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Merge branch 'master' into xsn/wasm_simd

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This commit is contained in:
Xuan Son Nguyen 2025-02-08 22:12:49 +01:00
commit 6278c767ee
187 changed files with 19559 additions and 5104 deletions
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10
.devops/tools.sh
View file

@ -13,9 +13,13 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
exec ./llama-quantize "$@"
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
exec ./llama-cli "$@"
elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then
exec ./llama-bench "$@"
elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then
exec ./llama-perplexity "$@"
elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
echo "Converting PTH to GGML..."
for i in `ls $1/$2/ggml-model-f16.bin*`; do
for i in $(ls $1/$2/ggml-model-f16.bin*); do
if [ -f "${i/f16/q4_0}" ]; then
echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
else
@ -30,6 +34,10 @@ else
echo "Available commands: "
echo " --run (-r): Run a model previously converted into ggml"
echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
echo " --bench (-b): Benchmark the performance of the inference for various parameters."
echo " ex: -m model.gguf"
echo " --perplexity (-p): Measure the perplexity of a model over a given text."
echo " ex: -m model.gguf -f file.txt"
echo " --convert (-c): Convert a llama model into ggml"
echo " ex: --outtype f16 \"/models/7B/\" "
echo " --quantize (-q): Optimize with quantization process ggml"

9
.devops/vulkan.Dockerfile
View file

@ -1,4 +1,4 @@
ARG UBUNTU_VERSION=22.04
ARG UBUNTU_VERSION=24.04
FROM ubuntu:$UBUNTU_VERSION AS build
@ -7,7 +7,7 @@ RUN apt update && apt install -y git build-essential cmake wget
# Install Vulkan SDK and cURL
RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
apt update -y && \
apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
@ -55,8 +55,9 @@ RUN apt-get update \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt \
python3-wheel \
&& pip install --break-system-packages --upgrade setuptools \
&& pip install --break-system-packages -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \

8
.editorconfig
View file

@ -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

263
.github/workflows/build.yml vendored
View file

@ -10,10 +10,10 @@ on:
push:
branches:
- master
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
pull_request:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@ -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
@ -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
@ -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
@ -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
- name: Dependencies
id: depends
run: |
@ -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: |
@ -336,17 +392,16 @@ 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
run: |
cd build
ctest -L main --verbose --timeout 900
# This is using llvmpipe and runs slower than other backends
ctest -L main --verbose --timeout 1800
ubuntu-22-cmake-hip:
runs-on: ubuntu-22.04
@ -363,6 +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: |
@ -395,6 +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: |
@ -434,17 +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
@ -478,18 +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
@ -499,6 +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
@ -509,9 +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 \
@ -520,7 +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
@ -530,6 +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
@ -540,9 +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 \
@ -551,7 +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
@ -565,6 +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
@ -575,17 +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
@ -606,6 +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 ${{ matrix.sys }}
uses: msys2/setup-msys2@v2
with:
@ -674,6 +768,13 @@ jobs:
with:
fetch-depth: 0
- name: ccache
uses: hendrikmuhs/ccache-action@v1.2.16
with:
key: windows-latest-cmake-${{ matrix.build }}
variant: sccache
evict-old-files: 1d
- name: Clone Kompute submodule
id: clone_kompute
if: ${{ matrix.build == 'kompute-x64' }}
@ -713,21 +814,19 @@ jobs:
run: |
git clone https://github.com/KhronosGroup/OpenCL-Headers
cd OpenCL-Headers
mkdir build && cd build
cmake .. `
cmake -B build `
-DBUILD_TESTING=OFF `
-DOPENCL_HEADERS_BUILD_TESTING=OFF `
-DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
-DCMAKE_INSTALL_PREFIX="$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
@ -812,6 +911,8 @@ jobs:
- name: Clone
id: checkout
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Install dependencies
env:
@ -820,6 +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 \
@ -846,6 +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: |
@ -902,11 +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: |
@ -986,6 +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
@ -1065,9 +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
@ -1097,6 +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: |
@ -1165,9 +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 \
@ -1176,8 +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
@ -1194,6 +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:
@ -1231,6 +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
View file

@ -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"

5
.github/workflows/docker.yml vendored
View file

@ -28,7 +28,7 @@ jobs:
push_to_registry:
name: Push Docker image to Docker Hub
runs-on: ubuntu-latest
runs-on: ubuntu-22.04
env:
COMMIT_SHA: ${{ github.sha }}
strategy:
@ -36,8 +36,7 @@ jobs:
matrix:
config:
# Multi-stage build
- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/arm64", full: true, light: true, server: true, freediskspace: false}
- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
- { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
- { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
- { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}

27
.github/workflows/server.yml vendored
View file

@ -81,13 +81,36 @@ jobs:
with:
node-version: '22.11.0'
- name: WebUI - Install dependencies
id: webui_lint
run: |
cd examples/server/webui
npm ci
- name: WebUI - Check code format
id: webui_format
run: |
git config --global --add safe.directory $(realpath .)
cd examples/server/webui
git status
npm run format
git status
modified_files="$(git status -s)"
echo "Modified files: ${modified_files}"
if [ -n "${modified_files}" ]; then
echo "Files do not follow coding style. To fix: npm run format"
echo "${modified_files}"
exit 1
fi
- name: Verify bundled index.html
id: verify_server_index_html
run: |
git config --global --add safe.directory $(realpath .)
cd examples/server/webui
git status
npm ci
npm run build
git status
modified_files="$(git status -s)"
@ -205,7 +228,7 @@ 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
View file

@ -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>

3
CMakeLists.txt
View file

@ -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)
@ -232,4 +233,4 @@ configure_file(cmake/llama.pc.in
@ONLY)
install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
DESTINATION lib/pkgconfig)
DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)

11
Makefile
View file

@ -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, $<)

8
README.md
View 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)
@ -186,6 +189,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- [ramalama](https://github.com/containers/ramalama) (MIT)
- [semperai/amica](https://github.com/semperai/amica) (MIT)
- [withcatai/catai](https://github.com/withcatai/catai) (MIT)
- [Autopen](https://github.com/blackhole89/autopen) (GPL)
</details>
@ -422,7 +426,7 @@ To learn more about model quantization, [read this documentation](examples/quant
</details>
[^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md)
[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
## [`llama-bench`](examples/llama-bench)

2
cmake/llama-config.cmake.in
View file

@ -9,7 +9,7 @@ set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
find_package(ggml REQUIRED)
find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake)
find_library(llama_LIBRARY llama
REQUIRED

10
cmake/llama.pc.in
View file

@ -1,10 +1,10 @@
prefix=@CMAKE_INSTALL_PREFIX@
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir=${prefix}/include
exec_prefix=@CMAKE_INSTALL_PREFIX@
libdir=@CMAKE_INSTALL_FULL_LIBDIR@
includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@
Name: llama
Description: Port of Facebook's LLaMA model in C/C++
Version: @PROJECT_VERSION@
Libs: -L${libdir} -lggml -lggml-base -lllama
Version: @LLAMA_INSTALL_VERSION@
Libs: -L${libdir} -lggml -lggml-base -lllama
Cflags: -I${includedir}

30
common/CMakeLists.txt
View file

@ -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)

65
common/arg.cpp
View file

@ -877,7 +877,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
[](common_params & params) {
params.warmup = false;
}
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING}));
add_opt(common_arg(
{"--spm-infill"},
string_format(
@ -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) {
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);
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);
}
));
@ -2311,5 +2324,47 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_examples({LLAMA_EXAMPLE_TTS}));
add_opt(common_arg(
{"--embd-bge-small-en-default"},
string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"),
[](common_params & params) {
params.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF";
params.hf_file = "bge-small-en-v1.5-q8_0.gguf";
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
params.embd_normalize = 2;
params.n_ctx = 512;
params.verbose_prompt = true;
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--embd-e5-small-en-default"},
string_format("use default e5-small-v2 model (note: can download weights from the internet)"),
[](common_params & params) {
params.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF";
params.hf_file = "e5-small-v2-q8_0.gguf";
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
params.embd_normalize = 2;
params.n_ctx = 512;
params.verbose_prompt = true;
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--embd-gte-small-default"},
string_format("use default gte-small model (note: can download weights from the internet)"),
[](common_params & params) {
params.hf_repo = "ggml-org/gte-small-Q8_0-GGUF";
params.hf_file = "gte-small-q8_0.gguf";
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
params.embd_normalize = 2;
params.n_ctx = 512;
params.verbose_prompt = true;
params.embedding = true;
}
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
return ctx_arg;
}

