end) {
|
||||
std::sregex_iterator rend;
|
||||
std::sregex_iterator rit(it, end, function_regex);
|
||||
if (rit == rend) {
|
||||
fprintf(stderr, "No more tool calls found\n");
|
||||
result.content += std::string(it, end);
|
||||
break;
|
||||
}
|
||||
auto name = rit->str(1);
|
||||
result.content += std::string(it, rit->prefix().second);
|
||||
it = rit->suffix().first;
|
||||
|
||||
json arguments;
|
||||
if (!parse_json(it, end, arguments)) {
|
||||
throw std::runtime_error("Failed to parse json tool call arguments");
|
||||
}
|
||||
if (!std::regex_search(it, end, match, close_regex)) {
|
||||
throw std::runtime_error("Malformed input, missing closing pattern");
|
||||
}
|
||||
it = match.suffix().first;
|
||||
result.tool_calls.push_back({name, arguments.is_string() ? arguments.get<std::string>() : arguments.dump(), /* id= */ ""});
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
static common_chat_msg parse_prefixed_json_tool_call_array(const std::string& input, const std::string & prefix, size_t rstrip_prefix = 0) {
|
||||
auto content_end = input.find(prefix);
|
||||
size_t tc_start = std::string::npos;
|
||||
|
||||
common_chat_msg result;
|
||||
result.role = "assistant";
|
||||
const auto process_tool_calls = [&](const json & tool_calls) {
|
||||
for (const auto & tool_call : tool_calls) {
|
||||
const auto & arguments = tool_call["arguments"];
|
||||
result.tool_calls.push_back({
|
||||
tool_call["name"],
|
||||
arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
|
||||
tool_call.contains("id") ? tool_call["id"] : "",
|
||||
});
|
||||
}
|
||||
};
|
||||
if (content_end == std::string::npos) {
|
||||
result.content = input;
|
||||
} else {
|
||||
tc_start = content_end + prefix.size() - rstrip_prefix;
|
||||
result.content = input.substr(0, content_end);
|
||||
auto tool_calls = json::parse(input.substr(tc_start));
|
||||
process_tool_calls(tool_calls);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
|
||||
for (const auto & tool : tools) {
|
||||
if (!tool.contains("type") || tool["type"] != "function" || !tool.contains("function")) {
|
||||
LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
|
||||
continue;
|
||||
}
|
||||
fn(tool);
|
||||
}
|
||||
}
|
||||
|
||||
static std::string apply(
|
||||
const common_chat_template & tmpl,
|
||||
const nlohmann::ordered_json & messages,
|
||||
const nlohmann::ordered_json & tools,
|
||||
bool add_generation_prompt,
|
||||
const nlohmann::ordered_json & extra_context = nlohmann::ordered_json())
|
||||
{
|
||||
minja::chat_template_inputs tmpl_inputs;
|
||||
tmpl_inputs.messages = messages;
|
||||
tmpl_inputs.tools = tools;
|
||||
tmpl_inputs.add_generation_prompt = add_generation_prompt;
|
||||
tmpl_inputs.extra_context = extra_context;
|
||||
// TODO: add flag to control date/time, if only for testing purposes.
|
||||
// tmpl_inputs.now = std::chrono::system_clock::now();
|
||||
|
||||
minja::chat_template_options tmpl_opts;
|
||||
tmpl_opts.use_bos_token = false;
|
||||
tmpl_opts.use_eos_token = false;
|
||||
|
||||
return tmpl.apply(tmpl_inputs, tmpl_opts);
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
common_chat_params data;
|
||||
|
||||
auto tool_call_schemas = json::array();
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
auto tool_schema = json {
|
||||
{"type", "object"},
|
||||
{"properties", {
|
||||
{"name", {
|
||||
{"type", "string"},
|
||||
{"const", function["name"]},
|
||||
}},
|
||||
{"arguments", function["parameters"]},
|
||||
}},
|
||||
{"required", json::array({"name", "arguments"})},
|
||||
};
|
||||
if (function.contains("description")) {
|
||||
tool_schema["description"] = function["description"];
|
||||
}
|
||||
if (inputs.parallel_tool_calls) {
|
||||
tool_schema["properties"]["id"] = {
|
||||
{"type", "string"},
|
||||
{"minLength", 4},
|
||||
};
|
||||
tool_schema["required"].push_back("id");
|
||||
}
|
||||
tool_call_schemas.emplace_back(tool_schema);
|
||||
});
|
||||
const auto tool_call =
|
||||
inputs.parallel_tool_calls
|
||||
? json {
|
||||
{"type", "object"},
|
||||
{"properties", {
|
||||
{"tool_calls", {
|
||||
{"type", "array"},
|
||||
{"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
|
||||
{"anyOf", tool_call_schemas},
|
||||
}},
|
||||
{"minItems", 1},
|
||||
}},
|
||||
}},
|
||||
{"required", json::array({"tool_calls"})},
|
||||
}
|
||||
: json {
|
||||
{"type", "object"},
|
||||
{"properties", {
|
||||
{"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
|
||||
{"anyOf", tool_call_schemas},
|
||||
}},
|
||||
}},
|
||||
{"required", json::array({"tool_call"})},
|
||||
};
|
||||
const auto schema =
|
||||
inputs.tool_choice != "required"
|
||||
? json {
|
||||
{"anyOf", json::array({
|
||||
tool_call,
|
||||
{
|
||||
{"type", "object"},
|
||||
{"properties", {
|
||||
{"response", inputs.json_schema.is_null()
|
||||
? json {{"type", "string"}}
|
||||
: inputs.json_schema
|
||||
},
|
||||
}},
|
||||
{"required", json::array({"response"})},
|
||||
},
|
||||
})}
|
||||
}
|
||||
: tool_call;
|
||||
|
||||
data.grammar_lazy = false;
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
builder.add_schema("root", schema);
|
||||
}, grammar_options);
|
||||
|
||||
auto tweaked_messages = common_chat_template::add_system(
|
||||
inputs.messages,
|
||||
"Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request");
|
||||
|
||||
data.prompt = apply(tmpl, tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
data.format = COMMON_CHAT_FORMAT_GENERIC;
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_generic(const std::string & input) {
|
||||
json data = json::parse(input);
|
||||
common_chat_msg result;
|
||||
result.role = "assistant";
|
||||
if (data.contains("tool_calls")) {
|
||||
for (const auto & tool_call : data["tool_calls"]) {
|
||||
result.tool_calls.push_back({
|
||||
tool_call["name"],
|
||||
tool_call["arguments"].dump(),
|
||||
tool_call.contains("id") ? tool_call["id"] : "",
|
||||
});
|
||||
}
|
||||
} else if (data.contains("tool_call")) {
|
||||
result.tool_calls.push_back({
|
||||
data["tool_call"]["name"],
|
||||
data["tool_call"]["arguments"].dump(),
|
||||
/* id= */ "",
|
||||
});
|
||||
} else if (data.contains("response")) {
|
||||
const auto & response = data["response"];
|
||||
result.content = response.is_string() ? response.get<std::string>() : response.dump(2);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
common_chat_params data;
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
auto schemas = json::array();
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
schemas.push_back({
|
||||
{"type", "object"},
|
||||
{"properties", {
|
||||
// Important note: the model is probably trained to take a JSON stringified arguments value.
|
||||
// It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object.
|
||||
{"name", {
|
||||
{"type", "string"},
|
||||
{"const", function["name"]},
|
||||
}},
|
||||
{"arguments", function["parameters"]},
|
||||
{"id", {
|
||||
{"type", "string"},
|
||||
// Nemo's template expects a 9-character alphanumeric ID.
|
||||
{"pattern", "^[a-zA-Z0-9]{9}$"},
|
||||
}},
|
||||
}},
|
||||
{"required", json::array({"name", "arguments", "id"})},
|
||||
});
|
||||
});
|
||||
auto schema = json {
|
||||
{"type", "array"},
|
||||
{"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
|
||||
{"minItems", 1},
|
||||
};
|
||||
if (!inputs.parallel_tool_calls) {
|
||||
schema["maxItems"] = 1;
|
||||
}
|
||||
builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema));
|
||||
}, grammar_options);
|
||||
data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true});
|
||||
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO;
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input) {
|
||||
return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]");
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
common_chat_params data;
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
auto schemas = json::array();
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
schemas.push_back({
|
||||
{"type", "object"},
|
||||
{"properties", {
|
||||
{"tool_call_id", {
|
||||
{"type", "string"},
|
||||
// Command-R's template expects an integer string.
|
||||
{"pattern", "^[0-9]{1,10}$"},
|
||||
}},
|
||||
{"tool_name", {
|
||||
{"type", "string"},
|
||||
{"const", function["name"]},
|
||||
}},
|
||||
{"parameters", function["parameters"]},
|
||||
}},
|
||||
{"required", json::array({"tool_call_id", "tool_name", "parameters"})},
|
||||
});
|
||||
});
|
||||
auto schema = json {
|
||||
{"type", "array"},
|
||||
{"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
|
||||
{"minItems", 1},
|
||||
};
|
||||
if (!inputs.parallel_tool_calls) {
|
||||
schema["maxItems"] = 1;
|
||||
}
|
||||
builder.add_rule("root", "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\"");
|
||||
}, grammar_options);
|
||||
data.grammar_triggers.push_back({"<|START_ACTION|>", /* .at_start = */ false});
|
||||
data.preserved_tokens = {
|
||||
"<|START_RESPONSE|>",
|
||||
"<|END_RESPONSE|>",
|
||||
"<|START_THINKING|>",
|
||||
"<|END_THINKING|>",
|
||||
"<|END_ACTION|>",
|
||||
};
|
||||
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
data.format = COMMON_CHAT_FORMAT_COMMAND_R7B;
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_command_r7b(const std::string & input) {
|
||||
static std::regex response_regex("<\\|START_RESPONSE\\|>([\\s\\S\\n\\r]*?)<\\|END_RESPONSE\\|>");
|
||||
static std::regex thought_action_regex("<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|><\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>");
|
||||
std::smatch match;
|
||||
|
||||
common_chat_msg result;
|
||||
result.role = "assistant";
|
||||
if (std::regex_match(input, match, response_regex)) {
|
||||
result.content = match[1].str();
|
||||
} else if (std::regex_match(input, match, thought_action_regex)) {
|
||||
result.tool_plan = match[1].str();
|
||||
auto actions_str = match[2].str();
|
||||
auto actions = json::parse(actions_str);
|
||||
for (const auto & action : actions) {
|
||||
result.tool_calls.push_back({
|
||||
/* .name = */ action["tool_name"],
|
||||
/* .arguments = */ action["parameters"].dump(),
|
||||
/* .id = */ action["tool_call_id"],
|
||||
});
|
||||
}
|
||||
} else {
|
||||
LOG_ERR("Failed to parse command_r output");
|
||||
result.content = input;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector<std::string> & expected_properties) {
|
||||
if (!parameters.is_object() || !parameters.contains("type") || parameters["type"] != "object" || !parameters.contains("properties") || !parameters.contains("required")) {
|
||||
throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties");
|
||||
}
|
||||
const auto & parameters_properties = parameters.at("properties");
|
||||
const auto & parameters_required = parameters.at("required");
|
||||
for (const auto & prop : expected_properties) {
|
||||
if (!parameters_properties.contains(prop)) {
|
||||
throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop);
|
||||
}
|
||||
if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) {
|
||||
throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop);
|
||||
}
|
||||
}
|
||||
if (parameters_properties.size() != expected_properties.size()) {
|
||||
throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", "));
|
||||
}
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct common_chat_inputs & inputs, bool allow_python_tag_builtin_tools) {
|
||||
auto builtin_tools = json::array();
|
||||
common_chat_params data;
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
std::vector<std::string> tool_rules;
|
||||
|
||||
auto handle_builtin_tool = [&](const std::string & name, const json & parameters) {
|
||||
if (name == "wolfram_alpha") {
|
||||
// https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py
|
||||
expect_tool_parameters(name, parameters, {"query"});
|
||||
} else if (name == "web_search" || name == "brave_search") {
|
||||
// https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py
|
||||
expect_tool_parameters(name, parameters, {"query"});
|
||||
} else if (name == "python" || name == "code_interpreter") {
|
||||
// https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
|
||||
expect_tool_parameters(name, parameters, {"code"});
|
||||
} else {
|
||||
return false;
|
||||
}
|
||||
|
||||
std::vector<std::string> kvs;
|
||||
for (const auto & [key, value] : parameters.at("properties").items()) {
|
||||
kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value));
|
||||
}
|
||||
|
||||
tool_rules.push_back(
|
||||
builder.add_rule(
|
||||
name + "-call",
|
||||
"\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\""));
|
||||
builtin_tools.push_back(name);
|
||||
|
||||
return true;
|
||||
};
|
||||
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
std::string name = function["name"];
|
||||
auto parameters = function["parameters"];
|
||||
builder.resolve_refs(parameters);
|
||||
|
||||
// https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime
|
||||
if (allow_python_tag_builtin_tools) {
|
||||
handle_builtin_tool(name, parameters);
|
||||
}
|
||||
tool_rules.push_back(
|
||||
builder.add_rule(
|
||||
name + "-call",
|
||||
"\"{\" space "
|
||||
"( \"\\\"type\\\":\" space \"\\\"function\\\",\" space )? "
|
||||
"\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " +
|
||||
builder.add_schema(name + "-args", parameters) +
|
||||
" \"}\""));
|
||||
data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true});
|
||||
});
|
||||
data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true});
|
||||
data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true});
|
||||
data.grammar_triggers.push_back({"{\n \"name\":", /* .at_start = */ true});
|
||||
data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true});
|
||||
data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true});
|
||||
data.grammar_triggers.push_back({"{\n \"type\": \"function\"", /* .at_start = */ true});
|
||||
if (!builtin_tools.empty()) {
|
||||
data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
|
||||
}
|
||||
builder.add_rule("root", string_join(tool_rules, " | "));
|
||||
}, grammar_options);
|
||||
data.additional_stops.push_back("<|eom_id|>");
|
||||
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
|
||||
{"tools_in_user_message", false},
|
||||
{"builtin_tools", builtin_tools.empty() ? json() : builtin_tools},
|
||||
});
|
||||
data.format = allow_python_tag_builtin_tools && !builtin_tools.empty()
|
||||
? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS
|
||||
: COMMON_CHAT_FORMAT_LLAMA_3_X;
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) {
|
||||
// TODO: tighten & simplify the parser, don't accept leading text context.
|
||||
static std::regex function_regex("\\{[\\s\\n\\r]*(?:\"type\"[\\s\\n\\r]*:[\\s\\n\\r]*\"function\"[\\s\\n\\r]*,[\\s\\n\\r]*|[\\s\\n\\r]*)\"name\"[\\s\\n\\r]*:[\\s\\n\\r]*\"([^\"]+)\"[\\s\\n\\r]*,[\\s\\n\\r]*\"parameters\": ");
|
||||
static std::regex close_regex("\\}");
|
||||
static std::regex builtin_call_regex("<\\|python_tag\\|>([^.(]+)\\.call\\((.*)\\)");
|
||||
|
||||
if (with_builtin_tools) {
|
||||
std::smatch match;
|
||||
if (std::regex_match(input, match, builtin_call_regex)) {
|
||||
auto name = match[1].str();
|
||||
auto raw_args = match[2].str();
|
||||
|
||||
// TODO: if/when builtin tools start accepting more than 1 argument, use parse_json for real parsing.
|
||||
auto it_eq = raw_args.find('=');
|
||||
auto arg_name = raw_args.substr(0, it_eq);
|
||||
auto arg_value_str = raw_args.substr(it_eq + 1);
|
||||
auto arg_value = json::parse(arg_value_str);
|
||||
|
||||
return {
|
||||
/* .role = */ "assistant",
|
||||
/* .content = */ match.prefix().str(),
|
||||
/* .tool_calls = */ {
|
||||
{
|
||||
/* .name = */ match[1],
|
||||
/* .arguments = */ (json {
|
||||
{arg_name, arg_value},
|
||||
}).dump(),
|
||||
/* .id = */ "",
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
}
|
||||
return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
common_chat_params data;
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
std::vector<std::string> tool_rules;
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
std::string name = function["name"];
|
||||
auto parameters = function["parameters"];
|
||||
auto args_rule = builder.add_schema(name + "-args", parameters);
|
||||
tool_rules.push_back(builder.add_rule(name + "-call",
|
||||
"\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\""));
|
||||
});
|
||||
data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false});
|
||||
data.preserved_tokens = {
|
||||
"<｜tool▁sep｜>",
|
||||
"<｜tool▁call▁end｜>",
|
||||
};
|
||||
builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space");
|
||||
}, grammar_options);
|
||||
auto prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
data.prompt = prompt;
|
||||
data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input) {
|
||||
static std::regex trigger_regex("<｜tool▁calls▁begin｜>");
|
||||
static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n");
|
||||
static std::regex close_regex("```<｜tool▁call▁end｜>");
|
||||
return parse_json_tool_calls(input, trigger_regex, function_regex, close_regex);
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
fprintf(stderr, "%s\n", __func__);
|
||||
common_chat_params data;
|
||||
data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
|
||||
{"datetime", "Jan 29 2025 13:00:00 GMT"},
|
||||
{"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
|
||||
});
|
||||
if (!inputs.tools.is_null() && !inputs.tools.empty()) {
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
auto schemas = json::array();
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
schemas.push_back({
|
||||
{"type", "object"},
|
||||
{"properties", {
|
||||
{"name", {
|
||||
{"type", "string"},
|
||||
{"const", function["name"]},
|
||||
}},
|
||||
{"arguments", function["parameters"]},
|
||||
}},
|
||||
{"required", json::array({"name", "arguments", "id"})},
|
||||
});
|
||||
});
|
||||
auto schema = json {
|
||||
{"type", "array"},
|
||||
{"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
|
||||
{"minItems", 1},
|
||||
};
|
||||
if (!inputs.parallel_tool_calls) {
|
||||
schema["maxItems"] = 1;
|
||||
}
|
||||
builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema));
|
||||
}, grammar_options);
|
||||
data.grammar_triggers.push_back({" functools[", /* .at_start = */ false});
|
||||
data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2;
|
||||
} else {
|
||||
data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
}
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_firefunction_v2(const std::string & input) {
|
||||
return parse_prefixed_json_tool_call_array(input, " functools[", /* rstrip_prefix= */ 1);
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
// >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}...
|
||||
// Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar
|
||||
common_chat_params data;
|
||||
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2;
|
||||
if (!inputs.tools.is_null() && !inputs.tools.empty()) {
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
std::vector<std::string> first_tool_rules;
|
||||
std::vector<std::string> subsequent_tool_rules;
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
std::string name = function["name"];
|
||||
auto parameters = function["parameters"];
|
||||
auto args_rule = builder.add_schema(name + "-args", parameters);
|
||||
first_tool_rules.push_back(builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule));
|
||||
subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>" + name + "\\n\" " + args_rule));
|
||||
data.grammar_triggers.push_back({name, /* .at_start = */ true});
|
||||
data.grammar_triggers.push_back({">>>" + name, /* .at_start = */ false});
|
||||
});
|
||||
auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space";
|
||||
if (inputs.parallel_tool_calls) {
|
||||
auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space";
|
||||
builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*");
|
||||
} else {
|
||||
builder.add_rule("root", first_rule);
|
||||
}
|
||||
|
||||
}, grammar_options);
|
||||
}
|
||||
return data;
|
||||
}
|
||||
|
||||
static bool consume(std::string::const_iterator & it, const std::string::const_iterator & end, const std::string & expected) {
|
||||
auto expected_it = expected.begin();
|
||||
auto tmp_it = it;
|
||||
while (tmp_it != end && expected_it != expected.end() && *tmp_it == *expected_it) {
|
||||
++tmp_it;
|
||||
++expected_it;
|
||||
}
|
||||
if (expected_it == expected.end()) {
|
||||
it = tmp_it;
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) {
|
||||
static std::regex function_regex(R"((?:>>>)?(\w+)\n)");
|
||||
static std::regex close_regex(R"($|(?=>>>))");
|
||||
|
||||
std::string content;
|
||||
auto it = input.begin();
|
||||
const auto end = input.end();
|
||||
|
||||
if (consume(it, end, "all\n")) {
|
||||
std::smatch match;
|
||||
if (std::regex_search(it, end, match, function_regex)) {
|
||||
auto fun_it = match.prefix().second;
|
||||
content = std::string(it, fun_it);
|
||||
it = fun_it;
|
||||
} else {
|
||||
common_chat_msg res;
|
||||
res.role = "assistant";
|
||||
res.content = std::string(it, end);
|
||||
return res;
|
||||
}
|
||||
}
|
||||
// TODO: tighten & simplify.
|
||||
try {
|
||||
auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex);
|
||||
res.content = content + res.content;
|
||||
return res;
|
||||
} catch (const std::exception & e) {
|
||||
LOG_ERR("Failed to parse functionary v3.2 input: %s\n", e.what());
|
||||
common_chat_msg res;
|
||||
res.role = "assistant";
|
||||
res.content = input;
|
||||
return res;
|
||||
}
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
// https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt
|
||||
common_chat_params data;
|
||||
json tools = inputs.tools.is_null() ? inputs.tools : json::array();
|
||||
std::string python_code_argument_name;
|
||||
auto has_raw_python = false;
|
||||
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
std::vector<std::string> tool_rules;
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
const auto & parameters = function["parameters"];
|
||||
std::string name = function["name"];
|
||||
if (name == "python" || name == "ipython") {
|
||||
if (!parameters.contains("type")) {
|
||||
throw std::runtime_error("Missing type in python tool");
|
||||
}
|
||||
has_raw_python = true;
|
||||
auto type = parameters.at("type");
|
||||
if (type == "object") {
|
||||
auto properties = parameters.at("properties");
|
||||
for (auto it = properties.begin(); it != properties.end(); ++it) {
|
||||
if (it.value().at("type") == "string") {
|
||||
if (!python_code_argument_name.empty()) {
|
||||
throw std::runtime_error("Multiple string arguments found in python tool");
|
||||
}
|
||||
python_code_argument_name = it.key();
|
||||
}
|
||||
}
|
||||
if (python_code_argument_name.empty()) {
|
||||
throw std::runtime_error("No string argument found in python tool");
|
||||
}
|
||||
} else if (type != "string") {
|
||||
throw std::runtime_error("Invalid type in python tool: " + type.dump());
|
||||
}
|
||||
}
|
||||
tool_rules.push_back(builder.add_rule(name + "-call", "\"<function=" + name + ">\" " + builder.add_schema(name + "-args", parameters) + " \"</function>\" space"));
|
||||
});
|
||||
if (has_raw_python) {
|
||||
tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*"));
|
||||
data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
|
||||
}
|
||||
auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space";
|
||||
builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
|
||||
data.grammar_triggers.push_back({"<function=", /* .at_start = */ false});
|
||||
}, grammar_options);
|
||||
|
||||
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
// TODO: if (has_raw_python)
|
||||
data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::string & input) {
|
||||
// This version of Functionary still supports the llama 3.1 tool call format for the python tool.
|
||||
static std::regex python_tag_regex(R"(<\|python_tag\|>([\s\S\n]*)$)");
|
||||
std::smatch match;
|
||||
if (std::regex_search(input, match, python_tag_regex)) {
|
||||
auto code = match[1].str();
|
||||
return {
|
||||
/* .role = */ "assistant",
|
||||
/* .content = */ match.prefix().str(),
|
||||
/* .tool_calls = */ {
|
||||
{
|
||||
/* .name = */ "python",
|
||||
/* .arguments = */ (json {{"code", code}}).dump(),
|
||||
/* .id = */ "",
|
||||
},
|
||||
}
|
||||
};
|
||||
}
|
||||
static std::regex function_regex(R"(<function=(\w+)>)");
|
||||
static std::regex close_regex(R"(</function>)");
|
||||
// TODO: tighten & simplify.
|
||||
return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
common_chat_params data;
|
||||
// (content)?(<tool_call>{"name": "foo", "arguments": {"a": 1}}</tool_call>)*
|
||||
data.grammar_lazy = inputs.tool_choice != "required";
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
std::vector<std::string> tool_rules;
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool["function"];
|
||||
std::string name = function["name"];
|
||||
auto parameters = function["parameters"];
|
||||
builder.resolve_refs(parameters);
|
||||
tool_rules.push_back(builder.add_schema(name + "-call", {
|
||||
{"type", "object"},
|
||||
{"properties", json {
|
||||
{"name", json {{"const", name}}},
|
||||
{"arguments", parameters},
|
||||
}},
|
||||
{"required", json::array({"name", "arguments"})},
|
||||
}));
|
||||
});
|
||||
auto tool_call = "\"<tool_call>\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"</tool_call>\" space";
|
||||
builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
|
||||
data.grammar_triggers.push_back({"<tool_call>", /* .at_start = */ false});
|
||||
data.preserved_tokens = { "</tool_call>" };
|
||||
}, grammar_options);
|
||||
|
||||
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
|
||||
return data;
|
||||
}
|
||||
static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input) {
|
||||
try {
|
||||
std::regex start_pattern(R"([\n\s]*<tool_call>)");
|
||||
std::regex middle_pattern(R"([\n\s]*</tool_call>[\n\s]*<tool_call>)");
|
||||
std::regex end_pattern(R"([\n\s]*</tool_call>[\n\s]*$)");
|
||||
|
||||
auto end = input.end();
|
||||
std::sregex_iterator rend;
|
||||
std::sregex_iterator rit(input.begin(), end, start_pattern);
|
||||
if (rit == rend) {
|
||||
return {
|
||||
/* .role = */ "assistant",
|
||||
/* .content = */ input,
|
||||
/* .tool_calls = */ {},
|
||||
};
|
||||
}
|
||||
|
||||
common_chat_msg result;
|
||||
result.role = "assistant";
|
||||
result.content = rit->prefix();
|
||||
|
||||
auto it = rit->suffix().first;
|
||||
while (it != end) {
|
||||
json call;
|
||||
if (!parse_json(it, end, call)) {
|
||||
throw std::runtime_error("Failed to parse json tool call");
|
||||
}
|
||||
const auto & arguments = call["arguments"];
|
||||
result.tool_calls.push_back({
|
||||
call["name"],
|
||||
arguments.dump(),
|
||||
// arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
|
||||
/* id= */ "",
|
||||
});
|
||||
rit = {it, end, middle_pattern};
|
||||
if (rit != rend) {
|
||||
it = rit->suffix().first;
|
||||
} else {
|
||||
rit = {it, end, end_pattern};
|
||||
if (rit == rend) {
|
||||
throw std::runtime_error("Malformed input, missing </tool_call>");
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
} catch (const std::exception & e) {
|
||||
return {
|
||||
/* .role = */ "assistant",
|
||||
/* .content = */ input,
|
||||
/* .tool_calls = */ {},
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
common_chat_params data;
|
||||
data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
|
||||
data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
data.grammar_lazy = false;
|
||||
if (!inputs.json_schema.is_null()) {
|
||||
if (!inputs.grammar.empty()) {
|
||||
throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both");
|
||||
}
|
||||
data.grammar = json_schema_to_grammar(inputs.json_schema);
|
||||
} else {
|
||||
data.grammar = inputs.grammar.empty();
|
||||
}
|
||||
return data;
|
||||
}
|
||||
|
||||
common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
|
||||
auto has_tools = !inputs.tools.is_null() && inputs.tool_choice != "none";
|
||||
LOG_DBG("[%s] has_tools=%s\n", __func__, has_tools ? "true" : "false");
|
||||
|
||||
if (has_tools && !inputs.grammar.empty()) {
|
||||
throw std::runtime_error("Cannot specify grammar with tools");
|
||||
}
|
||||
|
||||
const auto & src = tmpl.source();
|
||||
if (src.find(">>>all") != std::string::npos) {
|
||||
// Functionary prepends "all\n" to plain content outputs, so we use the parser no matter when
|
||||
return common_chat_params_init_functionary_v3_2(tmpl, inputs);
|
||||
}
|
||||
if (src.find(" functools[") != std::string::npos) {
|
||||
// Firefunction v2 requires datetime and functions in the context, even w/o tools.
|
||||
return common_chat_params_init_firefunction_v2(tmpl, inputs);
|
||||
}
|
||||
|
||||
if (!has_tools) {
|
||||
return common_chat_params_init_without_tools(tmpl, inputs);
|
||||
}
|
||||
|
||||
if (src.find("<tool_call>") != std::string::npos) {
|
||||
return common_chat_params_init_hermes_2_pro(tmpl, inputs);
|
||||
}
|
||||
if (src.find("<|start_header_id|>") != std::string::npos
|
||||
&& src.find("<function=") != std::string::npos) {
|
||||
return common_chat_params_init_functionary_v3_1_llama_3_1(tmpl, inputs);
|
||||
}
|
||||
if (src.find("<|start_header_id|>ipython<|end_header_id|>") != std::string::npos) {
|
||||
auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos;
|
||||
return common_chat_params_init_llama_3_1_tool_calls(tmpl, inputs, allow_python_tag_builtin_tools);
|
||||
}
|
||||
if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos) {
|
||||
return common_chat_params_init_deepseek_r1(tmpl, inputs);
|
||||
}
|
||||
if (src.find("[TOOL_CALLS]") != std::string::npos) {
|
||||
return common_chat_params_init_mistral_nemo(tmpl, inputs);
|
||||
}
|
||||
if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos) {
|
||||
return common_chat_params_init_command_r7b(tmpl, inputs);
|
||||
}
|
||||
return common_chat_params_init_generic(tmpl, inputs);
|
||||
}
|
||||
|
||||
static common_chat_msg common_chat_parse_content_only(const std::string & input) {
|
||||
return {
|
||||
/* .role = */ "assistant",
|
||||
/* .content = */ input,
|
||||
/* .tool_calls = */ {},
|
||||
};
|
||||
}
|
||||
|
||||
common_chat_msg common_chat_parse(const std::string & input, common_chat_format format) {
|
||||
switch (format) {
|
||||
case COMMON_CHAT_FORMAT_CONTENT_ONLY:
|
||||
return common_chat_parse_content_only(input);
|
||||
case COMMON_CHAT_FORMAT_GENERIC:
|
||||
return common_chat_parse_generic(input);
|
||||
case COMMON_CHAT_FORMAT_MISTRAL_NEMO:
|
||||
return common_chat_parse_mistral_nemo(input);
|
||||
case COMMON_CHAT_FORMAT_LLAMA_3_X:
|
||||
return common_chat_parse_llama_3_1(input);
|
||||
case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS:
|
||||
return common_chat_parse_llama_3_1(input, /* with_builtin_tools= */ true);
|
||||
case COMMON_CHAT_FORMAT_DEEPSEEK_R1:
|
||||
return common_chat_parse_deepseek_r1(input);
|
||||
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2:
|
||||
return common_chat_parse_functionary_v3_2(input);
|
||||
case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1:
|
||||
return common_chat_parse_functionary_v3_1_llama_3_1(input);
|
||||
case COMMON_CHAT_FORMAT_HERMES_2_PRO:
|
||||
return common_chat_parse_hermes_2_pro(input);
|
||||
case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
|
||||
return common_chat_parse_firefunction_v2(input);
|
||||
case COMMON_CHAT_FORMAT_COMMAND_R7B:
|
||||
return common_chat_parse_command_r7b(input);
|
||||
default:
|
||||
throw std::runtime_error("Unsupported format: " + common_chat_format_name(format));
|
||||
}
|
||||
}
|
||||
52
common/chat.hpp
Normal file
52
common/chat.hpp
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "common.h"
|
||||
#include <json.hpp>
|
||||
#include <optional>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
using json = nlohmann::ordered_json;
|
||||
|
||||
struct common_chat_inputs {
|
||||
json messages;
|
||||
json tools;
|
||||
json tool_choice;
|
||||
json json_schema;
|
||||
bool parallel_tool_calls;
|
||||
bool stream;
|
||||
std::string grammar;
|
||||
bool add_generation_prompt = true;
|
||||
};
|
||||
|
||||
enum common_chat_format {
|
||||
COMMON_CHAT_FORMAT_CONTENT_ONLY,
|
||||
COMMON_CHAT_FORMAT_GENERIC,
|
||||
COMMON_CHAT_FORMAT_MISTRAL_NEMO,
|
||||
COMMON_CHAT_FORMAT_LLAMA_3_X,
|
||||
COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
|
||||
COMMON_CHAT_FORMAT_DEEPSEEK_R1,
|
||||
COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
|
||||
COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
|
||||
COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
|
||||
COMMON_CHAT_FORMAT_HERMES_2_PRO,
|
||||
COMMON_CHAT_FORMAT_COMMAND_R7B,
|
||||
|
||||
COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
|
||||
};
|
||||
|
||||
struct common_chat_params {
|
||||
common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
json prompt;
|
||||
std::string grammar;
|
||||
bool grammar_lazy = false;
|
||||
std::vector<common_grammar_trigger> grammar_triggers;
|
||||
std::vector<std::string> preserved_tokens;
|
||||
std::vector<std::string> additional_stops;
|
||||
};
|
||||
|
||||
struct common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & params);
|
||||
std::string common_chat_format_name(common_chat_format format);
|
||||
common_chat_msg common_chat_parse( const std::string & input, common_chat_format format);
|
||||
|
|
@ -12,6 +12,7 @@
|
|||
#include "json.hpp"
|
||||
#include "json-schema-to-grammar.h"
|
||||
#include "llama.h"
|
||||
#include "chat.hpp"
|
||||
#include "chat-template.hpp"
|
||||
|
||||
#include <algorithm>
|
||||
|
|
@ -1774,11 +1775,13 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
|
|||
bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
|
||||
if (use_jinja) {
|
||||
try {
|
||||
auto chat_template = minja::chat_template(tmpl, "<s>", "</s>");
|
||||
chat_template.apply({{
|
||||
auto chat_template = common_chat_template(tmpl, "<s>", "</s>");
|
||||
common_chat_inputs inputs;
|
||||
inputs.messages = json::array({{
|
||||
{"role", "user"},
|
||||
{"content", "test"},
|
||||
}}, json(), true);
|
||||
}});
|
||||
common_chat_params_init(chat_template, inputs);
|
||||
return true;
|
||||
} catch (const std::exception & e) {
|
||||
LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what());
|
||||
|
|
@ -1800,7 +1803,10 @@ std::string common_chat_apply_template(
|
|||
for (const auto & msg : msgs) {
|
||||
messages.push_back({{"role", msg.role}, {"content", msg.content}});
|
||||
}
|
||||
return tmpl.apply(messages, /* tools= */ json(), add_ass);
|
||||
common_chat_inputs inputs;
|
||||
inputs.messages = messages;
|
||||
inputs.add_generation_prompt = add_ass;
|
||||
return common_chat_params_init(tmpl, inputs).prompt;
|
||||
}
|
||||
|
||||
int alloc_size = 0;
|
||||
|
|
@ -1855,19 +1861,27 @@ std::string common_chat_format_single(
|
|||
|
||||
std::string common_chat_format_example(const common_chat_template & tmpl, bool use_jinja) {
|
||||
std::vector<common_chat_msg> msgs = {
|
||||
{"system", "You are a helpful assistant"},
|
||||
{"user", "Hello"},
|
||||
{"assistant", "Hi there"},
|
||||
{"user", "How are you?"},
|
||||
{"system", "You are a helpful assistant", {}},
|
||||
{"user", "Hello", {}},
|
||||
{"assistant", "Hi there", {}},
|
||||
{"user", "How are you?", {}},
|
||||
};
|
||||
return common_chat_apply_template(tmpl, msgs, true, use_jinja);
|
||||
}
|
||||
|
||||
#define CHATML_TEMPLATE_SRC \
|
||||
"{%- for message in messages -%}\n" \
|
||||
" {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \
|
||||
"{%- endfor -%}\n" \
|
||||
"{%- if add_generation_prompt -%}\n" \
|
||||
" {{- '<|im_start|>assistant\n' -}}\n" \
|
||||
"{%- endif -%}"
|
||||
|
||||
common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override)
|
||||
{
|
||||
auto vocab = llama_model_get_vocab(model);
|
||||
std::string default_template_src = chat_template_override;
|
||||
std::string template_tool_use_src = chat_template_override;
|
||||
std::string default_template_src;
|
||||
std::string template_tool_use_src;
|
||||
|
||||
bool has_explicit_template = !chat_template_override.empty();
|
||||
if (chat_template_override.empty()) {
|
||||
auto str = llama_model_chat_template(model, /* name */ nullptr);
|
||||
|
|
@ -1880,21 +1894,17 @@ common_chat_templates common_chat_templates_from_model(const struct llama_model
|
|||
template_tool_use_src = str;
|
||||
has_explicit_template = true;
|
||||
}
|
||||
} else {
|
||||
default_template_src = chat_template_override;
|
||||
}
|
||||
if (default_template_src.empty() || default_template_src == "chatml") {
|
||||
if (!template_tool_use_src.empty()) {
|
||||
default_template_src = template_tool_use_src;
|
||||
} else {
|
||||
default_template_src = R"(
|
||||
{%- for message in messages -%}
|
||||
{{- "<|im_start|>" + message.role + "\n" + message.content + "<|im_end|>\n" -}}
|
||||
{%- endfor -%}
|
||||
{%- if add_generation_prompt -%}
|
||||
{{- "<|im_start|>assistant\n" -}}
|
||||
{%- endif -%}
|
||||
)";
|
||||
default_template_src = CHATML_TEMPLATE_SRC;
|
||||
}
|
||||
}
|
||||
auto vocab = llama_model_get_vocab(model);
|
||||
const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
|
||||
if (token == LLAMA_TOKEN_NULL) {
|
||||
if (default_template_src.find(jinja_variable_name) != std::string::npos
|
||||
|
|
@ -1908,13 +1918,22 @@ common_chat_templates common_chat_templates_from_model(const struct llama_model
|
|||
};
|
||||
auto token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
|
||||
auto token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
|
||||
try {
|
||||
return {
|
||||
has_explicit_template,
|
||||
std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos),
|
||||
template_tool_use_src.empty()
|
||||
? nullptr
|
||||
: std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos)
|
||||
: std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos),
|
||||
};
|
||||
} catch (const std::exception & e) {
|
||||
LOG_ERR("%s: failed to parse chat template: %s\n", __func__, e.what());
|
||||
return {
|
||||
has_explicit_template,
|
||||
std::make_unique<minja::chat_template>(CHATML_TEMPLATE_SRC, token_bos, token_eos),
|
||||
nullptr,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
//
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
|
||||
#include "llama-cpp.h"
|
||||
|
||||
#include <set>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <sstream>
|
||||
|
|
@ -109,6 +110,11 @@ enum common_conversation_mode {
|
|||
COMMON_CONVERSATION_MODE_AUTO = 2,
|
||||
};
|
||||
|
||||
struct common_grammar_trigger {
|
||||
std::string word;
|
||||
bool at_start;
|
||||
};
|
||||
|
||||
// sampling parameters
|
||||
struct common_params_sampling {
|
||||
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
|
||||
|
|
@ -155,6 +161,10 @@ struct common_params_sampling {
|
|||
};
|
||||
|
||||
std::string grammar; // optional BNF-like grammar to constrain sampling
|
||||
bool grammar_lazy = false;
|
||||
std::vector<common_grammar_trigger> grammar_trigger_words; // optional trigger words to trigger lazy grammar
|
||||
std::vector<llama_token> grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens.