403
common/chat-template.hpp
View file

@ -17,21 +17,54 @@ using json = nlohmann::ordered_json;
namespace minja {
struct chat_template_caps {
bool supports_tools = false;
bool supports_tool_calls = false;
bool supports_tool_responses = false;
bool supports_system_role = false;
bool supports_parallel_tool_calls = false;
bool supports_tool_call_id = false;
// meta-llama/Llama-3.1-8B-Instruct expects arguments to be an object.
// Most other templates (and OpenAI's API) expect the arguments object to be stringified.
bool requires_object_arguments = false;
// CohereForAI/c4ai-command-r-plus simple variant
bool requires_non_null_content = false;
// MiniMaxAI/MiniMax-Text-01 special
bool requires_typed_content = false;
};
struct chat_template_inputs {
nlohmann::ordered_json messages;
nlohmann::ordered_json tools;
bool add_generation_prompt = true;
nlohmann::ordered_json extra_context;
std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
};
struct chat_template_options {
bool apply_polyfills = true;
bool use_bos_token = true;
bool use_eos_token = true;
bool define_strftime_now = true;
bool polyfill_tools = true;
bool polyfill_tool_call_examples = true;
bool polyfill_tool_calls = true;
bool polyfill_tool_responses = true;
bool polyfill_system_role = true;
bool polyfill_object_arguments = true;
bool polyfill_typed_content = true;
};
class chat_template {
public:
private:
bool supports_tools_ = true;
// Meta-Llama-3.1-8B-Instruct's template expects arguments to be an object.
// Most other templates (and OpenAI's API) expect the arguments object to be stringified.
bool requires_object_arguments_ = false;
bool requires_typed_content_ = false;
bool supports_system_role_ = true;
bool supports_parallel_tool_calls_ = false;
chat_template_caps caps_;
std::string source_;
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,
@ -40,16 +73,28 @@ 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);
// fprintf(stderr, "Prompt: %s\n", prompt.c_str());
chat_template_inputs inputs;
inputs.messages = messages;
inputs.tools = tools;
inputs.add_generation_prompt = add_generation_prompt;
inputs.extra_context = extra_context;
// Use fixed date for tests
inputs.now = std::chrono::system_clock::from_time_t(0);
chat_template_options opts;
opts.apply_polyfills = false;
auto prompt = apply(inputs, opts);
// fprintf(stderr, "try_raw_render: %s\n", prompt.c_str());
return prompt;
} catch (const std::exception & e) {
// fprintf(stderr, "Error: %s\n", e.what());
// fprintf(stderr, "try_raw_render error: %s\n", e.what());
return "";
}
}
public:
chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token)
: source_(source), bos_token_(bos_token), eos_token_(eos_token)
{
@ -58,86 +103,239 @@ class chat_template {
/* .lstrip_blocks = */ true,
/* .keep_trailing_newline = */ false,
});
supports_tools_ = source.find("tools") != std::string::npos;
auto renders_string_arguments =
try_raw_render({
auto contains = [](const std::string & haystack, const std::string & needle) {
return haystack.find(needle) != std::string::npos;
};
const std::string user_needle = "<User Needle>";
const std::string sys_needle = "<System Needle>";
const json dummy_str_user_msg = {{"role", "user"}, {"content", user_needle}};
const json dummy_typed_user_msg = {{"role", "user"}, {"content", json::array({{{"type", "text"}, {"text", user_needle}}})}};
caps_.requires_typed_content =
!contains(try_raw_render(json::array({dummy_str_user_msg}), {}, false), user_needle)
&& contains(try_raw_render(json::array({dummy_typed_user_msg}), {}, false), user_needle);
const auto dummy_user_msg = caps_.requires_typed_content
? dummy_typed_user_msg
: dummy_str_user_msg;
const json needle_system_msg = {
{"role", "system"},
{"content", caps_.requires_typed_content ? json::array({{{"type", "text"}, {"text", sys_needle}}}) : json(sys_needle)},
};
caps_.supports_system_role = contains(try_raw_render({needle_system_msg, dummy_user_msg,}, {}, false), sys_needle);
auto out = try_raw_render(json::array({
dummy_user_msg
}), json::array({
{
{"name", "some_tool"},
{"type", "function"},
{"function", {
{"name", "some_tool"},
{"description", "Some tool."},
{"parameters", {
{"type", "object"},
{"properties", {
{"arg", {
{"type", "string"},
{"description", "Some argument."},
}},
}},
{"required", json::array({ "arg" })},
}},
}},
},
}), false);
caps_.supports_tools = contains(out, "some_tool");
auto make_tool_calls_msg = [&](const json & tool_calls) {
return json {
{"role", "assistant"},
{"content", nullptr},
{"tool_calls", tool_calls},
};
};
auto make_tool_call = [](const std::string & tool_name, const json & arguments) {
return json {
{"id", "call_1___"},
{"type", "function"},
{"function", {
{"arguments", arguments},
{"name", tool_name},
}},
};
};
const json dummy_args_obj {{"argument_needle", "print('Hello, World!')"}};
// Note: the arguments are rendered in both cases, but may be double-escaped, which we don't want.
out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})),
}), {}, false);
auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})),
}), {}, false);
auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments;
caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments;
auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);
if (caps_.supports_tool_calls) {
auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump());
auto tc1 = make_tool_call("test_tool1", dummy_args);
auto tc2 = make_tool_call("test_tool2", dummy_args);
auto out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({tc1, tc2})),
}), {}, false);
caps_.supports_parallel_tool_calls = contains(out, "test_tool1") && contains(out, "test_tool2");
out = try_raw_render(json::array({
dummy_user_msg,
make_tool_calls_msg(json::array({tc1})),
{
{"role", "tool"},
{"name", "test_tool1"},
{"content", "Some response!"},
{"tool_call_id", "call_911_"},
}
}), {}, false);
caps_.supports_tool_responses = contains(out, "Some response!");
caps_.supports_tool_call_id = contains(out, "call_911_");
}
try {
if (!caps_.supports_tools) {
const json user_msg {
{"role", "user"},
{"content", "Hey"}
},
{
{"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", {
{"arguments", "{\"code\": \"print('Hello, World!')\"}"},
{"name", "ipython"},
{"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);
}
}, {}, false).find("{\"code\": \"print") != std::string::npos;
if (!renders_string_arguments) {
auto renders_object_arguments =
try_raw_render({
{
{"role", "user"},
{"content", "Hey"}
},
{
{"role", "assistant"},
{"tool_calls", json::array({
{
{"id", "call_1___"},
{"type", "function"},
{"function", {
{"arguments", {
{"code", "print('Hello, World!')"},
}},
{"name", "ipython"},
}},
},
})},
{
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());
}
}, {}, false).find("{\"code\": \"print") != std::string::npos;
requires_object_arguments_ = renders_object_arguments;
}
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());
}
supports_parallel_tool_calls_ = source.find("tool_call_id") != std::string::npos;
supports_system_role_ = try_raw_render({
{{"role", "system"}, {"content", "<System Needle>"}},
{{"role", "user"}, {"content", "Hey"}}
}, {}, false).find("<System Needle>") != std::string::npos;
requires_typed_content_ = try_raw_render({{{"role", "user"}, {"content", "Hey"}}}, {}, false).find("Hey") == std::string::npos
&& try_raw_render({{{"role", "user"}, {"content", {{{"type", "text"}, {"text", "Hey"}}}}}}, {}, false).find("Hey") != std::string::npos;
}
const std::string & source() const { return source_; }
const std::string & bos_token() const { return bos_token_; }
const std::string & eos_token() const { return eos_token_; }
bool supports_tools() const { return supports_tools_; }
bool supports_parallel_tool_calls() const { return supports_parallel_tool_calls_; }
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;
// First, "fix" messages so they have a chance to be rendered correctly by the template
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;
}
}
if (adjust_inputs && (requires_object_arguments_ || !supports_system_role_ || !supports_tools_ || requires_typed_content_)) {
auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role;
auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools;
auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples;
auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls;
auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses;
auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments;
auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content;
auto needs_polyfills = opts.apply_polyfills && (false
|| polyfill_system_role
|| polyfill_tools
|| polyfill_tool_calls
|| polyfill_tool_responses
|| polyfill_object_arguments
|| polyfill_typed_content
);
if (needs_polyfills) {
actual_messages = json::array();
auto add_message = [&](const json & msg) {
if (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", {{
@ -160,7 +358,17 @@ class chat_template {
pending_system.clear();
}
};
for (const auto & message_ : 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());
@ -168,16 +376,22 @@ class chat_template {
std::string role = message.at("role");
if (message.contains("tool_calls")) {
if (requires_object_arguments_ || !supports_tools_) {
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");
std::string arguments = function.at("arguments");
function["arguments"] = json::parse(arguments);
auto & arguments = function.at("arguments");
if (arguments.is_string()) {
try {
arguments = json::parse(arguments.get<std::string>());
} catch (const std::exception & ecvt) {
fprintf(stderr, "Failed to parse arguments: %s\n", ecvt.what());
}
}
}
}
}
if (!supports_tools_) {
if (polyfill_tool_calls) {
auto content = message.at("content");
auto tool_calls = json::array();
for (const auto & tool_call : message.at("tool_calls")) {
@ -204,14 +418,16 @@ class chat_template {
message.erase("tool_calls");
}
}
if (!supports_tools_ && 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");
}
@ -219,7 +435,7 @@ class chat_template {
message.erase("name");
}
if (!message["content"].is_null() && !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";
@ -240,28 +456,59 @@ class chat_template {
}
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()));
}
}
return template_root_->render(context);
auto ret = template_root_->render(context);
// fprintf(stderr, "actual_messages: %s\n", actual_messages.dump(2).c_str());
// fprintf(stderr, "apply: %s\n\n", ret.c_str());
return ret;
}
static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) {
json messages_with_system = messages;
if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") {
std::string existing_system = messages_with_system.at(0).at("content");
messages_with_system[0] = json {
{"role", "system"},
{"content", existing_system + "\n\n" + system_prompt},
};
} else {
messages_with_system.insert(messages_with_system.begin(), json {
{"role", "system"},
{"content", system_prompt},
});
}
return messages_with_system;
}
};

966
common/chat.cpp Normal file
View 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
View 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);

71
common/common.cpp
View file

@ -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");
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)
};
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),
};
} 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,
};
}
}
//

20
common/common.h
View file

@ -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
@ -154,7 +160,11 @@ struct common_params_sampling {
COMMON_SAMPLER_TYPE_TEMPERATURE,
};
std::string grammar; // optional BNF-like grammar to constrain 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

24
common/json-schema-to-grammar.cpp
View file

@ -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);
},

12
common/json-schema-to-grammar.h
View file

@ -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
View 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 llama_sampler_init(
/* .iface = */ &llama_sampler_llg_i,
/* .ctx = */ ctx
);
}
#else
llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) {
LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
return nullptr;
}
#endif // LLAMA_USE_LLGUIDANCE

11
common/log.cpp
View file

@ -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
View file

@ -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__)

174
common/minja.hpp
View file

@ -628,7 +628,7 @@ class Context : public std::enable_shared_from_this<Context> {
if (parent_) return parent_->contains(key);
return false;
}
virtual void set(const Value & key, Value & value) {
virtual void set(const Value & key, const Value & value) {
values_.set(key, value);
}
};
@ -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());
body->render(out, loop_context);
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) {
@ -2648,31 +2692,38 @@ inline std::shared_ptr<Context> Context::builtins() {
return filter.call(context, actual_args);
});
};
// https://jinja.palletsprojects.com/en/3.0.x/templates/#jinja-filters.reject
globals.set("reject", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
args.expectArgs("reject", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
auto & items = args.args[0];
auto filter_fn = context->get(args.args[1]);
if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
auto select_or_reject = [make_filter](bool is_select) {
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_args = Value::array();
for (size_t i = 2, n = args.args.size(); i < n; i++) {
filter_args.push_back(args.args[i]);
}
auto filter = make_filter(filter_fn, filter_args);
auto filter_fn = context->get(args.args[1]);
if (filter_fn.is_null()) throw std::runtime_error("Undefined filter: " + args.args[1].dump());
auto res = Value::array();
for (size_t i = 0, n = items.size(); i < n; i++) {
auto & item = items.at(i);
ArgumentsValue filter_args;
filter_args.args.emplace_back(item);
auto pred_res = filter.call(context, filter_args);
if (!pred_res.to_bool()) {
res.push_back(item);
auto filter_args = Value::array();
for (size_t i = 2, n = args.args.size(); i < n; i++) {
filter_args.push_back(args.args[i]);
}
}
return res;
}));
auto filter = make_filter(filter_fn, filter_args);
auto res = Value::array();
for (size_t i = 0, n = items.size(); i < n; i++) {
auto & item = items.at(i);
ArgumentsValue filter_args;
filter_args.args.emplace_back(item);
auto pred_res = filter.call(context, filter_args);
if (pred_res.to_bool() == (is_select ? true : false)) {
res.push_back(item);
}
}
return res;
});
};
globals.set("select", select_or_reject(/* is_select= */ true));
globals.set("reject", select_or_reject(/* is_select= */ false));
globals.set("map", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
auto res = Value::array();
if (args.args.size() == 1 &&
@ -2720,41 +2771,46 @@ inline std::shared_ptr<Context> Context::builtins() {
if (!text.empty() && text.back() == '\n') out += "\n";
return out;
}));
globals.set("selectattr", Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
args.expectArgs("selectattr", {2, (std::numeric_limits<size_t>::max)()}, {0, 0});
auto & items = args.args[0];
if (items.is_null())
return Value::array();
auto attr_name = args.args[1].get<std::string>();
auto select_or_reject_attr = [](bool is_select) {
return Value::callable([=](const std::shared_ptr<Context> & context, ArgumentsValue & args) {
args.expectArgs(is_select ? "selectattr" : "rejectattr", {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 attr_name = args.args[1].get<std::string>();
bool has_test = false;
Value test_fn;
ArgumentsValue test_args {{Value()}, {}};
if (args.args.size() >= 3) {
has_test = true;
test_fn = context->get(args.args[2]);
if (test_fn.is_null()) throw std::runtime_error("Undefined test: " + args.args[2].dump());
for (size_t i = 3, n = args.args.size(); i < n; i++) {
test_args.args.emplace_back(args.args[i]);
}
test_args.kwargs = args.kwargs;
}
auto res = Value::array();
for (size_t i = 0, n = items.size(); i < n; i++) {
auto & item = items.at(i);
auto attr = item.get(attr_name);
if (has_test) {
test_args.args[0] = attr;
if (test_fn.call(context, test_args).to_bool()) {
res.push_back(item);
bool has_test = false;
Value test_fn;
ArgumentsValue test_args {{Value()}, {}};
if (args.args.size() >= 3) {
has_test = true;
test_fn = context->get(args.args[2]);
if (test_fn.is_null()) throw std::runtime_error("Undefined test: " + args.args[2].dump());
for (size_t i = 3, n = args.args.size(); i < n; i++) {
test_args.args.emplace_back(args.args[i]);
}
} else {
res.push_back(attr);
test_args.kwargs = args.kwargs;
}
}
return res;
}));
auto res = Value::array();
for (size_t i = 0, n = items.size(); i < n; i++) {
auto & item = items.at(i);
auto attr = item.get(attr_name);
if (has_test) {
test_args.args[0] = attr;
if (test_fn.call(context, test_args).to_bool() == (is_select ? true : false)) {
res.push_back(item);
}
} else {
res.push_back(attr);
}
}
return res;
});
};
globals.set("selectattr", select_or_reject_attr(/* is_select= */ true));
globals.set("rejectattr", select_or_reject_attr(/* is_select= */ false));
globals.set("range", Value::callable([=](const std::shared_ptr<Context> &, ArgumentsValue & args) {
std::vector<int64_t> startEndStep(3);
std::vector<bool> param_set(3);

23
common/sampling.cpp
View file

@ -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 = */ {},

3
common/sampling.h
View file

@ -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);

58
convert_hf_to_gguf.py
View file

@ -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.")

61
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@ -125,21 +125,66 @@ For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
## CUDA
This provides GPU acceleration using an NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from the [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
This provides GPU acceleration using an NVIDIA GPU. Make sure to have the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) installed.
If you are using Fedora (using Fedora Workstation, or an 'Atomic' variant such as Silverblue), or would like to set up CUDA in a toolbox, please consider our [Fedora CUDA guide](./cuda-fedora.md). Unfortunately, the process is not as simple as one might expect.
#### Download directly from NVIDIA
You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
- Using `CMake`:
```bash
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
```
#### Compile and run inside a Fedora Toolbox Container
We also have a [guide](./cuda-fedora.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/).
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
**Recommended for:**
- ***Particularly*** *convenient* for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/).
- Toolbox is installed by default: [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde).
- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download)
### Compilation
```bash
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
```
### Override Compute Capability Specifications
If `nvcc` cannot detect your gpu, you may get compile-warnings such as:
```text
nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used
```
To override the `native` GPU detection:
#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus).
```text
GeForce RTX 4090 8.9
GeForce RTX 3080 Ti 8.6
GeForce RTX 3070 8.6
```
#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list.
```bash
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89"
```
### Runtime CUDA environmental variables
You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime.
```bash
# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device.
CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf
```
### Unified Memory
The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
### Performance Tuning
The following compilation options are also available to tweak performance:
| Option | Legal values | Default | Description |