|
||||
std::set<llama_token> preserved_tokens;
|
||||
|
||||
std::vector<llama_logit_bias> logit_bias; // logit biases to apply
|
||||
|
||||
|
|
@ -602,10 +612,18 @@ std::string common_detokenize(
|
|||
// Chat template utils
|
||||
//
|
||||
|
||||
struct common_tool_call {
|
||||
std::string name;
|
||||
std::string arguments;
|
||||
std::string id;
|
||||
};
|
||||
|
||||
// same with llama_chat_message, but uses std::string
|
||||
struct common_chat_msg {
|
||||
std::string role;
|
||||
std::string content;
|
||||
std::vector<common_tool_call> tool_calls;
|
||||
std::string tool_plan = "";
|
||||
};
|
||||
|
||||
// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
|
||||
|
|
|
|||
|
|
@ -343,7 +343,7 @@ static std::string format_literal(const std::string & literal) {
|
|||
|
||||
class SchemaConverter {
|
||||
private:
|
||||
friend std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb);
|
||||
friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
|
||||
std::function<json(const std::string &)> _fetch_json;
|
||||
bool _dotall;
|
||||
std::map<std::string, std::string> _rules;
|
||||
|
|
@ -764,10 +764,11 @@ private:
|
|||
public:
|
||||
SchemaConverter(
|
||||
const std::function<json(const std::string &)> & fetch_json,
|
||||
bool dotall)
|
||||
bool dotall,
|
||||
bool compact_spaces)
|
||||
: _fetch_json(fetch_json), _dotall(dotall)
|
||||
{
|
||||
_rules["space"] = SPACE_RULE;
|
||||
_rules["space"] = compact_spaces ? "\" \"?" : SPACE_RULE;
|
||||
}
|
||||
|
||||
void resolve_refs(json & schema, const std::string & url) {
|
||||
|
|
@ -990,17 +991,24 @@ public:
|
|||
}
|
||||
};
|
||||
|
||||
std::string json_schema_to_grammar(const json & schema) {
|
||||
return build_grammar([&](const llama_grammar_builder & callbacks) {
|
||||
std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
|
||||
#ifdef LLAMA_USE_LLGUIDANCE
|
||||
if (!force_gbnf) {
|
||||
return "%llguidance {}\nstart: %json " + schema.dump();
|
||||
}
|
||||
#else
|
||||
(void)force_gbnf;
|
||||
#endif // LLAMA_USE_LLGUIDANCE
|
||||
return build_grammar([&](const common_grammar_builder & callbacks) {
|
||||
auto copy = schema;
|
||||
callbacks.resolve_refs(copy);
|
||||
callbacks.add_schema("", copy);
|
||||
});
|
||||
}
|
||||
|
||||
std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb) {
|
||||
SchemaConverter converter([&](const std::string &) { return json(); }, /* dotall= */ false);
|
||||
llama_grammar_builder builder {
|
||||
std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options) {
|
||||
SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall, options.compact_spaces);
|
||||
common_grammar_builder builder {
|
||||
/* .add_rule = */ [&](const std::string & name, const std::string & rule) {
|
||||
return converter._add_rule(name, rule);
|
||||
},
|
||||
|
|
|
|||
|
|
@ -5,12 +5,18 @@
|
|||
#define JSON_ASSERT GGML_ASSERT
|
||||
#include "json.hpp"
|
||||
|
||||
std::string json_schema_to_grammar(const nlohmann::ordered_json & schema);
|
||||
std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
|
||||
bool force_gbnf = false);
|
||||
|
||||
struct llama_grammar_builder {
|
||||
struct common_grammar_builder {
|
||||
std::function<std::string(const std::string &, const std::string &)> add_rule;
|
||||
std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
|
||||
std::function<void(nlohmann::ordered_json &)> resolve_refs;
|
||||
};
|
||||
|
||||
std::string build_grammar(const std::function<void(const llama_grammar_builder &)> & cb);
|
||||
struct common_grammar_options {
|
||||
bool dotall = false;
|
||||
bool compact_spaces = false;
|
||||
};
|
||||
|
||||
std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options = {});
|
||||
|
|
|
|||
270
common/llguidance.cpp
Normal file
270
common/llguidance.cpp
Normal file
|
|
@ -0,0 +1,270 @@
|
|||
#include "sampling.h"
|
||||
#include "log.h"
|
||||
|
||||
#ifdef LLAMA_USE_LLGUIDANCE
|
||||
|
||||
# include "llguidance.h"
|
||||
# include <cmath>
|
||||
|
||||
struct llama_sampler_llg {
|
||||
const llama_vocab * vocab;
|
||||
std::string grammar_kind;
|
||||
std::string grammar_data;
|
||||
LlgTokenizer * tokenizer;
|
||||
LlgConstraint * grammar;
|
||||
LlgMaskResult llg_res;
|
||||
bool has_llg_res;
|
||||
};
|
||||
|
||||
static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
|
||||
const char * grammar_data) {
|
||||
LlgConstraintInit cinit;
|
||||
llg_constraint_init_set_defaults(&cinit, tokenizer);
|
||||
const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL");
|
||||
if (log_level && *log_level) {
|
||||
cinit.log_stderr_level = atoi(log_level);
|
||||
}
|
||||
auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data);
|
||||
if (llg_get_error(c)) {
|
||||
LOG_ERR("llg error: %s\n", llg_get_error(c));
|
||||
llg_free_constraint(c);
|
||||
return nullptr;
|
||||
}
|
||||
return c;
|
||||
}
|
||||
|
||||
static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) {
|
||||
return "llguidance";
|
||||
}
|
||||
|
||||
static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) {
|
||||
auto * ctx = (llama_sampler_llg *) smpl->ctx;
|
||||
if (ctx->grammar) {
|
||||
LlgCommitResult res;
|
||||
llg_commit_token(ctx->grammar, token, &res);
|
||||
ctx->has_llg_res = false;
|
||||
}
|
||||
}
|
||||
|
||||
static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) {
|
||||
auto * ctx = (llama_sampler_llg *) smpl->ctx;
|
||||
if (ctx->grammar) {
|
||||
if (!ctx->has_llg_res) {
|
||||
if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) {
|
||||
ctx->has_llg_res = true;
|
||||
} else {
|
||||
LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar));
|
||||
llg_free_constraint(ctx->grammar);
|
||||
ctx->grammar = nullptr;
|
||||
}
|
||||
}
|
||||
if (ctx->has_llg_res) {
|
||||
if (ctx->llg_res.is_stop) {
|
||||
for (size_t i = 0; i < cur_p->size; ++i) {
|
||||
if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) {
|
||||
cur_p->data[i].logit = -INFINITY;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
const uint32_t * mask = ctx->llg_res.sample_mask;
|
||||
for (size_t i = 0; i < cur_p->size; ++i) {
|
||||
auto token = cur_p->data[i].id;
|
||||
if ((mask[token / 32] & (1 << (token % 32))) == 0) {
|
||||
cur_p->data[i].logit = -INFINITY;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void llama_sampler_llg_reset(llama_sampler * smpl) {
|
||||
auto * ctx = (llama_sampler_llg *) smpl->ctx;
|
||||
if (!ctx->grammar) {
|
||||
return;
|
||||
}
|
||||
|
||||
auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str());
|
||||
llg_free_constraint(ctx->grammar);
|
||||
ctx->grammar = grammar_new;
|
||||
ctx->has_llg_res = false;
|
||||
}
|
||||
|
||||
static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
|
||||
const auto * ctx = (const llama_sampler_llg *) smpl->ctx;
|
||||
|
||||
auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr);
|
||||
|
||||
// copy the state
|
||||
{
|
||||
auto * result_ctx = (llama_sampler_llg *) result->ctx;
|
||||
|
||||
if (ctx->grammar) {
|
||||
result_ctx->grammar_kind = ctx->grammar_kind;
|
||||
result_ctx->grammar_data = ctx->grammar_data;
|
||||
result_ctx->grammar = llg_clone_constraint(ctx->grammar);
|
||||
result_ctx->tokenizer = llg_clone_tokenizer(ctx->tokenizer);
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
static void llama_sampler_llg_free(llama_sampler * smpl) {
|
||||
const auto * ctx = (llama_sampler_llg *) smpl->ctx;
|
||||
|
||||
if (ctx->grammar) {
|
||||
llg_free_constraint(ctx->grammar);
|
||||
llg_free_tokenizer(ctx->tokenizer);
|
||||
}
|
||||
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
static llama_sampler_i llama_sampler_llg_i = {
|
||||
/* .name = */ llama_sampler_llg_name,
|
||||
/* .accept = */ llama_sampler_llg_accept_impl,
|
||||
/* .apply = */ llama_sampler_llg_apply,
|
||||
/* .reset = */ llama_sampler_llg_reset,
|
||||
/* .clone = */ llama_sampler_llg_clone,
|
||||
/* .free = */ llama_sampler_llg_free,
|
||||
};
|
||||
|
||||
static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
|
||||
uint32_t * output_tokens, size_t output_tokens_len) {
|
||||
const llama_vocab * vocab = (const llama_vocab *) user_data;
|
||||
int r = 0;
|
||||
try {
|
||||
r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false,
|
||||
true);
|
||||
} catch (const std::exception & e) {
|
||||
GGML_ABORT("llama_tokenize failed: %s\n", e.what());
|
||||
}
|
||||
if (r < 0) {
|
||||
return -r;
|
||||
}
|
||||
return r;
|
||||
}
|
||||
|
||||
static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) {
|
||||
// TODO store the tokenizer in the vocab somehow
|
||||
static const llama_vocab * vocab_cache;
|
||||
static LlgTokenizer * tokenizer_cache;
|
||||
|
||||
if (vocab_cache == vocab) {
|
||||
return llg_clone_tokenizer(tokenizer_cache);
|
||||
}
|
||||
|
||||
auto tok_eos = llama_vocab_eot(vocab);
|
||||
if (tok_eos == LLAMA_TOKEN_NULL) {
|
||||
tok_eos = llama_vocab_eos(vocab);
|
||||
}
|
||||
|
||||
size_t vocab_size = llama_vocab_n_tokens(vocab);
|
||||
|
||||
auto token_lens = new uint32_t[vocab_size];
|
||||
// we typically have ~7 bytes per token; let's go on the safe side here
|
||||
auto token_bytes_size = vocab_size * 16 + 1024 * 1024;
|
||||
auto token_bytes = new uint8_t[token_bytes_size];
|
||||
|
||||
size_t offset = 0;
|
||||
for (size_t i = 0; i < vocab_size; i++) {
|
||||
size_t max_token = 1024;
|
||||
if (token_bytes_size - offset < max_token) {
|
||||
GGML_ABORT("token_bytes buffer too small\n");
|
||||
}
|
||||
|
||||
llama_token token = i;
|
||||
auto dp = (char *) token_bytes + offset;
|
||||
auto size = llama_detokenize(vocab, &token, 1, dp, max_token, false, false);
|
||||
if (size < 0) {
|
||||
GGML_ABORT("llama_detokenize failed\n");
|
||||
}
|
||||
if (size == 0) {
|
||||
size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true);
|
||||
if (size < 0) {
|
||||
GGML_ABORT("llama_detokenize failed\n");
|
||||
}
|
||||
if (size != 0) {
|
||||
*dp = '\xff'; // special token prefix marker
|
||||
size += 1;
|
||||
}
|
||||
}
|
||||
|
||||
token_lens[i] = size;
|
||||
offset += size;
|
||||
}
|
||||
|
||||
LlgTokenizerInit tinit = {
|
||||
/* .vocab_size = */ (uint32_t) vocab_size,
|
||||
/* .tok_eos = */ (uint32_t) tok_eos,
|
||||
/* .token_lens = */ token_lens,
|
||||
/* .token_bytes = */ token_bytes,
|
||||
/* .tokenizer_json = */ nullptr,
|
||||
/* .tokenize_assumes_string = */ true,
|
||||
/* .tokenize_fn = */ llama_sampler_llg_tokenize_fn,
|
||||
/* .use_approximate_greedy_tokenize_fn = */ false,
|
||||
/* .tokenize_user_data = */ vocab,
|
||||
};
|
||||
|
||||
char error_buffer[1024];
|
||||
LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer));
|
||||
|
||||
delete[] token_bytes;
|
||||
delete[] token_lens;
|
||||
|
||||
if (tokenizer == nullptr) {
|
||||
LOG_ERR("llg tokenizer error: %s\n", error_buffer);
|
||||
return tokenizer;
|
||||
}
|
||||
|
||||
if (tokenizer_cache) {
|
||||
llg_free_tokenizer(tokenizer_cache);
|
||||
}
|
||||
vocab_cache = vocab;
|
||||
tokenizer_cache = tokenizer;
|
||||
|
||||
return llg_clone_tokenizer(tokenizer_cache);
|
||||
}
|
||||
|
||||
llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind,
|
||||
const char * grammar_data) {
|
||||
auto * ctx = new llama_sampler_llg;
|
||||
|
||||
if (grammar_kind != nullptr && grammar_kind[0] != '\0') {
|
||||
auto tokenizer = llama_sampler_llg_new_tokenizer(vocab);
|
||||
*ctx = {
|
||||
/* .vocab = */ vocab,
|
||||
/* .grammar_kind = */ grammar_kind,
|
||||
/* .grammar_data = */ grammar_data,
|
||||
/* .tokenizer = */ tokenizer,
|
||||
/* .grammar = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data),
|
||||
/* .llg_res = */ {},
|
||||
/* .has_llg_res = */ false,
|
||||
};
|
||||
} else {
|
||||
*ctx = {
|
||||
/* .vocab = */ vocab,
|
||||
/* .grammar_kind = */ {},
|
||||
/* .grammar_data = */ {},
|
||||
/* .tokenizer = */ nullptr,
|
||||
/* .grammar = */ nullptr,
|
||||
/* .llg_res = */ {},
|
||||
/* .has_llg_res = */ false,
|
||||
};
|
||||
}
|
||||
|
||||
return new llama_sampler{
|
||||
/* .iface = */ &llama_sampler_llg_i,
|
||||
/* .ctx = */ ctx,
|
||||
};
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) {
|
||||
LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
#endif // LLAMA_USE_LLGUIDANCE
|
||||
|
|
@ -14,16 +14,6 @@ void common_log_set_verbosity_thold(int verbosity) {
|
|||
common_log_verbosity_thold = verbosity;
|
||||
}
|
||||
|
||||
#define LOG_COL_DEFAULT "\033[0m"
|
||||
#define LOG_COL_BOLD "\033[1m"
|
||||
#define LOG_COL_RED "\033[31m"
|
||||
#define LOG_COL_GREEN "\033[32m"
|
||||
#define LOG_COL_YELLOW "\033[33m"
|
||||
#define LOG_COL_BLUE "\033[34m"
|
||||
#define LOG_COL_MAGENTA "\033[35m"
|
||||
#define LOG_COL_CYAN "\033[36m"
|
||||
#define LOG_COL_WHITE "\033[37m"
|
||||
|
||||
static int64_t t_us() {
|
||||
return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
|
||||
}
|
||||
|
|
@ -206,6 +196,7 @@ public:
|
|||
vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy);
|
||||
}
|
||||
#endif
|
||||
va_end(args_copy);
|
||||
}
|
||||
|
||||
entry.level = level;
|
||||
|
|
|
|||
10
common/log.h
10
common/log.h
|
|
@ -2,6 +2,16 @@
|
|||
|
||||
#include "ggml.h" // for ggml_log_level
|
||||
|
||||
#define LOG_COL_DEFAULT "\033[0m"
|
||||
#define LOG_COL_BOLD "\033[1m"
|
||||
#define LOG_COL_RED "\033[31m"
|
||||
#define LOG_COL_GREEN "\033[32m"
|
||||
#define LOG_COL_YELLOW "\033[33m"
|
||||
#define LOG_COL_BLUE "\033[34m"
|
||||
#define LOG_COL_MAGENTA "\033[35m"
|
||||
#define LOG_COL_CYAN "\033[36m"
|
||||
#define LOG_COL_WHITE "\033[37m"
|
||||
|
||||
#ifndef __GNUC__
|
||||
# define LOG_ATTRIBUTE_FORMAT(...)
|
||||
#elif defined(__MINGW32__)
|
||||
|
|
|
|||
|
|
@ -693,7 +693,7 @@ enum SpaceHandling { Keep, Strip, StripSpaces, StripNewline };
|
|||
|
||||
class TemplateToken {
|
||||
public:
|
||||
enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Generation, EndGeneration, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter };
|
||||
enum class Type { Text, Expression, If, Else, Elif, EndIf, For, EndFor, Generation, EndGeneration, Set, EndSet, Comment, Macro, EndMacro, Filter, EndFilter, Break, Continue };
|
||||
|
||||
static std::string typeToString(Type t) {
|
||||
switch (t) {
|
||||
|
|
@ -714,6 +714,8 @@ public:
|
|||
case Type::EndFilter: return "endfilter";
|
||||
case Type::Generation: return "generation";
|
||||
case Type::EndGeneration: return "endgeneration";
|
||||
case Type::Break: return "break";
|
||||
case Type::Continue: return "continue";
|
||||
}
|
||||
return "Unknown";
|
||||
}
|
||||
|
|
@ -815,6 +817,22 @@ struct CommentTemplateToken : public TemplateToken {
|
|||
CommentTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, const std::string& t) : TemplateToken(Type::Comment, location, pre, post), text(t) {}
|
||||
};
|
||||
|
||||
enum class LoopControlType { Break, Continue };
|
||||
|
||||
class LoopControlException : public std::runtime_error {
|
||||
public:
|
||||
LoopControlType control_type;
|
||||
LoopControlException(const std::string & message, LoopControlType control_type) : std::runtime_error(message), control_type(control_type) {}
|
||||
LoopControlException(LoopControlType control_type)
|
||||
: std::runtime_error((control_type == LoopControlType::Continue ? "continue" : "break") + std::string(" outside of a loop")),
|
||||
control_type(control_type) {}
|
||||
};
|
||||
|
||||
struct LoopControlTemplateToken : public TemplateToken {
|
||||
LoopControlType control_type;
|
||||
LoopControlTemplateToken(const Location & location, SpaceHandling pre, SpaceHandling post, LoopControlType control_type) : TemplateToken(Type::Break, location, pre, post), control_type(control_type) {}
|
||||
};
|
||||
|
||||
class TemplateNode {
|
||||
Location location_;
|
||||
protected:
|
||||
|
|
@ -825,6 +843,12 @@ public:
|
|||
void render(std::ostringstream & out, const std::shared_ptr<Context> & context) const {
|
||||
try {
|
||||
do_render(out, context);
|
||||
} catch (const LoopControlException & e) {
|
||||
// TODO: make stack creation lazy. Only needed if it was thrown outside of a loop.
|
||||
std::ostringstream err;
|
||||
err << e.what();
|
||||
if (location_.source) err << error_location_suffix(*location_.source, location_.pos);
|
||||
throw LoopControlException(err.str(), e.control_type);
|
||||
} catch (const std::exception & e) {
|
||||
std::ostringstream err;
|
||||
err << e.what();
|
||||
|
|
@ -897,6 +921,15 @@ public:
|
|||
}
|
||||
};
|
||||
|
||||
class LoopControlNode : public TemplateNode {
|
||||
LoopControlType control_type_;
|
||||
public:
|
||||
LoopControlNode(const Location & location, LoopControlType control_type) : TemplateNode(location), control_type_(control_type) {}
|
||||
void do_render(std::ostringstream &, const std::shared_ptr<Context> &) const override {
|
||||
throw LoopControlException(control_type_);
|
||||
}
|
||||
};
|
||||
|
||||
class ForNode : public TemplateNode {
|
||||
std::vector<std::string> var_names;
|
||||
std::shared_ptr<Expression> iterable;
|
||||
|
|
@ -961,7 +994,12 @@ public:
|
|||
loop.set("last", i == (n - 1));
|
||||
loop.set("previtem", i > 0 ? filtered_items.at(i - 1) : Value());
|
||||
loop.set("nextitem", i < n - 1 ? filtered_items.at(i + 1) : Value());
|
||||
try {
|
||||
body->render(out, loop_context);
|
||||
} catch (const LoopControlException & e) {
|
||||
if (e.control_type == LoopControlType::Break) break;
|
||||
if (e.control_type == LoopControlType::Continue) continue;
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
|
@ -2156,10 +2194,10 @@ private:
|
|||
}
|
||||
|
||||
TemplateTokenVector tokenize() {
|
||||
static std::regex comment_tok(R"(\{#([-~]?)(.*?)([-~]?)#\})");
|
||||
static std::regex comment_tok(R"(\{#([-~]?)([\s\S\r\n]*?)([-~]?)#\})");
|
||||
static std::regex expr_open_regex(R"(\{\{([-~])?)");
|
||||
static std::regex block_open_regex(R"(^\{%([-~])?[\s\n\r]*)");
|
||||
static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter)\b)");
|
||||
static std::regex block_keyword_tok(R"((if|else|elif|endif|for|endfor|generation|endgeneration|set|endset|block|endblock|macro|endmacro|filter|endfilter|break|continue)\b)");
|
||||
static std::regex non_text_open_regex(R"(\{\{|\{%|\{#)");
|
||||
static std::regex expr_close_regex(R"([\s\n\r]*([-~])?\}\})");
|
||||
static std::regex block_close_regex(R"([\s\n\r]*([-~])?%\})");
|
||||
|
|
@ -2291,6 +2329,9 @@ private:
|
|||
} else if (keyword == "endfilter") {
|
||||
auto post_space = parseBlockClose();
|
||||
tokens.push_back(std::make_unique<EndFilterTemplateToken>(location, pre_space, post_space));
|
||||
} else if (keyword == "break" || keyword == "continue") {
|
||||
auto post_space = parseBlockClose();
|
||||
tokens.push_back(std::make_unique<LoopControlTemplateToken>(location, pre_space, post_space, keyword == "break" ? LoopControlType::Break : LoopControlType::Continue));
|
||||
} else {
|
||||
throw std::runtime_error("Unexpected block: " + keyword);
|
||||
}
|
||||
|
|
@ -2414,6 +2455,8 @@ private:
|
|||
children.emplace_back(std::make_shared<FilterNode>(token->location, std::move(filter_token->filter), std::move(body)));
|
||||
} else if (dynamic_cast<CommentTemplateToken*>(token.get())) {
|
||||
// Ignore comments
|
||||
} else if (auto ctrl_token = dynamic_cast<LoopControlTemplateToken*>(token.get())) {
|
||||
children.emplace_back(std::make_shared<LoopControlNode>(token->location, ctrl_token->control_type));
|
||||
} else if (dynamic_cast<EndForTemplateToken*>(token.get())
|
||||
|| dynamic_cast<EndSetTemplateToken*>(token.get())
|
||||
|| dynamic_cast<EndMacroTemplateToken*>(token.get())
|
||||
|
|
@ -2572,6 +2615,7 @@ inline std::shared_ptr<Context> Context::builtins() {
|
|||
}));
|
||||
globals.set("join", simple_function("join", { "items", "d" }, [](const std::shared_ptr<Context> &, Value & args) {
|
||||
auto do_join = [](Value & items, const std::string & sep) {
|
||||
if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump());
|
||||
std::ostringstream oss;
|
||||
auto first = true;
|
||||
for (size_t i = 0, n = items.size(); i < n; ++i) {
|
||||
|
|
@ -2652,6 +2696,10 @@ inline std::shared_ptr<Context> Context::builtins() {
|
|||
return Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
|
||||
args.expectArgs(is_select ? "select" : "reject", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
|
||||
auto & items = args.args[0];
|
||||
if (items.is_null())
|
||||
return Value::array();
|
||||
if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump());
|
||||
|
||||
auto filter_fn = context->get(args.args[1]);
|
||||
if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
|
||||
|
||||
|
|
@ -2729,6 +2777,7 @@ inline std::shared_ptr<Context> Context::builtins() {
|
|||
auto & items = args.args[0];
|
||||
if (items.is_null())
|
||||
return Value::array();
|
||||
if (!items.is_array()) throw std::runtime_error("object is not iterable: " + items.dump());
|
||||
auto attr_name = args.args[1].get<std::string>();
|
||||
|
||||
bool has_test = false;
|
||||
|
|
|
|||
|
|
@ -151,9 +151,30 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
|
|||
|
||||
lparams.no_perf = params.no_perf;
|
||||
|
||||
std::vector<const char *> trigger_words;
|
||||
trigger_words.reserve(params.grammar_trigger_words.size());
|
||||
for (const auto & str : params.grammar_trigger_words) {
|
||||
trigger_words.push_back(str.word.c_str());
|
||||
}
|
||||
|
||||
struct llama_sampler * grmr;
|
||||
if (params.grammar.compare(0, 11, "%llguidance") == 0) {
|
||||
#ifdef LLAMA_USE_LLGUIDANCE
|
||||
grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
|
||||
#else
|
||||
GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
|
||||
#endif // LLAMA_USE_LLGUIDANCE
|
||||
} else {
|
||||
grmr = params.grammar_lazy
|
||||
? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root",
|
||||
trigger_words.data(), trigger_words.size(),
|
||||
params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size())
|
||||
: llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
|
||||
}
|
||||
|
||||
auto * result = new common_sampler {
|
||||
/* .params = */ params,
|
||||
/* .grmr = */ llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"),
|
||||
/* .grmr = */ grmr,
|
||||
/* .chain = */ llama_sampler_chain_init(lparams),
|
||||
/* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
|
||||
/* .cur = */ {},
|
||||
|
|
|
|||
|
|
@ -102,3 +102,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
|
|||
|
||||
std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
|
||||
std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
|
||||
|
||||
llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
|
||||
const char * grammar_kind, const char * grammar_data);
|
||||
|
|
|
|||
|
|
@ -648,7 +648,7 @@ class Model:
|
|||
if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
|
||||
# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
|
||||
res = "jina-v2-code"
|
||||
if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
|
||||
if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b" or chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
|
||||
# ref: https://huggingface.co/THUDM/glm-4-9b-chat
|
||||
res = "chatglm-bpe"
|
||||
if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
|
||||
|
|
@ -4513,7 +4513,7 @@ class JaisModel(Model):
|
|||
self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
|
||||
|
||||
|
||||
@Model.register("ChatGLMModel", "ChatGLMForConditionalGeneration")
|
||||
@Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
|
||||
class ChatGLMModel(Model):
|
||||
model_arch = gguf.MODEL_ARCH.CHATGLM
|
||||
|
||||
|
|
@ -4619,47 +4619,15 @@ class ChatGLMModel(Model):
|
|||
|
||||
from transformers import AutoTokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
|
||||
vocab_size = hparams["padded_vocab_size"]
|
||||
vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
|
||||
assert max(tokenizer.get_vocab().values()) < vocab_size
|
||||
|
||||
tokpre = self.get_vocab_base_pre(tokenizer)
|
||||
|
||||
merges = []
|
||||
vocab = {}
|
||||
mergeable_ranks = tokenizer.mergeable_ranks
|
||||
for token, rank in mergeable_ranks.items():
|
||||
vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
|
||||
if len(token) == 1:
|
||||
continue
|
||||
merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank)
|
||||
assert len(merged) >= 2 and len(merged) <= 7
|
||||
merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged)))
|
||||
|
||||
# for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
|
||||
added_vocab = tokenizer.get_added_vocab()
|
||||
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
|
||||
|
||||
for i in range(vocab_size):
|
||||
if i not in reverse_vocab:
|
||||
tokens.append(f"[PAD{i}]")
|
||||
toktypes.append(gguf.TokenType.UNUSED)
|
||||
elif reverse_vocab[i] in added_vocab:
|
||||
tokens.append(reverse_vocab[i])
|
||||
if tokenizer.added_tokens_decoder[i].special:
|
||||
toktypes.append(gguf.TokenType.CONTROL)
|
||||
else:
|
||||
toktypes.append(gguf.TokenType.USER_DEFINED)
|
||||
else:
|
||||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
tokens, toktypes, tokpre = self.get_vocab_base()
|
||||
self.gguf_writer.add_tokenizer_model("gpt2")
|
||||
self.gguf_writer.add_tokenizer_pre(tokpre)
|
||||
self.gguf_writer.add_token_list(tokens)
|
||||
self.gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
|
||||
special_vocab.merges = merges
|
||||
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
|
||||
# only add special tokens when they were not already loaded from config.json
|
||||
special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
|
||||
special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
|
||||
|
|
@ -4670,16 +4638,20 @@ class ChatGLMModel(Model):
|
|||
def set_gguf_parameters(self):
|
||||
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
|
||||
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
|
||||
n_head_kv = self.hparams.get("multi_query_group_num", n_head)
|
||||
n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
|
||||
self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
|
||||
self.gguf_writer.add_embedding_length(n_embed)
|
||||
self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", 4 * n_embed))
|
||||
self.gguf_writer.add_block_count(self.hparams["num_layers"])
|
||||
self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
|
||||
self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"]))
|
||||
self.gguf_writer.add_head_count(n_head)
|
||||
self.gguf_writer.add_head_count_kv(n_head_kv)
|
||||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layernorm_epsilon"])
|
||||
self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
self.gguf_writer.add_rope_dimension_count(64)
|
||||
if "attention_dim" in self.hparams:
|
||||
rope_dim = self.hparams["attention_dim"]
|
||||
else:
|
||||
rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
|
||||
self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
|
||||
self.gguf_writer.add_add_bos_token(False)
|
||||
rope_freq = 10000
|
||||
if "rope_ratio" in self.hparams:
|
||||
|
|
@ -4689,7 +4661,7 @@ class ChatGLMModel(Model):
|
|||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||||
del bid # unused
|
||||
|
||||
if name.endswith(".rotary_pos_emb.inv_freq"):
|
||||
if name.endswith(".rotary_pos_emb.inv_freq") or name.startswith("model.vision."):
|
||||
return []
|
||||
|
||||
name = name.removeprefix("transformer.")
|
||||
|
|
|
|||
51
docs/llguidance.md
Normal file
51
docs/llguidance.md
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
# LLGuidance Support in llama.cpp
|
||||
|
||||
[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently.
|
||||
|
||||
LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process.
|
||||
|
||||
## Building
|
||||
|
||||
To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option:
|
||||
|
||||
```sh
|
||||
cmake -B build -DLLAMA_LLGUIDANCE=ON
|
||||
make -C build -j
|
||||
```
|
||||
|
||||
This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install).
|
||||
|
||||
## Interface
|
||||
|
||||
There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance.
|
||||
|
||||
For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format.
|
||||
|
||||
## Performance
|
||||
|
||||
Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md).
|
||||
|
||||
## JSON Schema
|
||||
|
||||
LLGuidance adheres closely to the JSON Schema specification. For example:
|
||||
|
||||
- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed.
|
||||
- any whitespace is allowed.
|
||||
- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first).
|
||||
|
||||
Unsupported schemas result in an error message—no keywords are silently ignored.
|
||||
|
||||
## Why Not Reuse GBNF Format?
|
||||
|
||||
GBNF lacks the concept of a lexer.
|
||||
|
||||
Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes.
|
||||
LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest.
|
||||
|
||||
However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase.
|
||||
The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically.
|
||||
See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details.
|
||||
|
||||
## Error Handling
|
||||
|
||||
Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future.
|
||||
|
|
@ -31,6 +31,11 @@ defer {
|
|||
llama_model_free(model)
|
||||
}
|
||||
|
||||
guard let vocab = llama_model_get_vocab(model) else {
|
||||
print("Failed to get vocab")
|
||||
exit(1)
|
||||
}
|
||||
|
||||
var tokens = tokenize(text: prompt, add_bos: true)
|
||||
|
||||
let n_kv_req = UInt32(tokens.count) + UInt32((n_len - Int(tokens.count)) * n_parallel)
|
||||
|
|
@ -41,7 +46,7 @@ context_params.n_batch = UInt32(max(n_len, n_parallel))
|
|||
context_params.n_threads = 8
|
||||
context_params.n_threads_batch = 8
|
||||
|
||||
let context = llama_new_context_with_model(model, context_params)
|
||||
let context = llama_init_from_model(model, context_params)
|
||||
guard context != nil else {
|
||||
print("Failed to initialize context")
|
||||
exit(1)
|
||||
|
|
@ -141,7 +146,7 @@ while n_cur <= n_len {
|
|||
let new_token_id = llama_sampler_sample(smpl, context, i_batch[i])
|
||||
|
||||
// is it an end of stream? -> mark the stream as finished
|
||||
if llama_vocab_is_eog(model, new_token_id) || n_cur == n_len {
|
||||
if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len {
|
||||
i_batch[i] = -1
|
||||
// print("")
|
||||
if n_parallel > 1 {
|
||||
|
|
@ -207,7 +212,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
|
|||
let utf8Count = text.utf8.count
|
||||
let n_tokens = utf8Count + (add_bos ? 1 : 0)
|
||||
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
|
||||
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
|
||||
let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
|
||||
var swiftTokens: [llama_token] = []
|
||||
for i in 0 ..< tokenCount {
|
||||
swiftTokens.append(tokens[Int(i)])
|
||||
|
|
@ -218,12 +223,12 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
|
|||
|
||||
private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
|
||||
var result = [CChar](repeating: 0, count: 8)
|
||||
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), 0, false)
|
||||
let nTokens = llama_token_to_piece(vocab, token, &result, Int32(result.count), 0, false)
|
||||
if nTokens < 0 {
|
||||
let actualTokensCount = -Int(nTokens)
|
||||
result = .init(repeating: 0, count: actualTokensCount)
|
||||
let check = llama_token_to_piece(
|
||||
model,
|
||||
vocab,
|
||||
token,
|
||||
&result,
|
||||
Int32(result.count),
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ int main(int argc, char** argv) {
|
|||
grammar_str = buffer.str();
|
||||
}
|
||||
|
||||
llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root");
|
||||
llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root", false, nullptr, 0, nullptr, 0);
|
||||
if (grammar == nullptr) {
|
||||
fprintf(stdout, "Failed to initialize llama_grammar\n");
|
||||
return 1;
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama
|
|||
actor LlamaContext {
|
||||
private var model: OpaquePointer
|
||||
private var context: OpaquePointer
|
||||
private var vocab: OpaquePointer
|
||||
private var sampling: UnsafeMutablePointer<llama_sampler>
|
||||
private var batch: llama_batch
|
||||
private var tokens_list: [llama_token]
|
||||
|
|
@ -47,6 +48,7 @@ actor LlamaContext {
|
|||
self.sampling = llama_sampler_chain_init(sparams)
|
||||
llama_sampler_chain_add(self.sampling, llama_sampler_init_temp(0.4))
|
||||
llama_sampler_chain_add(self.sampling, llama_sampler_init_dist(1234))
|
||||
vocab = llama_model_get_vocab(model)
|
||||
}
|
||||
|
||||
deinit {
|
||||
|
|
@ -79,7 +81,7 @@ actor LlamaContext {
|
|||
ctx_params.n_threads = Int32(n_threads)
|
||||
ctx_params.n_threads_batch = Int32(n_threads)
|
||||
|
||||
let context = llama_new_context_with_model(model, ctx_params)
|
||||
let context = llama_init_from_model(model, ctx_params)
|
||||
guard let context else {
|
||||
print("Could not load context!")
|
||||
throw LlamaError.couldNotInitializeContext
|
||||
|
|
@ -151,7 +153,7 @@ actor LlamaContext {
|
|||
|
||||
new_token_id = llama_sampler_sample(sampling, context, batch.n_tokens - 1)
|
||||
|
||||
if llama_vocab_is_eog(model, new_token_id) || n_cur == n_len {
|
||||
if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len {
|
||||
print("\n")
|
||||
is_done = true
|
||||
let new_token_str = String(cString: temporary_invalid_cchars + [0])
|
||||
|
|
@ -297,7 +299,7 @@ actor LlamaContext {
|
|||
let utf8Count = text.utf8.count
|
||||
let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1
|
||||
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
|
||||
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
|
||||
let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
|
||||
|
||||
var swiftTokens: [llama_token] = []
|
||||
for i in 0..<tokenCount {
|
||||
|
|
@ -316,7 +318,7 @@ actor LlamaContext {
|
|||
defer {
|
||||
result.deallocate()
|
||||
}
|
||||
let nTokens = llama_token_to_piece(model, token, result, 8, 0, false)
|
||||
let nTokens = llama_token_to_piece(vocab, token, result, 8, 0, false)
|
||||
|
||||
if nTokens < 0 {
|
||||
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
|
||||
|
|
@ -324,7 +326,7 @@ actor LlamaContext {
|
|||
defer {
|
||||
newResult.deallocate()
|
||||
}
|
||||
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, 0, false)
|
||||
let nNewTokens = llama_token_to_piece(vocab, token, newResult, -nTokens, 0, false)
|
||||
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
|
||||
return Array(bufferPointer)
|
||||
} else {
|
||||
|
|
|
|||
43
examples/llava/README-glmedge.md
Normal file
43
examples/llava/README-glmedge.md
Normal file
|
|
@ -0,0 +1,43 @@
|
|||
# GLMV-EDGE
|
||||
|
||||
Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b).
|
||||
|
||||
## Usage
|
||||
Build with cmake or run `make llama-llava-cli` to build it.
|
||||
|
||||
After building, run: `./llama-llava-cli` to see the usage. For example:
|
||||
|
||||
```sh
|
||||
./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <image><|user|>\n prompt <|assistant|>\n"
|
||||
```
|
||||
|
||||
**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
|
||||
**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
|
||||
|
||||
## GGUF conversion
|
||||
|
||||
1. Clone a GLMV-EDGE model ([2B](https://huggingface.co/THUDM/glm-edge-v-2b) or [5B](https://huggingface.co/THUDM/glm-edge-v-5b)). For example:
|
||||
|
||||
```sh
|
||||
git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/THUDM/glm-edge-v-2b
|
||||
```
|
||||
|
||||
2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
|
||||
|
||||
```sh
|
||||
python ./examples/llava/glmedge-surgery.py -m ../model_path
|
||||
```
|
||||
|
||||
4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
|
||||
|
||||
```sh
|
||||
python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
|
||||
```
|
||||
|
||||
5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:
|
||||
|
||||
```sh
|
||||
python convert_hf_to_gguf.py ../model_path
|
||||
```
|
||||
|
||||
Now both the LLM part and the image encoder are in the `model_path` directory.
|
||||
|
|
@ -102,6 +102,7 @@ static std::string format(const char * fmt, ...) {
|
|||
#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
|
||||
#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
|
||||
#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector"
|
||||
#define KEY_HAS_GLM_PROJ "clip.has_glm_projector"
|
||||
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
|
||||
#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger"
|
||||
#define KEY_USE_GELU "clip.use_gelu"
|
||||
|
|
@ -160,6 +161,15 @@ static std::string format(const char * fmt, ...) {
|
|||
#define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
|
||||
#define TN_MINICPMV_LN "resampler.ln_%s.%s"
|
||||
|
||||
#define TN_GLM_ADAPER_CONV "adapter.conv.%s"
|
||||
#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s"
|
||||
#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s"
|
||||
#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
|
||||
#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s"
|
||||
#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
|
||||
#define TN_GLM_BOI_W "adapter.boi"
|
||||
#define TN_GLM_EOI_W "adapter.eoi"
|
||||
|
||||
|
||||
enum projector_type {
|
||||
PROJECTOR_TYPE_MLP,
|
||||
|
|
@ -167,6 +177,7 @@ enum projector_type {
|
|||
PROJECTOR_TYPE_LDP,
|
||||
PROJECTOR_TYPE_LDPV2,
|
||||
PROJECTOR_TYPE_RESAMPLER,
|
||||
PROJECTOR_TYPE_GLM_EDGE,
|
||||
PROJECTOR_TYPE_MERGER,
|
||||
PROJECTOR_TYPE_UNKNOWN,
|
||||
};
|
||||
|
|
@ -176,6 +187,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
|
|||
{ PROJECTOR_TYPE_LDP, "ldp" },
|
||||
{ PROJECTOR_TYPE_LDPV2, "ldpv2"},
|
||||
{ PROJECTOR_TYPE_RESAMPLER, "resampler"},
|
||||
{ PROJECTOR_TYPE_GLM_EDGE, "adapter"},
|
||||
{ PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
|
||||
};
|
||||
|
||||
|
|
@ -500,6 +512,12 @@ struct clip_vision_model {
|
|||
struct ggml_tensor * mm_4_w = NULL;
|
||||
struct ggml_tensor * mm_4_b = NULL;
|
||||
|
||||
//GLMV-Edge projection
|
||||
struct ggml_tensor * mm_model_adapter_conv_w;
|
||||
struct ggml_tensor * mm_model_adapter_conv_b;
|
||||
struct ggml_tensor * boi_w;
|
||||
struct ggml_tensor * eoi_w;
|
||||
|
||||
// MobileVLM projection
|
||||
struct ggml_tensor * mm_model_mlp_1_w;
|
||||
struct ggml_tensor * mm_model_mlp_1_b;
|
||||
|
|
@ -560,6 +578,7 @@ struct clip_ctx {
|
|||
bool has_vision_encoder = false;
|
||||
bool has_llava_projector = false;
|
||||
bool has_minicpmv_projector = false;
|
||||
bool has_glm_projector = false;
|
||||
bool has_qwen2vl_merger = false;
|
||||
int minicpmv_version = 2;
|
||||
|
||||
|
|
@ -638,7 +657,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
|
|||
|
||||
const int batch_size = imgs->size;
|
||||
|
||||
if (ctx->has_llava_projector || ctx->has_minicpmv_projector) {
|
||||
if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) {
|
||||
GGML_ASSERT(batch_size == 1);
|
||||
}
|
||||
|
||||
|
|
@ -734,8 +753,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
|
|||
}
|
||||
|
||||
// loop over layers
|
||||
if (ctx->has_minicpmv_projector || ctx->has_qwen2vl_merger) {
|
||||
// TODO: figure out why we doing thing in this way ???