159
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View file

@ -1,17 +1,16 @@
# Setting Up CUDA on Fedora
In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for:
- [Fedora Workstation](https://fedoraproject.org/workstation/)
- [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/)
- [Fedora Spins](https://fedoraproject.org/spins)
- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.`, `Arch Linux`, and `Ubuntu`.
- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.5`, `Arch Linux`, and `Ubuntu`.
## Table of Contents
- [Prerequisites](#prerequisites)
- [Monitoring NVIDIA CUDA Repositories](#monitoring-nvidia-cuda-repositories)
- [Using the Fedora 39 CUDA Repository](#using-the-fedora-39-cuda-repository)
- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository)
- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment)
- [Installing Essential Development Tools](#installing-essential-development-tools)
- [Adding the CUDA Repository](#adding-the-cuda-repository)
@ -29,44 +28,33 @@ In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox
## Prerequisites
- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/).
- **NVIDIA Drivers and Graphics Card installed on Host System (optional)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
- **Internet connectivity** to download packages.
### Monitoring NVIDIA CUDA Repositories
### Using the Fedora 41 CUDA Repository
Before proceeding, it is advisable to check if NVIDIA has updated their CUDA repositories for your Fedora version. NVIDIA's repositories can be found at:
The latest release is 41.
- [Fedora 40 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora40/x86_64/)
- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/)
As of the latest update, these repositories do not contain the `cuda` meta-package or are missing essential components.
### Using the Fedora 39 CUDA Repository
Since the newer repositories are incomplete, we'll use the Fedora 39 repository:
- [Fedora 39 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/)
**Note:** Fedora 39 is no longer maintained, so we recommend using a toolbox environment to prevent system conflicts.
**Note:** We recommend using a toolbox environment to prevent system conflicts.
## Creating a Fedora Toolbox Environment
This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using a Fedora 39 toolbox allows us to install the necessary packages without affecting the host system.
This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system.
**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker.
We do not recommend installing on the host system, as Fedora 39 is out-of-maintenance, and instead you should upgrade to a maintained version of Fedora for your host.
1. **Create a Fedora 39 Toolbox:**
1. **Create a Fedora 41 Toolbox:**
```bash
toolbox create --image registry.fedoraproject.org/fedora-toolbox:39 --container fedora-toolbox-39-cuda
toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda
```
2. **Enter the Toolbox:**
```bash
toolbox enter --container fedora-toolbox-39-cuda
toolbox enter --container fedora-toolbox-41-cuda
```
Inside the toolbox, you have root privileges and can install packages without affecting the host system.
@ -85,7 +73,7 @@ We do not recommend installing on the host system, as Fedora 39 is out-of-mainte
sudo dnf install vim-default-editor --allowerasing
```
The `--allowerasing` flag resolves any package conflicts.
The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package.
3. **Install Development Tools and Libraries:**
@ -100,7 +88,7 @@ We do not recommend installing on the host system, as Fedora 39 is out-of-mainte
Add the NVIDIA CUDA repository to your DNF configuration:
```bash
sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/cuda-fedora39.repo
sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo
```
After adding the repository, synchronize the package manager again:
@ -109,106 +97,62 @@ After adding the repository, synchronize the package manager again:
sudo dnf distro-sync
```
## Installing `nvidia-driver-libs`
## Installing `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
Attempt to install `nvidia-driver-libs`:
We need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go).
```bash
sudo dnf install nvidia-driver-libs
ls -la /usr/lib64/libcuda.so.1
```
**Explanation:**
- `nvidia-driver-libs` contains necessary NVIDIA driver libraries required by CUDA.
- This step might fail due to conflicts with existing NVIDIA drivers on the host system.
- `nvidia-driver-libs` and `nvidia-driver-cuda-libs` contains necessary NVIDIA driver libraries required by CUDA,
on hosts with NVIDIA drivers installed the Fedora Container will supply the host libraries.
## Manually Resolving Package Conflicts
### Install Nvidia Driver Libraries on Guest (if `libcuda.so.1` was NOT found).
```bash
sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
```
### Manually Updating the RPM database for host-supplied NVIDIA drivers (if `libcuda.so.1` was found).
If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files.
### 1. Download the `nvidia-driver-libs` RPM
#### 1. Download `nvidia-driver-libs` and `nvidia-driver-cuda-libs` RPM's (with dependencies)
```bash
sudo dnf download --arch x86_64 nvidia-driver-libs
sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-libs nvidia-driver-cuda-libs
```
You should see a file similar to:
```
nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
```
### 2. Attempt to Install the RPM
#### 2. Update the RPM database to assume the installation of these packages.
```bash
sudo dnf install nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
```
**Expected Error:**
Installation may fail with errors pointing to conflicts with `egl-gbm` and `egl-wayland`.
**Note: It is important to carefully read the error messages to identify the exact paths that need to be excluded.**
### 3. Download Dependencies
```bash
sudo dnf download --arch x86_64 egl-gbm egl-wayland
```
### 4. Install `egl-gbm` with Excluded Paths
Exclude conflicting files during installation:
```bash
sudo rpm --install --verbose --hash \
--excludepath=/usr/lib64/libnvidia-egl-gbm.so.1.1.2 \
--excludepath=/usr/share/egl/egl_external_platform.d/15_nvidia_gbm.json \
egl-gbm-1.1.2^20240919gitb24587d-3.fc39.x86_64.rpm
```
**Explanation:**
- The `--excludepath` option skips installing files that conflict with existing files.
- Adjust the paths based on the error messages you receive.
### 5. Install `egl-wayland` with Excluded Paths
```bash
sudo rpm --install --verbose --hash \
--excludepath=/usr/share/egl/egl_external_platform.d/10_nvidia_wayland.json \
egl-wayland-1.1.17^20241118giteeb29e1-5.fc39.x86_64.rpm
```
### 6. Install `nvidia-driver-libs` with Excluded Paths
```bash
sudo rpm --install --verbose --hash \
--excludepath=/usr/share/glvnd/egl_vendor.d/10_nvidia.json \
--excludepath=/usr/share/nvidia/nvoptix.bin \
nvidia-driver-libs-560.35.05-1.fc39.x86_64.rpm
sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/*
```
**Note:**
- Replace the paths with the ones causing conflicts in your installation if they differ.
- The `--verbose` and `--hash` options provide detailed output during installation.
- The `--justdb` option only updates the RPM database, without touching the filesystem.
## Finalizing the Installation of `nvidia-driver-libs`
#### Finalizing the Installation of `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
After manually installing the dependencies, run:
```bash
sudo dnf install nvidia-driver-libs
sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
```
You should receive a message indicating the package is already installed:
```
Package nvidia-driver-libs-3:560.35.05-1.fc39.x86_64 is already installed.
Dependencies resolved.
Updating and loading repositories:
Repositories loaded.
Package "nvidia-driver-libs-3:570.86.10-1.fc41.x86_64" is already installed.
Package "nvidia-driver-cuda-libs-3:570.86.10-1.fc41.x86_64" is already installed.
Nothing to do.
Complete!
```
## Installing the CUDA Meta-Package
@ -233,7 +177,7 @@ To use CUDA, add its binary directory to your system's `PATH`.
**Explanation:**
- We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
- We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
- The backslash `\` before `$PATH` ensures the variable is correctly written into the script.
2. **Make the Script Executable:**
@ -262,26 +206,33 @@ You should see output similar to:
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Tue_Oct_29_23:50:19_PDT_2024
Cuda compilation tools, release 12.6, V12.6.85
Build cuda_12.6.r12.6/compiler.35059454_0
Copyright (c) 2005-2025 NVIDIA Corporation
Built on Wed_Jan_15_19:20:09_PST_2025
Cuda compilation tools, release 12.8, V12.8.61
Build cuda_12.8.r12.8/compiler.35404655_0
```
This output confirms that the CUDA compiler is accessible and indicates the installed version.
## Conclusion
You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 39 CUDA repository. By manually resolving package conflicts and configuring the environment, you can develop CUDA applications without affecting your host system.
You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system.
## Troubleshooting
- **Installation Failures:**
- If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
- Use the `--excludepath` option with `rpm` to exclude conflicting files during manual installations.
- **Driver Conflicts:**
- Since the host system may already have NVIDIA drivers installed, conflicts can arise. Using the toolbox environment helps isolate these issues.
- If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
- You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations.
- **Rebooting the Container:**
- Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue:
```bash
# on the host system
podman container restart --all
```
- **Environment Variables Not Set:**
- If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`.
@ -291,10 +242,12 @@ You have successfully set up CUDA on Fedora within a toolbox environment using t
## Additional Notes
- **Updating CUDA in the Future:**
- Keep an eye on the official NVIDIA repositories for updates to your Fedora version.
- When an updated repository becomes available, adjust your `dnf` configuration accordingly.
- **Building `llama.cpp`:**
- With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support.
- Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration.

51
docs/llguidance.md Normal file
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@ -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.

15
examples/batched.swift/Sources/main.swift
View file

@ -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),

2
examples/gbnf-validator/gbnf-validator.cpp
View file

@ -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;

12
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -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 {

7
examples/llava/CMakeLists.txt
View file

@ -50,3 +50,10 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)
set(TARGET llama-llava-clip-quantize-cli)
add_executable(${TARGET} clip-quantize-cli.cpp)
set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

43
examples/llava/README-glmedge.md Normal file
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@ -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.

44