|
||||
if (ctx->has_minicpmv_projector || ctx->has_glm_projector || ctx->has_qwen2vl_merger) {
|
||||
n_layer += 1;
|
||||
}
|
||||
for (int il = 0; il < n_layer - 1; il++) {
|
||||
|
|
@ -1095,7 +1113,33 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
|
|||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
|
||||
// glm projector
|
||||
else if (ctx->has_glm_projector) {
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
|
||||
size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
|
||||
embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings,1,0,2,3));
|
||||
embeddings = ggml_reshape_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
|
||||
embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
|
||||
embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
|
||||
embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3));
|
||||
embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
|
||||
//GLU
|
||||
{
|
||||
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
|
||||
embeddings = ggml_norm(ctx0, embeddings, eps);
|
||||
embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
|
||||
embeddings = ggml_gelu_inplace(ctx0, embeddings);
|
||||
struct ggml_tensor * x = embeddings;
|
||||
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
|
||||
x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x);
|
||||
embeddings = ggml_silu_inplace(ctx0, embeddings);
|
||||
embeddings = ggml_mul(ctx0, embeddings,x);
|
||||
embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings);
|
||||
}
|
||||
} else {
|
||||
GGML_ABORT("fatel error");
|
||||
}
|
||||
} else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
|
||||
embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
|
||||
|
||||
embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
|
||||
|
|
@ -1284,6 +1328,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
|
||||
}
|
||||
|
||||
idx = gguf_find_key(ctx, KEY_HAS_GLM_PROJ);
|
||||
if (idx != -1) {
|
||||
new_clip->has_glm_projector = gguf_get_val_bool(ctx, idx);
|
||||
}
|
||||
|
||||
idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER);
|
||||
if (idx != -1) {
|
||||
new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx);
|
||||
|
|
@ -1309,6 +1358,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
LOG_INF("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
|
||||
LOG_INF("%s: minicpmv_projector: %d\n", __func__, new_clip->has_minicpmv_projector);
|
||||
LOG_INF("%s: minicpmv_version: %d\n", __func__, new_clip->minicpmv_version);
|
||||
LOG_INF("%s: glm_projector: %d\n", __func__, new_clip->has_glm_projector);
|
||||
LOG_INF("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
|
||||
LOG_INF("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
|
||||
}
|
||||
|
|
@ -1576,6 +1626,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
|
||||
vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
|
||||
}
|
||||
else if (new_clip->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
|
||||
vision_model.mm_model_adapter_conv_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "weight"));
|
||||
vision_model.mm_model_adapter_conv_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPER_CONV, "bias"));
|
||||
vision_model.mm_model_mlp_0_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_LINEAR,"weight"));
|
||||
vision_model.mm_model_ln_q_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"weight"));
|
||||
vision_model.mm_model_ln_q_b = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_NORM_1,"bias"));
|
||||
vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_H_2_4H,"weight"));
|
||||
vision_model.mm_model_mlp_2_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_GATE,"weight"));
|
||||
vision_model.mm_model_mlp_3_w = get_tensor(new_clip->ctx_data, format(TN_GLM_ADAPTER_D_4H_2_H,"weight"));
|
||||
vision_model.boi_w = get_tensor(new_clip->ctx_data, TN_GLM_BOI_W);
|
||||
vision_model.eoi_w = get_tensor(new_clip->ctx_data, TN_GLM_EOI_W);
|
||||
}
|
||||
else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) {
|
||||
vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
|
||||
vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
|
||||
|
|
@ -2116,6 +2178,20 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
|
|||
return true;
|
||||
}
|
||||
|
||||
if (ctx->has_glm_projector) {
|
||||
res_imgs->size = 1;
|
||||
res_imgs->data = new clip_image_f32[res_imgs->size];
|
||||
clip_image_u8 resized_image;
|
||||
int32_t sz=ctx->vision_model.hparams.image_size;
|
||||
bicubic_resize(*img, resized_image,sz,sz);
|
||||
clip_image_f32 * res = clip_image_f32_init();
|
||||
//clip_image_save_to_bmp(resized_image, "resized.bmp");
|
||||
normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std);
|
||||
res_imgs->data[0] = *res;
|
||||
clip_image_f32_free(res);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool pad_to_square = true;
|
||||
if (!ctx->has_vision_encoder) {
|
||||
LOG_ERR("This gguf file seems to have no vision encoder\n");
|
||||
|
|
@ -2301,7 +2377,8 @@ void clip_free(clip_ctx * ctx) {
|
|||
}
|
||||
|
||||
size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
|
||||
return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
|
||||
int extra_tokens = ctx->has_glm_projector ? 2 : 0;
|
||||
return (clip_n_patches(ctx) + extra_tokens) * clip_n_mmproj_embd(ctx) * sizeof(float);
|
||||
}
|
||||
|
||||
size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
|
||||
|
|
@ -2343,7 +2420,7 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
|
|||
|
||||
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
|
||||
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
|
||||
n_patches /= 4;
|
||||
} else if (ctx->proj_type == PROJECTOR_TYPE_RESAMPLER) {
|
||||
if (ctx->minicpmv_version == 2) {
|
||||
|
|
@ -2476,6 +2553,12 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
if (ctx->has_minicpmv_projector) {
|
||||
GGML_ASSERT(batch_size == 1);
|
||||
}
|
||||
if (ctx->has_glm_projector) {
|
||||
GGML_ASSERT(batch_size == 1);
|
||||
ggml_tensor * boi = ctx->vision_model.boi_w;
|
||||
ggml_backend_tensor_get(boi,vec,0,ggml_nbytes(boi));
|
||||
vec = (float*)(vec+ggml_nelements(boi)); //offset for boi
|
||||
}
|
||||
|
||||
// build the inference graph
|
||||
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
|
||||
|
|
@ -2628,7 +2711,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
|
||||
free(positions_data);
|
||||
|
||||
{
|
||||
if (!ctx->has_glm_projector) {
|
||||
struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
|
||||
int* patches_data = (int*)malloc(ggml_nbytes(patches));
|
||||
for (int i = 0; i < num_patches; i++) {
|
||||
|
|
@ -2652,6 +2735,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
// copy the embeddings to the location passed by the user
|
||||
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
|
||||
|
||||
if (ctx->has_glm_projector) {
|
||||
//eoi
|
||||
ggml_tensor * eoi = ctx->vision_model.eoi_w;
|
||||
int offset = ggml_nelements(embeddings);
|
||||
ggml_backend_tensor_get(eoi, vec+offset, 0, ggml_nbytes(eoi));
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -2813,6 +2903,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|||
return 3584;
|
||||
}
|
||||
}
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE){
|
||||
return ctx->vision_model.mm_model_mlp_3_w->ne[1];
|
||||
}
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
|
||||
return ctx->vision_model.mm_1_b->ne[0];
|
||||
}
|
||||
|
|
@ -2828,6 +2921,9 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
|
|||
return 0;
|
||||
}
|
||||
|
||||
bool clip_is_glm(const struct clip_ctx * ctx) {
|
||||
return ctx->has_glm_projector;
|
||||
}
|
||||
bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
|
||||
return ctx->has_qwen2vl_merger;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -93,6 +93,8 @@ CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
|
|||
|
||||
CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
|
||||
|
||||
CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
280
examples/llava/glmedge-convert-image-encoder-to-gguf.py
Normal file
280
examples/llava/glmedge-convert-image-encoder-to-gguf.py
Normal file
|
|
@ -0,0 +1,280 @@
|
|||
import argparse
|
||||
import os
|
||||
import json
|
||||
import re
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
from gguf import *
|
||||
|
||||
TEXT = "clip.text"
|
||||
VISION = "clip.vision"
|
||||
from transformers import SiglipVisionModel, SiglipVisionConfig
|
||||
|
||||
def k(raw_key: str, arch: str) -> str:
|
||||
return raw_key.format(arch=arch)
|
||||
|
||||
|
||||
def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
|
||||
if name in (
|
||||
"logit_scale",
|
||||
"text_model.embeddings.position_ids",
|
||||
"vision_model.embeddings.position_ids",
|
||||
):
|
||||
return True
|
||||
|
||||
if name in (
|
||||
"vision_model.head.probe",
|
||||
"vision_model.head.attention.in_proj_weight",
|
||||
"vision_model.head.attention.in_proj_bias",
|
||||
"vision_model.head.attention.out_proj.weight",
|
||||
"vision_model.head.attention.out_proj.bias",
|
||||
"vision_model.head.layernorm.weight",
|
||||
"vision_model.head.layernorm.bias",
|
||||
"vision_model.head.mlp.fc1.weight",
|
||||
"vision_model.head.mlp.fc1.bias",
|
||||
"vision_model.head.mlp.fc2.weight",
|
||||
"vision_model.head.mlp.fc2.bias"
|
||||
):
|
||||
return True
|
||||
|
||||
if name.startswith("v") and not has_vision:
|
||||
return True
|
||||
|
||||
if name.startswith("t") and not has_text:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def get_tensor_name(name: str) -> str:
|
||||
if "projection" in name:
|
||||
return name
|
||||
if "mm_projector" in name:
|
||||
name = name.replace("model.mm_projector", "mm")
|
||||
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
|
||||
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
|
||||
return name
|
||||
|
||||
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
|
||||
|
||||
|
||||
def bytes_to_unicode():
|
||||
"""
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||
The reversible bpe codes work on unicode strings.
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||
This is a significant percentage of your normal, say, 32K bpe vocab.
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||
"""
|
||||
bs = (
|
||||
list(range(ord("!"), ord("~") + 1))
|
||||
+ list(range(ord("¡"), ord("¬") + 1))
|
||||
+ list(range(ord("®"), ord("ÿ") + 1))
|
||||
)
|
||||
cs = bs[:]
|
||||
n = 0
|
||||
for b in range(2**8):
|
||||
if b not in bs:
|
||||
bs.append(b)
|
||||
cs.append(2**8 + n)
|
||||
n += 1
|
||||
cs = [chr(n) for n in cs]
|
||||
return dict(zip(bs, cs))
|
||||
|
||||
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
|
||||
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
|
||||
ap.add_argument("--text-only", action="store_true", required=False,
|
||||
help="Save a text-only model. It can't be used to encode images")
|
||||
ap.add_argument("--vision-only", action="store_true", required=False,
|
||||
help="Save a vision-only model. It can't be used to encode texts")
|
||||
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
|
||||
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
|
||||
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
|
||||
help="The clip model is from openclip (for ViT-SO400M type))")
|
||||
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
|
||||
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter")
|
||||
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
|
||||
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
|
||||
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
|
||||
default_image_mean = [0.5, 0.5, 0.5]
|
||||
default_image_std = [0.5, 0.5, 0.5]
|
||||
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
|
||||
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
|
||||
|
||||
# with proper
|
||||
args = ap.parse_args()
|
||||
|
||||
|
||||
if args.text_only and args.vision_only:
|
||||
print("--text-only and --image-only arguments cannot be specified at the same time.")
|
||||
exit(1)
|
||||
|
||||
if args.use_f32:
|
||||
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
|
||||
|
||||
# output in the same directory as the model if output_dir is None
|
||||
dir_model = args.model_dir
|
||||
|
||||
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
|
||||
vocab = None
|
||||
tokens = None
|
||||
else:
|
||||
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
|
||||
vocab = json.load(f)
|
||||
tokens = [key for key in vocab]
|
||||
|
||||
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
|
||||
config = json.load(f)
|
||||
if args.clip_model_is_vision:
|
||||
v_hparams = config
|
||||
t_hparams = None
|
||||
else:
|
||||
v_hparams = config["vision_config"]
|
||||
t_hparams = None
|
||||
|
||||
# possible data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
#
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
ftype = 1
|
||||
if args.use_f32:
|
||||
ftype = 0
|
||||
|
||||
vision_config = SiglipVisionConfig(**v_hparams)
|
||||
model = SiglipVisionModel(vision_config)
|
||||
model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip")))
|
||||
|
||||
fname_middle = None
|
||||
has_text_encoder = False
|
||||
has_vision_encoder = True
|
||||
has_glm_projector = True
|
||||
if args.text_only:
|
||||
fname_middle = "text-"
|
||||
has_vision_encoder = False
|
||||
elif args.llava_projector is not None:
|
||||
fname_middle = "mmproj-"
|
||||
has_text_encoder = False
|
||||
has_glm_projector = True
|
||||
elif args.vision_only:
|
||||
fname_middle = "vision-"
|
||||
has_text_encoder = False
|
||||
else:
|
||||
fname_middle = ""
|
||||
|
||||
output_dir = args.output_dir if args.output_dir is not None else dir_model
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
|
||||
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
|
||||
fout = GGUFWriter(path=fname_out, arch="clip")
|
||||
|
||||
fout.add_bool("clip.has_text_encoder", has_text_encoder)
|
||||
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
|
||||
fout.add_bool("clip.has_glm_projector", has_glm_projector)
|
||||
fout.add_file_type(ftype)
|
||||
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
|
||||
fout.add_name(model_name)
|
||||
if has_glm_projector:
|
||||
fout.add_description("image encoder for glm4v")
|
||||
fout.add_string("clip.projector_type", "adapter")
|
||||
else:
|
||||
fout.add_description("two-tower CLIP model")
|
||||
|
||||
if has_text_encoder:
|
||||
assert t_hparams is not None
|
||||
assert tokens is not None
|
||||
# text_model hparams
|
||||
fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
|
||||
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
|
||||
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
|
||||
fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
|
||||
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
|
||||
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
|
||||
fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
|
||||
fout.add_token_list(tokens)
|
||||
|
||||
if has_vision_encoder:
|
||||
# vision_model hparams
|
||||
fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
|
||||
fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
|
||||
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
|
||||
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
|
||||
fout.add_uint32("clip.vision.projection_dim", 0)
|
||||
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
|
||||
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
|
||||
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"])
|
||||
|
||||
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
|
||||
image_std = args.image_std if args.image_std is not None else default_image_std
|
||||
fout.add_array("clip.vision.image_mean", image_mean)
|
||||
fout.add_array("clip.vision.image_std", image_std)
|
||||
|
||||
fout.add_bool("clip.use_gelu", True)
|
||||
|
||||
|
||||
if has_glm_projector:
|
||||
# model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
|
||||
projector = torch.load(args.llava_projector)
|
||||
for name, data in projector.items():
|
||||
name = get_tensor_name(name)
|
||||
# pw and dw conv ndim==4
|
||||
if data.ndim == 2 or data.ndim == 4:
|
||||
data = data.squeeze().numpy().astype(np.float16)
|
||||
else:
|
||||
data = data.squeeze().numpy().astype(np.float32)
|
||||
if name.startswith("vision."):
|
||||
name=name.replace("vision.","")
|
||||
fout.add_tensor(name, data)
|
||||
print(f"Projector {name} - {data.dtype} - shape = {data.shape}")
|
||||
# print(f"Projector {name} tensors added\n")
|
||||
|
||||
state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
|
||||
for name, data in state_dict.items():
|
||||
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector):
|
||||
# we don't need this
|
||||
print(f"skipping parameter: {name}")
|
||||
continue
|
||||
|
||||
name = get_tensor_name(name)
|
||||
data = data.squeeze().numpy()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
|
||||
# ftype == 0 -> float32, ftype == 1 -> float16
|
||||
ftype_cur = 0
|
||||
if n_dims == 4:
|
||||
print(f"tensor {name} is always saved in f16")
|
||||
data = data.astype(np.float16)
|
||||
ftype_cur = 1
|
||||
elif ftype == 1:
|
||||
if name[-7:] == ".weight" and n_dims == 2:
|
||||
# print(" Converting to float16")
|
||||
data = data.astype(np.float16)
|
||||
ftype_cur = 1
|
||||
else:
|
||||
# print(" Converting to float32")
|
||||
data = data.astype(np.float32)
|
||||
ftype_cur = 0
|
||||
else:
|
||||
if data.dtype != np.float32:
|
||||
# print(" Converting to float32")
|
||||
data = data.astype(np.float32)
|
||||
ftype_cur = 0
|
||||
print(f"siglip {name} - {data.dtype} - shape = {data.shape}")
|
||||
# print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
|
||||
fout.add_tensor(name, data)
|
||||
|
||||
|
||||
fout.write_header_to_file()
|
||||
fout.write_kv_data_to_file()
|
||||
fout.write_tensors_to_file()
|
||||
fout.close()
|
||||
|
||||
print("Done. Output file: " + fname_out)
|
||||
33
examples/llava/glmedge-surgery.py
Normal file
33
examples/llava/glmedge-surgery.py
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
import argparse
|
||||
import os
|
||||
import torch
|
||||
from transformers import AutoModel
|
||||
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("-m", "--model", help="Path to GLM model")
|
||||
args = ap.parse_args()
|
||||
|
||||
# find the model part that includes the the multimodal projector weights
|
||||
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
|
||||
checkpoint = model.state_dict()
|
||||
|
||||
# get a list of mm tensor names
|
||||
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")]
|
||||
|
||||
# store these tensors in a new dictionary and torch.save them
|
||||
projector = {name: checkpoint[name].float() for name in mm_tensors}
|
||||
torch.save(projector, f"{args.model}/glm.projector")
|
||||
|
||||
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")]
|
||||
if len(clip_tensors) > 0:
|
||||
clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors}
|
||||
torch.save(clip, f"{args.model}/glm.clip")
|
||||
|
||||
# added tokens should be removed to be able to convert Mistral models
|
||||
if os.path.exists(f"{args.model}/added_tokens.json"):
|
||||
with open(f"{args.model}/added_tokens.json", "w") as f:
|
||||
f.write("{}\n")
|
||||
|
||||
print("Done!")
|
||||
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
|
||||
print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.")
|
||||
|
|
@ -311,6 +311,20 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
img_res_v.size = 0;
|
||||
img_res_v.data = nullptr;
|
||||
}
|
||||
else if (clip_is_glm(ctx_clip)){
|
||||
struct clip_image_size * load_image_size = clip_image_size_init();
|
||||
load_image_size->width = img_res_v.data[0].nx;
|
||||
load_image_size->height = img_res_v.data[0].ny;
|
||||
clip_add_load_image_size(ctx_clip, load_image_size);
|
||||
|
||||
bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd);
|
||||
int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2);
|
||||
*n_img_pos = (pos * pos + 2);
|
||||
if (!encoded){
|
||||
LOG_ERR("Unable to encode image \n");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
|
||||
// flat / default llava-1.5 type embedding
|
||||
*n_img_pos = clip_n_patches(ctx_clip);
|
||||
|
|
@ -395,6 +409,9 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
|
|||
if (clip_is_minicpmv(ctx_clip)) {
|
||||
num_max_patches = 10;
|
||||
}
|
||||
if (clip_is_glm(ctx_clip)) {
|
||||
num_max_patches = 1;
|
||||
}
|
||||
float * image_embd;
|
||||
if (clip_is_qwen2vl(ctx_clip)) {
|
||||
// qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
|
||||
|
|
|
|||
|
|
@ -254,7 +254,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
const bool add_bos = llama_vocab_get_add_bos(vocab);
|
||||
const bool add_bos = llama_vocab_get_add_bos(vocab) && !params.use_jinja;
|
||||
if (!llama_model_has_encoder(model)) {
|
||||
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
|
||||
}
|
||||
|
|
@ -264,9 +264,9 @@ int main(int argc, char ** argv) {
|
|||
std::vector<llama_token> embd_inp;
|
||||
|
||||
auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) {
|
||||
common_chat_msg new_msg{role, content};
|
||||
common_chat_msg new_msg{role, content, {}};
|
||||
auto formatted = common_chat_format_single(*chat_templates.template_default, chat_msgs, new_msg, role == "user", g_params->use_jinja);
|
||||
chat_msgs.push_back({role, content});
|
||||
chat_msgs.push_back({role, content, {}});
|
||||
LOG_DBG("formatted: '%s'\n", formatted.c_str());
|
||||
return formatted;
|
||||
};
|
||||
|
|
@ -503,12 +503,14 @@ int main(int argc, char ** argv) {
|
|||
|
||||
std::vector<llama_token> embd;
|
||||
|
||||
// tokenized antiprompts
|
||||
std::vector<std::vector<llama_token>> antiprompt_ids;
|
||||
// single-token antiprompts
|
||||
std::vector<llama_token> antiprompt_token;
|
||||
|
||||
antiprompt_ids.reserve(params.antiprompt.size());
|
||||
for (const std::string & antiprompt : params.antiprompt) {
|
||||
antiprompt_ids.emplace_back(::common_tokenize(ctx, antiprompt, false, true));
|
||||
auto ids = ::common_tokenize(ctx, antiprompt, false, true);
|
||||
if (ids.size() == 1) {
|
||||
antiprompt_token.push_back(ids[0]);
|
||||
}
|
||||
}
|
||||
|
||||
if (llama_model_has_encoder(model)) {
|
||||
|
|
@ -753,14 +755,11 @@ int main(int argc, char ** argv) {
|
|||
|
||||
// check for reverse prompt using special tokens
|
||||
llama_token last_token = common_sampler_last(smpl);
|
||||
for (std::vector<llama_token> ids : antiprompt_ids) {
|
||||
if (ids.size() == 1 && last_token == ids[0]) {
|
||||
if (std::find(antiprompt_token.begin(), antiprompt_token.end(), last_token) != antiprompt_token.end()) {
|
||||
if (params.interactive) {
|
||||
is_interacting = true;
|
||||
}
|
||||
is_antiprompt = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (is_antiprompt) {
|
||||
|
|
|
|||
|
|
@ -24,15 +24,16 @@
|
|||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "chat-template.hpp"
|
||||
#include "common.h"
|
||||
#include "json.hpp"
|
||||
#include "linenoise.cpp/linenoise.h"
|
||||
#include "llama-cpp.h"
|
||||
#include "chat-template.hpp"
|
||||
#include "log.h"
|
||||
|
||||
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || defined(_WIN32)
|
||||
[[noreturn]] static void sigint_handler(int) {
|
||||
printf("\n\033[0m");
|
||||
printf("\n" LOG_COL_DEFAULT);
|
||||
exit(0); // not ideal, but it's the only way to guarantee exit in all cases
|
||||
}
|
||||
#endif
|
||||
|
|
@ -65,6 +66,13 @@ static int printe(const char * fmt, ...) {
|
|||
return ret;
|
||||
}
|
||||
|
||||
static std::string strftime_fmt(const char * fmt, const std::tm & tm) {
|
||||
std::ostringstream oss;
|
||||
oss << std::put_time(&tm, fmt);
|
||||
|
||||
return oss.str();
|
||||
}
|
||||
|
||||
class Opt {
|
||||
public:
|
||||
int init(int argc, const char ** argv) {
|
||||
|
|
@ -698,6 +706,39 @@ class LlamaData {
|
|||
return download(url, bn, true);
|
||||
}
|
||||
|
||||
int s3_dl(const std::string & model, const std::string & bn) {
|
||||
const size_t slash_pos = model.find('/');
|
||||
if (slash_pos == std::string::npos) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
const std::string bucket = model.substr(0, slash_pos);
|
||||
const std::string key = model.substr(slash_pos + 1);
|
||||
const char * access_key = std::getenv("AWS_ACCESS_KEY_ID");
|
||||
const char * secret_key = std::getenv("AWS_SECRET_ACCESS_KEY");
|
||||
if (!access_key || !secret_key) {
|
||||
printe("AWS credentials not found in environment\n");
|
||||
return 1;
|
||||
}
|
||||
|
||||
// Generate AWS Signature Version 4 headers
|
||||
// (Implementation requires HMAC-SHA256 and date handling)
|
||||
// Get current timestamp
|
||||
const time_t now = time(nullptr);
|
||||
const tm tm = *gmtime(&now);
|
||||
const std::string date = strftime_fmt("%Y%m%d", tm);
|
||||
const std::string datetime = strftime_fmt("%Y%m%dT%H%M%SZ", tm);
|
||||
const std::vector<std::string> headers = {
|
||||
"Authorization: AWS4-HMAC-SHA256 Credential=" + std::string(access_key) + "/" + date +
|
||||
"/us-east-1/s3/aws4_request",
|
||||
"x-amz-content-sha256: UNSIGNED-PAYLOAD", "x-amz-date: " + datetime
|
||||
};
|
||||
|
||||
const std::string url = "https://" + bucket + ".s3.amazonaws.com/" + key;
|
||||
|
||||
return download(url, bn, true, headers);
|
||||
}
|
||||
|
||||
std::string basename(const std::string & path) {
|
||||
const size_t pos = path.find_last_of("/\\");
|
||||
if (pos == std::string::npos) {
|
||||
|
|
@ -738,6 +779,9 @@ class LlamaData {
|
|||
rm_until_substring(model_, "github:");
|
||||
rm_until_substring(model_, "://");
|
||||
ret = github_dl(model_, bn);
|
||||
} else if (string_starts_with(model_, "s3://")) {
|
||||
rm_until_substring(model_, "://");
|
||||
ret = s3_dl(model_, bn);
|
||||
} else { // ollama:// or nothing
|
||||
rm_until_substring(model_, "ollama.com/library/");
|
||||
rm_until_substring(model_, "://");
|
||||
|
|
@ -804,7 +848,15 @@ static int apply_chat_template(const common_chat_template & tmpl, LlamaData & ll
|
|||
});
|
||||
}
|
||||
try {
|
||||
auto result = tmpl.apply(messages, /* tools= */ json(), append);
|
||||
minja::chat_template_inputs tmpl_inputs;
|
||||
tmpl_inputs.messages = messages;
|
||||
tmpl_inputs.add_generation_prompt = append;
|
||||
|
||||
minja::chat_template_options tmpl_opts;
|
||||
tmpl_opts.use_bos_token = false;
|
||||
tmpl_opts.use_eos_token = false;
|
||||
|
||||
auto result = tmpl.apply(tmpl_inputs, tmpl_opts);
|
||||
llama_data.fmtted.resize(result.size() + 1);
|
||||
memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1);
|
||||
return result.size();
|
||||
|
|
@ -847,7 +899,7 @@ static int check_context_size(const llama_context_ptr & ctx, const llama_batch &
|
|||
const int n_ctx = llama_n_ctx(ctx.get());
|
||||
const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get());
|
||||
if (n_ctx_used + batch.n_tokens > n_ctx) {
|
||||
printf("\033[0m\n");
|
||||
printf(LOG_COL_DEFAULT "\n");
|
||||
printe("context size exceeded\n");
|
||||
return 1;
|
||||
}
|
||||
|
|
@ -910,7 +962,7 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
|
|||
batch = llama_batch_get_one(&new_token_id, 1);
|
||||
}
|
||||
|
||||
printf("\033[0m");
|
||||
printf(LOG_COL_DEFAULT);
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
|
@ -919,7 +971,7 @@ static int read_user_input(std::string & user_input) {
|
|||
#ifdef WIN32
|
||||
printf(
|
||||
"\r%*s"
|
||||
"\r\033[0m%s",
|
||||
"\r" LOG_COL_DEFAULT "%s",
|
||||
get_terminal_width(), " ", prompt_prefix);
|
||||
|
||||
std::getline(std::cin, user_input);
|
||||
|
|
@ -956,7 +1008,7 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
|
|||
const bool stdout_a_terminal) {
|
||||
// Set response color
|
||||
if (stdout_a_terminal) {
|
||||
printf("\033[33m");
|
||||
printf(LOG_COL_YELLOW);
|
||||
}
|
||||
|
||||
if (generate(llama_data, prompt, response)) {
|
||||
|
|
@ -965,7 +1017,7 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
|
|||
}
|
||||
|
||||
// End response with color reset and newline
|
||||
printf("\n%s", stdout_a_terminal ? "\033[0m" : "");
|
||||
printf("\n%s", stdout_a_terminal ? LOG_COL_DEFAULT : "");
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -126,7 +126,7 @@ The project is under active development, and we are [looking for feedback and co
|
|||
| `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') |
|
||||
| `--grammar-file FNAME` | file to read grammar from |
|
||||
| `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
|
||||
| `--jinja` | Enable experimental Jinja templating engine (needed for tool use) |
|
||||
| `--jinja` | Enable experimental Jinja templating engine (required for tool use) |
|
||||
|
||||
**Example-specific params**
|
||||
|
||||
|
|
@ -1069,7 +1069,7 @@ Given a ChatML-formatted json description in `messages`, it returns the predicte
|
|||
|
||||
*Options:*
|
||||
|
||||
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
|
||||
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). llama.cpp `/completion`-specific features such as `mirostat` are also supported.
|
||||
|
||||
The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers.
|
||||
|
||||
|
|
@ -1117,6 +1117,184 @@ curl http://localhost:8080/v1/chat/completions \
|
|||
}'
|
||||
```
|
||||
|
||||
*Tool call support*
|
||||
|
||||
[Function calling](https://platform.openai.com/docs/guides/function-calling) is supported for all models (see https://github.com/ggerganov/llama.cpp/pull/9639):
|
||||
|
||||
- Requires `--jinja` flag
|
||||
- Native tool call formats supported:
|
||||
- Llama 3.1 / 3.3 (including builtin tools support - tool names for `wolfram_alpha`, `web_search` / `brave_search`, `code_interpreter`), Llama 3.2
|
||||
- Functionary v3.1 / v3.2
|
||||
- Hermes 2/3, Qwen 2.5
|
||||
- Mistral Nemo
|
||||
- Firefunction v2
|
||||
- Command R7B
|
||||
- DeepSeek R1 (WIP / seems reluctant to call any tools?)
|
||||
|
||||
<details>
|
||||
<summary>Show some common templates and which format handler they use</summary>
|
||||
|
||||
| Template | Format |
|
||||
|----------|--------|
|
||||
| CohereForAI-c4ai-command-r-plus-default.jinja | generic tool calls |
|
||||
| CohereForAI-c4ai-command-r-plus-rag.jinja | generic tool calls |
|
||||
| CohereForAI-c4ai-command-r-plus-tool_use.jinja | generic tool calls |
|
||||
| MiniMaxAI-MiniMax-Text-01.jinja | generic tool calls |
|
||||
| NexaAIDev-Octopus-v2.jinja | generic tool calls |
|
||||
| NousResearch-Hermes-2-Pro-Llama-3-8B-default.jinja | generic tool calls |
|
||||
| NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja | hermes 2 pro tool calls |
|
||||
| NousResearch-Hermes-2-Pro-Mistral-7B-default.jinja | generic tool calls |
|
||||
| NousResearch-Hermes-2-Pro-Mistral-7B-tool_use.jinja | hermes 2 pro tool calls |
|
||||
| NousResearch-Hermes-3-Llama-3.1-70B-default.jinja | generic tool calls |
|
||||
| NousResearch-Hermes-3-Llama-3.1-70B-tool_use.jinja | hermes 2 pro tool calls |
|
||||
| OrionStarAI-Orion-14B-Chat.jinja | generic tool calls |
|
||||
| Qwen-QwQ-32B-Preview.jinja | hermes 2 pro tool calls |
|
||||
| Qwen-Qwen2-7B-Instruct.jinja | generic tool calls |
|
||||
| Qwen-Qwen2-VL-7B-Instruct.jinja | generic tool calls |
|
||||
| Qwen-Qwen2.5-7B-Instruct.jinja | hermes 2 pro tool calls |
|
||||
| Qwen-Qwen2.5-Math-7B-Instruct.jinja | hermes 2 pro tool calls |
|
||||
| TheBloke-FusionNet_34Bx2_MoE-AWQ.jinja | generic tool calls |
|
||||
| abacusai-Fewshot-Metamath-OrcaVicuna-Mistral.jinja | generic tool calls |
|
||||
| bofenghuang-vigogne-2-70b-chat.jinja | generic tool calls |
|
||||
| databricks-dbrx-instruct.jinja | generic tool calls |
|
||||
| deepseek-ai-DeepSeek-Coder-V2-Instruct.jinja | generic tool calls |
|
||||
| deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja | deepseek r1 tool calls |
|
||||
| deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja | deepseek r1 tool calls |
|
||||
| deepseek-ai-DeepSeek-R1-Distill-Qwen-7B.jinja | deepseek r1 tool calls |
|
||||
| deepseek-ai-DeepSeek-V2.5.jinja | deepseek r1 tool calls |
|
||||
| deepseek-ai-deepseek-coder-33b-instruct.jinja | generic tool calls |
|
||||
| google-gemma-2-2b-it.jinja | generic tool calls |
|
||||
| google-gemma-7b-it.jinja | generic tool calls |
|
||||
| indischepartij-MiniCPM-3B-OpenHermes-2.5-v2.jinja | generic tool calls |
|
||||
| mattshumer-Reflection-Llama-3.1-70B.jinja | generic tool calls |
|
||||
| meetkai-functionary-medium-v3.2.jinja | functionary v3.2 tool calls |
|
||||
| meta-llama-Llama-3.1-8B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) |
|
||||
| meta-llama-Llama-3.2-3B-Instruct.jinja | llama 3.x tool calls |
|
||||
| meta-llama-Llama-3.3-70B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) |
|
||||
| meta-llama-Meta-Llama-3.1-8B-Instruct.jinja | llama 3.x tool calls (w/ builtin tools) |
|
||||
| microsoft-Phi-3-medium-4k-instruct.jinja | generic tool calls |
|
||||
| microsoft-Phi-3-mini-4k-instruct.jinja | generic tool calls |
|
||||
| microsoft-Phi-3-small-8k-instruct.jinja | generic tool calls |
|
||||
| microsoft-Phi-3.5-mini-instruct.jinja | generic tool calls |
|
||||
| microsoft-Phi-3.5-vision-instruct.jinja | generic tool calls |
|
||||
| mistralai-Mistral-7B-Instruct-v0.2.jinja | generic tool calls |
|
||||
| mistralai-Mistral-Large-Instruct-2407.jinja | mistral nemo tool calls |
|
||||
| mistralai-Mistral-Large-Instruct-2411.jinja | generic tool calls |
|
||||
| mistralai-Mistral-Nemo-Instruct-2407.jinja | mistral nemo tool calls |
|
||||
| mistralai-Mixtral-8x7B-Instruct-v0.1.jinja | generic tool calls |
|
||||
| mlabonne-AlphaMonarch-7B.jinja | generic tool calls |
|
||||
| nvidia-Llama-3.1-Nemotron-70B-Instruct-HF.jinja | llama 3.x tool calls (w/ builtin tools) |
|
||||
| openchat-openchat-3.5-0106.jinja | generic tool calls |
|
||||
| teknium-OpenHermes-2.5-Mistral-7B.jinja | generic tool calls |
|
||||
|
||||
This table can be generated with:
|
||||
|
||||
```bash
|
||||
./build/bin/test-chat ../minja/build/tests/*.jinja 2>/dev/null
|
||||
|
||||
</details>
|
||||
|
||||
- Generic tool call is supported when the template isn't recognized by native format handlers (you'll see `Chat format: Generic` in the logs).
|
||||
- Use `--chat-template-file` to override the template when appropriate (see examples below)
|
||||
- Generic support may consume more tokens and be less efficient than a model's native format.
|
||||
|
||||
- Run with:
|
||||
|
||||
```shell
|
||||
# Native support:
|
||||
llama-server --jinja -fa -hf bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
|
||||
llama-server --jinja -fa -hf bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q6_K_L
|
||||
llama-server --jinja -fa -hf bartowski/functionary-small-v3.2-GGUF:Q4_K_M
|
||||
llama-server --jinja -fa -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
|
||||
|
||||
# Native support requires the right template for these GGUFs:
|
||||
|
||||
llama-server --jinja -fa -hf bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M \
|
||||
--chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-2-Pro-Llama-3-8B tool_use )
|
||||
|
||||
llama-server --jinja -fa -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M \
|
||||
--chat-template-file <( python scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use )
|
||||
|
||||
llama-server --jinja -fa -hf bartowski/firefunction-v2-GGUF -hff firefunction-v2-IQ1_M.gguf \
|
||||
--chat-template-file <( python scripts/get_chat_template.py fireworks-ai/llama-3-firefunction-v2 tool_use )
|
||||
|
||||
llama-server --jinja -fa -hf bartowski/c4ai-command-r7b-12-2024-GGUF:Q6_K_L \
|
||||
--chat-template-file <( python scripts/get_chat_template.py CohereForAI/c4ai-command-r7b-12-2024 tool_use )
|
||||
|
||||
# Generic format support
|
||||
llama-server --jinja -fa -hf bartowski/phi-4-GGUF:Q4_0
|
||||
llama-server --jinja -fa -hf bartowski/gemma-2-2b-it-GGUF:Q8_0
|
||||
llama-server --jinja -fa -hf bartowski/c4ai-command-r-v01-GGUF:Q2_K
|
||||
```
|
||||
|
||||
- Test in CLI:
|
||||
|
||||
```bash
|
||||
curl http://localhost:8080/v1/chat/completions -d '{
|
||||
"model": "gpt-3.5-turbo",
|
||||
"tools": [
|
||||
{
|
||||
"type":"function",
|
||||
"function":{
|
||||
"name":"get_current_weather",
|
||||
"description":"Get the current weather in a given location",
|
||||
"parameters":{
|
||||
"type":"object",
|
||||
"properties":{
|
||||
"location":{
|
||||
"type":"string",
|
||||
"description":"The city and state, e.g. San Francisco, CA"
|
||||
}
|
||||
},
|
||||
"required":["location"]
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the weather like in Istanbul?."
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Show output</summary>
|
||||
|
||||
```json
|
||||
{
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "tool",
|
||||
"index": 0,
|
||||
"message": {
|
||||
"content": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"name": "python",
|
||||
"arguments": "{\"code\":\" \\nprint(\\\"Hello, World!\\\")\"}"
|
||||
}
|
||||
],
|
||||
"role": "assistant"
|
||||
}
|
||||
}
|
||||
],
|
||||
"created": 1727287211,
|
||||
"model": "gpt-3.5-turbo",
|
||||
"object": "chat.completion",
|
||||
"usage": {
|
||||
"completion_tokens": 16,
|
||||
"prompt_tokens": 44,
|
||||
"total_tokens": 60
|
||||
},
|
||||
"id": "chatcmpl-Htbgh9feMmGM0LEH2hmQvwsCxq3c6Ni8"
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### POST `/v1/embeddings`: OpenAI-compatible embeddings API
|
||||
|
||||
This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm.
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -117,6 +117,7 @@ struct slot_params {
|
|||
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
|
||||
std::string oaicompat_model;
|
||||
std::string oaicompat_cmpl_id;
|
||||
common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
|
||||
json to_json() const {
|
||||
std::vector<std::string> samplers;
|
||||
|
|
@ -130,6 +131,11 @@ struct slot_params {
|
|||
lora.push_back({{"id", i}, {"scale", this->lora[i].scale}});
|
||||
}
|
||||
|
||||
std::vector<std::string> grammar_trigger_words;
|
||||
for (const auto & trigger : sampling.grammar_trigger_words) {
|
||||
grammar_trigger_words.push_back(trigger.word);
|
||||
}
|
||||
|
||||
return json {
|
||||
{"n_predict", n_predict}, // Server configured n_predict
|
||||
{"seed", sampling.seed},
|
||||
|
|
@ -164,6 +170,9 @@ struct slot_params {
|
|||
{"n_probs", sampling.n_probs},
|
||||
{"min_keep", sampling.min_keep},
|
||||
{"grammar", sampling.grammar},
|
||||
{"grammar_trigger_words", grammar_trigger_words},
|
||||
{"grammar_trigger_tokens", sampling.grammar_trigger_tokens},
|
||||
{"preserved_tokens", sampling.preserved_tokens},
|
||||
{"samplers", samplers},
|
||||
{"speculative.n_max", speculative.n_max},
|
||||
{"speculative.n_min", speculative.n_min},
|
||||
|
|
@ -325,12 +334,64 @@ struct server_task {
|
|||
if (data.contains("json_schema") && !data.contains("grammar")) {
|
||||
try {
|
||||
auto schema = json_value(data, "json_schema", json::object());
|
||||
LOG_DBG("JSON schema: %s\n", schema.dump(2).c_str());
|
||||
params.sampling.grammar = json_schema_to_grammar(schema);
|
||||
LOG_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
|
||||
} catch (const std::exception & e) {
|
||||
throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
|
||||
}
|
||||
} else {
|
||||
params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
|
||||
LOG_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
|
||||
params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
|
||||
LOG_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
|
||||
}
|
||||
|
||||
{
|
||||
auto it = data.find("chat_format");
|
||||
if (it != data.end()) {
|
||||
params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
|
||||
LOG_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
|
||||
} else {
|
||||
params.oaicompat_chat_format = defaults.oaicompat_chat_format;
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
const auto grammar_triggers = data.find("grammar_triggers");
|
||||
if (grammar_triggers != data.end()) {
|
||||
for (const auto & t : *grammar_triggers) {
|
||||
common_grammar_trigger trigger;
|
||||
trigger.word = t.at("word");
|
||||
trigger.at_start = t.at("at_start");
|
||||
|
||||
auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true);
|
||||
if (ids.size() == 1) {
|
||||
LOG_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
|
||||
params.sampling.grammar_trigger_tokens.push_back(ids[0]);
|
||||
params.sampling.preserved_tokens.insert(ids[0]);
|
||||
continue;
|
||||
}
|
||||
LOG_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
|
||||
params.sampling.grammar_trigger_words.push_back(trigger);
|
||||
}
|
||||
}
|
||||
const auto preserved_tokens = data.find("preserved_tokens");
|
||||
if (preserved_tokens != data.end()) {
|
||||
for (const auto & t : *preserved_tokens) {
|
||||
auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
|
||||
if (ids.size() == 1) {
|
||||
LOG_DBG("Preserved token: %d\n", ids[0]);
|
||||
params.sampling.preserved_tokens.insert(ids[0]);
|
||||
} else {
|
||||
// This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
|
||||
LOG_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get<std::string>().c_str());
|
||||
}
|
||||
}
|
||||
}
|
||||
if (params.sampling.grammar_lazy) {
|
||||
GGML_ASSERT(params.sampling.grammar_trigger_tokens.size() > 0 || params.sampling.grammar_trigger_words.size() > 0);
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
|
|
@ -382,22 +443,12 @@ struct server_task {
|
|||
}
|
||||
|
||||
{
|
||||
const auto & samplers = data.find("samplers");
|
||||
const auto samplers = data.find("samplers");
|
||||
if (samplers != data.end()) {
|
||||
if (samplers->is_array()) {
|
||||
std::vector<std::string> sampler_names;
|
||||
for (const auto & name : *samplers) {
|
||||
if (name.is_string()) {
|
||||
sampler_names.emplace_back(name);
|
||||
}
|
||||
}
|
||||
params.sampling.samplers = common_sampler_types_from_names(sampler_names, false);
|
||||
params.sampling.samplers = common_sampler_types_from_names(*samplers, false);
|
||||
} else if (samplers->is_string()){
|
||||
std::string sampler_string;
|
||||
for (const auto & name : *samplers) {
|
||||
sampler_string += name;
|
||||
}
|
||||
params.sampling.samplers = common_sampler_types_from_chars(sampler_string);
|
||||
params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
|
||||
}
|
||||
} else {
|
||||
params.sampling.samplers = defaults.sampling.samplers;
|
||||
|
|
@ -570,6 +621,7 @@ struct server_task_result_cmpl_final : server_task_result {
|
|||
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
|
||||
std::string oaicompat_model;
|
||||
std::string oaicompat_cmpl_id;
|
||||
common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
|
||||
virtual int get_index() override {
|
||||
return index;
|
||||
|
|
@ -663,18 +715,44 @@ struct server_task_result_cmpl_final : server_task_result {
|
|||
|
||||
json to_json_oaicompat_chat() {
|
||||
std::string finish_reason = "length";
|
||||
common_chat_msg msg;
|
||||
if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
|
||||
finish_reason = "stop";
|
||||
LOG_DBG("Parsing chat message: %s\n", content.c_str());
|
||||
msg = common_chat_parse(content, oaicompat_chat_format);
|
||||
finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
|
||||
} else {
|
||||
msg.content = content;
|
||||
}
|
||||
|
||||
json choice = json{
|
||||
json tool_calls;
|
||||
if (!msg.tool_calls.empty()) {
|
||||
tool_calls = json::array();
|
||||
for (const auto & tc : msg.tool_calls) {
|
||||
tool_calls.push_back({
|
||||
{"type", "function"},
|
||||
{"function", {
|
||||
{"name", tc.name},
|
||||
{"arguments", tc.arguments},
|
||||
}},
|
||||
{"id", tc.id},
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
json message {
|
||||
{"content", msg.content},
|
||||
{"tool_calls", tool_calls},
|
||||
{"role", "assistant"},
|
||||
};
|
||||
if (!msg.tool_plan.empty()) {
|
||||
message["tool_plan"] = msg.tool_plan;
|
||||
}
|
||||
|
||||
json choice {
|
||||
{"finish_reason", finish_reason},
|
||||
{"index", 0},
|
||||
{"message", json {
|
||||
{"content", content},
|
||||
{"role", "assistant"}
|
||||
}
|
||||
}};
|
||||
{"message", message},
|
||||
};
|
||||
|
||||
if (!stream && probs_output.size() > 0) {
|
||||
choice["logprobs"] = json{
|
||||
|
|
@ -716,7 +794,7 @@ struct server_task_result_cmpl_final : server_task_result {
|
|||
finish_reason = "stop";
|
||||
}
|
||||
|
||||
json choice = json{
|
||||
json choice = json {
|
||||
{"finish_reason", finish_reason},
|
||||
{"index", 0},
|
||||
{"delta", json::object()}
|
||||
|
|
@ -1191,6 +1269,8 @@ struct server_slot {
|
|||
|
||||
llama_token sampled;
|
||||
|
||||
common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
|
||||
// stats
|
||||
size_t n_sent_text = 0; // number of sent text character
|
||||
|
||||
|
|
@ -1804,7 +1884,12 @@ struct server_context {
|
|||
llama_init_dft.context.reset();
|
||||
}
|
||||
|
||||
if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) {
|
||||
LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
|
||||
chat_templates = common_chat_templates_from_model(model, "chatml");
|
||||
} else {
|
||||
chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
|
||||
}
|
||||
GGML_ASSERT(chat_templates.template_default.get() != nullptr);
|
||||
|
||||
return true;
|
||||
|
|
@ -1815,17 +1900,16 @@ struct server_context {
|
|||
|
||||
if (use_jinja) {
|
||||
auto templates = common_chat_templates_from_model(model, "");
|
||||
common_chat_inputs inputs;
|
||||
inputs.messages = json::array({{
|
||||
{"role", "user"},
|
||||
{"content", "test"},
|
||||
}});
|
||||
GGML_ASSERT(templates.template_default);
|
||||
try {
|
||||
templates.template_default->apply({{
|
||||
{"role", "user"},
|
||||
{"content", "test"},
|
||||
}}, json(), true);
|
||||
common_chat_params_init(*templates.template_default, inputs);
|
||||
if (templates.template_tool_use) {
|
||||
templates.template_tool_use->apply({{
|
||||
{"role", "user"},
|
||||
{"content", "test"},
|
||||
}}, json(), true);
|
||||
common_chat_params_init(*templates.template_tool_use, inputs);
|
||||
}
|
||||
return true;
|
||||
} catch (const std::exception & e) {
|
||||
|
|
@ -2275,11 +2359,11 @@ struct server_context {
|
|||
res->id_slot = slot.id;
|
||||
|
||||
res->index = slot.index;
|
||||
res->content = slot.generated_text;
|
||||
res->tokens = slot.generated_tokens;
|
||||
res->content = std::move(slot.generated_text);
|
||||
res->tokens = std::move(slot.generated_tokens);
|
||||
res->timings = slot.get_timings();
|
||||
res->prompt = common_detokenize(ctx, slot.prompt_tokens, true);
|
||||
res->response_fields = slot.params.response_fields;
|
||||
res->response_fields = std::move(slot.params.response_fields);
|
||||
|
||||
res->truncated = slot.truncated;
|
||||
res->n_decoded = slot.n_decoded;
|
||||
|
|
@ -2295,7 +2379,7 @@ struct server_context {
|
|||
res->oaicompat = slot.params.oaicompat;
|
||||
res->oaicompat_model = slot.params.oaicompat_model;
|
||||
res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
|
||||
|
||||
res->oaicompat_chat_format = slot.params.oaicompat_chat_format;
|
||||
// populate res.probs_output
|
||||
if (slot.params.sampling.n_probs > 0) {
|
||||
if (!slot.params.stream && slot.stop == STOP_TYPE_WORD) {
|
||||
|
|
@ -2773,6 +2857,10 @@ struct server_context {
|
|||
// track if given slot can be batched with slots already in the batch
|
||||
server_slot * slot_batched = nullptr;
|
||||
|
||||
auto accept_special_token = [&](server_slot & slot, llama_token token) {
|
||||
return params_base.special || slot.params.sampling.preserved_tokens.find(token) != slot.params.sampling.preserved_tokens.end();
|
||||
};
|
||||
|
||||
// frist, add sampled tokens from any ongoing sequences
|
||||
for (auto & slot : slots) {
|
||||
if (slot.state != SLOT_STATE_GENERATING) {
|
||||
|
|
@ -3136,7 +3224,7 @@ struct server_context {
|
|||
|
||||
completion_token_output result;
|
||||
result.tok = id;
|
||||
result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
|
||||
result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
|
||||
result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
|
||||
|
||||
if (slot.params.sampling.n_probs > 0) {
|
||||
|
|
@ -3225,7 +3313,7 @@ struct server_context {
|
|||
completion_token_output result;
|
||||
|
||||
result.tok = ids[i];
|
||||
result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special);
|
||||
result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
|
||||
result.prob = 1.0f; // set later
|
||||
|
||||
// TODO: set result.probs
|
||||
|
|
@ -3265,6 +3353,8 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp
|
|||
return;
|
||||
}
|
||||
|
||||
// reminder: this function is not covered by httplib's exception handler; if someone does more complicated stuff, think about wrapping it in try-catch
|
||||
|
||||
LOG_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
|
||||
|
||||
LOG_DBG("request: %s\n", req.body.c_str());
|
||||
|
|
@ -3351,9 +3441,13 @@ int main(int argc, char ** argv) {
|
|||
message = "Unknown Exception";
|
||||
}
|
||||
|
||||
try {
|
||||
json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
|
||||
LOG_WRN("got exception: %s\n", formatted_error.dump().c_str());
|
||||
res_error(res, formatted_error);
|
||||
} catch (const std::exception & e) {
|
||||
LOG_ERR("got another exception: %s | while hanlding exception: %s\n", e.what(), message.c_str());
|
||||
}
|
||||
});
|
||||
|
||||
svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
|
||||
|
|
@ -3575,11 +3669,11 @@ int main(int argc, char ** argv) {
|
|||
{"value", (uint64_t) res_metrics->kv_cache_tokens_count}
|
||||
},{
|
||||
{"name", "requests_processing"},
|
||||
{"help", "Number of request processing."},
|
||||
{"help", "Number of requests processing."},
|
||||
{"value", (uint64_t) res_metrics->n_processing_slots}
|
||||
},{
|
||||
{"name", "requests_deferred"},
|
||||
{"help", "Number of request deferred."},
|
||||
{"help", "Number of requests deferred."},
|
||||
{"value", (uint64_t) res_metrics->n_tasks_deferred}
|
||||
}}}
|
||||
};
|
||||
|
|
@ -3722,6 +3816,8 @@ int main(int argc, char ** argv) {
|
|||
{ "total_slots", ctx_server.params_base.n_parallel },
|
||||
{ "model_path", ctx_server.params_base.model },
|
||||
{ "chat_template", ctx_server.chat_templates.template_default->source() },
|
||||
{ "bos_token", ctx_server.chat_templates.template_default->bos_token() },
|
||||
{ "eos_token", ctx_server.chat_templates.template_default->eos_token() },
|
||||
{ "build_info", build_info },
|
||||
};
|
||||
if (ctx_server.params_base.use_jinja && ctx_server.chat_templates.template_tool_use) {
|
||||
|
|
@ -3763,7 +3859,9 @@ int main(int argc, char ** argv) {
|
|||
std::vector<server_task> tasks;
|
||||
|
||||
try {
|
||||
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, data.at("prompt"), true, true);
|
||||
const auto & prompt = data.at("prompt");
|
||||
LOG_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
|
||||
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, prompt, true, true);
|
||||
tasks.reserve(tokenized_prompts.size());
|
||||
for (size_t i = 0; i < tokenized_prompts.size(); i++) {
|
||||
server_task task = server_task(type);
|
||||
|
|
@ -3949,14 +4047,14 @@ int main(int argc, char ** argv) {
|
|||
};
|
||||
|
||||
const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
|
||||
LOG_DBG("request: %s\n", req.body.c_str());
|
||||
if (ctx_server.params_base.embedding) {
|
||||
res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
|
||||
return;
|
||||
}
|
||||
|
||||
auto body = json::parse(req.body);
|
||||
const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
|
||||
json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
|
||||
json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates);
|
||||
|
||||
return handle_completions_impl(
|
||||
SERVER_TASK_TYPE_COMPLETION,
|
||||
|
|
@ -3966,6 +4064,13 @@ int main(int argc, char ** argv) {
|
|||
OAICOMPAT_TYPE_CHAT);
|
||||
};
|
||||
|
||||
// same with handle_chat_completions, but without inference part
|
||||
const auto handle_apply_template = [&ctx_server, ¶ms, &res_ok](const httplib::Request & req, httplib::Response & res) {
|
||||
auto body = json::parse(req.body);
|
||||
json data = oaicompat_completion_params_parse(body, params.use_jinja, ctx_server.chat_templates);
|
||||
res_ok(res, {{ "prompt", std::move(data.at("prompt")) }});
|
||||
};
|
||||
|
||||
const auto handle_models = [¶ms, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
|
||||
json models = {
|
||||
{"object", "list"},
|
||||
|
|
@ -4124,14 +4229,6 @@ int main(int argc, char ** argv) {
|
|||
res_ok(res, root);
|
||||
};
|
||||
|
||||
const auto handle_apply_template = [&ctx_server, ¶ms, &res_ok](const httplib::Request & req, httplib::Response & res) {
|
||||
auto body = json::parse(req.body);
|
||||
const auto & chat_template = body.contains("tools") && ctx_server.chat_templates.template_tool_use ? *ctx_server.chat_templates.template_tool_use : *ctx_server.chat_templates.template_default;
|
||||
json data = oaicompat_completion_params_parse(body, chat_template, params.use_jinja);
|
||||
|
||||
res_ok(res, {{ "prompt", data.at("prompt") }});
|
||||
};
|
||||
|
||||
const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) {
|
||||
handle_embeddings_impl(req, res, OAICOMPAT_TYPE_NONE);
|
||||
};
|
||||
|
|
@ -4374,14 +4471,6 @@ int main(int argc, char ** argv) {
|
|||
|
||||
LOG_INF("%s: model loaded\n", __func__);
|
||||
|
||||
// if a custom chat template is not supplied, we will use the one that comes with the model (if any)
|
||||
if (params.chat_template.empty()) {
|
||||
if (!ctx_server.validate_builtin_chat_template(params.use_jinja)) {
|
||||
LOG_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
|
||||
params.chat_template = "chatml";
|
||||
}
|
||||
}
|
||||
|
||||
// print sample chat example to make it clear which template is used
|
||||
LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
|
||||
ctx_server.chat_templates.template_default->source().c_str(),
|
||||
|
|
|
|||
|
|
@ -31,8 +31,9 @@ It's possible to override some scenario steps values with environment variables:
|
|||
| `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/llama-server` |
|
||||
| `DEBUG` | to enable steps and server verbose mode `--verbose` |
|
||||
| `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` |
|
||||
| `LLAMA_CACHE` | by default server tests re-download models to the `tmp` subfolder. Set this to your cache (e.g. `$HOME/Library/Caches/llama.cpp` on Mac or `$HOME/.cache/llama.cpp` on Unix) to avoid this |
|
||||
|
||||
To run slow tests:
|
||||
To run slow tests (will download many models, make sure to set `LLAMA_CACHE` if needed):
|
||||
|
||||
```shell
|
||||
SLOW_TESTS=1 ./tests.sh
|
||||
|
|
@ -44,10 +45,16 @@ To run with stdout/stderr display in real time (verbose output, but useful for d
|
|||
DEBUG=1 ./tests.sh -s -v -x
|
||||
```
|
||||
|
||||
To run single test unit:
|
||||
To run all the tests in a file:
|
||||
|
||||
```shell
|
||||
./tests.sh unit/test_{name of test case here}.py -v -x
|
||||
./tests.sh unit/test_chat_completion.py.py -v -x
|
||||
```
|
||||
|
||||
To run a single test:
|
||||
|
||||
```shell
|
||||
./tests.sh unit/test_chat_completion.py::test_invalid_chat_completion_req
|
||||
```
|
||||
|
||||
Hint: You can compile and run test in single command, useful for local developement:
|
||||
|
|
|
|||
4
examples/server/tests/pytest.ini
Normal file
4
examples/server/tests/pytest.ini
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
[pytest]
|
||||
markers =
|
||||
slow: marks tests as slow (deselect with '-m "not slow"')
|
||||
serial
|
||||
|
|
@ -6,9 +6,18 @@ cd $SCRIPT_DIR
|
|||
|
||||
set -eu
|
||||
|
||||
if [[ "${SLOW_TESTS:-0}" == 1 ]]; then
|
||||
# Slow tests for tool calls need quite a few models ahead of time to avoid timing out.
|
||||
python $SCRIPT_DIR/../../../scripts/fetch_server_test_models.py
|
||||
fi
|
||||
|
||||
if [ $# -lt 1 ]
|
||||
then
|
||||
if [[ "${SLOW_TESTS:-0}" == 1 ]]; then
|
||||
pytest -v -x
|
||||
else
|
||||
pytest -v -x -m "not slow"
|
||||
fi
|
||||
else
|
||||
pytest "$@"
|
||||
fi
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import pytest
|
|||
from openai import OpenAI
|
||||
from utils import *
|
||||
|
||||
server = ServerPreset.tinyllama2()
|
||||
server: ServerProcess
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def create_server():
|
||||
|
|
@ -13,8 +13,11 @@ def create_server():
|
|||
@pytest.mark.parametrize(
|
||||
"model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason,jinja,chat_template",
|
||||
[
|
||||
(None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length", False, None),
|
||||
(None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length", True, None),
|
||||
(None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", False, None),
|
||||
(None, "Book", "Hey", 8, "But she couldn't", 69, 8, "length", True, None),
|
||||
(None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", False, None),
|
||||
(None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, None),
|
||||
(None, "Book", "What is the best book", 8, "(Suddenly)+|\\{ \" Sarax.", 77, 8, "length", True, 'chatml'),
|
||||
(None, "Book", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"),
|
||||
("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
|
||||
("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
|
||||
|
|
|
|||
418
examples/server/tests/unit/test_tool_call.py
Normal file
418
examples/server/tests/unit/test_tool_call.py
Normal file
|
|
@ -0,0 +1,418 @@
|
|||
import pytest
|
||||
from utils import *
|
||||
|
||||
server: ServerProcess
|
||||
|
||||
TIMEOUT_SERVER_START = 15*60
|
||||
TIMEOUT_HTTP_REQUEST = 60
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def create_server():
|
||||
global server
|
||||
server = ServerPreset.tinyllama2()
|
||||
server.model_alias = "tinyllama-2-tool-call"
|
||||
server.server_port = 8081
|
||||
|
||||
|
||||
TEST_TOOL = {
|
||||
"type":"function",
|
||||
"function": {
|
||||
"name": "test",
|
||||
"description": "",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"success": {"type": "boolean", "const": True},
|
||||
},
|
||||
"required": ["success"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
PYTHON_TOOL = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "python",
|
||||
"description": "Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"code": {
|
||||
"type": "string",
|
||||
"description": "The code to run in the ipython interpreter."
|
||||
}
|
||||
},
|
||||
"required": ["code"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
WEATHER_TOOL = {
|
||||
"type":"function",
|
||||
"function":{
|
||||
"name":"get_current_weather",
|
||||
"description":"Get the current weather in a given location",
|
||||
"parameters":{
|
||||
"type":"object",
|
||||
"properties":{
|
||||
"location":{
|
||||
"type":"string",
|
||||
"description":"The city and country/state, e.g. 'San Francisco, CA', or 'Paris, France'"
|
||||
}
|
||||
},
|
||||
"required":["location"]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def do_test_completion_with_required_tool_tiny(template_name: str, tool: dict, argument_key: str | None):
|
||||
global server
|
||||
n_predict = 512
|
||||
# server = ServerPreset.stories15m_moe()
|
||||
server.jinja = True
|
||||
server.n_predict = n_predict
|
||||
server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
|
||||
server.start(timeout_seconds=TIMEOUT_SERVER_START)
|
||||
res = server.make_request("POST", "/chat/completions", data={
|
||||
"max_tokens": n_predict,
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a coding assistant."},
|
||||
{"role": "user", "content": "Write an example"},
|
||||
],
|
||||
"tool_choice": "required",
|
||||
"tools": [tool],
|
||||
"parallel_tool_calls": False,
|
||||
"temperature": 0.0,
|
||||
"top_k": 1,
|
||||
"top_p": 1.0,
|
||||
})
|
||||
assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
|
||||
choice = res.body["choices"][0]
|
||||
tool_calls = choice["message"].get("tool_calls")
|
||||
assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
|
||||
tool_call = tool_calls[0]
|
||||
expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"]
|
||||
assert expected_function_name == tool_call["function"]["name"]
|
||||
actual_arguments = tool_call["function"]["arguments"]
|
||||
assert isinstance(actual_arguments, str)
|
||||
if argument_key is not None:
|
||||
actual_arguments = json.loads(actual_arguments)
|
||||
assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("template_name,tool,argument_key", [
|
||||
("google-gemma-2-2b-it", TEST_TOOL, "success"),
|
||||
("meta-llama-Llama-3.3-70B-Instruct", TEST_TOOL, "success"),
|
||||
("meta-llama-Llama-3.3-70B-Instruct", PYTHON_TOOL, "code"),
|
||||
])
|
||||
def test_completion_with_required_tool_tiny_fast(template_name: str, tool: dict, argument_key: str | None):
|
||||
do_test_completion_with_required_tool_tiny(template_name, tool, argument_key)
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.parametrize("template_name,tool,argument_key", [
|
||||
("meta-llama-Llama-3.1-8B-Instruct", TEST_TOOL, "success"),
|
||||
("meta-llama-Llama-3.1-8B-Instruct", PYTHON_TOOL, "code"),
|
||||
("meetkai-functionary-medium-v3.1", TEST_TOOL, "success"),
|
||||
("meetkai-functionary-medium-v3.1", PYTHON_TOOL, "code"),
|
||||
("meetkai-functionary-medium-v3.2", TEST_TOOL, "success"),
|
||||
("meetkai-functionary-medium-v3.2", PYTHON_TOOL, "code"),
|
||||
("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", TEST_TOOL, "success"),
|
||||
("NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use", PYTHON_TOOL, "code"),
|
||||
("meta-llama-Llama-3.2-3B-Instruct", TEST_TOOL, "success"),
|
||||
("meta-llama-Llama-3.2-3B-Instruct", PYTHON_TOOL, "code"),
|
||||
("mistralai-Mistral-Nemo-Instruct-2407", TEST_TOOL, "success"),
|
||||
("mistralai-Mistral-Nemo-Instruct-2407", PYTHON_TOOL, "code"),
|
||||
("NousResearch-Hermes-3-Llama-3.1-8B-tool_use", TEST_TOOL, "success"),
|
||||
("NousResearch-Hermes-3-Llama-3.1-8B-tool_use", PYTHON_TOOL, "code"),
|
||||
("deepseek-ai-DeepSeek-R1-Distill-Llama-8B", TEST_TOOL, "success"),
|
||||
("deepseek-ai-DeepSeek-R1-Distill-Llama-8B", PYTHON_TOOL, "code"),
|
||||
("fireworks-ai-llama-3-firefunction-v2", TEST_TOOL, "success"),
|
||||
("fireworks-ai-llama-3-firefunction-v2", PYTHON_TOOL, "code"),
|
||||
])
|
||||
def test_completion_with_required_tool_tiny_slow(template_name: str, tool: dict, argument_key: str | None):
|
||||
do_test_completion_with_required_tool_tiny(template_name, tool, argument_key)
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.parametrize("tool,argument_key,hf_repo,template_override", [
|
||||
(TEST_TOOL, "success", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
# Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it.
|
||||
(TEST_TOOL, "success", "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
|
||||
(PYTHON_TOOL, "code", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
|
||||
(PYTHON_TOOL, "code", "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
|
||||
(PYTHON_TOOL, "code", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
|
||||
(PYTHON_TOOL, "code", "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
|
||||
(PYTHON_TOOL, "code", "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", ("meetkai/functionary-medium-v3.2", None)),
|
||||
(PYTHON_TOOL, "code", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", ("meetkai/functionary-medium-v3.2", None)),
|
||||
(PYTHON_TOOL, "code", "bartowski/functionary-small-v3.2-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
|
||||
(PYTHON_TOOL, "code", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
|
||||
(PYTHON_TOOL, "code", "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(TEST_TOOL, "success", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
|
||||
(PYTHON_TOOL, "code", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
|
||||
(PYTHON_TOOL, "code", "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
# TODO: fix these
|
||||
# (TEST_TOOL, "success", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
|
||||
# (PYTHON_TOOL, "code", "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
|
||||
])
|
||||
def test_completion_with_required_tool_real_model(tool: dict, argument_key: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
|
||||
global server
|
||||
n_predict = 512
|
||||
server.n_slots = 1
|
||||
server.jinja = True
|
||||
server.n_ctx = 8192
|
||||
server.n_predict = n_predict
|
||||
server.model_hf_repo = hf_repo
|
||||
server.model_hf_file = None
|
||||
if isinstance(template_override, tuple):
|
||||
(template_hf_repo, template_variant) = template_override
|
||||
server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
|
||||
assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
|
||||
elif isinstance(template_override, str):
|
||||
server.chat_template = template_override
|
||||
server.start(timeout_seconds=TIMEOUT_SERVER_START)
|
||||
res = server.make_request("POST", "/chat/completions", data={
|
||||
"max_tokens": n_predict,
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a coding assistant."},
|
||||
{"role": "user", "content": "Write an example"},
|
||||
],
|
||||
"tool_choice": "required",
|
||||
"tools": [tool],
|
||||
"parallel_tool_calls": False,
|
||||
"temperature": 0.0,
|
||||
"top_k": 1,
|
||||
"top_p": 1.0,
|
||||
}, timeout=TIMEOUT_HTTP_REQUEST)
|
||||
assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
|
||||
choice = res.body["choices"][0]
|
||||
tool_calls = choice["message"].get("tool_calls")
|
||||
assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
|
||||
tool_call = tool_calls[0]
|
||||
expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"]
|
||||
assert expected_function_name == tool_call["function"]["name"]
|
||||
actual_arguments = tool_call["function"]["arguments"]
|
||||
assert isinstance(actual_arguments, str)
|
||||
if argument_key is not None:
|
||||
actual_arguments = json.loads(actual_arguments)
|
||||
assert argument_key in actual_arguments, f"tool arguments: {json.dumps(actual_arguments)}, expected: {argument_key}"
|
||||
|
||||
|
||||
def do_test_completion_without_tool_call(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
|
||||
global server
|
||||
server.jinja = True
|
||||
server.n_predict = n_predict
|
||||
server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
|
||||
server.start(timeout_seconds=TIMEOUT_SERVER_START)
|
||||
res = server.make_request("POST", "/chat/completions", data={
|
||||
"max_tokens": n_predict,
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a coding assistant."},
|
||||
{"role": "user", "content": "say hello world with python"},
|
||||
],
|
||||
"tools": tools if tools else None,
|
||||
"tool_choice": tool_choice,
|
||||
"temperature": 0.0,
|
||||
"top_k": 1,
|
||||
"top_p": 1.0,
|
||||
}, timeout=TIMEOUT_HTTP_REQUEST)
|
||||
assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
|
||||
choice = res.body["choices"][0]
|
||||
assert choice["message"].get("tool_calls") is None, f'Expected no tool call in {choice["message"]}'
|
||||
|
||||
|
||||
@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [
|
||||
("meta-llama-Llama-3.3-70B-Instruct", 128, [], None),
|
||||
("meta-llama-Llama-3.3-70B-Instruct", 128, [TEST_TOOL], None),
|
||||
("meta-llama-Llama-3.3-70B-Instruct", 128, [PYTHON_TOOL], 'none'),
|
||||
])
|
||||
def test_completion_without_tool_call_fast(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
|
||||
do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice)
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.parametrize("template_name,n_predict,tools,tool_choice", [
|
||||
("meetkai-functionary-medium-v3.2", 256, [], None),
|
||||
("meetkai-functionary-medium-v3.2", 256, [TEST_TOOL], None),
|
||||
("meetkai-functionary-medium-v3.2", 256, [PYTHON_TOOL], 'none'),
|
||||
("meetkai-functionary-medium-v3.1", 256, [], None),
|
||||
("meetkai-functionary-medium-v3.1", 256, [TEST_TOOL], None),
|
||||
("meetkai-functionary-medium-v3.1", 256, [PYTHON_TOOL], 'none'),
|
||||
("meta-llama-Llama-3.2-3B-Instruct", 256, [], None),
|
||||
("meta-llama-Llama-3.2-3B-Instruct", 256, [TEST_TOOL], None),
|
||||
("meta-llama-Llama-3.2-3B-Instruct", 256, [PYTHON_TOOL], 'none'),
|
||||
])
|
||||
def test_completion_without_tool_call_slow(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
|
||||
do_test_completion_without_tool_call(template_name, n_predict, tools, tool_choice)
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.parametrize("hf_repo,template_override", [
|
||||
("bartowski/c4ai-command-r7b-12-2024-GGUF:Q4_K_M", ("CohereForAI/c4ai-command-r7b-12-2024", "tool_use")),
|
||||
("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
|
||||
("bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None),
|
||||
("bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None),
|
||||
("bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
|
||||
("bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-3-Llama-3.1-8B", "tool_use")),
|
||||
("bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
|
||||
("bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
("bartowski/functionary-small-v3.2-GGUF:Q8_0", ("meetkai/functionary-medium-v3.2", None)),
|
||||
("bartowski/functionary-small-v3.2-GGUF:Q8_0", "chatml"),
|
||||
|
||||
("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
|
||||
("bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
# Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it.
|
||||
("bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None),
|
||||
|
||||
# ("bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama/Llama-3.2-3B-Instruct", None)),
|
||||
# ("bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
|
||||
])
|
||||
def test_weather(hf_repo: str, template_override: Tuple[str, str | None] | None):
|
||||
global server
|
||||
n_predict = 512
|
||||
server.n_slots = 1
|
||||
server.jinja = True
|
||||
server.n_ctx = 8192
|
||||
server.n_predict = n_predict
|
||||
server.model_hf_repo = hf_repo
|
||||
server.model_hf_file = None
|
||||
if isinstance(template_override, tuple):
|
||||
(template_hf_repo, template_variant) = template_override
|
||||
server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
|
||||
assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
|
||||
elif isinstance(template_override, str):
|
||||
server.chat_template = template_override
|
||||
server.start(timeout_seconds=TIMEOUT_SERVER_START)
|
||||
res = server.make_request("POST", "/chat/completions", data={
|
||||
"max_tokens": n_predict,
|
||||
"messages": [
|
||||
{"role": "user", "content": "What is the weather in Istanbul?"},
|
||||
],
|
||||
"tools": [WEATHER_TOOL],
|
||||
}, timeout=TIMEOUT_HTTP_REQUEST)
|
||||
assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
|
||||
choice = res.body["choices"][0]
|
||||
tool_calls = choice["message"].get("tool_calls")
|
||||
assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
|
||||
tool_call = tool_calls[0]
|
||||
assert tool_call["function"]["name"] == WEATHER_TOOL["function"]["name"]
|
||||
actual_arguments = json.loads(tool_call["function"]["arguments"])
|
||||
assert 'location' in actual_arguments, f"location not found in {json.dumps(actual_arguments)}"
|
||||
location = actual_arguments["location"]
|
||||
assert isinstance(location, str), f"Expected location to be a string, got {type(location)}: {json.dumps(location)}"
|
||||
assert re.match('^Istanbul(, (TR|Turkey|Türkiye))?$', location), f'Expected Istanbul for location, got {location}'
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.parametrize("expected_arguments_override,hf_repo,template_override", [
|
||||
(None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", None),
|
||||
(None, "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(None, "bartowski/functionary-small-v3.2-GGUF:Q8_0", ("meetkai-functionary-medium-v3.2", None)),
|
||||
(None, "bartowski/functionary-small-v3.2-GGUF:Q8_0", "chatml"),
|
||||
|
||||
(None, "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", None),
|
||||
('{"code":"print("}', "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
('{"code":"print("}', "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", ("meta-llama-Llama-3.2-3B-Instruct", None)),
|
||||
(None, "bartowski/Llama-3.2-1B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
('{"code":"print("}', "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", ("meta-llama-Llama-3.2-3B-Instruct", None)),
|
||||
('{"code":"print("}', "bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(None, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", None),
|
||||
(None, "bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(None, "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", ("NousResearch/Hermes-2-Pro-Llama-3-8B", "tool_use")),
|
||||
(None, "bartowski/Hermes-2-Pro-Llama-3-8B-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(None, "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", ("NousResearch-Hermes-3-Llama-3.1-8B", "tool_use")),
|
||||
(None, "bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
(None, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", None),
|
||||
(None, "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M", "chatml"),
|
||||
|
||||
# Note: gemma-2-2b-it knows itself as "model", not "assistant", so we don't test the ill-suited chatml on it.
|
||||
(None, "bartowski/gemma-2-2b-it-GGUF:Q4_K_M", None),
|
||||
|
||||
# (None, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
|
||||
])
|
||||
def test_hello_world_tool_call(expected_arguments_override: str | None, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
|
||||
global server
|
||||
server.n_slots = 1
|
||||
server.jinja = True
|
||||
server.n_ctx = 8192
|
||||
server.n_predict = 128
|
||||
server.model_hf_repo = hf_repo
|
||||
server.model_hf_file = None
|
||||
if isinstance(template_override, tuple):
|
||||
(template_hf_repo, template_variant) = template_override
|
||||
server.chat_template_file = f"../../../models/templates/{template_hf_repo.replace('/', '-') + ('-' + template_variant if template_variant else '')}.jinja"
|
||||
assert os.path.exists(server.chat_template_file), f"Template file {server.chat_template_file} does not exist. Run `python scripts/get_chat_template.py {template_hf_repo} {template_variant} > {server.chat_template_file}` to download the template."
|
||||
elif isinstance(template_override, str):
|
||||
server.chat_template = template_override
|
||||
server.start(timeout_seconds=TIMEOUT_SERVER_START)
|
||||
res = server.make_request("POST", "/chat/completions", data={
|
||||
"max_tokens": 256,
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a coding assistant."},
|
||||
{"role": "user", "content": "say hello world with python"},
|
||||
],
|
||||
"tools": [PYTHON_TOOL],
|
||||
# Note: without these greedy params, Functionary v3.2 writes `def hello_world():\n print("Hello, World!")\nhello_world()` which is correct but a pain to test.
|
||||
"temperature": 0.0,
|
||||
"top_k": 1,
|
||||
"top_p": 1.0,
|
||||
}, timeout=TIMEOUT_HTTP_REQUEST)
|
||||
assert res.status_code == 200, f"Expected status code 200, got {res.status_code}"
|
||||
choice = res.body["choices"][0]
|
||||
tool_calls = choice["message"].get("tool_calls")
|
||||
assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
|
||||
tool_call = tool_calls[0]
|
||||
assert tool_call["function"]["name"] == PYTHON_TOOL["function"]["name"]
|
||||
actual_arguments = tool_call["function"]["arguments"]
|
||||
if expected_arguments_override is not None:
|
||||
assert actual_arguments == expected_arguments_override
|
||||
else:
|
||||
actual_arguments = json.loads(actual_arguments)
|
||||
assert 'code' in actual_arguments, f"code not found in {json.dumps(actual_arguments)}"
|
||||
code = actual_arguments["code"]
|
||||
assert isinstance(code, str), f"Expected code to be a string, got {type(code)}: {json.dumps(code)}"
|
||||
assert re.match(r'''print\(("[Hh]ello,? [Ww]orld!?"|'[Hh]ello,? [Ww]orld!?')\)''', code), f'Expected hello world, got {code}'
|
||||
|
|
@ -26,7 +26,7 @@ from re import RegexFlag
|
|||
import wget
|
||||
|
||||
|
||||
DEFAULT_HTTP_TIMEOUT = 10 if "LLAMA_SANITIZE" not in os.environ else 30
|
||||
DEFAULT_HTTP_TIMEOUT = 12 if "LLAMA_SANITIZE" not in os.environ else 30
|
||||
|
||||
|
||||
class ServerResponse:
|
||||
|
|
@ -41,7 +41,7 @@ class ServerProcess:
|
|||
server_port: int = 8080
|
||||
server_host: str = "127.0.0.1"
|
||||
model_hf_repo: str = "ggml-org/models"
|
||||
model_hf_file: str = "tinyllamas/stories260K.gguf"
|
||||
model_hf_file: str | None = "tinyllamas/stories260K.gguf"
|
||||
model_alias: str = "tinyllama-2"
|
||||
temperature: float = 0.8
|
||||
seed: int = 42
|
||||
|
|
@ -191,7 +191,7 @@ class ServerProcess:
|
|||
creationflags=flags,
|
||||
stdout=sys.stdout,
|
||||
stderr=sys.stdout,
|
||||
env={**os.environ, "LLAMA_CACHE": "tmp"},
|
||||
env={**os.environ, "LLAMA_CACHE": "tmp"} if "LLAMA_CACHE" not in os.environ else None,
|
||||
)
|
||||
server_instances.add(self)
|
||||
|
||||
|
|
|
|||
|
|
@ -5,10 +5,6 @@
|
|||
#include "llama.h"
|
||||
#include "common/base64.hpp"
|
||||
|
||||
#ifndef NDEBUG
|
||||
// crash the server in debug mode, otherwise send an http 500 error
|
||||
#define CPPHTTPLIB_NO_EXCEPTIONS 1
|
||||
#endif
|
||||
// increase max payload length to allow use of larger context size
|
||||
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
|
||||
#include "httplib.h"
|
||||
|
|
@ -17,6 +13,7 @@
|
|||
#define JSON_ASSERT GGML_ASSERT
|
||||
#include "json.hpp"
|
||||
#include "minja.hpp"
|
||||
#include "chat.hpp"
|
||||
#include "chat-template.hpp"
|
||||
|
||||
#include <random>
|
||||
|
|
@ -376,7 +373,7 @@ inline std::string format_chat(const common_chat_template & tmpl, const std::vec
|
|||
throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
|
||||
}
|
||||
|
||||
chat.push_back({role, content});
|
||||
chat.push_back({role, content, /* tool_calls= */ {}});
|
||||
}
|
||||
|
||||
const auto formatted_chat = common_chat_apply_template(tmpl, chat, true, /* use_jinja= */ false);
|
||||
|
|
@ -580,21 +577,30 @@ static json oaicompat_completion_params_parse(const json & body) {
|
|||
|
||||
static json oaicompat_completion_params_parse(
|
||||
const json & body, /* openai api json semantics */
|
||||
const common_chat_template & tmpl,
|
||||
bool use_jinja)
|
||||
bool use_jinja,
|
||||
const common_chat_templates & chat_templates)
|
||||
{
|
||||
json llama_params;
|
||||
const auto & tmpl = body.contains("tools") && chat_templates.template_tool_use
|
||||
? *chat_templates.template_tool_use
|
||||
: *chat_templates.template_default;
|
||||
|
||||
auto tools = json_value(body, "tools", json());
|
||||
auto has_tools = tools.is_array() && !tools.empty();
|
||||
auto stream = json_value(body, "stream", false);
|
||||
|
||||
if (has_tools) {
|
||||
if (use_jinja) {
|
||||
LOG_WRN("tools param is not fully supported yet\n");
|
||||
} else {
|
||||
if (tools.is_array() && !tools.empty()) {
|
||||
if (stream) {
|
||||
throw std::runtime_error("Cannot use tools with stream");
|
||||
}
|
||||
if (!use_jinja) {
|
||||
throw std::runtime_error("tools param requires --jinja flag");
|
||||
}
|
||||
}
|
||||
if (!use_jinja) {
|
||||
if (body.contains("tool_choice") && !body.at("tool_choice").is_null()) {
|
||||
throw std::runtime_error("Unsupported param: tool_choice");
|
||||
}
|
||||
}
|
||||
|
||||
// Handle "stop" field
|
||||
if (body.contains("stop") && body.at("stop").is_string()) {
|
||||
|
|
@ -619,7 +625,43 @@ static json oaicompat_completion_params_parse(
|
|||
|
||||
// Apply chat template to the list of messages
|
||||
if (use_jinja) {
|
||||
llama_params["prompt"] = tmpl.apply(body.at("messages"), tools, /* add_generation_prompt= */ true);
|
||||
auto tool_choice = json_value(body, "tool_choice", std::string("auto"));
|
||||
if (tool_choice != "none" && tool_choice != "auto" && tool_choice != "required") {
|
||||
throw std::runtime_error("Invalid tool_choice: " + tool_choice);
|
||||
}
|
||||
if (tool_choice != "none" && llama_params.contains("grammar")) {
|
||||
throw std::runtime_error("Cannot use custom grammar constraints with tools.");
|
||||
}
|
||||
common_chat_inputs inputs;
|
||||
inputs.messages = body.at("messages");
|
||||
inputs.tools = tools;
|
||||
inputs.tool_choice = tool_choice;
|
||||
inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false);
|
||||
if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) {
|
||||
LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n");
|
||||
inputs.parallel_tool_calls = false;
|
||||
}
|
||||
inputs.stream = stream;
|
||||
// TODO: support mixing schema w/ tools beyond generic format.
|
||||
inputs.json_schema = json_value(llama_params, "json_schema", json());
|
||||
auto chat_params = common_chat_params_init(tmpl, inputs);
|
||||
|
||||
llama_params["chat_format"] = static_cast<int>(chat_params.format);
|
||||
llama_params["prompt"] = chat_params.prompt;
|
||||
llama_params["grammar"] = chat_params.grammar;
|
||||
llama_params["grammar_lazy"] = chat_params.grammar_lazy;
|
||||
auto grammar_triggers = json::array();
|
||||
for (const auto & trigger : chat_params.grammar_triggers) {
|
||||
grammar_triggers.push_back({
|
||||
{"word", trigger.word},
|
||||
{"at_start", trigger.at_start},
|
||||
});
|
||||
}
|
||||
llama_params["grammar_triggers"] = grammar_triggers;
|
||||
llama_params["preserved_tokens"] = chat_params.preserved_tokens;
|
||||
for (const auto & stop : chat_params.additional_stops) {
|
||||
llama_params["stop"].push_back(stop);
|
||||
}
|
||||
} else {
|
||||
llama_params["prompt"] = format_chat(tmpl, body.at("messages"));
|
||||
}
|
||||
|
|
@ -638,14 +680,6 @@ static json oaicompat_completion_params_parse(
|
|||
throw std::runtime_error("top_logprobs requires logprobs to be set to true");
|
||||
}
|
||||
|
||||
// Params supported by OAI but unsupported by llama.cpp
|
||||
static const std::vector<std::string> unsupported_params { "tool_choice" };
|
||||
for (const auto & param : unsupported_params) {
|
||||
if (body.contains(param)) {
|
||||
throw std::runtime_error("Unsupported param: " + param);
|
||||
}
|
||||
}
|
||||
|
||||
// Copy remaining properties to llama_params
|
||||
// This allows user to use llama.cpp-specific params like "mirostat", ... via OAI endpoint.