examples/llava/README-quantize.md Normal file
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@ -0,0 +1,44 @@
# Quantizing CLIP Visual Projector
This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance.
## Usage
To quantize a CLIP visual projector model, use the following command:
```sh
./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf <type>
```
After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc).
### Arguments
- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format.
- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved.
- `<type>`: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`.
### Quantization Types
The following quantization types are supported, based on the `enum ggml_type` definition:
- `2` - `q4_0`: 4-bit quantization with a single scale value.
- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block.
- `6` - `q5_0`: 5-bit quantization with a single scale value.
- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block.
- `8` - `q8_0`: 8-bit quantization with a single scale value.
### Example
To quantize a model using the `q4_0` quantization type, you would run:
```sh
./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2
```
This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method.
## Notes
- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements.
- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments.

59
examples/llava/clip-quantize-cli.cpp Normal file
View file

@ -0,0 +1,59 @@
#include "arg.h"
#include "base64.hpp"
#include "log.h"
#include "common.h"
#include "sampling.h"
#include "clip.h"
#include "llava.h"
#include "llama.h"
#include "ggml.h"
static void print_usage(int argc, char ** argv) {
(void) argc;
fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]);
fprintf(stderr, " type = 2 - q4_0\n");
fprintf(stderr, " type = 3 - q4_1\n");
fprintf(stderr, " type = 6 - q5_0\n");
fprintf(stderr, " type = 7 - q5_1\n");
fprintf(stderr, " type = 8 - q8_0\n");
}
int main(int argc, char ** argv) {
if (argc != 4) {
print_usage(argc, argv);
return 1;
}
const std::string fname_inp = argv[1];
const std::string fname_out = argv[2];
const int itype = atoi(argv[3]);
const int64_t t_main_start_us = ggml_time_us();
int64_t t_quantize_us = 0;
// load the model
{
const int64_t t_start_us = ggml_time_us();
if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}
t_quantize_us = ggml_time_us() - t_start_us;
}
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
printf("\n");
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
}
return 0;
}

118
examples/llava/clip.cpp
View file

@ -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);
@ -1308,6 +1357,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
LOG_INF("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
LOG_INF("%s: minicpmv_projector: %d\n", __func__, new_clip->has_minicpmv_projector);
LOG_INF("%s: glm_projector: %d\n", __func__, new_clip->has_glm_projector);
LOG_INF("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
LOG_INF("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
}
@ -1575,6 +1625,18 @@ 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"));
@ -2115,6 +2177,20 @@ 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");
@ -2300,7 +2376,8 @@ 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) {
@ -2342,7 +2419,7 @@ 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) {
@ -2475,6 +2552,12 @@ 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);
@ -2627,7 +2710,7 @@ 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++) {
@ -2651,14 +2734,19 @@ 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;
}
bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype) {
ggml_type type = GGML_TYPE_Q4_1;
assert(itype < GGML_TYPE_COUNT);
type = static_cast<ggml_type>(itype);
ggml_type type = static_cast<ggml_type>(itype);
auto * ctx_clip = clip_model_load(fname_inp, 2);
@ -2711,8 +2799,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
}
}
// quantize only 2D tensors
quantize &= (ggml_n_dims(cur) == 2);
// quantize only 2D tensors and bigger than block size
quantize &= (ggml_n_dims(cur) == 2) && cur->ne[0] > ggml_blck_size(type);
if (quantize) {
new_type = type;
@ -2812,6 +2900,9 @@ 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];
}
@ -2827,6 +2918,9 @@ 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;
}

2
examples/llava/clip.h
View file

@ -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
View 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
View 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.")

17
examples/llava/llava.cpp
View 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.

27
examples/main/main.cpp
View file

@ -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 (params.interactive) {
is_interacting = true;
}
is_antiprompt = true;
break;
if (std::find(antiprompt_token.begin(), antiprompt_token.end(), last_token) != antiprompt_token.end()) {
if (params.interactive) {
is_interacting = true;
}
is_antiprompt = true;
}
if (is_antiprompt) {

245
examples/run/run.cpp
View file

@ -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) {
@ -181,6 +189,10 @@ class Opt {
}
}
if (model_.empty()){
return 1;
}
return 0;
}
@ -319,6 +331,10 @@ class HttpClient {
public:
int init(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
const bool progress, std::string * response_str = nullptr) {
if (std::filesystem::exists(output_file)) {
return 0;
}
std::string output_file_partial;
curl = curl_easy_init();
if (!curl) {
@ -330,7 +346,7 @@ class HttpClient {
if (!output_file.empty()) {
output_file_partial = output_file + ".partial";
if (!out.open(output_file_partial, "ab")) {
printe("Failed to open file\n");
printe("Failed to open file for writing\n");
return 1;
}
@ -346,7 +362,11 @@ class HttpClient {
data.file_size = set_resume_point(output_file_partial);
set_progress_options(progress, data);
set_headers(headers);
perform(url);
CURLcode res = perform(url);
if (res != CURLE_OK){
printe("Fetching resource '%s' failed: %s\n", url.c_str(), curl_easy_strerror(res));
return 1;
}
if (!output_file.empty()) {
std::filesystem::rename(output_file_partial, output_file);
}
@ -411,16 +431,12 @@ class HttpClient {
}
}
void perform(const std::string & url) {
CURLcode res;
CURLcode perform(const std::string & url) {
curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https");
curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L);
res = curl_easy_perform(curl);
if (res != CURLE_OK) {
printe("curl_easy_perform() failed: %s\n", curl_easy_strerror(res));
}
return curl_easy_perform(curl);
}
static std::string human_readable_time(double seconds) {
@ -558,13 +574,14 @@ class LlamaData {
}
sampler = initialize_sampler(opt);
return 0;
}
private:
#ifdef LLAMA_USE_CURL
int download(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
const bool progress, std::string * response_str = nullptr) {
int download(const std::string & url, const std::string & output_file, const bool progress,
const std::vector<std::string> & headers = {}, std::string * response_str = nullptr) {
HttpClient http;
if (http.init(url, headers, output_file, progress, response_str)) {
return 1;
@ -573,48 +590,85 @@ class LlamaData {
return 0;
}
#else
int download(const std::string &, const std::vector<std::string> &, const std::string &, const bool,
int download(const std::string &, const std::string &, const bool, const std::vector<std::string> & = {},
std::string * = nullptr) {
printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
return 1;
}
#endif
int huggingface_dl(const std::string & model, const std::vector<std::string> headers, const std::string & bn) {
// Find the second occurrence of '/' after protocol string
size_t pos = model.find('/');
pos = model.find('/', pos + 1);
if (pos == std::string::npos) {
return 1;
}
const std::string hfr = model.substr(0, pos);
const std::string hff = model.substr(pos + 1);
const std::string url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
return download(url, headers, bn, true);
}
int ollama_dl(std::string & model, const std::vector<std::string> headers, const std::string & bn) {
if (model.find('/') == std::string::npos) {
model = "library/" + model;
}
std::string model_tag = "latest";
size_t colon_pos = model.find(':');
// Helper function to handle model tag extraction and URL construction
std::pair<std::string, std::string> extract_model_and_tag(std::string & model, const std::string & base_url) {
std::string model_tag = "latest";
const size_t colon_pos = model.find(':');
if (colon_pos != std::string::npos) {
model_tag = model.substr(colon_pos + 1);
model = model.substr(0, colon_pos);
}
std::string manifest_url = "https://registry.ollama.ai/v2/" + model + "/manifests/" + model_tag;
std::string url = base_url + model + "/manifests/" + model_tag;
return { model, url };
}
// Helper function to download and parse the manifest
int download_and_parse_manifest(const std::string & url, const std::vector<std::string> & headers,
nlohmann::json & manifest) {
std::string manifest_str;
const int ret = download(manifest_url, headers, "", false, &manifest_str);
int ret = download(url, "", false, headers, &manifest_str);
if (ret) {
return ret;
}
nlohmann::json manifest = nlohmann::json::parse(manifest_str);
std::string layer;
manifest = nlohmann::json::parse(manifest_str);
return 0;
}
int huggingface_dl(std::string & model, const std::string & bn) {
// Find the second occurrence of '/' after protocol string
size_t pos = model.find('/');
pos = model.find('/', pos + 1);
std::string hfr, hff;
std::vector<std::string> headers = { "User-Agent: llama-cpp", "Accept: application/json" };
std::string url;
if (pos == std::string::npos) {
auto [model_name, manifest_url] = extract_model_and_tag(model, "https://huggingface.co/v2/");
hfr = model_name;
nlohmann::json manifest;
int ret = download_and_parse_manifest(manifest_url, headers, manifest);
if (ret) {
return ret;
}
hff = manifest["ggufFile"]["rfilename"];
} else {
hfr = model.substr(0, pos);
hff = model.substr(pos + 1);
}
url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
return download(url, bn, true, headers);
}
int ollama_dl(std::string & model, const std::string & bn) {
const std::vector<std::string> headers = { "Accept: application/vnd.docker.distribution.manifest.v2+json" };
if (model.find('/') == std::string::npos) {
model = "library/" + model;
}
auto [model_name, manifest_url] = extract_model_and_tag(model, "https://registry.ollama.ai/v2/");
nlohmann::json manifest;
int ret = download_and_parse_manifest(manifest_url, {}, manifest);
if (ret) {
return ret;
}
std::string layer;
for (const auto & l : manifest["layers"]) {
if (l["mediaType"] == "application/vnd.ollama.image.model") {
layer = l["digest"];
@ -622,8 +676,67 @@ class LlamaData {
}
}
std::string blob_url = "https://registry.ollama.ai/v2/" + model + "/blobs/" + layer;
return download(blob_url, headers, bn, true);
std::string blob_url = "https://registry.ollama.ai/v2/" + model_name + "/blobs/" + layer;
return download(blob_url, bn, true, headers);
}
int github_dl(const std::string & model, const std::string & bn) {
std::string repository = model;
std::string branch = "main";
const size_t at_pos = model.find('@');
if (at_pos != std::string::npos) {
repository = model.substr(0, at_pos);
branch = model.substr(at_pos + 1);
}
const std::vector<std::string> repo_parts = string_split(repository, "/");
if (repo_parts.size() < 3) {
printe("Invalid GitHub repository format\n");
return 1;
}
const std::string & org = repo_parts[0];
const std::string & project = repo_parts[1];
std::string url = "https://raw.githubusercontent.com/" + org + "/" + project + "/" + branch;
for (size_t i = 2; i < repo_parts.size(); ++i) {
url += "/" + repo_parts[i];
}
return download(url, bn, true);
}
int s3_dl(const std::string & model, const std::string & bn) {
const size_t slash_pos = model.find('/');
if (slash_pos == std::string::npos) {
return 1;
}
const std::string bucket = model.substr(0, slash_pos);
const std::string key = model.substr(slash_pos + 1);
const char * access_key = std::getenv("AWS_ACCESS_KEY_ID");
const char * secret_key = std::getenv("AWS_SECRET_ACCESS_KEY");
if (!access_key || !secret_key) {
printe("AWS credentials not found in environment\n");
return 1;
}
// Generate AWS Signature Version 4 headers
// (Implementation requires HMAC-SHA256 and date handling)
// Get current timestamp
const time_t now = time(nullptr);
const tm tm = *gmtime(&now);
const std::string date = strftime_fmt("%Y%m%d", tm);
const std::string datetime = strftime_fmt("%Y%m%dT%H%M%SZ", tm);
const std::vector<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) {
@ -653,22 +766,26 @@ class LlamaData {
return ret;
}
const std::string bn = basename(model_);
const std::vector<std::string> headers = { "--header",
"Accept: application/vnd.docker.distribution.manifest.v2+json" };
if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
rm_until_substring(model_, "://");
ret = huggingface_dl(model_, headers, bn);
} else if (string_starts_with(model_, "hf.co/")) {
const std::string bn = basename(model_);
if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://") ||
string_starts_with(model_, "hf.co/")) {
rm_until_substring(model_, "hf.co/");
ret = huggingface_dl(model_, headers, bn);
} else if (string_starts_with(model_, "ollama://")) {
rm_until_substring(model_, "://");
ret = ollama_dl(model_, headers, bn);
} else if (string_starts_with(model_, "https://")) {
ret = download(model_, headers, bn, true);
} else {
ret = ollama_dl(model_, headers, bn);
ret = huggingface_dl(model_, bn);
} else if ((string_starts_with(model_, "https://") || string_starts_with(model_, "http://")) &&
!string_starts_with(model_, "https://ollama.com/library/")) {
ret = download(model_, bn, true);
} else if (string_starts_with(model_, "github:") || string_starts_with(model_, "github://")) {
rm_until_substring(model_, "github:");
rm_until_substring(model_, "://");
ret = github_dl(model_, bn);
} else if (string_starts_with(model_, "s3://")) {
rm_until_substring(model_, "://");
ret = s3_dl(model_, bn);
} else { // ollama:// or nothing
rm_until_substring(model_, "ollama.com/library/");
rm_until_substring(model_, "://");
ret = ollama_dl(model_, bn);
}
model_ = bn;
@ -731,7 +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();
@ -774,7 +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;
}
@ -837,7 +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;
}
@ -846,7 +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);
@ -883,7 +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)) {
@ -892,7 +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;
}

208
examples/server/README.md
View file

@ -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**
@ -220,7 +220,7 @@ services:
The project includes a web-based user interface that enables interaction with the model through the `/chat/completions` endpoint.
The web UI is developed using:
- `vue` framework for frontend development
- `react` framework for frontend development
- `tailwindcss` and `daisyui` for styling
- `vite` for build tooling
@ -236,9 +236,13 @@ npm i
# to run the dev server
npm run dev
# to build the public/index.html
# to build the public/index.html.gz
npm run build
```
After `public/index.html.gz` has been generated we need to generate the c++
headers (like build/examples/server/index.html.gz.hpp) that will be included
by server.cpp. This is done by building `llama-server` as described in the
[build](#build) section above.
NOTE: if you are using the vite dev server, you can change the API base URL to llama.cpp. To do that, run this code snippet in browser's console:
@ -456,7 +460,7 @@ These words will not be included in the completion, so make sure to add them to
- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements:
```json
```
{
"content": "<the generated completion text>",
"tokens": [ generated token ids if requested ],
@ -557,7 +561,7 @@ If `with_pieces` is `true`:
```
With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
```json
```
{
"tokens": [
{"id": 198, "piece": [195]}, // hex C3
@ -572,6 +576,18 @@ With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
`tokens`: Set the tokens to detokenize.
### POST `/apply-template`: Apply chat template to a conversation
Uses the server's prompt template formatting functionality to convert chat messages to a single string expected by a chat model as input, but does not perform inference. Instead, the prompt string is returned in the `prompt` field of the JSON response. The prompt can then be modified as desired (for example, to insert "Sure!" at the beginning of the model's response) before sending to `/completion` to generate the chat response.
*Options:*
`messages`: (Required) Chat turns in the same format as `/v1/chat/completions`.
**Response format**
Returns a JSON object with a field `prompt` containing a string of the input messages formatted according to the model's chat template format.
### POST `/embedding`: Generate embedding of a given text
> [!IMPORTANT]
@ -764,7 +780,7 @@ Same as the `/v1/embeddings` endpoint.
**Response format**
```json
```
[
{
"index": 0,
@ -1053,7 +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.
@ -1101,6 +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.

BIN
examples/server/public/index.html.gz
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258
examples/server/server.cpp
View file

@ -14,7 +14,7 @@
// mime type for sending response
#define MIMETYPE_JSON "application/json; charset=utf-8"
// auto generated files (update with ./deps.sh)
// auto generated files (see README.md for details)
#include "index.html.gz.hpp"
#include "loading.html.hpp"
@ -113,10 +113,11 @@ struct slot_params {
struct common_params_speculative speculative;
// OAI-compat fields
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
json to_json() const {
std::vector<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());
params.sampling.grammar = json_schema_to_grammar(schema);
SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
params.sampling.grammar = json_schema_to_grammar(schema);
SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
} catch (const std::exception & e) {
throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
}
} else {
params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar);
SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
}
{
auto it = data.find("chat_format");
if (it != data.end()) {
params.oaicompat_chat_format = static_cast<common_chat_format>(it->get<int>());
SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_format).c_str());
} else {
params.oaicompat_chat_format = defaults.oaicompat_chat_format;
}
}
{
const auto grammar_triggers = data.find("grammar_triggers");
if (grammar_triggers != data.end()) {
for (const auto & t : *grammar_triggers) {
common_grammar_trigger trigger;
trigger.word = t.at("word");
trigger.at_start = t.at("at_start");
auto ids = common_tokenize(vocab, trigger.word, /* add_special= */ false, /* parse_special= */ true);
if (ids.size() == 1) {
SRV_DBG("Grammar trigger token: %d (`%s`)\n", ids[0], trigger.word.c_str());
params.sampling.grammar_trigger_tokens.push_back(ids[0]);
params.sampling.preserved_tokens.insert(ids[0]);
continue;
}
SRV_DBG("Grammar trigger word: `%s`\n", trigger.word.c_str());
params.sampling.grammar_trigger_words.push_back(trigger);
}
}
const auto preserved_tokens = data.find("preserved_tokens");
if (preserved_tokens != data.end()) {
for (const auto & t : *preserved_tokens) {
auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
if (ids.size() == 1) {
SRV_DBG("Preserved token: %d\n", ids[0]);
params.sampling.preserved_tokens.insert(ids[0]);
} else {
// This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
SRV_WRN("Not preserved because more than 1 token (wrong chat template override?): %s\n", t.get<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;
@ -544,7 +595,7 @@ struct completion_token_output {
struct server_task_result_cmpl_final : server_task_result {
int index = 0;
std::string content;
std::string content;
llama_tokens tokens;
bool stream;
@ -566,10 +617,11 @@ struct server_task_result_cmpl_final : server_task_result {
slot_params generation_params;
// OAI-compat fields
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
bool verbose = false;
oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE;
std::string oaicompat_model;
std::string oaicompat_cmpl_id;
common_chat_format oaicompat_chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
virtual int get_index() override {
return index;
@ -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";
SRV_DBG("Parsing chat message: %s\n", content.c_str());
msg = common_chat_parse(content, oaicompat_chat_format);
finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
} else {
msg.content = content;
}
json 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();
}
chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) {
SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
chat_templates = common_chat_templates_from_model(model, "chatml");
} else {
chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
}
GGML_ASSERT(chat_templates.template_default.get() != nullptr);
return true;
@ -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;
@ -2290,12 +2374,12 @@ struct server_context {
res->stop = slot.stop;
res->post_sampling_probs = slot.params.post_sampling_probs;
res->verbose = slot.params.verbose;
res->stream = slot.params.stream;
res->oaicompat = slot.params.oaicompat;
res->oaicompat_model = slot.params.oaicompat_model;
res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
res->verbose = slot.params.verbose;
res->stream = slot.params.stream;
res->oaicompat = slot.params.oaicompat;
res->oaicompat_model = slot.params.oaicompat_model;
res->oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
res->oaicompat_chat_format = slot.params.oaicompat_chat_format;
// populate res.probs_output
if (slot.params.sampling.n_probs > 0) {
if (!slot.params.stream && slot.stop == STOP_TYPE_WORD) {
@ -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
@ -3265,10 +3353,12 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp
return;
}
LOG_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
// 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_DBG("request: %s\n", req.body.c_str());
LOG_DBG("response: %s\n", res.body.c_str());
SRV_INF("request: %s %s %s %d\n", req.method.c_str(), req.path.c_str(), req.remote_addr.c_str(), res.status);
SRV_DBG("request: %s\n", req.body.c_str());
SRV_DBG("response: %s\n", res.body.c_str());
}
std::function<void(int)> shutdown_handler;
@ -3351,9 +3441,13 @@ int main(int argc, char ** argv) {