|
||||
// See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
|
||||
|
|
|
|||
|
|
@ -154,8 +154,6 @@
|
|||
placeholder="Type a message (Shift+Enter to add a new line)"
|
||||
v-model="inputMsg"
|
||||
@keydown.enter.exact.prevent="sendMessage"
|
||||
@keydown.enter.shift.exact.prevent="inputMsg += '\n'"
|
||||
:disabled="isGenerating"
|
||||
id="msg-input"
|
||||
dir="auto"
|
||||
></textarea>
|
||||
|
|
|
|||
|
|
@ -468,7 +468,10 @@ const mainApp = createApp({
|
|||
URL.revokeObjectURL(url);
|
||||
},
|
||||
async sendMessage() {
|
||||
if (!this.inputMsg) return;
|
||||
// prevent sending empty message
|
||||
// also allow typing the message while generating, but does not allow sending it (to match UX/UI behavior of other chat apps)
|
||||
if (!this.inputMsg || this.isGenerating) return;
|
||||
|
||||
const currConvId = this.viewingConvId;
|
||||
|
||||
StorageUtils.appendMsg(currConvId, {
|
||||
|
|
|
|||
|
|
@ -274,22 +274,25 @@ endif()
|
|||
|
||||
# Generate version info based on git commit.
|
||||
|
||||
find_program(GIT_EXE NAMES git git.exe REQUIRED NO_CMAKE_FIND_ROOT_PATH)
|
||||
execute_process(COMMAND ${GIT_EXE} rev-list --count HEAD
|
||||
if(NOT DEFINED GGML_BUILD_NUMBER)
|
||||
find_program(GIT_EXE NAMES git git.exe REQUIRED NO_CMAKE_FIND_ROOT_PATH)
|
||||
execute_process(COMMAND ${GIT_EXE} rev-list --count HEAD
|
||||
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
|
||||
OUTPUT_VARIABLE GGML_BUILD_NUMBER
|
||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
||||
)
|
||||
)
|
||||
|
||||
if(GGML_BUILD_NUMBER EQUAL 1)
|
||||
if(GGML_BUILD_NUMBER EQUAL 1)
|
||||
message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
|
||||
execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
|
||||
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
|
||||
OUTPUT_VARIABLE GGML_BUILD_COMMIT
|
||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
||||
)
|
||||
)
|
||||
endif()
|
||||
|
||||
|
||||
# Capture variables prefixed with GGML_.
|
||||
|
||||
|
|
|
|||
|
|
@ -1775,7 +1775,7 @@ extern "C" {
|
|||
struct ggml_tensor * a,
|
||||
int k);
|
||||
|
||||
#define GGML_KQ_MASK_PAD 32
|
||||
#define GGML_KQ_MASK_PAD 64
|
||||
|
||||
// q: [n_embd, n_batch, n_head, 1]
|
||||
// k: [n_embd, n_kv, n_head_kv, 1]
|
||||
|
|
|
|||
|
|
@ -93,12 +93,18 @@ endif()
|
|||
|
||||
if (GGML_CCACHE)
|
||||
find_program(GGML_CCACHE_FOUND ccache)
|
||||
find_program(GGML_SCCACHE_FOUND sccache)
|
||||
|
||||
if (GGML_CCACHE_FOUND)
|
||||
if (GGML_CCACHE_FOUND OR GGML_SCCACHE_FOUND)
|
||||
if(GGML_CCACHE_FOUND)
|
||||
set(GGML_CCACHE_VARIANT ccache)
|
||||
else()
|
||||
set(GGML_CCACHE_VARIANT sccache)
|
||||
endif()
|
||||
# TODO: should not be set globally
|
||||
set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache)
|
||||
set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE "${GGML_CCACHE_VARIANT}")
|
||||
set(ENV{CCACHE_SLOPPINESS} time_macros)
|
||||
message(STATUS "ccache found, compilation results will be cached. Disable with GGML_CCACHE=OFF.")
|
||||
message(STATUS "${GGML_CCACHE_VARIANT} found, compilation results will be cached. Disable with GGML_CCACHE=OFF.")
|
||||
else()
|
||||
message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF")
|
||||
endif ()
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ if (CUDAToolkit_FOUND)
|
|||
list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h")
|
||||
|
||||
file(GLOB GGML_SOURCES_CUDA "*.cu")
|
||||
file(GLOB SRCS "template-instances/fattn-wmma*.cu")
|
||||
file(GLOB SRCS "template-instances/fattn-mma*.cu")
|
||||
list(APPEND GGML_SOURCES_CUDA ${SRCS})
|
||||
file(GLOB SRCS "template-instances/mmq*.cu")
|
||||
list(APPEND GGML_SOURCES_CUDA ${SRCS})
|
||||
|
|
|
|||
|
|
@ -61,6 +61,13 @@
|
|||
#define GGML_CUDA_CC_RDNA2 (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
|
||||
#define GGML_CUDA_CC_RDNA3 (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
|
||||
|
||||
#define GGML_CUDA_CC_IS_RDNA(cc) (cc >= GGML_CUDA_CC_RDNA1)
|
||||
#define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
|
||||
#define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
|
||||
#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3)
|
||||
#define GGML_CUDA_CC_IS_GCN(cc) (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA)
|
||||
#define GGML_CUDA_CC_IS_CDNA(cc) (cc >= GGML_CUDA_CC_CDNA && cc < GGML_CUDA_CC_RDNA1)
|
||||
|
||||
#define GGML_CUDA_CC_QY1 210
|
||||
#define GGML_CUDA_CC_QY2 220
|
||||
|
||||
|
|
@ -148,7 +155,7 @@ typedef float2 dfloat2;
|
|||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
|
||||
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
|
||||
#define INT8_MMA_AVAILABLE
|
||||
#define NEW_MMA_AVAILABLE
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
|
||||
|
||||
#if !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ <= GGML_CUDA_CC_QY1)
|
||||
|
|
@ -159,14 +166,24 @@ static constexpr bool fast_fp16_available(const int cc) {
|
|||
return cc >= GGML_CUDA_CC_PASCAL && cc != 610;
|
||||
}
|
||||
|
||||
// Any FP16 tensor cores are available.
|
||||
static constexpr bool fp16_mma_available(const int cc) {
|
||||
return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_VOLTA;
|
||||
}
|
||||
|
||||
static constexpr bool int8_mma_available(const int cc) {
|
||||
// Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
|
||||
static constexpr bool new_mma_available(const int cc) {
|
||||
return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_TURING;
|
||||
}
|
||||
|
||||
static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
|
||||
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
|
||||
return __AMDGCN_WAVEFRONT_SIZE;
|
||||
#else
|
||||
return 32;
|
||||
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
|
||||
}
|
||||
|
||||
[[noreturn]]
|
||||
static __device__ void no_device_code(
|
||||
const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
|
||||
|
|
@ -190,53 +207,46 @@ static __device__ void no_device_code(
|
|||
#define NO_DEVICE_CODE //GGML_ABORT("NO_DEVICE_CODE not valid in host code.")
|
||||
#endif // __CUDA_ARCH__
|
||||
|
||||
template<int width = WARP_SIZE>
|
||||
static __device__ __forceinline__ int warp_reduce_sum(int x) {
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
return __reduce_add_sync(0xffffffff, x);
|
||||
#else
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 0; offset >>= 1) {
|
||||
x += __shfl_xor_sync(0xffffffff, x, offset, 32);
|
||||
for (int offset = width/2; offset > 0; offset >>= 1) {
|
||||
x += __shfl_xor_sync(0xffffffff, x, offset, width);
|
||||
}
|
||||
return x;
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
}
|
||||
|
||||
template<int width = WARP_SIZE>
|
||||
static __device__ __forceinline__ float warp_reduce_sum(float x) {
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 0; offset >>= 1) {
|
||||
x += __shfl_xor_sync(0xffffffff, x, offset, 32);
|
||||
for (int offset = width/2; offset > 0; offset >>= 1) {
|
||||
x += __shfl_xor_sync(0xffffffff, x, offset, width);
|
||||
}
|
||||
return x;
|
||||
}
|
||||
|
||||
template<int width = WARP_SIZE>
|
||||
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 0; offset >>= 1) {
|
||||
a.x += __shfl_xor_sync(0xffffffff, a.x, offset, 32);
|
||||
a.y += __shfl_xor_sync(0xffffffff, a.y, offset, 32);
|
||||
for (int offset = width/2; offset > 0; offset >>= 1) {
|
||||
a.x += __shfl_xor_sync(0xffffffff, a.x, offset, width);
|
||||
a.y += __shfl_xor_sync(0xffffffff, a.y, offset, width);
|
||||
}
|
||||
return a;
|
||||
}
|
||||
|
||||
template<int width = WARP_SIZE>
|
||||
static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
|
||||
#ifdef FP16_AVAILABLE
|
||||
|
||||
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 0; offset >>= 1) {
|
||||
const half2 a_other = __shfl_xor_sync(0xffffffff, a, offset, 32);
|
||||
reinterpret_cast<half&>(a.x) += __low2half(a_other);
|
||||
reinterpret_cast<half&>(a.y) += __high2half(a_other);
|
||||
for (int offset = width/2; offset > 0; offset >>= 1) {
|
||||
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, width));
|
||||
}
|
||||
return a;
|
||||
#else
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 0; offset >>= 1) {
|
||||
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, 32));
|
||||
}
|
||||
return a;
|
||||
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
|
||||
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
|
|
@ -244,10 +254,11 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
|
|||
#endif // FP16_AVAILABLE
|
||||
}
|
||||
|
||||
template<int width = WARP_SIZE>
|
||||
static __device__ __forceinline__ float warp_reduce_max(float x) {
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 0; offset >>= 1) {
|
||||
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
|
||||
for (int offset = width/2; offset > 0; offset >>= 1) {
|
||||
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, width));
|
||||
}
|
||||
return x;
|
||||
}
|
||||
|
|
@ -269,35 +280,34 @@ static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b
|
|||
}
|
||||
|
||||
static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) {
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
|
||||
#if CUDART_VERSION >= CUDART_HMAX
|
||||
#if defined(GGML_USE_HIP) && HIP_VERSION >= 50700000
|
||||
return half2(__hmax(a.x, b.x), __hmax(a.y, b.y));
|
||||
#elif !defined(GGML_USE_HIP) && CUDART_VERSION >= CUDART_HMAX
|
||||
return __hmax2(a, b);
|
||||
#else
|
||||
#elif !defined(GGML_USE_HIP)
|
||||
half2 ret;
|
||||
reinterpret_cast<half&>(ret.x) = __float2half(fmaxf( __low2float(a), __low2float(b)));
|
||||
reinterpret_cast<half&>(ret.y) = __float2half(fmaxf(__high2float(a), __high2float(b)));
|
||||
return ret;
|
||||
#endif // CUDART_VERSION >= CUDART_HMAX
|
||||
|
||||
#else
|
||||
GGML_UNUSED(a);
|
||||
GGML_UNUSED(b);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
#endif
|
||||
}
|
||||
|
||||
template<int width = WARP_SIZE>
|
||||
static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 0; offset >>= 1) {
|
||||
x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
|
||||
for (int offset = width/2; offset > 0; offset >>= 1) {
|
||||
x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, width));
|
||||
}
|
||||
return x;
|
||||
#else
|
||||
GGML_UNUSED(x);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
|
||||
}
|
||||
|
||||
#if CUDART_VERSION < CUDART_HMASK
|
||||
|
|
@ -520,6 +530,7 @@ struct ggml_cuda_device_info {
|
|||
bool vmm; // virtual memory support
|
||||
size_t vmm_granularity; // granularity of virtual memory
|
||||
size_t total_vram;
|
||||
int warp_size; // Number of threads in a dispatch
|
||||
};
|
||||
|
||||
cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {};
|
||||
|
|
|
|||
|
|
@ -516,6 +516,114 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
|
|||
nullptr;
|
||||
}
|
||||
|
||||
// The HIP compiler for some reason complains that it can't unroll a loop because of the jt*ncols + j >= ne01 conditional.
|
||||
#ifdef __clang__
|
||||
#pragma clang diagnostic push
|
||||
#pragma clang diagnostic ignored "-Wpass-failed"
|
||||
#endif // __clang__
|
||||
|
||||
template<int D, int ncols, int KQ_stride> // D == head size
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(D, 1)
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_stream_k_fixup(
|
||||
float * __restrict__ dst, const float2 * __restrict__ dst_fixup, const int ne01, const int ne02, const int ne11) {
|
||||
const float * dst_fixup_data = ((const float *) dst_fixup) + gridDim.x*(2*2*ncols);
|
||||
|
||||
const int iter_k = ne11 / KQ_stride;
|
||||
const int iter_j = (ne01 + (ncols - 1)) / ncols;
|
||||
|
||||
const int bidx0 = blockIdx.x;
|
||||
|
||||
const int kbc0 = (bidx0 + 0)*iter_k*iter_j*ne02 / gridDim.x;
|
||||
const int kbc0_stop = (bidx0 + 1)*iter_k*iter_j*ne02 / gridDim.x;
|
||||
|
||||
const bool did_not_have_any_data = kbc0 == kbc0_stop;
|
||||
const bool wrote_beginning_of_tile = kbc0 % iter_k == 0;
|
||||
const bool did_not_write_last = kbc0/iter_k == kbc0_stop/iter_k && kbc0_stop % iter_k != 0;
|
||||
if (did_not_have_any_data || wrote_beginning_of_tile || did_not_write_last) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int channel = kbc0 / (iter_k*iter_j);
|
||||
const int jt = (kbc0 - channel*iter_k*iter_j) / iter_k;
|
||||
|
||||
dst += jt*ncols*ne02*D + channel*D;
|
||||
|
||||
// Load the partial result that needs a fixup:
|
||||
float dst_val[ncols] = {0.0f};
|
||||
float max_val[ncols] = {0.0f};
|
||||
float rowsum[ncols] = {0.0f};
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
if (jt*ncols + j >= ne01) {
|
||||
break;
|
||||
}
|
||||
dst_val[j] = dst[j*ne02*D + threadIdx.x];
|
||||
|
||||
const float2 tmp = dst_fixup[bidx0*ncols + j];
|
||||
max_val[j] = tmp.x;
|
||||
rowsum[j] = tmp.y;
|
||||
}
|
||||
|
||||
// Iterate over previous blocks and compute the combined results.
|
||||
// All CUDA blocks that get here must have a previous block that needs a fixup.
|
||||
int bidx = bidx0 - 1;
|
||||
int kbc_stop = kbc0;
|
||||
while(true) {
|
||||
const int kbc = bidx*iter_k*iter_j*ne02 / gridDim.x;
|
||||
if (kbc == kbc_stop) { // Did not have any data.
|
||||
bidx--;
|
||||
kbc_stop = kbc;
|
||||
continue;
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
if (jt*ncols + j >= ne01) {
|
||||
break;
|
||||
}
|
||||
const float dst_add = dst_fixup_data[bidx*ncols*D + j*D + threadIdx.x];
|
||||
|
||||
const float2 tmp = dst_fixup[(gridDim.x + bidx)*ncols + j];
|
||||
|
||||
// Scale the current and new value accumulators depending on the max. values.
|
||||
const float max_val_new = fmaxf(max_val[j], tmp.x);
|
||||
|
||||
const float diff_val = max_val[j] - max_val_new;
|
||||
const float diff_add = tmp.x - max_val_new;
|
||||
|
||||
const float scale_val = diff_val >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_val) : 0.0f;
|
||||
const float scale_add = diff_add >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_add) : 0.0f;
|
||||
|
||||
dst_val[j] = scale_val*dst_val[j] + scale_add*dst_add;
|
||||
rowsum[j] = scale_val*rowsum[j] + scale_add*tmp.y;
|
||||
|
||||
max_val[j] = max_val_new;
|
||||
}
|
||||
|
||||
// If this block started in a previous tile we are done and don't need to combine additional partial results.
|
||||
if (kbc % iter_k == 0 || kbc/iter_k < kbc0/iter_k) {
|
||||
break;
|
||||
}
|
||||
bidx--;
|
||||
kbc_stop = kbc;
|
||||
}
|
||||
|
||||
// Write back final result:
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols; ++j) {
|
||||
if (jt*ncols + j >= ne01) {
|
||||
return;
|
||||
}
|
||||
dst[j*ne02*D + threadIdx.x] = dst_val[j] / rowsum[j];
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef __clang__
|
||||
#pragma clang diagnostic pop
|
||||
#endif // __clang__
|
||||
|
||||
template<int D, int parallel_blocks> // D == head size
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(D, 1)
|
||||
|
|
@ -581,10 +689,11 @@ static void on_no_fattn_vec_case(const int D) {
|
|||
}
|
||||
}
|
||||
|
||||
template <int D, int parallel_blocks>
|
||||
// parallel_blocks == 0 is stream-k decomposition
|
||||
template <int D, int cols_per_block, int parallel_blocks, int KQ_stride>
|
||||
void launch_fattn(
|
||||
ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel,
|
||||
const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V
|
||||
const int nwarps, const size_t nbytes_shared, const bool need_f16_K, const bool need_f16_V
|
||||
) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
const ggml_tensor * K = dst->src[1];
|
||||
|
|
@ -603,20 +712,23 @@ void launch_fattn(
|
|||
|
||||
GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
|
||||
|
||||
GGML_ASSERT(Q->ne[3] == 1);
|
||||
|
||||
ggml_cuda_pool & pool = ctx.pool();
|
||||
cudaStream_t main_stream = ctx.stream();
|
||||
const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
|
||||
|
||||
ggml_cuda_pool_alloc<half> K_f16(pool);
|
||||
ggml_cuda_pool_alloc<half> V_f16(pool);
|
||||
ggml_cuda_pool_alloc<float> dst_tmp(pool);
|
||||
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
|
||||
|
||||
char * K_data = (char *) K->data;
|
||||
const char * K_data = (const char *) K->data;
|
||||
size_t nb11 = K->nb[1];
|
||||
size_t nb12 = K->nb[2];
|
||||
size_t nb13 = K->nb[3];
|
||||
|
||||
char * V_data = (char *) V->data;
|
||||
const char * V_data = (const char *) V->data;
|
||||
size_t nb21 = V->nb[1];
|
||||
size_t nb22 = V->nb[2];
|
||||
size_t nb23 = V->nb[3];
|
||||
|
|
@ -649,39 +761,60 @@ void launch_fattn(
|
|||
nb23 = nb23*bs*sizeof(half)/ts;
|
||||
}
|
||||
|
||||
const int ntiles_x = ((Q->ne[1] + cols_per_block - 1) / cols_per_block);
|
||||
const int ntiles_total = ntiles_x*Q->ne[2]*Q->ne[3];
|
||||
|
||||
const dim3 block_dim(WARP_SIZE, nwarps, 1);
|
||||
dim3 blocks_num;
|
||||
if (parallel_blocks == 0) {
|
||||
// For short contexts it can be faster to have the SMs work on whole tiles because this lets us skip the fixup.
|
||||
const int tiles_nwaves = (ntiles_total - nsm - 1) / nsm;
|
||||
const bool tiles_inefficient = 3*nsm < 2*tiles_nwaves*ntiles_total;
|
||||
const bool short_context = K->ne[1] < 4096;
|
||||
|
||||
const int nblocks_stream_k = 2*nsm;
|
||||
|
||||
blocks_num.x = short_context && !tiles_inefficient ? ntiles_total : nblocks_stream_k;
|
||||
blocks_num.y = 1;
|
||||
blocks_num.z = 1;
|
||||
|
||||
dst_tmp_meta.alloc(blocks_num.x*cols_per_block * (2*2 + D) * sizeof(float));
|
||||
} else {
|
||||
blocks_num.x = parallel_blocks*ntiles_x;
|
||||
blocks_num.y = Q->ne[2];
|
||||
blocks_num.z = Q->ne[3];
|
||||
|
||||
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;
|
||||
float logit_softcap = 0.0f;
|
||||
|
||||
memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float));
|
||||
memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
|
||||
memcpy(&logit_softcap, (float *) KQV->op_params + 2, sizeof(float));
|
||||
memcpy(&scale, (const float *) KQV->op_params + 0, sizeof(float));
|
||||
memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
|
||||
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
|
||||
|
||||
if (logit_softcap != 0.0f) {
|
||||
scale /= logit_softcap;
|
||||
}
|
||||
|
||||
const uint32_t n_head = Q->ne[2];
|
||||
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
|
||||
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<<<blocks_num, block_dim, shmem, main_stream>>>(
|
||||
fattn_kernel<<<blocks_num, block_dim, nbytes_shared, main_stream>>>(
|
||||
(const char *) Q->data,
|
||||
K_data,
|
||||
V_data,
|
||||
mask ? ((const char *) mask->data) : nullptr,
|
||||
(parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
|
||||
(parallel_blocks) > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
|
||||
scale, max_bias, m0, m1, n_head_log2, logit_softcap,
|
||||
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
|
||||
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
|
||||
|
|
@ -693,16 +826,22 @@ void launch_fattn(
|
|||
);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
|
||||
if ((parallel_blocks) == 1) {
|
||||
return;
|
||||
}
|
||||
if constexpr (parallel_blocks == 0) {
|
||||
if (blocks_num.x % ntiles_total != 0) { // Fixup is only needed if the SMs work on fractional tiles.
|
||||
const dim3 block_dim_combine(D, 1, 1);
|
||||
const dim3 blocks_num_combine = blocks_num;
|
||||
|
||||
flash_attn_stream_k_fixup<D, cols_per_block, KQ_stride>
|
||||
<<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
|
||||
((float *) KQV->data, dst_tmp_meta.ptr, Q->ne[1], Q->ne[2], K->ne[1]);
|
||||
}
|
||||
} else if constexpr (parallel_blocks > 1) {
|
||||
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<D, parallel_blocks>
|
||||
<<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
|
||||
<<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
|
||||
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
|
||||
}
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
}
|
||||
|
|
|
|||
637
ggml/src/ggml-cuda/fattn-mma-f16.cuh
Normal file
637
ggml/src/ggml-cuda/fattn-mma-f16.cuh
Normal file
|
|
@ -0,0 +1,637 @@
|
|||
#include "common.cuh"
|
||||
#include "mma.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
|
||||
template<int D, int ncols, int nwarps, int KQ_stride, bool use_logit_softcap, bool needs_fixup, bool is_fixup>
|
||||
static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
|
||||
const float2 * const __restrict__ Q_f2,
|
||||
const half2 * const __restrict__ K_h2,
|
||||
const half2 * const __restrict__ V_h2,
|
||||
const half * const __restrict__ maskh,
|
||||
float2 * const __restrict__ dstk,
|
||||
float2 * const __restrict__ dstk_fixup,
|
||||
const float scale,
|
||||
const float slope,
|
||||
const float logit_softcap,
|
||||
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 nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3,
|
||||
const int jt,
|
||||
const int kb0_start,
|
||||
const int kb0_stop) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
typedef mma_A_I16K8<half2> mma_A;
|
||||
typedef mma_B_J8K8<half2> mma_B;
|
||||
typedef mma_C_I16J8<float> mma_C_KQ;
|
||||
typedef mma_C_I16J8<half2> mma_C_VKQ;
|
||||
|
||||
static_assert(nwarps*mma_B::J % ncols == 0, "bad nwarps");
|
||||
constexpr int np = nwarps*mma_B::J / ncols; // Number of parallel CUDA warps per Q column.
|
||||
|
||||
static_assert(D % nwarps == 0, "bad D");
|
||||
static_assert(KQ_stride % nwarps == 0, "bad KQ_stride");
|
||||
|
||||
constexpr int D2_padded = D/2 + 4; // Size of D in half2, padded to avoid shared memory bank conflicts.
|
||||
extern __shared__ half2 tile_KV[]; // Temporary shared buffer for loading K/V data with KQ_stride*D logical elements.
|
||||
|
||||
const int stride_Q = nb01 / sizeof(float2);
|
||||
const int stride_KV = nb11 / sizeof(half2);
|
||||
const int stride_mask = nb31 / sizeof(half);
|
||||
|
||||
mma_B Q_B[D/(2*mma_B::K)];
|
||||
mma_C_VKQ VKQ_C[D/mma_C_VKQ::I];
|
||||
|
||||
float2 KQ_rowsum = {0.0f, 0.0f};
|
||||
float2 KQ_max = {-FLT_MAX/2.0f, -FLT_MAX/2.0f};
|
||||
float2 KQ_max_scale = {0.0f, 0.0f};
|
||||
|
||||
// Temporarily load Q data into tile_KV, will be loaded into registers afterwards.
|
||||
// The loading is done with decreasing granularity for D for better memory bandwidth.
|
||||
const half2 scale_h2 = make_half2(scale, scale);
|
||||
#pragma unroll
|
||||
for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
|
||||
const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
|
||||
const int k0_stop = D/2 - (D/2) % (1*stride_k);
|
||||
const int stride_j = WARP_SIZE / stride_k;
|
||||
|
||||
if (nwarps*stride_j > ncols && threadIdx.y*stride_j >= ncols) {
|
||||
break;
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps*stride_j) {
|
||||
const int j = j0 + threadIdx.y*stride_j + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
|
||||
|
||||
if (jt*ncols + j < ne01) {
|
||||
#pragma unroll
|
||||
for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
|
||||
const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
|
||||
|
||||
const float2 tmp = Q_f2[(jt*ncols + j)*stride_Q + k];
|
||||
tile_KV[j*D2_padded + k] = scale_h2 * make_half2(tmp.x, tmp.y);
|
||||
}
|
||||
} else {
|
||||
#pragma unroll
|
||||
for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
|
||||
const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
|
||||
|
||||
tile_KV[j*D2_padded + k] = make_half2(0.0f, 0.0f);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
{
|
||||
const int j0 = (threadIdx.y / np) * mma_B::J;
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < D/2; k0 += mma_B::K) {
|
||||
Q_B[k0/mma_B::K].load_ldmatrix(tile_KV + j0*D2_padded + k0, D2_padded);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Iterate over ne11 == previous tokens:
|
||||
for (int kb0 = kb0_start; kb0 < kb0_stop; ++kb0) {
|
||||
const int k_VKQ_0 = kb0*KQ_stride;
|
||||
mma_C_KQ KQ_C[KQ_stride/(np*mma_C_KQ::I)];
|
||||
|
||||
// Load K data into tile with decreasing granularity for D for better memory bandwidth:
|
||||
static_assert(KQ_stride % (4*nwarps) == 0, "out of bounds");
|
||||
#pragma unroll
|
||||
for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
|
||||
const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
|
||||
const int k0_stop = D/2 - (D/2) % (1*stride_k);
|
||||
const int stride_i = WARP_SIZE / stride_k;
|
||||
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < KQ_stride; i_KQ_0 += nwarps*stride_i) {
|
||||
const int i_KQ = i_KQ_0 + threadIdx.y*stride_i + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
|
||||
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = k0_start; k_KQ_0 < k0_stop; k_KQ_0 += stride_k) {
|
||||
const int k_KQ = k_KQ_0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
|
||||
|
||||
tile_KV[i_KQ*D2_padded + k_KQ] = K_h2[(k_VKQ_0 + i_KQ)*stride_KV + k_KQ];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Calculate tile of KQ:
|
||||
#pragma unroll
|
||||
for (int i_KQ_00 = 0; i_KQ_00 < KQ_stride; i_KQ_00 += np*mma_A::I) {
|
||||
const int i_KQ_0 = i_KQ_00 + (threadIdx.y % np)*mma_A::I;
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += mma_A::K) {
|
||||
mma_A K_A;
|
||||
K_A.load_ldmatrix(tile_KV + i_KQ_0*D2_padded + k_KQ_0, D2_padded);
|
||||
KQ_C[i_KQ_00/(np*mma_A::I)].mma(K_A, Q_B[k_KQ_0/mma_A::K]);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
if (use_logit_softcap) {
|
||||
static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
|
||||
#pragma unroll
|
||||
for (int i = 0; i < KQ_stride/(np*mma_C_KQ::I); ++i) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < mma_C_KQ::ne; ++l) {
|
||||
KQ_C[i].x[l] = logit_softcap*tanhf(KQ_C[i].x[l]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (maskh) {
|
||||
static_assert(KQ_stride % (np *mma_C_KQ::I) == 0, "bad loop size");
|
||||
static_assert(ncols % (nwarps/np*mma_C_KQ::J) == 0, "bad loop size");
|
||||
#pragma unroll
|
||||
for (int i00 = 0; i00 < KQ_stride; i00 += np*mma_C_KQ::I) {
|
||||
const int i0 = i00 + (threadIdx.y % np)*mma_C_KQ::I;
|
||||
#pragma unroll
|
||||
for (int l = 0; l < mma_C_KQ::ne; ++l) {
|
||||
const int i = i0 + mma_C_KQ::get_i(l);
|
||||
const int j = (threadIdx.y / np)*mma_C_KQ::J + mma_C_KQ::get_j(l);
|
||||
|
||||
KQ_C[i00/(np*mma_C_KQ::I)].x[l] += slope*__half2float(maskh[j*stride_mask + k_VKQ_0 + i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Calculate softmax for each KQ column using the current max. value.
|
||||
// The divisor is stored in KQ_rowsum and will be applied at the end.
|
||||
float2 KQ_max_new = KQ_max;
|
||||
static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
|
||||
#pragma unroll
|
||||
for (int k = 0; k < KQ_stride/(np*mma_C_KQ::I); ++k) {
|
||||
#pragma unroll
|
||||
for (int l0 = 0; l0 < mma_C_KQ::ne; l0 += 2) {
|
||||
KQ_max_new.x = fmaxf(KQ_max_new.x, KQ_C[k].x[l0 + 0]);
|
||||
KQ_max_new.y = fmaxf(KQ_max_new.y, KQ_C[k].x[l0 + 1]);
|
||||
}
|
||||
}
|
||||
|
||||
// Values per KQ column are spread across 8 threads, does not need full warp reduce:
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 2; offset >>= 1) {
|
||||
KQ_max_new.x = fmaxf(KQ_max_new.x, __shfl_xor_sync(0xFFFFFFFF, KQ_max_new.x, offset, WARP_SIZE));
|
||||
KQ_max_new.y = fmaxf(KQ_max_new.y, __shfl_xor_sync(0xFFFFFFFF, KQ_max_new.y, offset, WARP_SIZE));
|
||||
}
|
||||
|
||||
{
|
||||
const float2 diff = make_float2(KQ_max.x - KQ_max_new.x, KQ_max.y - KQ_max_new.y);
|
||||
KQ_max_scale = make_float2(expf(diff.x), expf(diff.y));
|
||||
if (diff.x <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_max_scale.x = 0.0f;
|
||||
}
|
||||
if (diff.y <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_max_scale.y = 0.0f;
|
||||
}
|
||||
KQ_max = KQ_max_new;
|
||||
}
|
||||
|
||||
float2 KQ_rowsum_add = make_float2(0.0f, 0.0f);
|
||||
static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
|
||||
#pragma unroll
|
||||
for (int k = 0; k < KQ_stride/(np*mma_C_KQ::I); ++k) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < mma_C_KQ::ne; ++l) {
|
||||
const float KQ_max_l = l % 2 == 0 ? KQ_max.x : KQ_max.y;
|
||||
const float diff = KQ_C[k].x[l] - KQ_max_l;
|
||||
KQ_C[k].x[l] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_C[k].x[l] = 0.0f;
|
||||
}
|
||||
|
||||
if (l % 2 == 0) {
|
||||
KQ_rowsum_add.x += KQ_C[k].x[l];
|
||||
} else {
|
||||
KQ_rowsum_add.y += KQ_C[k].x[l];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum.x = KQ_max_scale.x*KQ_rowsum.x + KQ_rowsum_add.x;
|
||||
KQ_rowsum.y = KQ_max_scale.y*KQ_rowsum.y + KQ_rowsum_add.y;
|
||||
|
||||
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale.x, KQ_max_scale.y);
|
||||
#pragma unroll
|
||||
for (int i = 0; i < D/mma_C_VKQ::I; ++i) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < mma_C_VKQ::ne; ++l) {
|
||||
VKQ_C[i].x[l] *= KQ_max_scale_h2;
|
||||
}
|
||||
}
|
||||
|
||||
// Convert KQ C tiles into B tiles for VKQ calculation:
|
||||
mma_B B[KQ_stride/(np*2*mma_B::K)];
|
||||
static_assert(KQ_stride % (np*2*mma_B::K) == 0, "bad loop size");
|
||||
#pragma unroll
|
||||
for (int k = 0; k < KQ_stride/(np*2*mma_B::K); ++k) {
|
||||
B[k] = KQ_C[k].to_mma_B();
|
||||
}
|
||||
|
||||
// Load V data into tile with decreasing granularity for D for better memory bandwidth:
|
||||
static_assert(KQ_stride % (4*nwarps) == 0, "out of bounds");
|
||||
#pragma unroll
|
||||
for (int stride_i : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
|
||||
const int i0_start = stride_i == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_i);
|
||||
const int i0_stop = D/2 - (D/2) % (1*stride_i);
|
||||
const int stride_k = WARP_SIZE / stride_i;
|
||||
|
||||
#pragma unroll
|
||||
for (int k_V_0 = 0; k_V_0 < KQ_stride; k_V_0 += nwarps*stride_k) {
|
||||
const int k_V = k_V_0 + threadIdx.y*stride_k + (stride_i == WARP_SIZE ? 0 : threadIdx.x / stride_i);
|
||||
|
||||
#pragma unroll
|
||||
for (int i_V_0 = i0_start; i_V_0 < i0_stop; i_V_0 += stride_i) {
|
||||
const int i_V = i_V_0 + (stride_i == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_i);
|
||||
|
||||
tile_KV[k_V*D2_padded + i_V] = V_h2[(k_VKQ_0 + k_V)*stride_KV + i_V];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Calculate VKQ tile:
|
||||
#pragma unroll
|
||||
for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += mma_C_VKQ::I) {
|
||||
static_assert((KQ_stride/2) % (np*mma_A::K) == 0, "bad loop size");
|
||||
#pragma unroll
|
||||
for (int k00 = 0; k00 < KQ_stride/2; k00 += np*mma_A::K) {
|
||||
const int k0 = k00 + (threadIdx.y % np)*mma_A::K;
|
||||
|
||||
mma_A A;
|
||||
A.load_ldmatrix_trans(tile_KV + 2*k0*D2_padded + i_VKQ_0/2, D2_padded);
|
||||
VKQ_C[i_VKQ_0/mma_C_VKQ::I].mma(A, B[k00/(np*mma_A::K)]);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
// Finally, sum up partial KQ rowsums.
|
||||
// The partial sums are spread across 8 threads each, does not need full reduce.
|
||||
#pragma unroll
|
||||
for (int offset = 16; offset > 2; offset >>= 1) {
|
||||
KQ_rowsum.x += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum.x, offset, WARP_SIZE);
|
||||
KQ_rowsum.y += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum.y, offset, WARP_SIZE);
|
||||
}
|
||||
|
||||
// Write VKQ accumulators to shared memory in column-major format.
|
||||
// It's faster to do small writes to shared memory, then large write to VRAM than to do small writes to VRAM.
|
||||
// Also for np > 1 the combination is done via these values in shared memory.
|
||||
const int j_cwd = threadIdx.y*mma_B::J + mma_B::get_j(-1); // j combine write data
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < D/2; k0 += mma_B::K) {
|
||||
const mma_B B = VKQ_C[k0/mma_B::K].to_mma_B(); // Conversion of C to B matrix puts it in column-major format.
|
||||
|
||||
#pragma unroll
|
||||
for (int l = 0; l < mma_B::ne; ++l) {
|
||||
const int k = k0 + mma_B::get_k(l);
|
||||
|
||||
tile_KV[j_cwd*D2_padded + k] = B.x[l];
|
||||
}
|
||||
}
|
||||
|
||||
const int j_cwmo = (threadIdx.x % (2*mma_C_VKQ::J)) / mma_C_VKQ::J; // j combine write meta offset
|
||||
const int j_cwm = threadIdx.y*(2*mma_C_VKQ::J) + 2*mma_C_VKQ::get_j(-1) + j_cwmo; // j combine write meta
|
||||
const float2 KQ_cmr = make_float2(((const float *) &KQ_max)[j_cwmo], ((const float *) &KQ_rowsum)[j_cwmo]); // KQ combine max rowsum
|
||||
|
||||
if (((!needs_fixup && !is_fixup) || np > 1) && threadIdx.x < 2*mma_C_VKQ::J) {
|
||||
// Use the 16 bytes of padding in each row to store the meta data: KQ max, KQ rowsum, KQ max scale.
|
||||
((float2 *) tile_KV)[j_cwm*(D2_padded/2) + D/4] = KQ_cmr;
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
static_assert(np == 1 || np == 2 || np == 4, "bad np");
|
||||
if (np == 1) {
|
||||
// No combination is needed, the meta data can be directly written from registers to VRAM.
|
||||
if (needs_fixup && threadIdx.x < mma_B::J) {
|
||||
float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
|
||||
dstk_fixup_meta[j_cwm] = KQ_cmr;
|
||||
}
|
||||
if (is_fixup && threadIdx.x < mma_B::J) {
|
||||
float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
|
||||
dstk_fixup_meta[j_cwm] = KQ_cmr;
|
||||
}
|
||||
} else if (threadIdx.y % np == 0) {
|
||||
// Combine the meta data for parallel warps via shared memory.
|
||||
// Warps with threadIdx.y % np != 0 must NOT return early.
|
||||
// All threads must return simultaneously to avoid race conditions with work on the next tile.
|
||||
|
||||
float * meta_j = (float *) tile_KV + (threadIdx.y*mma_B::J + threadIdx.x)*D2_padded + D/2;
|
||||
|
||||
float KQ_cm = -FLT_MAX/2; // KQ combine max per parallel warp.
|
||||
if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
|
||||
KQ_cm = meta_j[0];
|
||||
}
|
||||
|
||||
float KQ_cmn = KQ_cm; // KQ combine max new, max between all parallel warps.
|
||||
#pragma unroll
|
||||
for (int offset = np*mma_B::J/2; offset >= mma_B::J; offset >>= 1) {
|
||||
KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, WARP_SIZE));
|
||||
}
|
||||
|
||||
const float KQ_cms = expf(KQ_cm - KQ_cmn); // KQ combine max scale per warp.
|
||||
float KQ_crs = 0.0f; // KQ combine rowsum, scaled sum of all parallel warps.
|
||||
if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
|
||||
KQ_crs = KQ_cms*meta_j[1];
|
||||
}
|
||||
#pragma unroll
|
||||
for (int offset = np*mma_B::J/2; offset >= mma_B::J; offset >>= 1) {
|
||||
KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, WARP_SIZE);
|
||||
}
|
||||
|
||||
// Write back combined meta data:
|
||||
if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
|
||||
meta_j[0] = KQ_cmn; // Combined max. KQ values.
|
||||
meta_j[1] = KQ_crs; // Combined KQ rowsums.
|
||||
meta_j[2] = KQ_cms; // KQ max scales per parallel warp.
|
||||
}
|
||||
if (needs_fixup && threadIdx.x < mma_B::J) {
|
||||
float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
|
||||
dstk_fixup_meta[(threadIdx.y/np)*mma_B::J + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
|
||||
}
|
||||
if (is_fixup && threadIdx.x < mma_B::J) {
|
||||
float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
|
||||
dstk_fixup_meta[(threadIdx.y/np)*mma_B::J + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
|
||||
}
|
||||
}
|
||||
|
||||
if (np > 1) {
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
if (np == 1 || threadIdx.y % np == 0) {
|
||||
// The first 2*2*gridDim.x*ncols floats in dstk_fixup are for storing max. values and row sums.
|
||||
// The values after that are for the partial results of the individual blocks.
|
||||
float2 * dstk_fixup_data = dstk_fixup + gridDim.x*(2*ncols) + blockIdx.x*(ncols*(D/2));
|
||||
|
||||
#pragma unroll
|
||||
for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
|
||||
const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
|
||||
const int k0_stop = D/2 - (D/2) % (1*stride_k);
|
||||
const int stride_j = WARP_SIZE / stride_k;
|
||||
|
||||
if (nwarps*stride_j > ncols && threadIdx.y*stride_j >= ncols) {
|
||||
break;
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j0_dst = 0; j0_dst < ncols; j0_dst += (nwarps/np)*stride_j) {
|
||||
const int j_dst = j0_dst + (threadIdx.y/np)*stride_j + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
|
||||
const int j_tile_KV = (j_dst/mma_B::J)*(np*mma_B::J) + j_dst % mma_B::J;
|
||||
|
||||
if (!is_fixup && jt*ncols + j_dst >= ne01) {
|
||||
continue;
|
||||
}
|
||||
const float * meta_j = (const float *) tile_KV + j_tile_KV*D2_padded + D/2;
|
||||
#pragma unroll
|
||||
for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
|
||||
const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
|
||||
|
||||
float2 dstk_val = make_float2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int ip = 0; ip < np; ++ip) {
|
||||
const float KQ_crs = np == 1 ? 1.0f : meta_j[ip*mma_B::J*D2_padded + 2];
|
||||
const float2 dstk_val_add = __half22float2(tile_KV[(j_tile_KV + ip*mma_B::J)*D2_padded + k]);
|
||||
dstk_val.x += dstk_val_add.x*KQ_crs;
|
||||
dstk_val.y += dstk_val_add.y*KQ_crs;
|
||||
}
|
||||
|
||||
if (!needs_fixup && !is_fixup) {
|
||||
const float KQ_rowsum_j = meta_j[1];
|
||||
dstk_val.x /= KQ_rowsum_j;
|
||||
dstk_val.y /= KQ_rowsum_j;
|
||||
}
|
||||
|
||||
if (is_fixup) {
|
||||
dstk_fixup_data[j_dst*(D/2) + k] = dstk_val;
|
||||
} else {
|
||||
dstk[(jt*ncols + j_dst)*ne02*(D/2) + k] = dstk_val;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (np > 1) {
|
||||
__syncthreads();
|
||||
}
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
template<int D, int ncols, int nwarps, int KQ_stride, bool use_logit_softcap>
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(nwarps*WARP_SIZE, 2)
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_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 float logit_softcap,
|
||||
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 nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
// Skip unused kernel variants for faster compilation:
|
||||
if (use_logit_softcap && !(D == 128 || D == 256)) {
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
}
|
||||
|
||||
static_assert(FATTN_KQ_STRIDE % KQ_stride == 0, "bad KQ_stride");
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
|
||||
const int iter_k = ne11 / KQ_stride;
|
||||
const int iter_j = (ne01 + (ncols - 1)) / ncols;
|
||||
|
||||
// kbc == k block continuous, current index in continuous ijk space.