message = "Unknown Exception";
}
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);
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 +3859,11 @@ 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");
// TODO: this log can become very long, put it behind a flag or think about a more compact format
//SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<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);
@ -3779,8 +3879,8 @@ int main(int argc, char ** argv) {
task.id_selected_slot = json_value(data, "id_slot", -1);
// OAI-compat
task.params.oaicompat = oaicompat;
task.params.oaicompat_cmpl_id = completion_id;
task.params.oaicompat = oaicompat;
task.params.oaicompat_cmpl_id = completion_id;
// oaicompat_model is already populated by params_from_json_cmpl
tasks.push_back(task);
@ -3949,14 +4049,14 @@ int main(int argc, char ** argv) {
};
const auto handle_chat_completions = [&ctx_server, &params, &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 +4066,13 @@ 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, &params, &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 = [&params, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
json models = {
{"object", "list"},
@ -4300,6 +4407,7 @@ int main(int argc, char ** argv) {
svr->Post("/v1/reranking", handle_rerank);
svr->Post("/tokenize", handle_tokenize);
svr->Post("/detokenize", handle_detokenize);
svr->Post("/apply-template", handle_apply_template);
// LoRA adapters hotswap
svr->Get ("/lora-adapters", handle_lora_adapters_list);
svr->Post("/lora-adapters", handle_lora_adapters_apply);
@ -4365,24 +4473,18 @@ 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(),
common_chat_format_example(*ctx_server.chat_templates.template_default, ctx_server.params_base.use_jinja).c_str());
ctx_server.queue_tasks.on_new_task(std::bind(
&server_context::process_single_task, &ctx_server, std::placeholders::_1));
ctx_server.queue_tasks.on_new_task([&ctx_server](const server_task & task) {
ctx_server.process_single_task(task);
});
ctx_server.queue_tasks.on_update_slots(std::bind(
&server_context::update_slots, &ctx_server));
ctx_server.queue_tasks.on_update_slots([&ctx_server]() {
ctx_server.update_slots();
});
shutdown_handler = [&](int) {
ctx_server.queue_tasks.terminate();

13
examples/server/tests/README.md
View file

@ -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
View file

@ -0,0 +1,4 @@
[pytest]
markers =
slow: marks tests as slow (deselect with '-m "not slow"')
serial

11
examples/server/tests/tests.sh
View file

@ -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 [ $# -lt 1 ]
then
pytest -v -x
if [[ "${SLOW_TESTS:-0}" == 1 ]]; then
pytest -v -x
else
pytest -v -x -m "not slow"
fi
else
pytest "$@"
fi

26
examples/server/tests/unit/test_chat_completion.py
View file

@ -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():
@ -13,9 +13,12 @@ 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", "What is the best book", 8, "^ blue", 23, 8, "length", True, "This is not a chat template, it is"),
(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),
]
@ -121,6 +124,21 @@ def test_chat_template():
assert res.body["__verbose"]["prompt"] == "<s> <|start_header_id|>system<|end_header_id|>\n\nBook<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the best book<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
def test_apply_chat_template():
global server
server.chat_template = "command-r"
server.start()
res = server.make_request("POST", "/apply-template", data={
"messages": [
{"role": "system", "content": "You are a test."},
{"role": "user", "content":"Hi there"},
]
})
assert res.status_code == 200
assert "prompt" in res.body
assert res.body["prompt"] == "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a test.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
@pytest.mark.parametrize("response_format,n_predicted,re_content", [
({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""),
({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"),

4
examples/server/tests/unit/test_completion.py
View file

@ -87,7 +87,7 @@ def test_completion_stream_vs_non_stream():
assert content_stream == res_non_stream.body["content"]
def test_completion_stream_with_openai_library():
def test_completion_with_openai_library():
global server
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
@ -102,7 +102,7 @@ def test_completion_stream_with_openai_library():
assert match_regex("(going|bed)+", res.choices[0].text)
def test_completion_with_openai_library():
def test_completion_stream_with_openai_library():
global server
server.start()
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")

418
examples/server/tests/unit/test_tool_call.py Normal file
View 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}'

6
examples/server/tests/utils.py
View file

@ -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)

76
examples/server/utils.hpp
View file

@ -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

24
examples/server/webui/.gitignore vendored Normal file
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@ -0,0 +1,24 @@
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
lerna-debug.log*
node_modules
dist
dist-ssr
*.local
# Editor directories and files
.vscode/*
!.vscode/extensions.json
.idea
.DS_Store
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?

10
examples/server/webui/.prettierignore Normal file
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@ -0,0 +1,10 @@
**/.vscode
**/.github
**/.git
**/.svn
**/.hg
**/node_modules
**/dist
**/build
*.config.js

26
examples/server/webui/eslint.config.js Normal file
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@ -0,0 +1,26 @@
import js from '@eslint/js'
import globals from 'globals'
import reactHooks from 'eslint-plugin-react-hooks'
import reactRefresh from 'eslint-plugin-react-refresh'
import tseslint from 'typescript-eslint'
export default tseslint.config(
{ ignores: ['dist'] },
{
extends: [js.configs.recommended, ...tseslint.configs.recommended],
files: ['**/*.{ts,tsx}'],
languageOptions: {
ecmaVersion: 2020,
globals: globals.browser,
},
plugins: {
'react-hooks': reactHooks,
'react-refresh': reactRefresh,
},
rules: {
...reactHooks.configs.recommended.rules,
'react-refresh/only-export-components': 'off',
'@typescript-eslint/no-unused-vars': 'off',
},
},
)

355
examples/server/webui/index.html
View file

@ -1,343 +1,16 @@
<!DOCTYPE html>
<!doctype html>
<html>
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
<meta name="color-scheme" content="light dark">
<title>🦙 llama.cpp - chat</title>
</head>
<body>
<div id="app" class="opacity-0"> <!-- opacity-0 will be removed on app mounted -->
<div class="flex flex-row drawer lg:drawer-open">
<input id="toggle-drawer" type="checkbox" class="drawer-toggle" checked />
<!-- sidebar -->
<div class="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
<label for="toggle-drawer" aria-label="close sidebar" class="drawer-overlay"></label>
<div class="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
<div class="flex flex-row items-center justify-between mb-4 mt-4">
<h2 class="font-bold ml-4">Conversations</h2>
<!-- close sidebar button -->
<label for="toggle-drawer" class="btn btn-ghost lg:hidden">
<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-arrow-bar-left" viewBox="0 0 16 16">
<path fill-rule="evenodd" d="M12.5 15a.5.5 0 0 1-.5-.5v-13a.5.5 0 0 1 1 0v13a.5.5 0 0 1-.5.5M10 8a.5.5 0 0 1-.5.5H3.707l2.147 2.146a.5.5 0 0 1-.708.708l-3-3a.5.5 0 0 1 0-.708l3-3a.5.5 0 1 1 .708.708L3.707 7.5H9.5a.5.5 0 0 1 .5.5"/>
</svg>
</label>
</div>
<!-- list of conversations -->
<div :class="{
'btn btn-ghost justify-start': true,
'btn-active': messages.length === 0,
}" @click="newConversation">
+ New conversation
</div>
<div v-for="conv in conversations" :class="{
'btn btn-ghost justify-start font-normal': true,
'btn-active': conv.id === viewingConvId,
}" @click="setViewingConv(conv.id)" dir="auto">
<span class="truncate">{{ conv.messages[0].content }}</span>
</div>
<div class="text-center text-xs opacity-40 mt-auto mx-4">
Conversations are saved to browser's localStorage
</div>
</div>
</div>
<!-- main view -->
<div class="chat-screen drawer-content grow flex flex-col h-screen w-screen mx-auto px-4">
<!-- header -->
<div class="flex flex-row items-center mt-6 mb-6">
<!-- open sidebar button -->
<label for="toggle-drawer" class="btn btn-ghost lg:hidden">
<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-list" viewBox="0 0 16 16">
<path fill-rule="evenodd" d="M2.5 12a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5"/>
</svg>
</label>
<div class="grow text-2xl font-bold ml-2">llama.cpp</div>
<!-- action buttons (top right) -->
<div class="flex items-center">
<div v-if="messages.length > 0" class="dropdown dropdown-end">
<!-- "..." button -->
<button tabindex="0" role="button" class="btn m-1" :disabled="isGenerating">
<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-three-dots-vertical" viewBox="0 0 16 16">
<path d="M9.5 13a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0"/>
</svg>
</button>
<!-- "delete" dropdown menu -->
<ul tabindex="0" class="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow">
<li @click="downloadConv(viewingConvId)"><a>Download</a></li>
<li class="text-error" @click="deleteConv(viewingConvId)"><a>Delete</a></li>
</ul>
</div>
<div class="tooltip tooltip-bottom" data-tip="Settings">
<button class="btn" @click="showConfigDialog = true" :disabled="isGenerating">
<!-- settings button -->
<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-gear" viewBox="0 0 16 16">
<path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0"/>
<path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z"/>
</svg>
</button>
</div>
<!-- theme controller is copied from https://daisyui.com/components/theme-controller/ -->
<div class="tooltip tooltip-bottom" data-tip="Themes">
<div class="dropdown dropdown-end dropdown-bottom">
<div tabindex="0" role="button" class="btn m-1">
<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-palette2" viewBox="0 0 16 16">
<path d="M0 .5A.5.5 0 0 1 .5 0h5a.5.5 0 0 1 .5.5v5.277l4.147-4.131a.5.5 0 0 1 .707 0l3.535 3.536a.5.5 0 0 1 0 .708L10.261 10H15.5a.5.5 0 0 1 .5.5v5a.5.5 0 0 1-.5.5H3a3 3 0 0 1-2.121-.879A3 3 0 0 1 0 13.044m6-.21 7.328-7.3-2.829-2.828L6 7.188zM4.5 13a1.5 1.5 0 1 0-3 0 1.5 1.5 0 0 0 3 0M15 15v-4H9.258l-4.015 4zM0 .5v12.495zm0 12.495V13z"/>
</svg>
</div>
<ul tabindex="0" class="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto">
<li>
<button
class="btn btn-sm btn-block btn-ghost justify-start"
:class="{ 'btn-active': selectedTheme === 'auto' }"
@click="setSelectedTheme('auto')">
auto
</button>
</li>
<li v-for="theme in themes">
<input
type="radio"
name="theme-dropdown"
class="theme-controller btn btn-sm btn-block btn-ghost justify-start"
:aria-label="theme"
:value="theme"
:checked="selectedTheme === theme"
@click="setSelectedTheme(theme)" />
</li>
</ul>
</div>
</div>
</div>
</div>
<!-- chat messages -->
<div id="messages-list" class="flex flex-col grow overflow-y-auto">
<div class="mt-auto flex justify-center">
<!-- placeholder to shift the message to the bottom -->
{{ messages.length === 0 ? 'Send a message to start' : '' }}
</div>
<div v-for="msg in messages" class="group">
<message-bubble
:config="config"
:msg="msg"
:key="msg.id"