|
||||
int kbc = (blockIdx.x + 0)*iter_k*iter_j*ne02 / gridDim.x;
|
||||
const int kbc_stop = (blockIdx.x + 1)*iter_k*iter_j*ne02 / gridDim.x;
|
||||
|
||||
// If the seams of 2 CUDA blocks fall within an output tile their results need to be combined.
|
||||
// For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup).
|
||||
// In the most general case >2 seams can fall into the same tile.
|
||||
|
||||
// kb0 == k start index when in the output tile.
|
||||
int kb0_start = kbc % iter_k;
|
||||
int kb0_stop = min(iter_k, kb0_start + kbc_stop - kbc);
|
||||
while (kbc < kbc_stop && kb0_stop == iter_k) {
|
||||
const int channel = kbc / (iter_k*iter_j);
|
||||
const int jt = (kbc - channel*iter_k*iter_j) / iter_k; // j index of current tile.
|
||||
|
||||
const float2 * Q_f2 = (const float2 *) (Q + nb02* channel);
|
||||
const half2 * K_h2 = (const half2 *) (K + nb12*(channel / gqa_ratio));
|
||||
const half2 * V_h2 = (const half2 *) (V + nb12*(channel / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = mask ? (const half *) mask + (nb31/sizeof(half))*jt*ncols : nullptr;
|
||||
float2 * dstk = ((float2 *) dst) + channel*(D/2);
|
||||
|
||||
const float slope = get_alibi_slope(max_bias, channel, n_head_log2, m0, m1);
|
||||
|
||||
constexpr bool is_fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
|
||||
if (kb0_start == 0) {
|
||||
constexpr bool needs_fixup = false; // CUDA block is working on an entire tile.
|
||||
flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
|
||||
(Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
|
||||
ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
|
||||
jt, kb0_start, kb0_stop);
|
||||
} else {
|
||||
constexpr bool needs_fixup = true; // CUDA block is working on the beginning of a tile.
|
||||
flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
|
||||
(Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
|
||||
ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
|
||||
jt, kb0_start, kb0_stop);
|
||||
}
|
||||
|
||||
kbc += iter_k;
|
||||
kbc -= kbc % iter_k;
|
||||
|
||||
kb0_start = 0;
|
||||
kb0_stop = min(iter_k, kbc_stop - kbc);
|
||||
}
|
||||
|
||||
if (kbc >= kbc_stop) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int channel = kbc / (iter_k*iter_j);
|
||||
const int jt = (kbc - channel*iter_k*iter_j) / iter_k; // j index of current tile.
|
||||
|
||||
const float2 * Q_f2 = (const float2 *) (Q + nb02* channel);
|
||||
const half2 * K_h2 = (const half2 *) (K + nb12*(channel / gqa_ratio));
|
||||
const half2 * V_h2 = (const half2 *) (V + nb12*(channel / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = mask ? (const half *) mask + (nb31/sizeof(half))*jt*ncols : nullptr;
|
||||
float2 * dstk = ((float2 *) dst) + channel*(D/2);
|
||||
|
||||
const float slope = get_alibi_slope(max_bias, channel, n_head_log2, m0, m1);
|
||||
|
||||
constexpr bool is_fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
|
||||
constexpr bool needs_fixup = false;
|
||||
flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
|
||||
(Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
|
||||
ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
|
||||
jt, kb0_start, kb0_stop);
|
||||
}
|
||||
|
||||
template <int D, int cols_per_block>
|
||||
void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
typedef mma_A_I16K8<half2> mma_A;
|
||||
typedef mma_B_J8K8<half2> mma_B;
|
||||
|
||||
static_assert(D % mma_B::K == 0, "bad D");
|
||||
static_assert(cols_per_block % mma_B::J == 0, "bad cols_per_block");
|
||||
|
||||
const ggml_tensor * KQV = dst;
|
||||
|
||||
constexpr int KQ_stride = D <= 128 ? 64 : 32;
|
||||
constexpr int nwarps = (KQ_stride == 32 && cols_per_block <= 16) ?
|
||||
cols_per_block/mma_B::J * KQ_stride/mma_A::I : (cols_per_block <= 8 ? 4 : 8);
|
||||
constexpr size_t nbytes_shared = std::max(KQ_stride, nwarps*mma_B::J) * (D + 8) * sizeof(half);
|
||||
|
||||
float logit_softcap;
|
||||
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
|
||||
|
||||
fattn_kernel_t fattn_kernel;
|
||||
if (logit_softcap == 0.0f) {
|
||||
constexpr bool use_logit_softcap = false;
|
||||
fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, KQ_stride, use_logit_softcap>;
|
||||
} else {
|
||||
constexpr bool use_logit_softcap = true;
|
||||
fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, KQ_stride, use_logit_softcap>;
|
||||
}
|
||||
launch_fattn<D, cols_per_block, 0, KQ_stride>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
|
||||
}
|
||||
|
||||
#define DECL_FATTN_MMA_F16_CASE(D, cols_per_block) \
|
||||
template void ggml_cuda_flash_attn_ext_mma_f16_case \
|
||||
<D, cols_per_block>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
|
||||
|
||||
extern DECL_FATTN_MMA_F16_CASE( 64, 8);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 80, 8);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 96, 8);
|
||||
extern DECL_FATTN_MMA_F16_CASE(112, 8);
|
||||
extern DECL_FATTN_MMA_F16_CASE(128, 8);
|
||||
extern DECL_FATTN_MMA_F16_CASE(256, 8);
|
||||
|
||||
extern DECL_FATTN_MMA_F16_CASE( 64, 16);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 80, 16);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 96, 16);
|
||||
extern DECL_FATTN_MMA_F16_CASE(112, 16);
|
||||
extern DECL_FATTN_MMA_F16_CASE(128, 16);
|
||||
extern DECL_FATTN_MMA_F16_CASE(256, 16);
|
||||
|
||||
extern DECL_FATTN_MMA_F16_CASE( 64, 32);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 80, 32);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 96, 32);
|
||||
extern DECL_FATTN_MMA_F16_CASE(112, 32);
|
||||
extern DECL_FATTN_MMA_F16_CASE(128, 32);
|
||||
extern DECL_FATTN_MMA_F16_CASE(256, 32);
|
||||
|
||||
extern DECL_FATTN_MMA_F16_CASE( 64, 64);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 80, 64);
|
||||
extern DECL_FATTN_MMA_F16_CASE( 96, 64);
|
||||
extern DECL_FATTN_MMA_F16_CASE(112, 64);
|
||||
extern DECL_FATTN_MMA_F16_CASE(128, 64);
|
||||
extern DECL_FATTN_MMA_F16_CASE(256, 64);
|
||||
|
|
@ -45,7 +45,17 @@ static __global__ void flash_attn_tile_ext_f16(
|
|||
const int ne2,
|
||||
const int ne3) {
|
||||
#ifdef FP16_AVAILABLE
|
||||
|
||||
#ifndef FLASH_ATTN_AVAILABLE
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
#endif // FLASH_ATTN_AVAILABLE
|
||||
|
||||
// Skip unused kernel variants for faster compilation:
|
||||
#ifdef FP16_MMA_AVAILABLE
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
if (use_logit_softcap && !(D == 128 || D == 256)) {
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
|
|
@ -290,14 +300,16 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
|
|||
case 64: {
|
||||
constexpr int D = 64;
|
||||
constexpr int nwarps = 8;
|
||||
constexpr size_t nbytes_shared = 0;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
|
||||
} break;
|
||||
case 128: {
|
||||
constexpr int D = 128;
|
||||
constexpr int nwarps = 8;
|
||||
constexpr size_t nbytes_shared = 0;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
|
||||
} break;
|
||||
default: {
|
||||
GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
|
||||
|
|
|
|||
|
|
@ -48,7 +48,12 @@ static __global__ void flash_attn_tile_ext_f32(
|
|||
NO_DEVICE_CODE;
|
||||
return;
|
||||
#endif // FLASH_ATTN_AVAILABLE
|
||||
|
||||
// Skip unused kernel variants for faster compilation:
|
||||
#ifdef FP16_MMA_AVAILABLE
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
if (use_logit_softcap && !(D == 128 || D == 256)) {
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
|
|
@ -289,14 +294,16 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
|
|||
case 64: {
|
||||
constexpr int D = 64;
|
||||
constexpr int nwarps = 8;
|
||||
constexpr size_t nbytes_shared = 0;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
|
||||
} break;
|
||||
case 128: {
|
||||
constexpr int D = 128;
|
||||
constexpr int nwarps = 8;
|
||||
constexpr size_t nbytes_shared = 0;
|
||||
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
|
||||
} break;
|
||||
default: {
|
||||
GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
|
||||
|
|
|
|||
|
|
@ -42,6 +42,12 @@ static __global__ void flash_attn_vec_ext_f16(
|
|||
const int ne2,
|
||||
const int ne3) {
|
||||
#ifdef FP16_AVAILABLE
|
||||
|
||||
#ifndef FLASH_ATTN_AVAILABLE
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
#endif // FLASH_ATTN_AVAILABLE
|
||||
|
||||
// Skip unused kernel variants for faster compilation:
|
||||
if (use_logit_softcap && !(D == 128 || D == 256)) {
|
||||
NO_DEVICE_CODE;
|
||||
|
|
@ -303,7 +309,8 @@ void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx,
|
|||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
|
||||
constexpr bool need_f16_K = D != 128;
|
||||
constexpr bool need_f16_V = D != 128 && D != 64;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
|
||||
constexpr size_t nbytes_shared = 0;
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, need_f16_K, need_f16_V);
|
||||
}
|
||||
|
||||
template <int D, ggml_type type_K, ggml_type type_V>
|
||||
|
|
|
|||
|
|
@ -41,6 +41,11 @@ static __global__ void flash_attn_vec_ext_f32(
|
|||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#ifndef FLASH_ATTN_AVAILABLE
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
#endif // FLASH_ATTN_AVAILABLE
|
||||
|
||||
// Skip unused kernel variants for faster compilation:
|
||||
if (use_logit_softcap && !(D == 128 || D == 256)) {
|
||||
NO_DEVICE_CODE;
|
||||
|
|
@ -284,7 +289,8 @@ void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx,
|
|||
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
|
||||
constexpr bool need_f16_K = D != 128;
|
||||
constexpr bool need_f16_V = D != 128 && D != 64;
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
|
||||
constexpr size_t nbytes_shared = 0;
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, need_f16_K, need_f16_V);
|
||||
}
|
||||
|
||||
template <int D, ggml_type type_K, ggml_type type_V>
|
||||
|
|
|
|||
648
ggml/src/ggml-cuda/fattn-wmma-f16.cu
Normal file
648
ggml/src/ggml-cuda/fattn-wmma-f16.cu
Normal file
|
|
@ -0,0 +1,648 @@
|
|||
// Old and deprecated WMMA FlashAttention implementation.
|
||||
// It is still needed for Volta since the memory layout of NVIDIA tensor cores changed with Turing.
|
||||
// Long-term the WMMA code should be replaced with a dedicated Volta implementation.
|
||||
|
||||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
#include "fattn-wmma-f16.cuh"
|
||||
|
||||
#ifdef FP16_MMA_AVAILABLE
|
||||
#include <mma.h>
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
|
||||
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
|
||||
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t, bool use_logit_softcap>
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(nwarps*WARP_SIZE, 1)
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_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 float logit_softcap,
|
||||
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 nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
|
||||
// Skip unused kernel variants for faster compilation:
|
||||
if (use_logit_softcap && !(D == 128 || D == 256)) {
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
}
|
||||
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first 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.
|
||||
|
||||
static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
|
||||
static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
|
||||
constexpr int frag_m = ncols == 8 ? 32 : 16;
|
||||
constexpr int frag_n = ncols == 8 ? 8 : 16;
|
||||
static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
|
||||
|
||||
constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
|
||||
constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
|
||||
static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
|
||||
|
||||
// Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
|
||||
constexpr int D_padded = D + 8;
|
||||
constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
|
||||
constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
|
||||
const half * K_h = (const half *) (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 + (nb31/sizeof(half))* ic0;
|
||||
const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
|
||||
|
||||
const int stride_Q = nb01 / sizeof(float);
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
|
||||
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);
|
||||
|
||||
const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
|
||||
|
||||
frag_b Q_b[D/16][ncols/frag_n];
|
||||
|
||||
// A single buffer for temporarily holding tiles of KQ and VKQ parts:
|
||||
constexpr int mem_KQ = ncols*kqs_padded*kqar;
|
||||
constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
|
||||
__shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
|
||||
float * KQ_f = (float *) KQ;
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
float KQ_rowsum_f[ncols/nwarps] = {0.0f};
|
||||
float KQ_max_f[ncols/nwarps];
|
||||
float KQ_max_scale_f[ncols/nwarps] = {0.0f};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_f[j] = -FLT_MAX/2.0f;
|
||||
}
|
||||
|
||||
half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
half2 KQ_max_h2[ncols/nwarps];
|
||||
half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
|
||||
}
|
||||
|
||||
__shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
|
||||
half2 * VKQ2 = (half2 *) VKQ;
|
||||
#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;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
|
||||
}
|
||||
}
|
||||
|
||||
// Convert Q to half and apply scale, temporarily store in KQ:
|
||||
#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 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Load Q into tensor core fragments/registers since it will be used frequently:
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += 16) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Iterate over ne11 == previous tokens:
|
||||
for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
|
||||
// Calculate tile of KQ:
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
|
||||
frag_c_KQ KQ_c[ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
|
||||
}
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
|
||||
frag_a_K K_a;
|
||||
nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
|
||||
}
|
||||
}
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Calculate softmax for each KQ column using the current max. value.
|
||||
// The divisor is stored in KQ_rowsum and will be applied at the end.
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
|
||||
|
||||
if (use_logit_softcap) {
|
||||
KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
}
|
||||
|
||||
float KQ_max_new = KQ_max_f[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
|
||||
KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = warp_reduce_max(KQ_max_new);
|
||||
|
||||
const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_f[j0/nwarps] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_max_scale_f[j0/nwarps] = 0.0f;
|
||||
}
|
||||
KQ_max_f[j0/nwarps] = KQ_max_new;
|
||||
|
||||
float KQ_rowsum_add = 0.0f;
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
|
||||
KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
|
||||
}
|
||||
KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
|
||||
KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
|
||||
} else {
|
||||
half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
|
||||
|
||||
if (use_logit_softcap) {
|
||||
// There is no dedicated tangens hyperbolicus function for half2.
|
||||
KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
|
||||
KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
|
||||
/(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
|
||||
}
|
||||
}
|
||||
|
||||
half2 KQ_max_new = KQ_max_h2[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
|
||||
KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
|
||||
const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
|
||||
KQ_max_h2[j0/nwarps] = KQ_max_new;
|
||||
|
||||
half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
|
||||
KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
|
||||
KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
|
||||
KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
nvcuda::wmma::load_matrix_sync(
|
||||
KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
|
||||
KQ + j0*(kqar*kqs_padded) + k,
|
||||
kqar*kqs_padded);
|
||||
}
|
||||
}
|
||||
|
||||
frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
|
||||
frag_a_V v_a;
|
||||
nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync(
|
||||
KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
|
||||
VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
|
||||
D_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
half2 VKQ_scale;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
|
||||
} else {
|
||||
VKQ_scale = KQ_max_scale_h2[j0/nwarps];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 VKQ_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int l = 0; l < VKQ_ratio; ++l) {
|
||||
VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j_VKQ = j0 + threadIdx.y;
|
||||
if (ic0 + j_VKQ >= ne01) {
|
||||
return;
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
|
||||
float KQ_rowsum_j;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
|
||||
} else {
|
||||
KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
float dst_val = VKQ[j_VKQ*D_padded + i];
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= KQ_rowsum_j;
|
||||
}
|
||||
dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks == 1 || threadIdx.x != 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
float2 dst_meta_val;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
dst_meta_val.x = KQ_max_f[j0/nwarps];
|
||||
} else {
|
||||
dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
|
||||
}
|
||||
dst_meta_val.y = KQ_rowsum_j;
|
||||
dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
|
||||
}
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
|
||||
}
|
||||
|
||||
constexpr int get_max_power_of_2(int x) {
|
||||
return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
|
||||
}
|
||||
|
||||
static_assert(get_max_power_of_2(1) == 1, "Test failed.");
|
||||
static_assert(get_max_power_of_2(2) == 2, "Test failed.");
|
||||
static_assert(get_max_power_of_2(4) == 4, "Test failed.");
|
||||
static_assert(get_max_power_of_2(6) == 2, "Test failed.");
|
||||
|
||||
// Number of VKQ rows calculated in parallel:
|
||||
constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
|
||||
return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
|
||||
}
|
||||
|
||||
static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
|
||||
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 <int D, int cols_per_block, typename KQ_acc_t>
|
||||
void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * KQV = dst;
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
constexpr int nwarps = 4;
|
||||
|
||||
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;
|
||||
|
||||
float logit_softcap;
|
||||
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
|
||||
|
||||
if (4*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 4;
|
||||
fattn_kernel_t fattn_kernel;
|
||||
if (logit_softcap == 0.0f) {
|
||||
constexpr bool use_logit_softcap = false;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
} else {
|
||||
constexpr bool use_logit_softcap = true;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
}
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
|
||||
return;
|
||||
}
|
||||
if (2*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 2;
|
||||
fattn_kernel_t fattn_kernel;
|
||||
if (logit_softcap == 0.0f) {
|
||||
constexpr bool use_logit_softcap = false;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
} else {
|
||||
constexpr bool use_logit_softcap = true;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
}
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
|
||||
return;
|
||||
}
|
||||
constexpr int parallel_blocks = 1;
|
||||
fattn_kernel_t fattn_kernel;
|
||||
if (logit_softcap == 0.0f) {
|
||||
constexpr bool use_logit_softcap = false;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
} else {
|
||||
constexpr bool use_logit_softcap = true;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
}
|
||||
launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
|
||||
}
|
||||
|
||||
void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * KQV = dst;
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
|
||||
|
||||
if (prec != GGML_PREC_DEFAULT) {
|
||||
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
|
||||
constexpr int cols_per_block = 16;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
constexpr int cols_per_block = 32;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
// case 256:
|
||||
// ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
|
||||
// break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
|
||||
constexpr int cols_per_block = 8;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 32) {
|
||||
constexpr int cols_per_block = 16;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 32;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
|
@ -1,543 +1,3 @@
|
|||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
|
||||
#ifdef FP16_MMA_AVAILABLE
|
||||
#include <mma.h>
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
|
||||
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
|
||||
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t, bool use_logit_softcap>
|
||||
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
__launch_bounds__(nwarps*WARP_SIZE, 1)
|
||||
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
|
||||
static __global__ void flash_attn_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 float logit_softcap,
|
||||
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 nb21,
|
||||
const int nb22,
|
||||
const int nb23,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#ifdef FP16_MMA_AVAILABLE
|
||||
// Skip unused kernel variants for faster compilation:
|
||||
if (use_logit_softcap && !(D == 128 || D == 256)) {
|
||||
NO_DEVICE_CODE;
|
||||
return;
|
||||
}
|
||||
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first 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.
|
||||
|
||||
static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
|
||||
static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
|
||||
constexpr int frag_m = ncols == 8 ? 32 : 16;
|
||||
constexpr int frag_n = ncols == 8 ? 8 : 16;
|
||||
static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
|
||||
|
||||
constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
|
||||
constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
|
||||
static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
|
||||
|
||||
// Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
|
||||
constexpr int D_padded = D + 8;
|
||||
constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
|
||||
constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
|
||||
const half * K_h = (const half *) (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 + (nb31/sizeof(half))* ic0;
|
||||
const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
|
||||
|
||||
const int stride_Q = nb01 / sizeof(float);
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
|
||||
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);
|
||||
|
||||
const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
|
||||
|
||||
frag_b Q_b[D/16][ncols/frag_n];
|
||||
|
||||
// A single buffer for temporarily holding tiles of KQ and VKQ parts:
|
||||
constexpr int mem_KQ = ncols*kqs_padded*kqar;
|
||||
constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
|
||||
__shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
|
||||
float * KQ_f = (float *) KQ;
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
float KQ_rowsum_f[ncols/nwarps] = {0.0f};
|
||||
float KQ_max_f[ncols/nwarps];
|
||||
float KQ_max_scale_f[ncols/nwarps] = {0.0f};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_f[j] = -FLT_MAX/2.0f;
|
||||
}
|
||||
|
||||
half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
half2 KQ_max_h2[ncols/nwarps];
|
||||
half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
|
||||
}
|
||||
|
||||
__shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
|
||||
half2 * VKQ2 = (half2 *) VKQ;
|
||||
#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;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
|
||||
}
|
||||
}
|
||||
|
||||
// Convert Q to half and apply scale, temporarily store in KQ:
|
||||
#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 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Load Q into tensor core fragments/registers since it will be used frequently:
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += 16) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Iterate over ne11 == previous tokens:
|
||||
for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
|
||||
// Calculate tile of KQ:
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
|
||||
frag_c_KQ KQ_c[ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
|
||||
}
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
|
||||
frag_a_K K_a;
|
||||
nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
|
||||
}
|
||||
}
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Calculate softmax for each KQ column using the current max. value.
|
||||
// The divisor is stored in KQ_rowsum and will be applied at the end.
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
|
||||
|
||||
if (use_logit_softcap) {
|
||||
KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
}
|
||||
|
||||
float KQ_max_new = KQ_max_f[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
|
||||
KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = warp_reduce_max(KQ_max_new);
|
||||
|
||||
const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_f[j0/nwarps] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_max_scale_f[j0/nwarps] = 0.0f;
|
||||
}
|
||||
KQ_max_f[j0/nwarps] = KQ_max_new;
|
||||
|
||||
float KQ_rowsum_add = 0.0f;
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
|
||||
KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
|
||||
}
|
||||
KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
|
||||
KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
|
||||
} else {
|
||||
half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
|
||||
|
||||
if (use_logit_softcap) {
|
||||
// There is no dedicated tangens hyperbolicus function for half2.
|
||||
KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
|
||||
KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
|
||||
/(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
|
||||
}
|
||||
}
|
||||
|
||||
half2 KQ_max_new = KQ_max_h2[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
|
||||
KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
|
||||
const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
|
||||
KQ_max_h2[j0/nwarps] = KQ_max_new;
|
||||
|
||||
half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
|
||||
KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
|
||||
KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
|
||||
KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
nvcuda::wmma::load_matrix_sync(
|
||||
KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
|
||||
KQ + j0*(kqar*kqs_padded) + k,
|
||||
kqar*kqs_padded);
|
||||
}
|
||||
}
|
||||
|
||||
frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
|
||||
frag_a_V v_a;
|
||||
nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync(
|
||||
KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
|
||||
VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
|
||||
D_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
half2 VKQ_scale;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
|
||||
} else {
|
||||
VKQ_scale = KQ_max_scale_h2[j0/nwarps];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 VKQ_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int l = 0; l < VKQ_ratio; ++l) {
|
||||
VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j_VKQ = j0 + threadIdx.y;
|
||||
if (ic0 + j_VKQ >= ne01) {
|
||||
return;
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
|
||||
float KQ_rowsum_j;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
|
||||
} else {
|
||||
KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
float dst_val = VKQ[j_VKQ*D_padded + i];
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= KQ_rowsum_j;
|
||||
}
|
||||
dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks == 1 || threadIdx.x != 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
float2 dst_meta_val;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
dst_meta_val.x = KQ_max_f[j0/nwarps];
|
||||
} else {
|
||||
dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
|
||||
}
|
||||
dst_meta_val.y = KQ_rowsum_j;
|
||||
dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
|
||||
}
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
constexpr int get_max_power_of_2(int x) {
|
||||
return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
|
||||
}
|
||||
|
||||
static_assert(get_max_power_of_2(1) == 1, "Test failed.");
|
||||
static_assert(get_max_power_of_2(2) == 2, "Test failed.");
|
||||
static_assert(get_max_power_of_2(4) == 4, "Test failed.");
|
||||
static_assert(get_max_power_of_2(6) == 2, "Test failed.");
|
||||
|
||||
// Number of VKQ rows calculated in parallel:
|
||||
constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
|
||||
return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
|
||||
}
|
||||
|
||||
static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
|
||||
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 <int D, int cols_per_block, typename KQ_acc_t>
|
||||
void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * KQV = dst;
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
constexpr int nwarps = 4;
|
||||
|
||||
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;
|
||||
|
||||
float logit_softcap;
|
||||
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
|
||||
|
||||
if (4*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 4;
|
||||
fattn_kernel_t fattn_kernel;
|
||||
if (logit_softcap == 0.0f) {
|
||||
constexpr bool use_logit_softcap = false;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
} else {
|
||||
constexpr bool use_logit_softcap = true;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
}
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
|
||||
return;
|
||||
}
|
||||
if (2*blocks_num_pb1 < 2*nsm) {
|
||||
constexpr int parallel_blocks = 2;
|
||||
fattn_kernel_t fattn_kernel;
|
||||
if (logit_softcap == 0.0f) {
|
||||
constexpr bool use_logit_softcap = false;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
} else {
|
||||
constexpr bool use_logit_softcap = true;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
}
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
|
||||
return;
|
||||
}
|
||||
constexpr int parallel_blocks = 1;
|
||||
fattn_kernel_t fattn_kernel;
|
||||
if (logit_softcap == 0.0f) {
|
||||
constexpr bool use_logit_softcap = false;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
} else {
|
||||
constexpr bool use_logit_softcap = true;
|
||||
fattn_kernel = flash_attn_ext_f16<
|
||||
D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
|
||||
}
|
||||
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
|
||||
}
|
||||
|
||||
#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t) \
|
||||
template void ggml_cuda_flash_attn_ext_wmma_f16_case \
|
||||
<D, cols_per_block, KQ_acc_t>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 16, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 32, float);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 32, float);
|
||||
// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 8, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 8, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 8, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 8, half);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 16, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
|
||||
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(112, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(128, 32, half);
|
||||
extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
|
||||
void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
#include "common.cuh"
|
||||
#include "fattn-common.cuh"
|
||||
#include "fattn-mma-f16.cuh"
|
||||
#include "fattn-tile-f16.cuh"
|
||||
#include "fattn-tile-f32.cuh"
|
||||
#include "fattn-vec-f16.cuh"
|
||||
|
|
@ -7,144 +8,56 @@
|
|||
#include "fattn-wmma-f16.cuh"
|
||||
#include "fattn.cuh"
|
||||
|
||||
#include <cstdint>
|
||||
|
||||
static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * KQV = dst;
|
||||
template <int cols_per_block>
|
||||
static void ggml_cuda_flash_attn_ext_mma_f16_switch_hs(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
|
||||
|
||||
if (prec != GGML_PREC_DEFAULT) {
|
||||
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
|
||||
constexpr int cols_per_block = 16;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
|
||||
ggml_cuda_flash_attn_ext_mma_f16_case< 64, cols_per_block>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
|
||||
ggml_cuda_flash_attn_ext_mma_f16_case< 80, cols_per_block>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
|
||||
ggml_cuda_flash_attn_ext_mma_f16_case< 96, cols_per_block>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
|
||||
ggml_cuda_flash_attn_ext_mma_f16_case<112, cols_per_block>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
ggml_cuda_flash_attn_ext_mma_f16_case<128, cols_per_block>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
constexpr int cols_per_block = 32;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
break;
|
||||
// case 256:
|
||||
// ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
|
||||
// break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
|
||||
constexpr int cols_per_block = 8;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 32) {
|
||||
constexpr int cols_per_block = 16;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 32;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 80:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 96:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 112:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 128:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
|
||||
break;
|
||||
case 256:
|
||||
ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
|
||||
ggml_cuda_flash_attn_ext_mma_f16_case<256, cols_per_block>(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
|
||||
if (Q->ne[1] <= 8) {
|
||||
ggml_cuda_flash_attn_ext_mma_f16_switch_hs<8>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 16) {
|
||||
ggml_cuda_flash_attn_ext_mma_f16_switch_hs<16>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 32) {
|
||||
ggml_cuda_flash_attn_ext_mma_f16_switch_hs<32>(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
ggml_cuda_flash_attn_ext_mma_f16_switch_hs<64>(ctx, dst);
|
||||
}
|
||||
|
||||
#define FATTN_VEC_F16_CASE(D, type_K, type_V) \
|
||||
if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \
|
||||
ggml_cuda_flash_attn_ext_vec_f16_case<D, type_K, type_V>(ctx, dst); \
|
||||
|
|
@ -323,11 +236,19 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
|
|||
}
|
||||
|
||||
if (!fp16_mma_available(cc)) {
|
||||
if (prec == GGML_PREC_DEFAULT) {
|
||||
if (Q->ne[1] <= 8) {
|
||||
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
|
||||
} else {
|
||||
ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
|
||||
}
|
||||
} else {
|
||||
if (Q->ne[1] <= 8) {
|
||||
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
|
||||
} else {
|
||||
ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
|
|
@ -341,5 +262,11 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
|
|||
}
|
||||
}
|
||||
|
||||
// The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
|
||||
if (cc == GGML_CUDA_CC_VOLTA) {
|
||||
ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@
|
|||
#include "ggml-cuda/upscale.cuh"
|
||||
#include "ggml-cuda/wkv6.cuh"
|
||||
#include "ggml-cuda/gla.cuh"
|
||||
#include "ggml.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <array>
|
||||
|
|
@ -242,6 +243,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
|
|||
|
||||
info.devices[id].nsm = prop.multiProcessorCount;
|
||||
info.devices[id].smpb = prop.sharedMemPerBlock;
|
||||
info.devices[id].warp_size = prop.warpSize;
|
||||
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
|
||||
info.devices[id].smpbo = prop.sharedMemPerBlock;
|
||||
|
||||
|
|
@ -256,8 +258,9 @@ static ggml_cuda_device_info ggml_cuda_init() {
|
|||
info.devices[id].cc += prop.minor * 0x10;
|
||||
}
|
||||
}
|
||||
GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s\n",
|
||||
id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff, device_vmm ? "yes" : "no");
|
||||
GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d\n",
|
||||
id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff,
|
||||
device_vmm ? "yes" : "no", prop.warpSize);
|
||||
#else
|
||||
info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
|
||||
info.devices[id].cc = 100*prop.major + 10*prop.minor;
|
||||
|
|
@ -1203,7 +1206,7 @@ static void ggml_cuda_op_mul_mat_cublas(
|
|||
|
||||
CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
|
||||
|
||||
if (compute_capability == GGML_CUDA_CC_CDNA) {
|
||||
if (GGML_CUDA_CC_IS_CDNA(compute_capability)) {
|
||||
const float alpha = 1.0f;
|
||||
const float beta = 0.0f;
|
||||
CUBLAS_CHECK(
|
||||
|
|
@ -1748,7 +1751,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
|
|||
beta = &beta_f32;
|
||||
}
|
||||
|
||||
if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
|
||||
if (GGML_CUDA_CC_IS_CDNA(ggml_cuda_info().devices[ctx.device].cc)) {
|
||||
cu_compute_type = CUBLAS_COMPUTE_32F;
|
||||
alpha = &alpha_f32;
|
||||
beta = &beta_f32;
|
||||
|
|
@ -3137,6 +3140,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
|||
break;
|
||||
case GGML_OP_NORM:
|
||||
case GGML_OP_RMS_NORM:
|
||||
return true;
|
||||
case GGML_OP_RMS_NORM_BACK:
|
||||
return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0;
|
||||
break;
|
||||
|
|
@ -3179,7 +3183,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
|||
case GGML_OP_SUM_ROWS:
|
||||
case GGML_OP_ARGSORT:
|
||||
case GGML_OP_ACC:
|
||||
return true;
|
||||
case GGML_OP_GROUP_NORM:
|
||||
return ggml_is_contiguous(op->src[0]);
|
||||
case GGML_OP_UPSCALE:
|
||||
case GGML_OP_PAD:
|
||||
case GGML_OP_ARANGE:
|
||||
|
|
|
|||
|
|
@ -1,11 +1,67 @@
|
|||
// This file contains primitives that expose the tensor core PTX instructions for CUDA code.
|
||||
// The primitives can be used in a similar way as the nvcuda::wmma interface but with a well-defined memory layout.
|
||||
// The documentation for the PTX instructions can be found under:
|
||||
// https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-multiply-accumulate-operation-using-mma-instruction
|
||||
//
|
||||
// Like with nvcuda::wmma there are three types of matrix tiles: A, B, and C with A @ B = C.
|
||||
// A is a row-major matrix with shape I x K.
|
||||
// B is a column-major matrix with shape K x J.
|
||||
// C is a column-major matrix with shape I x J.
|
||||
// Note that along their lowest dimension I, J, and K are measured in physical 32 bit elements instead of logical elements.
|
||||
// The functions get_i, get_j, and get_k can be used to get the physical 32 bit index of the lth element of a thread within a tile.
|
||||
// All matrix tiles have ne physical 32 bit elements per warp.
|
||||
//
|
||||
// As described in the documentation, all pointers for load_ldmatrix must be to shared memory and aligned to 16 bytes.
|
||||
|
||||
#include "common.cuh"
|
||||
|
||||
struct mma_int_A_I16K4 {
|
||||
|
||||
#if CUDART_VERSION >= 11800
|
||||
|
||||
static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
|
||||
int ret = 0;
|
||||
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
asm("movmatrix.sync.aligned.m8n8.trans.b16 %0, %1;"
|
||||
: "+r"(ret) : "r"(x));
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // defined(NEW_MMA_AVAILABLE)
|
||||
return ret;
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
|
||||
// Imagine transposing row-major matrix to column-major matrix.
|
||||
const int src_i_low = 2 * (threadIdx.x % 4);
|
||||
const int src_i_high = src_i_low + 1;
|
||||
const int src_j = threadIdx.x / 4;
|
||||
|
||||
const int src_laneid_low = src_i_low * 4 + src_j / 2;
|
||||
const int src_laneid_high = src_i_high * 4 + src_j / 2;
|
||||
|
||||
const int shift_low = ((src_j + 0) % 2) * 16;
|
||||
const int shift_high = ((src_j + 1) % 2) * 16;
|
||||
|
||||
const int ret_low = (__shfl_sync(0xFFFFFFFF, x, src_laneid_low, WARP_SIZE) >> shift_low) & 0x0000FFFF;
|
||||
const int ret_high = (__shfl_sync(0xFFFFFFFF, x, src_laneid_high, WARP_SIZE) << shift_high) & 0xFFFF0000;
|
||||
|
||||
return ret_low | ret_high;
|
||||
}
|
||||
|
||||
#endif // CUDART_VERSION >= 11800
|
||||
|
||||
|
||||
template <typename T>
|
||||
struct mma_A_I16K4 {
|
||||
static_assert(sizeof(T) == 4, "bad type size");
|
||||
|
||||
static constexpr int I = 16;
|
||||
static constexpr int K = 4;
|
||||
static constexpr int ne = 2;
|
||||
|
||||
int x[ne] = {0};
|
||||
T x[ne];
|
||||
|
||||
static __device__ __forceinline__ int get_i(const int l) {
|
||||
const int ret = (l%2) * (I/2) + threadIdx.x / K;
|
||||
|
|
@ -21,27 +77,35 @@ struct mma_int_A_I16K4 {
|
|||
return ret;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
|
||||
#if defined(INT8_MMA_AVAILABLE)
|
||||
const int * xs = xs0 + (threadIdx.x%I)*stride;
|
||||
asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
|
||||
: "+r"(x[0]), "+r"(x[1])
|
||||
: "l"(xs));
|
||||
#else
|
||||
__device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < ne; ++l) {
|
||||
x[l] = xs0[get_i(l)*stride + get_k(l)];
|
||||
}
|
||||
#endif // defined(INT8_MMA_AVAILABLE)
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
int * xi = (int *) x;
|
||||
const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride;
|
||||
asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
|
||||
: "+r"(xi[0]), "+r"(xi[1])
|
||||
: "l"(xs));
|
||||
#else
|
||||
load_generic(xs0, stride);
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
};
|
||||
|
||||
struct mma_int_A_I16K8 {
|
||||
template <typename T>
|
||||
struct mma_A_I16K8 {
|
||||
static_assert(sizeof(T) == 4, "bad type size");
|
||||
|
||||
static constexpr int I = 16;
|
||||
static constexpr int K = 8;
|
||||
static constexpr int ne = 4;
|
||||
|
||||
int x[ne] = {0};
|
||||
T x[ne];
|
||||
|
||||
static __device__ __forceinline__ int get_i(const int l) {
|
||||
const int ret = (l%2) * (I/2) + threadIdx.x / (K/2);
|
||||
|
|
@ -57,31 +121,62 @@ struct mma_int_A_I16K8 {
|
|||
return ret;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
|
||||
#if defined(INT8_MMA_AVAILABLE)
|
||||
const int * xs = xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
|
||||
asm("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
|
||||
: "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
|
||||
: "l"(xs));
|
||||
#else
|
||||
__device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < ne; ++l) {
|
||||
x[l] = xs0[get_i(l)*stride + get_k(l)];
|
||||
}
|
||||
#endif // defined(INT8_MMA_AVAILABLE)
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load_low(const int * __restrict__ xs0, const int & stride) {
|
||||
((mma_int_A_I16K4 *) x)[0].load(xs0, stride);
|
||||
__device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
int * xi = (int * ) x;
|
||||
const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
|
||||
asm("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
|
||||
: "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
|
||||
: "l"(xs));
|
||||
#else
|
||||
GGML_UNUSED(xs0);
|
||||
GGML_UNUSED(stride);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load_ldmatrix_trans(const T * __restrict__ xs0, const int & stride) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
int * xi = (int * ) x;
|
||||
const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
|
||||
asm("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
|
||||
: "+r"(xi[0]), "+r"(xi[2]), "+r"(xi[1]), "+r"(xi[3])
|
||||
: "l"(xs));
|
||||
#else
|
||||
GGML_UNUSED(xs0);
|
||||
GGML_UNUSED(stride);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void transpose() {
|
||||
int * xi = (int *) x;
|
||||
xi[0] = ggml_cuda_movmatrix(xi[0]);
|
||||
|
||||
const int tmp = ggml_cuda_movmatrix(xi[1]);
|
||||
xi[1] = ggml_cuda_movmatrix(xi[2]);
|
||||
xi[2] = tmp;
|
||||
|
||||
xi[3] = ggml_cuda_movmatrix(xi[3]);
|
||||
}
|
||||
};
|
||||
|
||||
struct mma_int_B_J8K4 {
|
||||
template <typename T>
|
||||
struct mma_B_J8K4 {
|
||||
static_assert(sizeof(T) == 4, "bad type size");
|
||||
|
||||
static constexpr int J = 8;
|
||||
static constexpr int K = 4;
|
||||
static constexpr int ne = 1;
|
||||
|
||||
int x[ne] = {0};
|
||||
T x[ne];
|
||||
|
||||
static __device__ __forceinline__ int get_j(const int /* l */) {
|
||||
const int ret = threadIdx.x / K;
|
||||
|
|
@ -97,27 +192,34 @@ struct mma_int_B_J8K4 {
|
|||
return ret;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
|
||||
#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
|
||||
const int * xs = xs0 + (threadIdx.x%J)*stride;
|
||||
asm("ldmatrix.sync.aligned.m8n8.x1.b16 {%0}, [%1];"
|
||||
: "+r"(x[0])
|
||||
: "l"(xs));
|
||||
#else
|
||||
__device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < ne; ++l) {
|
||||
x[l] = xs0[get_j(l)*stride + get_k(l)];
|
||||
}
|
||||
#endif // defined(INT8_MMA_AVAILABLE)
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
int * xi = (int *) x;
|
||||
const int * xs = (const int *) xs0 + (threadIdx.x%J)*stride;
|
||||
asm("ldmatrix.sync.aligned.m8n8.x1.b16 {%0}, [%1];"
|
||||
: "+r"(xi[0]) : "l"(xs));
|
||||
#else
|
||||
load_generic(xs0, stride);
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
};
|
||||
|
||||
struct mma_int_B_J8K8 {
|
||||
template <typename T>
|
||||
struct mma_B_J8K8 {
|
||||
static_assert(sizeof(T) == 4, "bad type size");
|
||||
|
||||
static constexpr int J = 8;
|
||||
static constexpr int K = 8;
|
||||
static constexpr int ne = 2;
|
||||
|
||||
int x[ne] = {0};
|
||||
T x[ne];
|
||||
|
||||
static __device__ __forceinline__ int get_j(const int /* l */) {
|
||||
const int ret = threadIdx.x / (K/2);
|
||||
|
|
@ -133,22 +235,31 @@ struct mma_int_B_J8K8 {
|
|||
return ret;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
|
||||
#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
|
||||
const int * xs = xs0 + (threadIdx.x%J)*stride + ((threadIdx.x/J)*(K/2)) % K;
|
||||
asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
|
||||
: "+r"(x[0]), "+r"(x[1])
|
||||
: "l"(xs));
|
||||
#else
|
||||
__device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < ne; ++l) {
|
||||
x[l] = xs0[get_j(l)*stride + get_k(l)];
|
||||
}
|
||||
#endif // defined(INT8_MMA_AVAILABLE)
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
int * xi = (int *) x;
|
||||
const int * xs = (const int *) xs0 + (threadIdx.x%J)*stride + ((threadIdx.x/J)*(K/2)) % K;
|
||||
asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
|
||||
: "+r"(xi[0]), "+r"(xi[1])
|
||||
: "l"(xs));
|
||||
#else
|
||||
load_generic(xs0, stride);
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
};
|
||||
|
||||
struct mma_int_C_I16J8 {
|
||||
template <typename T>
|
||||
struct mma_C_I16J8 {};
|
||||
|
||||
template <>
|
||||
struct mma_C_I16J8<int> {
|
||||
static constexpr int I = 16;
|
||||
static constexpr int J = 8;
|
||||
static constexpr int ne = 4;
|
||||
|
|
@ -169,8 +280,8 @@ struct mma_int_C_I16J8 {
|
|||
return ret;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) {
|
||||
#ifdef INT8_MMA_AVAILABLE
|
||||
__device__ __forceinline__ void mma(const mma_A_I16K4<int> & mma_A, const mma_B_J8K4<int> & mma_B) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
|
||||
: "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
|
||||
|
|
@ -188,11 +299,11 @@ struct mma_int_C_I16J8 {
|
|||
GGML_UNUSED(mma_A);
|
||||
GGML_UNUSED(mma_B);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // INT8_MMA_AVAILABLE
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) {
|
||||
#ifdef INT8_MMA_AVAILABLE
|
||||
__device__ __forceinline__ void mma(const mma_A_I16K8<int> & mma_A, const mma_B_J8K8<int> & mma_B) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
|
||||
: "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
|
||||
|
|
@ -216,6 +327,132 @@ struct mma_int_C_I16J8 {
|
|||
GGML_UNUSED(mma_A);
|
||||
GGML_UNUSED(mma_B);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // INT8_MMA_AVAILABLE
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct mma_C_I16J8<half2> {
|
||||
static constexpr int I = 16;
|
||||
static constexpr int J = 4;
|
||||
static constexpr int ne = 2;
|
||||
|
||||
half2 x[ne] = {{0.0f, 0.0f}, {0.0f, 0.0f}};
|
||||
|
||||
static __device__ __forceinline__ int get_i(const int l) {
|
||||
const int ret = l * (I/2) + threadIdx.x / J;
|
||||
GGML_CUDA_ASSUME(ret >= 0);
|
||||
GGML_CUDA_ASSUME(ret < I);
|
||||
return ret;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ int get_j(const int /* l */) {
|
||||
const int ret = threadIdx.x % J;
|
||||
GGML_CUDA_ASSUME(ret >= 0);
|
||||
GGML_CUDA_ASSUME(ret < J);
|
||||
return ret;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void mma(const mma_A_I16K8<half2> & mma_A, const mma_B_J8K8<half2> & mma_B) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
int * Axi = (int *) mma_A.x;
|
||||
int * Bxi = (int *) mma_B.x;
|
||||
int * xi = (int *) x;
|
||||
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
asm("mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3, %4, %5}, {%6, %7}, {%0, %1};"
|
||||
: "+r"(xi[0]), "+r"(xi[1])
|
||||
: "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
|
||||
#else
|
||||
// On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
|
||||
asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
|
||||
: "+r"(xi[0]), "+r"(xi[1])
|
||||
: "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
|
||||
asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
|
||||
: "+r"(xi[0]), "+r"(xi[1])
|
||||
: "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
|
||||
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
#else
|
||||
GGML_UNUSED(mma_A);
|
||||
GGML_UNUSED(mma_B);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
__device__ __forceinline__ mma_B_J8K8<half2> to_mma_B() {
|
||||
mma_B_J8K8<half2> mma_B;
|
||||
|
||||
int * xi = (int *) x;
|
||||
int * Bxi = (int *) mma_B.x;
|
||||
Bxi[0] = ggml_cuda_movmatrix(xi[0]);
|
||||
Bxi[1] = ggml_cuda_movmatrix(xi[1]);
|
||||
|
||||
return mma_B;
|
||||
}
|
||||
};
|
||||
|
||||
template <>
|
||||
struct mma_C_I16J8<float> {
|
||||
static constexpr int I = 16;
|
||||
static constexpr int J = 8;
|
||||
static constexpr int ne = 4;
|
||||
|
||||
float x[ne] = {0.0f, 0.0f, 0.0f, 0.0f};
|
||||
|
||||
static __device__ __forceinline__ int get_i(const int l) {
|
||||
const int ret = (l/2) * (I/2) + threadIdx.x / (J/2);
|
||||
GGML_CUDA_ASSUME(ret >= 0);
|
||||
GGML_CUDA_ASSUME(ret < I);
|
||||
return ret;
|
||||
}
|
||||
|
||||
static __device__ __forceinline__ int get_j(const int l) {
|
||||
const int ret = 2 * (threadIdx.x % (J/2)) + l%2;
|
||||
GGML_CUDA_ASSUME(ret >= 0);
|
||||
GGML_CUDA_ASSUME(ret < J);
|
||||
return ret;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void mma(const mma_A_I16K8<half2> & mma_A, const mma_B_J8K8<half2> & mma_B) {
|
||||
#ifdef NEW_MMA_AVAILABLE
|
||||
int * Axi = (int *) mma_A.x;
|
||||
int * Bxi = (int *) mma_B.x;
|
||||
int * xi = (int *) x;
|
||||
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
asm("mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
|
||||
: "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
|
||||
: "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
|
||||
#else
|
||||
// On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
|
||||
asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
|
||||
: "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
|
||||
: "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
|
||||
asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
|
||||
: "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
|
||||
: "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
|
||||
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
|
||||
#else
|
||||
GGML_UNUSED(mma_A);
|
||||
GGML_UNUSED(mma_B);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // NEW_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
__device__ __forceinline__ mma_B_J8K8<half2> to_mma_B() {
|
||||
mma_B_J8K8<half2> mma_B;
|
||||
mma_B.x[0] = make_half2(x[0], x[1]);
|
||||
mma_B.x[1] = make_half2(x[2], x[3]);
|
||||
|
||||
int * Bxi = (int *) mma_B.x;
|
||||
Bxi[0] = ggml_cuda_movmatrix(Bxi[0]);
|
||||
Bxi[1] = ggml_cuda_movmatrix(Bxi[1]);
|
||||
|
||||
return mma_B;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void load_generic(const float * __restrict__ xs0, const int & stride) {
|
||||
#pragma unroll
|
||||
for (int l = 0; l < ne; ++l) {
|
||||
x[l] = xs0[get_j(l)*stride + get_i(l)];
|
||||
}
|
||||
}
|
||||
};
|
||||
|
|
|
|||
|
|
@ -132,7 +132,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
|
|||
return false;
|
||||
}
|
||||
|
||||
if (int8_mma_available(cc)) {
|
||||
if (new_mma_available(cc)) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -148,5 +148,5 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
|
|||
return cc < GGML_CUDA_CC_VOLTA || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
|
||||
}
|
||||
|
||||
return (cc < GGML_CUDA_CC_RDNA3 && cc != GGML_CUDA_CC_CDNA && cc != GGML_CUDA_CC_VEGA20) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
|
||||
return (!GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc) && !GGML_CUDA_CC_IS_GCN(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
|
|
@ -8,6 +8,7 @@ static __global__ void mul_mat_vec(
|
|||
const int64_t row = blockIdx.x;
|
||||
const int64_t channel = blockIdx.z;
|
||||
const int tid = threadIdx.x;
|
||||
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
||||
|
||||
x += (channel/channel_ratio)*stride_channel_x + row*stride_row;
|
||||
y += channel *stride_channel_y;
|
||||
|
|
@ -18,8 +19,8 @@ static __global__ void mul_mat_vec(
|
|||
extern __shared__ char data_mmv[];
|
||||
float * buf_iw = (float *) data_mmv;
|
||||
|
||||
if (block_size > WARP_SIZE) {
|
||||
if (tid < WARP_SIZE) {
|
||||
if (block_size > warp_size) {
|
||||
if (tid < warp_size) {
|
||||
buf_iw[tid] = 0.0f;
|
||||
}
|
||||
__syncthreads();
|
||||
|
|
@ -67,16 +68,16 @@ static __global__ void mul_mat_vec(
|
|||
static_assert(std::is_same<T, void>::value, "unsupported type");
|
||||
}
|
||||
|
||||
sumf = warp_reduce_sum(sumf);
|
||||
sumf = warp_reduce_sum<warp_size>(sumf);
|
||||
|
||||
if (block_size > WARP_SIZE) {
|
||||
buf_iw[tid/WARP_SIZE] = sumf;
|
||||
if (block_size > warp_size) {
|
||||
buf_iw[tid/warp_size] = sumf;
|
||||
__syncthreads();
|
||||
if (tid >= WARP_SIZE) {
|
||||
if (tid >= warp_size) {
|
||||
return;
|
||||
}
|
||||
sumf = buf_iw[tid];
|
||||
sumf = warp_reduce_sum(sumf);
|
||||
sumf = warp_reduce_sum<warp_size>(sumf);
|
||||
}
|
||||
|
||||
if (tid != 0) {
|
||||
|
|
@ -96,10 +97,19 @@ static void launch_mul_mat_vec_cuda(
|
|||
GGML_ASSERT(stride_row % 2 == 0);
|
||||
GGML_ASSERT(nchannels_y % nchannels_x == 0);
|
||||
const int64_t channel_ratio = nchannels_y / nchannels_x;
|
||||
int device;
|
||||
int warp_size;
|
||||
|
||||
int64_t block_size_best = WARP_SIZE;
|
||||
int64_t niter_best = (ncols + 2*WARP_SIZE - 1) / (2*WARP_SIZE);
|
||||
for (int64_t block_size = 2*WARP_SIZE; block_size <= 256; block_size += WARP_SIZE) {
|
||||
CUDA_CHECK(cudaGetDevice(&device));
|
||||
warp_size = ggml_cuda_info().devices[device].warp_size;
|
||||
|
||||
int64_t block_size_best = warp_size;
|
||||
int64_t niter_best = (ncols + 2*warp_size - 1) / (2*warp_size);
|
||||
int64_t max_block_size = 256;
|
||||
if(ggml_cuda_info().devices[device].cc > GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_info().devices[device].cc < GGML_CUDA_CC_RDNA1) {
|
||||
max_block_size = 128;
|
||||
}
|
||||
for (int64_t block_size = 2*warp_size; block_size <= max_block_size; block_size += warp_size) {
|
||||
const int64_t niter = (ncols + 2*block_size - 1) / (2*block_size);
|
||||
if (niter < niter_best) {
|
||||
niter_best = niter;
|
||||
|
|
@ -107,7 +117,7 @@ static void launch_mul_mat_vec_cuda(
|
|||
}
|
||||
}
|
||||
|
||||
const int smem = WARP_SIZE*sizeof(float);
|
||||
const int smem = warp_size*sizeof(float);
|
||||
const dim3 block_nums(nrows, 1, nchannels_y);
|
||||
const dim3 block_dims(block_size_best, 1, 1);
|
||||
switch (block_size_best) {
|
||||
|
|
|
|||
|
|
@ -1,12 +1,20 @@
|
|||
#include "norm.cuh"
|
||||
#include <cstdint>
|
||||
|
||||
template <int block_size>
|
||||
static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) {
|
||||
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
static __global__ void norm_f32(
|
||||
const float * x, float * dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
|
||||
const int64_t stride_sample, const float eps) {
|
||||
const int nrows = gridDim.x;
|
||||
const int nchannels = gridDim.y;
|
||||
|
||||
const int row = blockIdx.x;
|
||||
const int channel = blockIdx.y;
|
||||
const int sample = blockIdx.z;
|
||||
const int tid = threadIdx.x;
|
||||
|
||||
x += int64_t(row)*ncols;
|
||||
dst += int64_t(row)*ncols;
|
||||
x += sample*stride_sample + channel*stride_channel + row*stride_row;
|
||||
dst += ((sample*nchannels + channel)*nrows + row)*ncols;
|
||||
|
||||
float2 mean_var = make_float2(0.0f, 0.0f);
|
||||
|
||||
|
|
@ -97,12 +105,19 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
|
|||
}
|
||||
|
||||
template <int block_size>
|
||||
static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
|
||||
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
static __global__ void rms_norm_f32(
|
||||
const float * x, float * dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
|
||||
const int64_t stride_sample, const float eps) {
|
||||
const int nrows = gridDim.x;
|
||||
const int nchannels = gridDim.y;
|
||||
|
||||
const int row = blockIdx.x;
|
||||
const int channel = blockIdx.y;
|
||||
const int sample = blockIdx.z;
|
||||
const int tid = threadIdx.x;
|
||||
|
||||
x += int64_t(row)*ncols;
|
||||
dst += int64_t(row)*ncols;
|
||||
x += sample*stride_sample + channel*stride_channel + row*stride_row;
|
||||
dst += ((sample*nchannels + channel)*nrows + row)*ncols;
|
||||
|
||||
float tmp = 0.0f; // partial sum for thread in warp
|
||||
|
||||
|
|
@ -186,13 +201,16 @@ static __global__ void rms_norm_back_f32(
|
|||
}
|
||||
}
|
||||
|
||||
static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
|
||||
static void norm_f32_cuda(
|
||||
const float * x, float * dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
|
||||
const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample, const float eps, cudaStream_t stream) {
|
||||
const dim3 blocks_num(nrows, nchannels, nsamples);
|
||||
if (ncols < 1024) {
|
||||
const dim3 block_dims(WARP_SIZE, 1, 1);
|
||||
norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
||||
norm_f32<WARP_SIZE><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
|
||||
} else {
|
||||
const dim3 block_dims(1024, 1, 1);
|
||||
norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
||||
norm_f32<1024><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -207,13 +225,16 @@ static void group_norm_f32_cuda(
|
|||
}
|
||||
}
|
||||
|
||||
static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
|
||||
static void rms_norm_f32_cuda(
|
||||
const float * x, float * dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
|
||||
const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample, const float eps, cudaStream_t stream) {
|
||||
const dim3 blocks_num(nrows, nchannels, nsamples);
|
||||
if (ncols < 1024) {
|
||||
const dim3 block_dims(WARP_SIZE, 1, 1);
|
||||
rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
||||
rms_norm_f32<WARP_SIZE><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
|
||||
} else {
|
||||
const dim3 block_dims(1024, 1, 1);
|
||||
rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
||||
rms_norm_f32<1024><<<blocks_num, block_dims, 0, stream>>>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -229,23 +250,26 @@ static void rms_norm_back_f32_cuda(const float * grad, const float * xf, float *
|
|||
|
||||
void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * src0 = dst->src[0];
|
||||
const float * src0_d = (const float *)src0->data;
|
||||
float * dst_d = (float *)dst->data;
|
||||
const float * src0_d = (const float *) src0->data;
|
||||
float * dst_d = (float *) dst->data;
|
||||
cudaStream_t stream = ctx.stream();
|
||||
|
||||
GGML_ASSERT(ggml_is_contiguous(src0));
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
||||
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t nrows = ggml_nrows(src0);
|
||||
GGML_TENSOR_UNARY_OP_LOCALS;
|
||||
|
||||
float eps;
|
||||
memcpy(&eps, dst->op_params, sizeof(float));
|
||||
GGML_ASSERT(eps >= 0.0f);
|
||||
|
||||
norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
|
||||
const size_t ts0 = ggml_type_size(src0->type);
|
||||
GGML_ASSERT(nb00 == ts0);
|
||||
const int64_t s01 = nb01 / ts0;
|
||||
const int64_t s02 = nb02 / ts0;
|
||||
const int64_t s03 = nb03 / ts0;
|
||||
|
||||
norm_f32_cuda(src0_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, eps, stream);
|
||||
}
|
||||
|
||||
void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
|
|
@ -254,8 +278,6 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
|
|||
float * dst_d = (float *)dst->data;
|
||||
cudaStream_t stream = ctx.stream();
|
||||
|
||||
GGML_ASSERT(ggml_is_contiguous(src0));
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
||||
|
||||
|
|
@ -271,23 +293,26 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
|
|||
|
||||
void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * src0 = dst->src[0];
|
||||
const float * src0_d = (const float *)src0->data;
|
||||
float * dst_d = (float *)dst->data;
|
||||
const float * src0_d = (const float *) src0->data;
|
||||
float * dst_d = (float *) dst->data;
|
||||
cudaStream_t stream = ctx.stream();
|
||||
|
||||
GGML_ASSERT(ggml_is_contiguous(src0));
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
||||
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t nrows = ggml_nrows(src0);
|
||||
GGML_TENSOR_UNARY_OP_LOCALS;
|
||||
|
||||
float eps;
|
||||
memcpy(&eps, dst->op_params, sizeof(float));
|
||||
GGML_ASSERT(eps >= 0.0f);
|
||||
|
||||
rms_norm_f32_cuda(src0_d, dst_d, ne00, nrows, eps, stream);
|
||||
const size_t ts0 = ggml_type_size(src0->type);
|
||||
GGML_ASSERT(nb00 == ts0);
|
||||
const int64_t s01 = nb01 / ts0;
|
||||
const int64_t s02 = nb02 / ts0;
|
||||
const int64_t s03 = nb03 / ts0;
|
||||
|
||||
rms_norm_f32_cuda(src0_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, eps, stream);
|
||||
}
|
||||
|
||||
void ggml_cuda_op_rms_norm_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ __device__ float __forceinline__ t2f32<half>(half val) {
|
|||
#ifdef __clang__
|
||||
#pragma clang diagnostic push
|
||||
#pragma clang diagnostic ignored "-Wpass-failed"
|
||||
#endif
|
||||
#endif // __clang__
|
||||
template <bool use_shared, int ncols_template, int block_size_template, typename T>
|
||||
static __global__ void soft_max_f32(
|
||||
const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y,
|
||||
|
|
@ -126,7 +126,7 @@ static __global__ void soft_max_f32(
|
|||
}
|
||||
#ifdef __clang__
|
||||
#pragma clang diagnostic pop
|
||||
#endif
|
||||
#endif // __clang__
|
||||
|
||||
static __global__ void soft_max_back_f32(
|
||||
const float * grad, const float * dstf, float * dst, const int ncols, const float scale) {
|
||||
|
|
|
|||
|
|
@ -0,0 +1,10 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-mma-f16.cuh"
|
||||
|
||||
DECL_FATTN_MMA_F16_CASE(64, 16);
|
||||
DECL_FATTN_MMA_F16_CASE(80, 16);
|
||||
DECL_FATTN_MMA_F16_CASE(96, 16);
|
||||
DECL_FATTN_MMA_F16_CASE(112, 16);
|
||||
DECL_FATTN_MMA_F16_CASE(128, 16);
|
||||
DECL_FATTN_MMA_F16_CASE(256, 16);
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-mma-f16.cuh"
|
||||
|
||||
DECL_FATTN_MMA_F16_CASE(64, 32);
|
||||
DECL_FATTN_MMA_F16_CASE(80, 32);
|
||||
DECL_FATTN_MMA_F16_CASE(96, 32);
|
||||
DECL_FATTN_MMA_F16_CASE(112, 32);
|
||||
DECL_FATTN_MMA_F16_CASE(128, 32);
|
||||
DECL_FATTN_MMA_F16_CASE(256, 32);
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-mma-f16.cuh"
|
||||
|
||||
DECL_FATTN_MMA_F16_CASE(64, 64);
|
||||
DECL_FATTN_MMA_F16_CASE(80, 64);
|
||||
DECL_FATTN_MMA_F16_CASE(96, 64);
|
||||
DECL_FATTN_MMA_F16_CASE(112, 64);
|
||||
DECL_FATTN_MMA_F16_CASE(128, 64);
|
||||
DECL_FATTN_MMA_F16_CASE(256, 64);
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-mma-f16.cuh"
|
||||
|
||||
DECL_FATTN_MMA_F16_CASE(64, 8);
|
||||
DECL_FATTN_MMA_F16_CASE(80, 8);
|
||||
DECL_FATTN_MMA_F16_CASE(96, 8);
|
||||
DECL_FATTN_MMA_F16_CASE(112, 8);
|
||||
DECL_FATTN_MMA_F16_CASE(128, 8);
|
||||
DECL_FATTN_MMA_F16_CASE(256, 8);
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-wmma-f16.cuh"
|
||||
|
||||
DECL_FATTN_WMMA_F16_CASE(64, 16, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(80, 16, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(96, 16, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(112, 16, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(128, 16, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(256, 16, float);
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-wmma-f16.cuh"
|
||||
|
||||
DECL_FATTN_WMMA_F16_CASE(64, 32, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(80, 32, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(96, 32, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(112, 32, float);
|
||||
DECL_FATTN_WMMA_F16_CASE(128, 32, float);
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-wmma-f16.cuh"
|
||||
|
||||
DECL_FATTN_WMMA_F16_CASE(64, 16, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(80, 16, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(96, 16, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(112, 16, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(128, 16, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(256, 16, half);
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-wmma-f16.cuh"
|
||||
|
||||
DECL_FATTN_WMMA_F16_CASE(64, 32, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(80, 32, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(96, 32, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(112, 32, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(128, 32, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(256, 32, half);
|
||||
|
|
@ -1,8 +0,0 @@
|
|||
// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-wmma-f16.cuh"
|
||||
|
||||
DECL_FATTN_WMMA_F16_CASE(64, 8, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(96, 8, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(128, 8, half);
|
||||
DECL_FATTN_WMMA_F16_CASE(256, 8, half);
|
||||
|
|
@ -12,13 +12,13 @@ SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.p
|
|||
DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v});
|
||||
"""
|
||||
|
||||
SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
SOURCE_FATTN_MMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
|
||||
|
||||
#include "../fattn-wmma-f16.cuh"
|
||||
#include "../fattn-mma-f16.cuh"
|
||||
|
||||
"""
|
||||
|
||||
SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n"
|
||||
SOURCE_FATTN_MMA_CASE = "DECL_FATTN_MMA_F16_CASE({head_size}, {cols_per_block});\n"
|
||||
|
||||
TYPES_MMQ = [
|
||||
"GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
|
||||
|
|
@ -57,20 +57,12 @@ for vkq_size in [16, 32]:
|
|||
with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f:
|
||||
f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v))
|
||||
|
||||
for kq_acc_t in ["half", "float"]:
|
||||
for cols_per_block in [8, 16, 32]:
|
||||
if kq_acc_t == "float" and cols_per_block == 8:
|
||||
continue
|
||||
|
||||
with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f:
|
||||
f.write(SOURCE_FATTN_WMMA_START)
|
||||
for cols_per_block in [8, 16, 32, 64]:
|
||||
with open(f"fattn-mma-f16-instance-cpb{cols_per_block}.cu", "w") as f:
|
||||
f.write(SOURCE_FATTN_MMA_START)
|
||||
|
||||
for head_size in [64, 80, 96, 112, 128, 256]:
|
||||
if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32
|
||||
continue
|
||||
if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance
|
||||
continue
|
||||
f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size))
|
||||
f.write(SOURCE_FATTN_MMA_CASE.format(cols_per_block=cols_per_block, head_size=head_size))
|
||||
|
||||
for type in TYPES_MMQ:
|
||||
with open(f"mmq-instance-{get_short_name(type)}.cu", "w") as f:
|
||||
|
|
|
|||
3
ggml/src/ggml-cuda/vendors/hip.h
vendored
3
ggml/src/ggml-cuda/vendors/hip.h
vendored
|
|
@ -1,5 +1,6 @@
|
|||
#pragma once
|
||||
|
||||
#define HIP_ENABLE_WARP_SYNC_BUILTINS 1
|
||||
#include <hip/hip_runtime.h>
|
||||
#include <hipblas/hipblas.h>
|
||||
#include <hip/hip_fp16.h>
|
||||
|
|
@ -8,6 +9,7 @@
|
|||
// for rocblas_initialize()
|
||||
#include "rocblas/rocblas.h"
|
||||
#endif // __HIP_PLATFORM_AMD__
|
||||
|
||||
#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
|
||||
#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
|
||||
#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
|
||||
|
|
@ -25,6 +27,7 @@
|
|||
#define CU_MEM_LOCATION_TYPE_DEVICE hipMemLocationTypeDevice
|
||||
#define CU_MEM_ACCESS_FLAGS_PROT_READWRITE hipMemAccessFlagsProtReadWrite
|
||||
#define CU_CHECK(fn) {hipError_t err = fn; if(err != hipSuccess) { GGML_ABORT("HipVMM Failure: %s\n", hipGetErrorString(err)); }}
|
||||
#define __shfl_sync(mask, var, laneMask, width) __shfl(var, laneMask, width)
|
||||
#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
|
||||
#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
|
||||
#define cublasCreate hipblasCreate
|
||||
|
|
|
|||
|
|
@ -40,13 +40,20 @@ find_package(hip REQUIRED)
|
|||
find_package(hipblas REQUIRED)
|
||||
find_package(rocblas REQUIRED)
|
||||
|
||||
if (${hip_VERSION} VERSION_LESS 5.5)
|
||||
message(FATAL_ERROR "At least ROCM/HIP V5.5 is required")
|
||||
endif()
|
||||
|
||||
message(STATUS "HIP and hipBLAS found")
|
||||
|
||||
# Workaround old compilers
|
||||
set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} --gpu-max-threads-per-block=1024")
|
||||
|
||||
file(GLOB GGML_HEADERS_ROCM "../ggml-cuda/*.cuh")
|
||||
list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h")
|
||||
|
||||
file(GLOB GGML_SOURCES_ROCM "../ggml-cuda/*.cu")
|
||||
file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu")
|
||||
file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
|
||||
list(APPEND GGML_SOURCES_ROCM ${SRCS})
|
||||
file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu")
|
||||
list(APPEND GGML_SOURCES_ROCM ${SRCS})
|
||||
|
|
|
|||
|
|
@ -20,7 +20,10 @@
|
|||
#define GGML_METAL_MAX_COMMAND_BUFFERS 8
|
||||
|
||||
// create residency sets only on macOS >= 15.0
|
||||
#if TARGET_OS_OSX && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000
|
||||
#if TARGET_OS_OSX && __MAC_OS_X_VERSION_MAX_ALLOWED >= 150000 || \
|
||||
TARGET_OS_IOS && __IPHONE_OS_VERSION_MAX_ALLOWED >= 180000 || \
|
||||
TARGET_OS_TV && __TV_OS_VERSION_MAX_ALLOWED >= 180000 || \
|
||||
TARGET_OS_VISION && __VISION_OS_VERSION_MAX_ALLOWED >= 200000
|
||||
#define GGML_METAL_HAS_RESIDENCY_SETS 1
|
||||
#endif
|
||||
|
||||
|
|
@ -1071,7 +1074,7 @@ static bool ggml_backend_metal_buffer_rset_init(
|
|||
}
|
||||
|
||||
#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
|
||||
if (@available(macOS 15.0, *)) {
|
||||
if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) {
|
||||
MTLResidencySetDescriptor * desc = [[MTLResidencySetDescriptor alloc] init];
|
||||
desc.label = @"ggml_backend_metal";
|
||||
desc.initialCapacity = ctx->n_buffers;
|
||||
|
|
@ -1106,7 +1109,7 @@ static bool ggml_backend_metal_buffer_rset_init(
|
|||
// rset free
|
||||
static void ggml_backend_metal_buffer_rset_free(struct ggml_backend_metal_buffer_context * ctx) {
|
||||
#if defined(GGML_METAL_HAS_RESIDENCY_SETS)
|
||||
if (@available(macOS 15.0, *)) {
|
||||
if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) {
|
||||
if (ctx->rset) {
|
||||
[ctx->rset endResidency];
|
||||
[ctx->rset removeAllAllocations];
|
||||
|
|
@ -1203,10 +1206,11 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
|
|||
case GGML_OP_GROUP_NORM:
|
||||
return has_simdgroup_reduction;
|
||||
case GGML_OP_RMS_NORM:
|
||||
return has_simdgroup_reduction && (op->ne[0] % 4 == 0);
|
||||
return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
|
||||
case GGML_OP_ARGMAX:
|
||||
case GGML_OP_NORM:
|
||||
return true;
|
||||
case GGML_OP_NORM:
|
||||
return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
|
||||
case GGML_OP_ROPE:
|
||||
{
|
||||
const int mode = ((const int32_t *) op->op_params)[2];
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ if (MUSAToolkit_FOUND)
|
|||
list(APPEND GGML_HEADERS_MUSA "../../include/ggml-cuda.h")
|
||||
|
||||
file(GLOB GGML_SOURCES_MUSA "../ggml-cuda/*.cu")
|
||||
file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu")
|
||||
file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
|
||||
list(APPEND GGML_SOURCES_MUSA ${SRCS})
|
||||
file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu")
|
||||
list(APPEND GGML_SOURCES_MUSA ${SRCS})
|
||||
|
|
|
|||
|
|
@ -8182,9 +8182,11 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
|
|||
case GGML_OP_VIEW:
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
return true;
|
||||
case GGML_OP_NORM:
|
||||
case GGML_OP_GROUP_NORM:
|
||||
case GGML_OP_RMS_NORM:
|
||||
return ggml_is_contiguous(op->src[0]);
|
||||
case GGML_OP_ADD:
|
||||
case GGML_OP_ACC:
|
||||
case GGML_OP_MUL:
|
||||
|
|
|
|||
|
|
@ -1357,6 +1357,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
|
|
|
|||
|
|
@ -1199,6 +1199,18 @@ extern "C" {
|
|||
const char * grammar_str,
|
||||
const char * grammar_root);
|
||||
|
||||
/// @details Lazy grammar sampler, introduced in https://github.com/ggerganov/llama.cpp/pull/9639
|
||||
/// @param trigger_words A list of words that will trigger the grammar sampler. This may be updated to a loose regex syntax (w/ ^) in a near future.
|
||||
/// @param trigger_tokens A list of tokens that will trigger the grammar sampler.
|
||||
LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy(
|
||||
const struct llama_vocab * vocab,
|
||||
const char * grammar_str,
|
||||
const char * grammar_root,
|
||||
const char ** trigger_words,
|
||||
size_t num_trigger_words,
|
||||
const llama_token * trigger_tokens,
|
||||
size_t num_trigger_tokens);
|
||||
|
||||
/// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
|
||||
LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
|
||||
int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size)
|
||||
|
|
|
|||
202
models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja
Normal file
202
models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja
Normal file
|
|
@ -0,0 +1,202 @@
|
|||
|
||||
{%- macro json_to_python_type(json_spec) %}
|
||||
{%- set basic_type_map = {
|
||||
"string": "str",
|
||||
"number": "float",
|
||||
"integer": "int",
|
||||
"boolean": "bool"
|
||||
} %}
|
||||
|
||||
{%- if basic_type_map[json_spec.type] is defined %}
|
||||
{{- basic_type_map[json_spec.type] }}
|
||||
{%- elif json_spec.type == "array" %}
|
||||
{{- "List[" + json_to_python_type(json_spec.items) + "]"}}
|
||||
{%- elif json_spec.type == "object" %}
|
||||
{{- "Dict[str, " + json_to_python_type(json_spec.additionalProperties) + ']'}}
|
||||
{%- elif json_spec.type is iterable %}
|
||||
{{- "Union[" }}
|
||||
{%- for t in json_spec.type %}
|
||||
{{- json_to_python_type({"type": t}) }}
|
||||
{%- if not loop.last %}
|
||||
{{- "," }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- "]" }}
|
||||
{%- else %}
|
||||
{{- "Any" }}
|
||||
{%- endif %}
|
||||
{%- endmacro %}
|
||||
|
||||
{%- macro old_tool_parser(tools) %}
|
||||
{%- for tool in tools %}
|
||||
{%- if loop.index0 != 0 %}
|
||||
{{- '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- '```python\ndef ' + tool.name + '(' }}
|
||||
{%- for param_name, param_fields in tool.parameter_definitions|items %}
|
||||
{%- if loop.index0 != 0 %}
|
||||
{{- ', '}}
|
||||
{%- endif %}
|
||||
{{- param_name + ': ' }}
|
||||
{%- if not param_fields.required %}
|
||||
{{- 'Optional[' + param_fields.type + '] = None'}}
|
||||
{%- else %}
|
||||
{{- param_fields.type }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ') -> List[Dict]:\n """'}}
|
||||
{{- tool.description }}
|
||||
{%- if tool.parameter_definitions|length != 0 %}
|
||||
{{- '\n\n Args:\n '}}
|
||||
{%- for param_name, param_fields in tool.parameter_definitions|items %}
|
||||
{%- if loop.index0 != 0 %}
|
||||
{{- '\n ' }}
|
||||
{%- endif %}
|
||||
{{- param_name + ' ('}}
|
||||
{%- if not param_fields.required %}
|
||||
{{- 'Optional[' + param_fields.type + ']'}}
|
||||
{%- else %}
|
||||
{{- param_fields.type }}
|
||||
{%- endif %}
|
||||
{{- '): ' + param_fields.description }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '\n """\n pass\n```' }}
|
||||
{%- endfor %}
|
||||
{%- endmacro %}
|
||||
|
||||
{%- macro new_tool_parser(tools) %}
|
||||
{%- for tool in tools %}
|
||||
{%- if loop.index0 != 0 %}
|
||||
{{- '\n\n'}}
|
||||
{%- endif %}
|
||||
{%- if tool.function is defined %}
|
||||
{%- set tool = tool.function %}
|
||||
{%- endif %}
|
||||
{{-'```python
|
||||
def ' + tool.name + '('}}
|
||||
{%- for param_name, param_fields in tool.parameters.properties|items %}
|
||||
{%- if loop.index0 != 0 %}
|
||||
{{- ', '}}
|
||||
{%- endif %}
|
||||
{{-param_name + ": "}}
|
||||
{%- if not param_name in tool.parameters.required %}
|
||||
{{-'Optional[' + json_to_python_type(param_fields) + '] = None'}}
|
||||
{%- else %}
|
||||
{{- json_to_python_type(param_fields) }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ') -> List[Dict]:
|
||||
"""'}}
|
||||
{{- tool.description }}
|
||||
{%- if tool.parameters.properties|length != 0 %}
|
||||
{{- '\n\n Args:\n '}}
|
||||
{%- for param_name, param_fields in tool.parameters.properties|items %}
|
||||
{%- if loop.index0 != 0 %}
|
||||
{{- '\n ' }}
|
||||
{%- endif %}
|
||||
{{- param_name + ' ('}}
|
||||
{%- if not param_name in tool.parameters.required %}
|
||||
{{-'Optional[' + json_to_python_type(param_fields) + ']'}}
|
||||
{%- else %}
|
||||
{{- json_to_python_type(param_fields) }}
|
||||
{%- endif %}
|
||||
{{- '): ' + param_fields.description }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '\n """\n pass\n```' }}
|
||||
{%- endfor %}
|
||||
{%- endmacro %}
|
||||
|
||||
{{- bos_token }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{%- set loop_messages = messages[1:] %}
|
||||
{%- set system_message = messages[0]['content'] %}
|
||||
{%- else %}
|
||||
{%- set loop_messages = messages %}
|
||||
{%- set system_message = '## Task and Context\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user\'s needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.' %}
|
||||
{%- endif %}
|
||||
{{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' }}
|
||||
{{- '# Safety Preamble' }}
|
||||
{{- '
|
||||
The instructions in this section override those in the task description and style guide sections. Don\'t answer questions that are harmful or immoral.' }}
|
||||
{{- '
|
||||
|
||||
# System Preamble' }}
|
||||
{{- '
|
||||
## Basic Rules' }}
|
||||
{{- '
|
||||
You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user\'s requests, you cite your sources in your answers, according to those instructions.' }}
|
||||
{{- '
|
||||
|
||||
# User Preamble' }}
|
||||
{{- '
|
||||
' + system_message }}
|
||||
{{-'
|
||||
|
||||
## Available Tools
|
||||
Here is a list of tools that you have available to you:
|
||||
|
||||
'}}
|
||||
{%- set ns = namespace(new_tools=true) %}
|
||||
{%- for tool in tools %}
|
||||
{%- if tool.parameter_definitions is defined %}
|
||||
{%- set ns.new_tools = false %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if ns.new_tools %}
|
||||
{{- new_tool_parser(tools) }}
|
||||
{%- else %}
|
||||
{{- old_tool_parser(tools) }}
|
||||
{%- endif %}
|
||||
{{- '<|END_OF_TURN_TOKEN|>'}}
|
||||
{%- for message in loop_messages %}
|
||||
{%- set content = message['content'] %}
|
||||
{%- if message.role == 'user' %}
|
||||
{{- '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }}
|
||||
{%- elif message.role == 'system' %}
|
||||
{{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }}
|
||||
{%- elif message.role == 'assistant' and message.tool_calls is defined %}
|
||||
{{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}
|
||||
{%- if message.content is defined %}
|
||||
{{- message.content|trim }}
|
||||
{%- endif %}
|
||||
{{- '\nAction:\n```json\n[\n' }}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '{\n'|indent(4, first=true) }}
|
||||
{{- '"tool_name": "'|indent(8, first=true) + tool_call.name + '",\n' }}
|
||||
{{- '"parameters": '|indent(8, first=true) }}
|
||||
{%- if tool_call.arguments is defined and tool_call.arguments|length > 0 %}
|
||||
{{- tool_call.arguments|tojson(indent=4)|indent(8) }}
|
||||
{{- '\n' }}
|
||||
{%- else %}
|
||||
{{- '{}\n' }}
|
||||
{%- endif %}
|
||||
{{- '}'|indent(4, first=true) }}
|
||||
{%- if not loop.last %}
|
||||
{{- ',\n' }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- "\n]```\n" }}
|
||||
{%- elif message.role == 'assistant' %}
|
||||
{{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }}
|
||||
{%- elif message.role == 'tool' %}
|
||||
{{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results>\n' }}
|
||||
{{- message.content|trim }}
|
||||
{{- '</results><|END_OF_TURN_TOKEN|>' }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{-'<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write \'Action:\' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user\'s last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
|
||||
```json
|
||||
[
|
||||
{
|
||||
"tool_name": title of the tool in the specification,
|
||||
"parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
|
||||
}
|
||||
]```<|END_OF_TURN_TOKEN|>'}}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}
|
||||
{%- endif %}
|
||||
|
|
@ -0,0 +1,156 @@
|
|||
{{ bos_token }}{%- macro document_turn(documents) -%}
|
||||
{# format documents into chat turn #}
|
||||
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[
|
||||
{"tool_call_id": "0", "tool_name": "direct-injected-document", "parameters": {}}
|
||||
]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
|
||||
{
|
||||
"tool_call_id": "0",
|
||||
"results": {
|
||||
{% for doc in documents %}
|
||||
"{{ loop.index0 }}": {{doc|tojson}}{% if not loop.last %},
|
||||
{% endif %}
|
||||
{% endfor %}
|
||||
|
||||
},
|
||||
"is_error": null
|
||||
}
|
||||
]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}
|
||||
{%- macro tool_call_id_to_int(messages, tool_call_id) %}
|
||||
{%- set counter = namespace(value=0) %}
|
||||
{%- set tool_call_id_seen = namespace(value=false) %}
|
||||
{%- for msg in messages %}
|
||||
{%- if msg.tool_calls %}
|
||||
{%- for tool_call in msg.tool_calls %}
|
||||
{%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}
|
||||
{{ counter.value }}
|
||||
{%- set tool_call_id_seen.value = true %}
|
||||
{%- endif %}
|
||||
{%- set counter.value = counter.value + 1 %}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- endmacro %}
|
||||
{%- macro format_tool_message(messages, tool_msg) -%}
|
||||
{# format tool message #}
|
||||
{
|
||||
"tool_call_id": "{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}",
|
||||
"results": {
|
||||
"0": {{ tool_msg.content|tojson }}
|
||||
},
|
||||
"is_error": null
|
||||
}
|
||||
{%- endmacro -%}
|
||||
{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}
|
||||
{%- set tool_idx = namespace(value=0) %}
|
||||
{%- set tool_ids_seen = namespace(value=[]) %}
|
||||
{%- set sent_documents = namespace(value=false) %}
|
||||
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble
|
||||
You are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.
|
||||
|
||||
Your information cutoff date is June 2024.
|
||||
|
||||
You have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.
|
||||
{% if tools or documents %}
|
||||
|
||||
You have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.
|
||||
|
||||
## Tool Use
|
||||
Think about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.
|
||||
|
||||
0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.
|
||||
You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.
|
||||
NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.
|
||||
|
||||
Then carry out your plan by repeatedly executing the following steps.
|
||||
1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing "tool_name" and "parameters" fields.
|
||||
When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.
|
||||
2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.
|
||||
Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its "tool_call_id".
|
||||
3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.
|
||||
You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.
|
||||
NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.
|
||||
|
||||
You can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.
|
||||
|
||||
4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.
|
||||
{% if enable_citations %}
|
||||
|
||||
## Grounding
|
||||
Importantly, note that "Reflection" and "Response" above can be grounded.
|
||||
Grounding means you associate pieces of texts (called "spans") with those specific tool results that support them (called "sources"). And you use a pair of tags "<co>" and "</co>" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as "{tool_call_id}:[{list of result indices}]", before they are joined together by ",". E.g., "<co>span</co: 0:[1,2],1:[0]>" means that "span" is supported by result 1 and 2 from "tool_call_id=0" as well as result 0 from "tool_call_id=1".
|
||||
{% endif %}
|
||||
|
||||
## Available Tools
|
||||
Here is the list of tools that you have available to you.
|
||||
You can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.
|
||||
Each tool is represented as a JSON object with fields like "name", "description", "parameters" (per JSON Schema), and optionally, "responses" (per JSON Schema).
|
||||
|
||||
```json
|
||||
[
|
||||
{% if documents %}
|
||||
{"name": "direct-injected-document", "description": "This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!", "parameters": {"type": "object", "properties": {}, "required": []}, "responses": {"200": {"description": "Successfully returned a list of chunked text snippets from the directly uploaded documents.", "content": {"application/json": {"schema": {"type": "array", "items": {"type": "object", "required": ["url", "snippet"], "properties": {"url": {"type": "string", "description": "The url of the uploaded document."}, "snippet": {"type": "string", "description": "The text snippet for the returned document chunk."}}}}}}}}}{%- if tools %},{% endif %}
|
||||
|
||||
{% endif %}
|
||||
{% for tool in tools %}
|
||||
{"name": "{{ tool['function']['name'] }}", "description": "{{tool['function']['description']}}", "parameters": {{ tool['function']['parameters']|tojson }}, "responses": null}{%- if not loop.last %},{% endif %}
|
||||
|
||||
{% endfor %}
|
||||
]
|
||||
```
|
||||
|
||||
{% endif %}
|
||||
# Default Preamble
|
||||
The following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.
|
||||
- Your name is Command.
|
||||
- You are a large language model built by Cohere.
|
||||
- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.
|
||||
- If the input is ambiguous, ask clarifying follow-up questions.
|
||||
- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).
|
||||
- Use LaTeX to generate mathematical notation for complex equations.
|
||||
- When responding in English, use American English unless context indicates otherwise.
|
||||
- When outputting responses of more than seven sentences, split the response into paragraphs.
|
||||
- Prefer the active voice.
|
||||
- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.
|
||||
- Use gender-neutral pronouns for unspecified persons.
|
||||
- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.
|
||||
- Use the third person when asked to write a summary.
|
||||
- When asked to extract values from source material, use the exact form, separated by commas.
|
||||
- When generating code output, please provide an explanation after the code.
|
||||
- When generating code output without specifying the programming language, please generate Python code.
|
||||
- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.
|
||||
{%- if developer_preamble %}
|
||||
|
||||
|
||||
# Developer Preamble
|
||||
The following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.
|
||||
{{ developer_preamble }}
|
||||
{%- endif -%}
|
||||
<|END_OF_TURN_TOKEN|>
|
||||
{%- for message in messages %}
|
||||
{%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}
|
||||
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>
|
||||
{%- elif message.role|lower == 'user' %}
|
||||
<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}
|
||||
{%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}
|
||||
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[
|
||||
{% for tc in message.tool_calls %}
|
||||
{"tool_call_id": "{{ tool_idx.value }}", "tool_name": "{{ tc['function']['name'] }}", "parameters": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}
|
||||
|
||||
{% set tool_idx.value = tool_idx.value + 1 %}
|
||||
{% endfor %}
|
||||
]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}
|
||||
{% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}
|
||||
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
|
||||
{{ format_tool_message(messages, message) }}
|
||||
{%- for msg in messages[loop.index0 + 1:] %}
|
||||
{%- if msg.role|lower == 'tool' %},
|
||||
{{ format_tool_message(messages, msg) }}
|
||||
{%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}
|
||||
{%- else %}
|
||||
{%- break %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
|
||||
]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>
|
||||
{%- endif %}
|
||||
{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
||||
|
|
@ -0,0 +1,152 @@
|
|||
{%- macro json_to_python_type(json_spec) %}
|
||||
{%- set basic_type_map = {
|
||||
"string": "str",
|
||||
"number": "float",
|
||||
"integer": "int",
|
||||
"boolean": "bool"
|
||||
} %}
|
||||
|
||||
{%- if basic_type_map[json_spec.type] is defined %}
|
||||
{{- basic_type_map[json_spec.type] }}
|
||||
{%- elif json_spec.type == "array" %}
|
||||
{{- "list[" + json_to_python_type(json_spec|items) + "]"}}
|
||||
{%- elif json_spec.type == "object" %}
|
||||
{%- if json_spec.additionalProperties is defined %}
|
||||
{{- "dict[str, " + json_to_python_type(json_spec.additionalProperties) + ']'}}
|
||||
{%- else %}
|
||||
{{- "dict" }}
|
||||
{%- endif %}
|
||||
{%- elif json_spec.type is iterable %}
|
||||
{{- "Union[" }}
|
||||
{%- for t in json_spec.type %}
|
||||
{{- json_to_python_type({"type": t}) }}
|
||||
{%- if not loop.last %}
|
||||
{{- "," }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- "]" }}
|
||||
{%- else %}
|
||||
{{- "Any" }}
|
||||
{%- endif %}
|
||||
{%- endmacro %}
|
||||
|
||||
|
||||
{{- bos_token }}
|
||||
{{- '<|im_start|>system
|
||||
' }}
|
||||
{{- "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> " }}
|
||||
{%- for tool in tools %}
|
||||
{%- if tool.function is defined %}
|
||||
{%- set tool = tool.function %}
|
||||
{%- endif %}
|
||||
{{- '{"type": "function", "function": ' }}
|
||||
{{- '{"name": "' + tool.name + '", ' }}
|
||||
{{- '"description": "' + tool.name + '(' }}
|
||||
{%- for param_name, param_fields in tool.parameters.properties|items %}
|
||||
{{- param_name + ": " + json_to_python_type(param_fields) }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- if tool.return is defined %}
|
||||
{{- " -> " + json_to_python_type(tool.return) }}
|
||||
{%- endif %}
|
||||
{{- " - " + tool.description + "
|
||||
|
||||
" }}
|
||||
{%- for param_name, param_fields in tool.parameters.properties|items %}
|
||||
{%- if loop.first %}
|
||||
{{- " Args:
|
||||
" }}
|
||||
{%- endif %}
|
||||
{{- " " + param_name + "(" + json_to_python_type(param_fields) + "): " + param_fields.description|trim }}
|
||||
{%- endfor %}
|
||||
{%- if tool.return is defined and tool.return.description is defined %}
|
||||
{{- "
|
||||
Returns:
|
||||
" + tool.return.description }}
|
||||
{%- endif %}
|
||||
{{- '"' }}
|
||||
{{- ', "parameters": ' }}
|
||||
{%- if tool.parameters.properties | length == 0 %}
|
||||
{{- "{}" }}
|
||||
{%- else %}
|
||||
{{- tool.parameters|tojson }}
|
||||
{%- endif %}
|
||||
{{- "}" }}
|
||||
{%- if not loop.last %}
|
||||
{{- "
|
||||
" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- " </tools>" }}
|
||||
{{- 'Use the following pydantic model json schema for each tool call you will make: {"properties": {"name": {"title": "Name", "type": "string"}, "arguments": {"title": "Arguments", "type": "object"}}, "required": ["name", "arguments"], "title": "FunctionCall", "type": "object"}}
|
||||
' }}
|
||||
{{- "For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
|
||||
" }}
|
||||
{{- "<tool_call>
|
||||
" }}
|
||||
{{- '{"name": <function-name>, "arguments": <args-dict>}
|
||||
' }}
|
||||
{{- '</tool_call><|im_end|>
|
||||
' }}
|
||||
{%- for message in messages %}
|
||||
{%- if message.role == "user" or message.role == "system" or (message.role == "assistant" and message.tool_calls is not defined) %}
|
||||
{{- '<|im_start|>' + message.role + '
|
||||
' + message.content + '<|im_end|>' + '
|
||||
' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{{- '
|
||||
<tool_call>
|
||||
' }} {%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '{' }}
|
||||
{{- '"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '"' }}
|
||||
{{- ', '}}
|
||||
{%- if tool_call.arguments is defined %}
|
||||
{{- '"arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments|tojson }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{{- '}' }}
|
||||
{{- '
|
||||
</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>
|
||||
' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.previtem and loop.previtem.role != "tool" %}
|
||||
{{- '<|im_start|>tool
|
||||
' }}
|
||||
{%- endif %}
|
||||
{{- '<tool_response>
|
||||
' }}
|
||||
{{- message.content }}
|
||||
{%- if not loop.last %}
|
||||
{{- '
|
||||
</tool_response>
|
||||
' }}
|
||||
{%- else %}
|
||||
{{- '
|
||||
</tool_response>' }}
|
||||
{%- endif %}
|
||||
{%- if not loop.last and loop.nextitem.role != "tool" %}
|
||||
{{- '<|im_end|>' }}
|
||||
{%- elif loop.last %}
|
||||
{{- '<|im_end|>' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant
|
||||
' }}
|
||||
{%- endif %}
|
||||
|
|
@ -0,0 +1,152 @@
|
|||
{%- macro json_to_python_type(json_spec) %}
|
||||
{%- set basic_type_map = {
|
||||
"string": "str",
|
||||
"number": "float",
|
||||
"integer": "int",
|
||||
"boolean": "bool"
|
||||
} %}
|
||||
|
||||
{%- if basic_type_map[json_spec.type] is defined %}
|
||||
{{- basic_type_map[json_spec.type] }}
|
||||
{%- elif json_spec.type == "array" %}
|
||||
{{- "list[" + json_to_python_type(json_spec|items) + "]"}}
|
||||
{%- elif json_spec.type == "object" %}
|
||||
{%- if json_spec.additionalProperties is defined %}
|
||||
{{- "dict[str, " + json_to_python_type(json_spec.additionalProperties) + ']'}}
|
||||
{%- else %}
|
||||
{{- "dict" }}
|
||||
{%- endif %}
|
||||
{%- elif json_spec.type is iterable %}
|
||||
{{- "Union[" }}
|
||||
{%- for t in json_spec.type %}
|
||||
{{- json_to_python_type({"type": t}) }}
|
||||
{%- if not loop.last %}
|
||||
{{- "," }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- "]" }}
|
||||
{%- else %}
|
||||
{{- "Any" }}
|
||||
{%- endif %}
|
||||
{%- endmacro %}
|
||||
|
||||
|
||||
{{- bos_token }}
|
||||
{{- '<|im_start|>system
|
||||
' }}
|
||||
{{- "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> " }}
|
||||
{%- for tool in tools %}
|
||||
{%- if tool.function is defined %}
|
||||
{%- set tool = tool.function %}
|
||||
{%- endif %}
|
||||
{{- '{"type": "function", "function": ' }}
|
||||
{{- '{"name": "' + tool.name + '", ' }}
|
||||
{{- '"description": "' + tool.name + '(' }}
|
||||
{%- for param_name, param_fields in tool.parameters.properties|items %}
|
||||
{{- param_name + ": " + json_to_python_type(param_fields) }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- if tool.return is defined %}
|
||||
{{- " -> " + json_to_python_type(tool.return) }}
|
||||
{%- endif %}
|
||||
{{- " - " + tool.description + "
|
||||
|
||||
" }}
|
||||
{%- for param_name, param_fields in tool.parameters.properties|items %}
|
||||
{%- if loop.first %}
|
||||
{{- " Args:
|
||||
" }}
|
||||
{%- endif %}
|
||||
{{- " " + param_name + "(" + json_to_python_type(param_fields) + "): " + param_fields.description|trim }}
|
||||
{%- endfor %}
|
||||
{%- if tool.return is defined and tool.return.description is defined %}
|
||||
{{- "
|
||||
Returns:
|
||||
" + tool.return.description }}
|
||||
{%- endif %}
|
||||
{{- '"' }}
|
||||
{{- ', "parameters": ' }}
|
||||
{%- if tool.parameters.properties | length == 0 %}
|
||||
{{- "{}" }}
|
||||
{%- else %}
|
||||
{{- tool.parameters|tojson }}
|
||||
{%- endif %}
|
||||
{{- "}" }}
|
||||
{%- if not loop.last %}
|
||||
{{- "
|
||||
" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- " </tools>" }}
|
||||
{{- 'Use the following pydantic model json schema for each tool call you will make: {"properties": {"name": {"title": "Name", "type": "string"}, "arguments": {"title": "Arguments", "type": "object"}}, "required": ["name", "arguments"], "title": "FunctionCall", "type": "object"}}
|
||||
' }}
|
||||
{{- "For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
|
||||
" }}
|
||||
{{- "<tool_call>
|
||||
" }}
|
||||
{{- '{"name": <function-name>, "arguments": <args-dict>}
|
||||
' }}
|
||||
{{- '</tool_call><|im_end|>
|
||||
' }}
|
||||
{%- for message in messages %}
|
||||
{%- if message.role == "user" or message.role == "system" or (message.role == "assistant" and message.tool_calls is not defined) %}
|
||||
{{- '<|im_start|>' + message.role + '
|
||||
' + message.content + '<|im_end|>' + '
|
||||
' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{{- '
|
||||
<tool_call>
|
||||
' }} {%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '{' }}
|
||||
{{- '"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '"' }}
|
||||
{{- ', '}}
|
||||
{%- if tool_call.arguments is defined %}
|
||||
{{- '"arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments|tojson }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{{- '}' }}
|
||||
{{- '
|
||||
</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>
|
||||
' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.previtem and loop.previtem.role != "tool" %}
|
||||
{{- '<|im_start|>tool
|
||||
' }}
|
||||
{%- endif %}
|
||||
{{- '<tool_response>
|
||||
' }}
|
||||
{{- message.content }}
|
||||
{%- if not loop.last %}
|
||||
{{- '
|
||||
</tool_response>
|
||||
' }}
|
||||
{%- else %}
|
||||
{{- '
|
||||
</tool_response>' }}
|
||||
{%- endif %}
|
||||
{%- if not loop.last and loop.nextitem.role != "tool" %}
|
||||
{{- '<|im_end|>' }}
|
||||
{%- elif loop.last %}
|
||||
{{- '<|im_end|>' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant
|
||||
' }}
|
||||
{%- endif %}
|
||||
54
models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
Normal file
54
models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
Normal file
|
|
@ -0,0 +1,54 @@
|
|||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
{% if not add_generation_prompt is defined %}
|
||||
{% set add_generation_prompt = false %}
|
||||
{% endif %}
|
||||
{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}
|
||||
{%- for message in messages %}
|
||||
{%- if message['role'] == 'system' %}
|
||||
{% set ns.system_prompt = message['content'] %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{bos_token}}
|
||||
{{ns.system_prompt}}
|
||||
{%- for message in messages %}
|
||||
{%- if message['role'] == 'user' %}
|
||||
{%- set ns.is_tool = false -%}
|
||||
{{'<｜User｜>' + message['content']}}
|
||||
{%- endif %}
|
||||
{%- if message['role'] == 'assistant' and message['content'] is none %}
|
||||
{%- set ns.is_tool = false -%}
|
||||
{%- for tool in message['tool_calls']%}
|
||||
{%- if not ns.is_first %}
|
||||
{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}
|
||||
{%- set ns.is_first = true -%}
|
||||
{%- else %}
|
||||
{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}
|
||||
{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{%- if message['role'] == 'assistant' and message['content'] is not none %}
|
||||
{%- if ns.is_tool %}
|
||||
{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}
|
||||
{%- set ns.is_tool = false -%}
|
||||
{%- else %}
|
||||
{% set content = message['content'] %}
|
||||
{% if '</think>' in content %}
|
||||
{% set content = content.split('</think>')[-1] %}
|
||||
{% endif %}
|
||||
{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if message['role'] == 'tool' %}
|
||||
{%- set ns.is_tool = true -%}
|
||||
{%- if ns.is_output_first %}
|
||||
{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}
|
||||
{%- set ns.is_output_first = false %}
|
||||
{%- else %}
|
||||
{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor -%}
|
||||
{% if ns.is_tool %}
|
||||
{{'<｜tool▁outputs▁end｜>'}}
|
||||
{% endif %}
|
||||
{% if add_generation_prompt and not ns.is_tool %}
|
||||
{{'<｜Assistant｜>'}}
|
||||
{% endif %}
|
||||
57
models/templates/fireworks-ai-llama-3-firefunction-v2.jinja
Normal file
57
models/templates/fireworks-ai-llama-3-firefunction-v2.jinja
Normal file
|
|
@ -0,0 +1,57 @@
|
|||
{%- set loop_messages = messages -%}
|
||||
{%- set message_roles = ['system', 'user', 'assistant', 'tool'] -%}
|
||||
{%- set system_prompt_suffix -%}
|
||||
{%- filter trim -%}
|
||||
In addition to plain text responses, you can chose to call one or more of the provided functions.
|
||||
|
||||
Use the following rule to decide when to call a function:
|
||||
* if the response can be generated from your internal knowledge (e.g., as in the case of queries like "What is the capital of Poland?"), do so
|
||||
* if you need external information that can be obtained by calling one or more of the provided functions, generate a function calls
|
||||
|
||||
If you decide to call functions:
|
||||
* prefix function calls with functools marker (no closing marker required)
|
||||
* all function calls should be generated in a single JSON list formatted as functools[{"name": [function name], "arguments": [function arguments as JSON]}, ...]
|
||||
* follow the provided JSON schema. Do not hallucinate arguments or values. Do to blindly copy values from the provided samples
|
||||
* respect the argument type formatting. E.g., if the type if number and format is float, write value 7 as 7.0
|
||||
* make sure you pick the right functions that match the user intent
|
||||
|
||||
Available functions as JSON spec:
|
||||
{%- endfilter -%}
|
||||
{%- endset -%}
|
||||
{%- set system_prompt_suffix = system_prompt_suffix + "\n" + functions -%}
|
||||
{%- set system_prompt_suffix = system_prompt_suffix + '\nToday is ' + datetime + '.' -%}
|
||||
{%- set ns = namespace(role='', content='') -%}
|
||||
{#- Basic consistency checks -#}
|
||||
{%- if not loop_messages -%}
|
||||
{{ raise_exception('Expected non-empty messages') }}
|
||||
{%- endif -%}
|
||||
{%- for message in loop_messages -%}
|
||||
{%- set ns.role = message['role'] | lower -%}
|
||||
{%- if ns.role not in message_roles -%}
|
||||
{%- set message_roles_string = message_roles | join(', ') -%}
|
||||
{{ raise_exception('Invalid role ' + message['role'] + '. Only ' + message_roles_string + ' are supported.') }}
|
||||
{%- endif -%}
|
||||
{%- set msg_content = message['content'] | default('', true) | trim -%}
|
||||
{%- if loop.index0 == 0 -%}
|
||||
{%- if ns.role == 'system' -%}
|
||||
{%- set system_prompt = '<|start_header_id|>' + 'system' + '<|end_header_id|>\n\n' + message['content'] | trim + '\n' + system_prompt_suffix + '<|eot_id|>' -%}
|
||||
{%- else -%}
|
||||
{%- set system_prompt = '<|start_header_id|>' + 'system' + '<|end_header_id|>\n\nYou are a helpful assistant with access to functions.\n' + system_prompt_suffix + '<|eot_id|>' -%}
|
||||
{%- endif -%}
|
||||
{%- set ns.content = bos_token + system_prompt -%}
|
||||
{{- ns.content -}}
|
||||
{%- endif -%}
|
||||
{%- if loop.index0 > 0 or ns.role != 'system' -%}
|
||||
{%- set ns.content = '<|start_header_id|>' + ns.role + '<|end_header_id|>\n\n' + msg_content -%}
|
||||
{%- if 'tool_calls' in message and message['tool_calls'] -%}
|
||||
{%- set tool = namespace(calls=[]) -%}
|
||||
{%- for call in message['tool_calls'] -%}
|
||||
{%- set tool.calls = tool.calls + ['{"name": "' + call['function']['name'] + '", "arguments": ' + call['function']['arguments'] + '}'] -%}
|
||||
{%- endfor -%}
|
||||
{%- set ns.content = ns.content + ' functools[' + tool.calls | join(', ') + ']' -%}
|
||||
{%- endif -%}
|
||||
{%- set ns.content = ns.content + '<|eot_id|>' -%}
|
||||
{{- ns.content -}}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
4
models/templates/google-gemma-2-2b-it.jinja
Normal file
4
models/templates/google-gemma-2-2b-it.jinja
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
|
||||
' + message['content'] | trim + '<end_of_turn>
|
||||
' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
|
||||
'}}{% endif %}
|
||||
58
models/templates/meetkai-functionary-medium-v3.1.jinja
Normal file
58
models/templates/meetkai-functionary-medium-v3.1.jinja
Normal file
|
|
@ -0,0 +1,58 @@
|
|||
{# version=v3-llama3.1 #}{%- if not tools is defined -%}
|
||||
{%- set tools = none -%}
|
||||
{%- endif -%}
|
||||
|
||||
{%- set has_code_interpreter = tools | selectattr("type", "equalto", "code_interpreter") | list | length > 0 -%}
|
||||
{%- if has_code_interpreter -%}
|
||||
{%- set tools = tools | rejectattr("type", "equalto", "code_interpreter") | list -%}
|
||||
{%- endif -%}
|
||||
|
||||
{#- System message + builtin tools #}
|
||||
{{- bos_token + "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
||||
{%- if has_code_interpreter %}
|
||||
{{- "Environment: ipython\n\n" }}
|
||||
{%- else -%}
|
||||
{{ "\n"}}
|
||||
{%- endif %}
|
||||
{{- "Cutting Knowledge Date: December 2023\n\n" }}
|
||||
{%- if tools %}
|
||||
{{- "\nYou have access to the following functions:\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{%- if "type" in t -%}
|
||||
{{ "Use the function '"|safe + t["function"]["name"] + "' to '"|safe + t["function"]["description"] + "'\n"|safe + t["function"] | tojson() }}
|
||||
{%- else -%}
|
||||
{{ "Use the function '"|safe + t["name"] + "' to '"|safe + t["description"] + "'\n"|safe + t | tojson() }}
|
||||
{%- endif -%}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- '\nThink very carefully before calling functions.\nIf a you choose to call a function ONLY reply in the following format:\n<{start_tag}={function_name}>{parameters}{end_tag}\nwhere\n\nstart_tag => `<function`\nparameters => a JSON dict with the function argument name as key and function argument value as value.\nend_tag => `</function>`\n\nHere is an example,\n<function=example_function_name>{"example_name": "example_value"}</function>\n\nReminder:\n- If looking for real time information use relevant functions before falling back to brave_search\n- Function calls MUST follow the specified format, start with <function= and end with </function>\n- Required parameters MUST be specified\n- Only call one function at a time\n- Put the entire function call reply on one line\n\n' -}}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" -}}
|
||||
|
||||
{%- for message in messages -%}
|
||||
{%- if message['role'] == 'user' or message['role'] == 'system' -%}
|
||||
{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
|
||||
{%- elif message['role'] == 'tool' -%}
|
||||
{{ '<|start_header_id|>ipython<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
|
||||
{%- else -%}
|
||||
{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}}
|
||||
{%- if message['content'] -%}
|
||||
{{ message['content'] }}
|
||||
{%- endif -%}
|
||||
{%- if 'tool_calls' in message and message['tool_calls'] -%}
|
||||
{%- for tool_call in message['tool_calls'] -%}
|
||||
{%- if tool_call["function"]["name"] == "python" -%}
|
||||
{{ '<|python_tag|>' + tool_call['function']['arguments'] }}
|
||||
{%- else -%}
|
||||
{{ '<function=' + tool_call['function']['name'] + '>' + tool_call['function']['arguments'] + '</function>' }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{{ '<|eom_id|>' }}
|
||||
{%- else -%}
|
||||
{{ '<|eot_id|>' }}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- if add_generation_prompt -%}
|
||||
{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif -%}
|
||||
287
models/templates/meetkai-functionary-medium-v3.2.jinja
Normal file
287
models/templates/meetkai-functionary-medium-v3.2.jinja
Normal file
|
|
@ -0,0 +1,287 @@
|
|||
{# version=v3.llama3 #}{%- macro append_new_param_info(param_declaration, comment_info, examples_info, depth) -%}
|
||||
{%- set offset = "" -%}
|
||||
{%- if depth >= 1 -%}
|
||||
{%- set offset = " " * depth -%}
|
||||
{%- endif -%}
|
||||
{%- if comment_info != "<|NONE|>" -%}
|
||||
{{ "\n" + offset + comment_info }}
|
||||
{%- if examples_info | length > 0 -%}
|
||||
{# Append each example info #}
|
||||
{%- for example in examples_info -%}
|
||||
{{ "\n" + offset + "// " + example|string|replace("'", '"') }}
|
||||
{%- endfor -%}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{{ "\n" + offset + param_declaration }}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro convert_data_type(param_type) -%}
|
||||
{%- if param_type == "integer" or param_type == "float" -%}
|
||||
{{ "number" }}
|
||||
{%- else -%}
|
||||
{{ param_type }}
|
||||
{%- endif -%}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro get_param_type(param) -%}
|
||||
{%- set param_type = "any" -%}
|
||||
|
||||
{%- if "type" in param -%}
|
||||
{%- set raw_param_type = param["type"] -%}
|
||||
{%- if raw_param_type is iterable and raw_param_type is not string -%}
|
||||
{%- set param_type = raw_param_type | join(" | ") -%}
|
||||
{%- else -%}
|
||||
{%- set param_type = raw_param_type -%}
|
||||
{%- endif -%}
|
||||
{{ convert_data_type(param_type) }}
|
||||
{%- elif "oneOf" in param -%}
|
||||
{%- set one_of_types = param["oneOf"]|selectattr("type", "defined")|list -%}
|
||||
{%- set one_of_types = one_of_types|map(attribute="type")|unique|list -%}
|
||||
{{ convert_data_type(one_of_types | join(" | ")) }}
|
||||
{%- endif -%}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro get_format_param(param) -%}
|
||||
{%- if "format" in param -%}
|
||||
{{ param["format"] }}
|
||||
{%- elif "oneOf" in param -%}
|
||||
{%- set formats = [] -%}
|
||||
{%- for item in param["oneOf"] -%}
|
||||
{%- if "format" in item -%}
|
||||
{%- if item["format"] == param["oneOf"][-1]["format"] -%}
|
||||
{{ item["format"] }}
|
||||
{%- else -%}
|
||||
{{ item["format"] + " or "}}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- else -%}
|
||||
{{ "<|NONE|>" }}
|
||||
{%- endif -%}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro get_param_info(param) -%}
|
||||
{%- set param_type = param.get("type", "any") -%}
|
||||
{%- set format_param = get_format_param(param) -%}
|
||||
|
||||
{%- if "description" in param or "default" in param or format_param != "<|NONE|>" or param["maximum"] or param["minimum"] or param["maxLength"] or param["minLength"] -%}
|
||||
{{ "//" }}
|
||||
{%- if "description" in param -%}
|
||||
{%- set desc = param["description"] -%}
|
||||
{%- if not desc.endswith(".") -%}
|
||||
{%- set desc = desc + "." -%}
|
||||
{%- endif -%}
|
||||
{{ " " + desc }}
|
||||
{%- endif -%}
|
||||
|
||||
{%- if "default" in param -%}
|
||||
{%- set default_value = param["default"] -%}
|
||||
{%- if param_type == "string" -%}
|
||||
{%- set default_value = '"' ~ default_value ~ '"' -%}
|
||||
{%- endif -%}
|
||||
{{ " Default=" ~ default_value ~ "." }}
|
||||
{%- endif -%}
|
||||
|
||||
{%- set format_param = get_format_param(param) -%}
|
||||
{%- if format_param != "<|NONE|>" -%}
|
||||
{{ " Format=" ~ format_param }}
|
||||
{%- endif -%}
|
||||
|
||||
{%- for field, field_name in [("maximum", "Maximum"), ("minimum", "Minimum"), ("maxLength", "Maximum length"), ("minLength", "Minimum length")] -%}
|
||||
{%- if field in param -%}
|
||||
{{ " " + field_name ~ "=" ~ param[field] }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- else -%}
|
||||
{{ "<|NONE|>"}}
|
||||
{%- endif -%}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro get_enum_option_str(enum_options) -%}
|
||||
{%- for v in enum_options -%}
|
||||
{%- if v is string -%}
|
||||
{{ '"' + v + '"' }}
|
||||
{%- else -%}
|
||||
{{ v }}
|
||||
{%- endif -%}
|
||||
{%- if enum_options|length > 0 and v != enum_options[-1] -%}
|
||||
{{ " | " }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro get_array_typescript(param_name, param_dic, depth) -%}
|
||||
{%- set offset = '' -%}
|
||||
{%- if depth >= 1 -%}
|
||||
{%- set offset = " " * depth -%}
|
||||
{%- endif -%}
|
||||
{%- set items_info = param_dic.get('items', {}) -%}
|
||||
|
||||
{%- if items_info|length == 0 -%}
|
||||
{%- if param_name -%}
|
||||
{{ "\n" + offset + param_name + ": []" }}
|
||||
{%- else -%}
|
||||
{{ "\n" + offset + "[]" }}
|
||||
{%- endif -%}
|
||||
{%- else -%}
|
||||
{%- set array_type = get_param_type(items_info) -%}
|
||||
{%- if array_type == 'object' -%}
|
||||
{%- if param_name -%}
|
||||
{{ "\n" + offset + param_name + ": {" }}
|
||||
{%- else -%}
|
||||
{{ "\n" + offset + "{" }}
|
||||
{%- endif -%}
|
||||
{{ get_parameter_typescript(items_info.get('properties', {}), items_info.get('required', []), depth + 1) -}}
|
||||
{{- "\n" + offset + "}[]" }}
|
||||
{%- elif array_type == 'array' -%}
|
||||
{%- set item_info = get_array_typescript(None, items_info, depth + 1) -%}
|
||||
{%- if not param_name -%}
|
||||
{{ "\n" + item_info + "[]" }}
|
||||
{%- else -%}
|
||||
{{ "\n" + offset + param_name + ": " + item_info|trim + "[]" }}
|
||||
{%- endif -%}
|
||||
{%- else -%}
|
||||
{%- if 'enum' in items_info -%}
|
||||
{%- set item_type = get_enum_option_str(items_info['enum']) -%}
|
||||
{%- if param_name is none -%}
|
||||
{{ "(" + item_type + ")[]"}}
|
||||
{%- else -%}
|
||||
{{ "\n" + offset + param_name + ": (" + item_type + ")[]" }}
|
||||
{%- endif -%}
|
||||
{%- else -%}
|
||||
{%- if param_name is none -%}
|
||||
{{ "\n" + array_type + "[]" }}
|
||||
{%- else -%}
|
||||
{{ "\n" + offset + param_name + ": " + array_type + "[]," }}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro get_parameter_typescript(properties, required_params, depth=0) -%}
|
||||
{%- set res = "" -%}
|
||||
{%- for param_name, param in properties.items() -%}
|
||||
{%- if param is mapping -%}
|
||||
{%- set comment_info = get_param_info(param) -%}
|
||||
{# Param Examples #}
|
||||
{%- set examples_info = [] -%}
|
||||
{%- if "examples" in param -%}
|
||||
{%- set examples_info = ["Example " + param_name + ":"] -%}
|
||||
{%- set examples_info = examples_info + param["examples"] -%}
|
||||
{%- endif -%}
|
||||
|
||||
{# Param Name declaration #}
|
||||
{%- set param_declaration = param_name -%}
|
||||
{%- if required_params is iterable and param_name not in required_params -%}
|
||||
{%- set param_declaration = param_declaration + "?" -%}
|
||||
{%- endif -%}
|
||||
|
||||
{%- set param_type = get_param_type(param) -%}
|
||||
|
||||
{# Handle indentation based on depth #}
|
||||
{%- set offset = "" -%}
|
||||
{%- if depth >= 1 -%}
|
||||
{%- set offset = " " * depth -%}
|
||||
{%- endif -%}
|
||||
|
||||
{%- if param_type == "object" -%}
|
||||
{%- if comment_info != "<|NONE|>" -%}
|
||||
{{ "\n" + offset + comment_info }}
|
||||
{%- endif -%}
|
||||
{%- if examples_info|length > 0 -%}
|
||||
{%- for example in examples_info -%}
|
||||
{{ "\n" + offset + "// " + example|string|replace("'", '"') }}
|
||||
{%- endfor -%}
|
||||
{%- endif -%}
|
||||
{%- set param_declaration = param_declaration + ": {" -%}
|
||||
{{ "\n" + offset + param_declaration -}}
|
||||
{{- get_parameter_typescript(param.get("properties", {}), param.get("required", []), depth + 1) -}}
|
||||
{{- "\n" + offset + "}," }}
|
||||
{%- elif param_type == "array" -%}
|
||||
{%- set item_info = param.get("items", {}) -%}
|
||||
{%- if "type" not in item_info -%}
|
||||
{%- set param_declaration = param_declaration + ": []," -%}
|
||||
{{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }}
|
||||
{%- else -%}
|
||||
{%- if comment_info != "<|NONE|>" -%}
|
||||
{{ "\n" + offset + comment_info }}
|
||||
{%- endif -%}
|
||||
{%- if examples_info|length > 0 -%}
|
||||
{%- for example in examples_info -%}
|
||||
{{ "\n" + offset + "// " + example|string|replace("'", '"') }}
|
||||
{%- endfor -%}
|
||||
{%- endif -%}
|
||||
{%- set array_declaration = get_array_typescript(param_declaration, param, depth) -%}
|
||||
{%- if not array_declaration.endswith(",") -%}
|
||||
{%- set array_declaration = array_declaration + "," -%}
|
||||
{%- endif -%}
|
||||
{{ array_declaration}}
|
||||
{%- endif -%}
|
||||
{%- else -%}
|
||||
{%- if "enum" in param -%}
|
||||
{%- set param_type = get_enum_option_str(param["enum"]) -%}
|
||||
{%- endif -%}
|
||||
{%- if "nullable" in param and param["nullable"] -%}
|
||||
{%- set param_type = param_type + " | null" -%}
|
||||
{%- endif -%}
|
||||
{%- set param_declaration = param_declaration + ": " + param_type + "," -%}
|
||||
{{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- endmacro -%}
|
||||
|
||||
{%- macro generate_schema_from_functions(functions, namespace='functions') -%}
|
||||
{{ "// Supported function definitions that should be called when necessary.\n" -}}
|
||||
{{- "namespace " + namespace + " {\n\n" -}}
|
||||
|
||||
{%- for function in functions -%}
|
||||
{%- if function.get("function") -%}
|
||||
{%- set function = function.get("function") -%}
|
||||
{%- endif -%}
|
||||
|
||||
{%- set function_name = function.get("name") -%}
|
||||
{%- if function_name -%}
|
||||
{%- set description = function.get('description', '') -%}
|
||||
{%- set parameters = function.get('parameters', {}) -%}
|
||||
{{- "// " + description + "\n" -}}
|
||||
{{- "type " + function_name -}}
|
||||
{%- if parameters and parameters.get("properties") -%}
|
||||
{{- " = (_: {" -}}
|
||||
{%- set required_params = parameters.get("required", []) -%}
|
||||
{{ get_parameter_typescript(parameters.get("properties"), required_params, 0) -}}
|
||||
{{- "\n}) => any;\n\n" }}
|
||||
{%- else -%}
|
||||
{{ " = () => any;\n\n" }}
|
||||
{%- endif -%}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{{ "} // namespace " + namespace }}
|
||||
{%- endmacro -%}
|
||||
{%- if not tools -%}
|
||||
{%- set tools = [] -%}
|
||||
{%- endif -%}
|
||||
{{ bos_token + '<|start_header_id|>system<|end_header_id|>\n\nYou are capable of executing available function(s) if required.\nOnly execute function(s) when absolutely necessary.\nAsk for the required input to:recipient==all\nUse JSON for function arguments.\nRespond in this format:\n>>>${recipient}\n${content}\nAvailable functions:\n' + generate_schema_from_functions(tools) + '<|eot_id|>' -}}
|
||||
{%- if tools|length > 0 and tools|selectattr("type", "equalto", "code_interpreter")|list|length > 0 -%}
|
||||
{{ '<|start_header_id|>system<|end_header_id|>\n\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at \'/mnt/data\' can be used to save and persist user files.<|eot_id|>' }}
|
||||
{%- endif -%}
|
||||
{%- for message in messages -%}
|
||||
{%- if message['role'] == 'user' or message['role'] == 'system' -%}
|
||||
{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
|
||||
{%- elif message['role'] == 'tool' -%}
|
||||
{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}
|
||||
{%- else -%}
|
||||
{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}}
|
||||
{%- if message['content'] -%}
|
||||
{{ '>>>all\n' + message['content'] }}
|
||||
{%- endif -%}
|
||||
{%- if 'tool_calls' in message and message['tool_calls'] -%}
|
||||
{%- for tool_call in message['tool_calls'] -%}
|
||||
{{ '>>>' + tool_call['function']['name'] + '\n' + tool_call['function']['arguments'] }}
|
||||
{%- endfor -%}
|
||||
{%- endif -%}
|
||||
{{ '<|eot_id|>' }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n>>>' }}{% endif %}
|
||||
109
models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja
Normal file
109
models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja
Normal file
|
|
@ -0,0 +1,109 @@
|
|||
{{- bos_token }}
|
||||
{%- if custom_tools is defined %}
|
||||
{%- set tools = custom_tools %}
|
||||
{%- endif %}
|
||||
{%- if not tools_in_user_message is defined %}
|
||||
{%- set tools_in_user_message = true %}
|
||||
{%- endif %}
|
||||
{%- if not date_string is defined %}
|
||||
{%- set date_string = "26 Jul 2024" %}
|
||||
{%- endif %}
|
||||
{%- if not tools is defined %}
|
||||
{%- set tools = none %}
|
||||
{%- endif %}
|
||||
|
||||
{#- This block extracts the system message, so we can slot it into the right place. #}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{%- set system_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{%- set system_message = "" %}
|
||||
{%- endif %}
|
||||
|
||||
{#- System message + builtin tools #}
|
||||
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
||||
{%- if builtin_tools is defined or tools is not none %}
|
||||
{{- "Environment: ipython\n" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
|
||||
{%- endif %}
|
||||
{{- "Cutting Knowledge Date: December 2023\n" }}
|
||||
{{- "Today Date: " + date_string + "\n\n" }}
|
||||
{%- if tools is not none and not tools_in_user_message %}
|
||||
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
||||
{#- Custom tools are passed in a user message with some extra guidance #}
|
||||
{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
||||
{%- if messages | length != 0 %}
|
||||
{%- set first_user_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
||||
{%- elif 'tool_calls' in message %}
|
||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
||||
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
||||
{{- arg_name + '="' + arg_val + '"' }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{#- This means we're in ipython mode #}
|
||||
{{- "<|eom_id|>" }}
|
||||
{%- else %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
93
models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja
Normal file
93
models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja
Normal file
|
|
@ -0,0 +1,93 @@
|
|||
{{- bos_token }}
|
||||
{%- if custom_tools is defined %}
|
||||
{%- set tools = custom_tools %}
|
||||
{%- endif %}
|
||||
{%- if not tools_in_user_message is defined %}
|
||||
{%- set tools_in_user_message = true %}
|
||||
{%- endif %}
|
||||
{%- if not date_string is defined %}
|
||||
{%- if strftime_now is defined %}
|
||||
{%- set date_string = strftime_now("%d %b %Y") %}
|
||||
{%- else %}
|
||||
{%- set date_string = "26 Jul 2024" %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if not tools is defined %}
|
||||
{%- set tools = none %}
|
||||
{%- endif %}
|
||||
|
||||
{#- This block extracts the system message, so we can slot it into the right place. #}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{%- set system_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{%- set system_message = "" %}
|
||||
{%- endif %}
|
||||
|
||||
{#- System message #}
|
||||
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
||||
{%- if tools is not none %}
|
||||
{{- "Environment: ipython\n" }}
|
||||
{%- endif %}
|
||||
{{- "Cutting Knowledge Date: December 2023\n" }}
|
||||
{{- "Today Date: " + date_string + "\n\n" }}
|
||||
{%- if tools is not none and not tools_in_user_message %}
|
||||
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
||||
{#- Custom tools are passed in a user message with some extra guidance #}
|
||||
{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
||||
{%- if messages | length != 0 %}
|
||||
{%- set first_user_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
||||
{%- elif 'tool_calls' in message %}
|
||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
109
models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja
Normal file
109
models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja
Normal file
|
|
@ -0,0 +1,109 @@
|
|||
{{- bos_token }}
|
||||
{%- if custom_tools is defined %}
|
||||
{%- set tools = custom_tools %}
|
||||
{%- endif %}
|
||||
{%- if not tools_in_user_message is defined %}
|
||||
{%- set tools_in_user_message = true %}
|
||||
{%- endif %}
|
||||
{%- if not date_string is defined %}
|
||||
{%- set date_string = "26 Jul 2024" %}
|
||||
{%- endif %}
|
||||
{%- if not tools is defined %}
|
||||
{%- set tools = none %}
|
||||
{%- endif %}
|
||||
|
||||
{#- This block extracts the system message, so we can slot it into the right place. #}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{%- set system_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{%- set system_message = "" %}
|
||||
{%- endif %}
|
||||
|
||||
{#- System message + builtin tools #}
|
||||
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
||||
{%- if builtin_tools is defined or tools is not none %}
|
||||
{{- "Environment: ipython\n" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
|
||||
{%- endif %}
|
||||
{{- "Cutting Knowledge Date: December 2023\n" }}
|
||||
{{- "Today Date: " + date_string + "\n\n" }}
|
||||
{%- if tools is not none and not tools_in_user_message %}
|
||||
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
||||
{#- Custom tools are passed in a user message with some extra guidance #}
|
||||
{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
||||
{%- if messages | length != 0 %}
|
||||
{%- set first_user_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
||||
{%- elif 'tool_calls' in message %}
|
||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
||||
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
||||
{{- arg_name + '="' + arg_val + '"' }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{#- This means we're in ipython mode #}
|
||||
{{- "<|eom_id|>" }}
|
||||
{%- else %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
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Reference in a new issue