:is-generating="isGenerating"
:edit-user-msg-and-regenerate="editUserMsgAndRegenerate"
:regenerate-msg="regenerateMsg"></message-bubble>
</div>
<!-- pending (ongoing) assistant message -->
<div id="pending-msg" class="group">
<message-bubble
v-if="pendingMsg"
:config="config"
:msg="pendingMsg"
:key="pendingMsg.id"
:is-generating="isGenerating"
:show-thought-in-progress="config.showThoughtInProgress"
:edit-user-msg-and-regenerate="() => {}"
:regenerate-msg="() => {}"></message-bubble>
</div>
</div>
<!-- chat input -->
<div class="flex flex-row items-center mt-8 mb-6">
<textarea
class="textarea textarea-bordered w-full"
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>
<button v-if="!isGenerating" class="btn btn-primary ml-2" @click="sendMessage" :disabled="inputMsg.length === 0">Send</button>
<button v-else class="btn btn-neutral ml-2" @click="stopGeneration">Stop</button>
</div>
</div>
</div>
<!-- modal for editing config -->
<dialog class="modal" :class="{'modal-open': showConfigDialog}">
<div class="modal-box">
<h3 class="text-lg font-bold mb-6">Settings</h3>
<div class="h-[calc(90vh-12rem)] overflow-y-auto">
<p class="opacity-40 mb-6">Settings below are saved in browser's localStorage</p>
<settings-modal-short-input :config-key="'apiKey'" :config-default="configDefault" :config-info="configInfo" v-model="config.apiKey"></settings-modal-short-input>
<label class="form-control mb-2">
<div class="label">System Message</div>
<textarea class="textarea textarea-bordered h-24" :placeholder="'Default: ' + configDefault.systemMessage" v-model="config.systemMessage"></textarea>
</label>
<template v-for="configKey in ['temperature', 'top_k', 'top_p', 'min_p', 'max_tokens']">
<settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
</template>
<!-- TODO: add more sampling-related configs, please regroup them into different "collapse" sections -->
<!-- Section: Other sampler settings -->
<details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary class="collapse-title font-bold">Other sampler settings</summary>
<div class="collapse-content">
<!-- Samplers queue -->
<settings-modal-short-input label="Samplers queue" :config-key="'samplers'" :config-default="configDefault" :config-info="configInfo" v-model="config.samplers"></settings-modal-short-input>
<!-- Samplers -->
<template v-for="configKey in ['dynatemp_range', 'dynatemp_exponent', 'typical_p', 'xtc_probability', 'xtc_threshold']">
<settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
</template>
</div>
</details>
<!-- Section: Penalties settings -->
<details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary class="collapse-title font-bold">Penalties settings</summary>
<div class="collapse-content">
<template v-for="configKey in ['repeat_last_n', 'repeat_penalty', 'presence_penalty', 'frequency_penalty', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_penalty_last_n']">
<settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
</template>
</div>
</details>
<!-- Section: Reasoning models -->
<details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary class="collapse-title font-bold">Reasoning models</summary>
<div class="collapse-content">
<div class="flex flex-row items-center mb-2">
<input type="checkbox" class="checkbox" v-model="config.showThoughtInProgress" />
<span class="ml-4">Expand though process by default for generating message</span>
</div>
<div class="flex flex-row items-center mb-2">
<input type="checkbox" class="checkbox" v-model="config.excludeThoughtOnReq" />
<span class="ml-4">Exclude thought process when sending request to API (Recommended for DeepSeek-R1)</span>
</div>
</div>
</details>
<!-- Section: Advanced config -->
<details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary class="collapse-title font-bold">Advanced config</summary>
<div class="collapse-content">
<div class="flex flex-row items-center mb-2" v-if="isDev">
<!-- this button only shows in dev mode, used to import a demo conversation to test message rendering -->
<button class="btn" @click="debugImportDemoConv()">(debug) Import demo conversation</button>
</div>
<div class="flex flex-row items-center mb-2">
<input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
<span class="ml-4">Show tokens per second</span>
</div>
<label class="form-control mb-2">
<!-- Custom parameters input -->
<div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
<textarea class="textarea textarea-bordered h-24" placeholder="Example: { &quot;mirostat&quot;: 1, &quot;min_p&quot;: 0.1 }" v-model="config.custom"></textarea>
</label>
</div>
</details>
</div>
<!-- action buttons -->
<div class="modal-action">
<button class="btn" @click="resetConfigDialog">Reset to default</button>
<button class="btn" @click="closeAndDiscardConfigDialog">Close</button>
<button class="btn btn-primary" @click="closeAndSaveConfigDialog">Save</button>
</div>
</div>
</dialog>
</div>
<!-- Template to be used as message bubble -->
<template id="message-bubble">
<div :class="{
'chat': true,
'chat-start': msg.role !== 'user',
'chat-end': msg.role === 'user',
}">
<div :class="{
'chat-bubble markdown': true,
'chat-bubble-base-300': msg.role !== 'user',
}">
<!-- textarea for editing message -->
<template v-if="editingContent !== null">
<textarea
dir="auto"
class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
v-model="editingContent"></textarea>
<br/>
<button class="btn btn-ghost mt-2 mr-2" @click="editingContent = null">Cancel</button>
<button class="btn mt-2" @click="editMsg()">Submit</button>
</template>
<template v-else>
<!-- show loading dots for pending message -->
<span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
<!-- render message as markdown -->
<div v-else dir="auto">
<details v-if="msg.role === 'assistant' && splitMsgContent.cot" class="collapse bg-base-200 collapse-arrow mb-4" :open="splitMsgContent.isThinking && showThoughtInProgress">
<summary class="collapse-title">
<span v-if="splitMsgContent.isThinking">
<span v-if="isGenerating" class="loading loading-spinner loading-md mr-2" style="vertical-align: middle;"></span>
<b>Thinking</b>
</span>
<b v-else>Thought Process</b>
</summary>
<vue-markdown :source="splitMsgContent.cot" dir="auto" class="collapse-content"></vue-markdown>
</details>
<vue-markdown :source="splitMsgContent.content"></vue-markdown>
</div>
<!-- render timings if enabled -->
<div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
<div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>
<div class="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
<b>Prompt</b><br/>
- Tokens: {{ timings.prompt_n }}<br/>
- Time: {{ timings.prompt_ms }} ms<br/>
- Speed: {{ timings.prompt_per_second.toFixed(1) }} t/s<br/>
<b>Generation</b><br/>
- Tokens: {{ timings.predicted_n }}<br/>
- Time: {{ timings.predicted_ms }} ms<br/>
- Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s<br/>
</div>
</div>
</template>
</div>
</div>
<!-- actions for each message -->
<div :class="{'text-right': msg.role === 'user', 'opacity-0': isGenerating}" class="mx-4 mt-2 mb-2">
<!-- user message -->
<button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingContent = msg.content" :disabled="isGenerating">
✍️ Edit
</button>
<!-- assistant message -->
<button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
🔄 Regenerate
</button>
<button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg()" :disabled="isGenerating">
📋 Copy
</button>
</div>
</template>
<!-- Template to be used by settings modal -->
<template id="settings-modal-short-input">
<label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
<!-- Show help message on hovering on the input label -->
<div class="dropdown dropdown-hover">
<div tabindex="0" role="button" class="font-bold">{{ label || configKey }}</div>
<div class="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
{{ configInfo[configKey] || '(no help message available)' }}
</div>
</div>
<!-- Here we forward v-model from parent to child component, see: https://stackoverflow.com/questions/47311936/v-model-and-child-components -->
<input type="text" class="grow" :placeholder="'Default: ' + (configDefault[configKey] || 'none')" :value="modelValue" @input="$emit('update:modelValue', $event.target.value)" />
</label>
</template>
<script type="module" src="/src/main.js"></script>
</body>
<head>
<meta charset="UTF-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1, maximum-scale=1"
/>
<meta name="color-scheme" content="light dark" />
<title>🦙 llama.cpp - chat</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.tsx"></script>
</body>
</html>

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examples/server/webui/package-lock.json generated
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48
examples/server/webui/package.json
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@ -5,13 +5,10 @@
"type": "module",
"scripts": {
"dev": "vite",
"build": "vite build",
"preview": "vite preview",
"analyze": "ANALYZE=1 npx vite-bundle-visualizer"
},
"devDependencies": {
"sass-embedded": "^1.83.0",
"vite": "^5.4.10"
"build": "tsc -b && vite build",
"format": "eslint . && prettier --write .",
"lint": "eslint .",
"preview": "vite preview"
},
"dependencies": {
"@sec-ant/readable-stream": "^0.6.0",
@ -20,11 +17,42 @@
"daisyui": "^4.12.14",
"highlight.js": "^11.10.0",
"katex": "^0.16.15",
"markdown-it": "^14.1.0",
"postcss": "^8.4.49",
"react": "^18.3.1",
"react-dom": "^18.3.1",
"react-markdown": "^9.0.3",
"react-router": "^7.1.5",
"rehype-highlight": "^7.0.2",
"rehype-katex": "^7.0.1",
"remark-breaks": "^4.0.0",
"remark-gfm": "^4.0.0",
"remark-math": "^6.0.0",
"tailwindcss": "^3.4.15",
"textlinestream": "^1.1.1",
"vite-plugin-singlefile": "^2.0.3",
"vue": "^3.5.13"
"vite-plugin-singlefile": "^2.0.3"
},
"devDependencies": {
"@eslint/js": "^9.17.0",
"@types/markdown-it": "^14.1.2",
"@types/node": "^22.13.1",
"@types/react": "^18.3.18",
"@types/react-dom": "^18.3.5",
"@vitejs/plugin-react": "^4.3.4",
"eslint": "^9.17.0",
"eslint-plugin-react-hooks": "^5.0.0",
"eslint-plugin-react-refresh": "^0.4.16",
"globals": "^15.14.0",
"prettier": "^3.4.2",
"sass-embedded": "^1.83.4",
"typescript": "~5.6.2",
"typescript-eslint": "^8.18.2",
"vite": "^6.0.5"
},
"prettier": {
"trailingComma": "es5",
"tabWidth": 2,
"semi": true,
"singleQuote": true,
"bracketSameLine": false
}
}

2
examples/server/webui/public/demo-conversation.json
View file

@ -11,7 +11,7 @@
{
"id": 1734087548327,
"role": "assistant",
"content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
"content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\n$2x + y = z$\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
"timings": {
"prompt_n": 1,
"prompt_ms": 28.923,

36
examples/server/webui/src/App.tsx Normal file
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@ -0,0 +1,36 @@
import { HashRouter, Outlet, Route, Routes } from 'react-router';
import Header from './components/Header';
import Sidebar from './components/Sidebar';
import { AppContextProvider } from './utils/app.context';
import ChatScreen from './components/ChatScreen';
function App() {
return (
<HashRouter>
<div className="flex flex-row drawer lg:drawer-open">
<AppContextProvider>
<Routes>
<Route element={<AppLayout />}>
<Route path="/chat/:convId" element={<ChatScreen />} />
<Route path="*" element={<ChatScreen />} />
</Route>
</Routes>
</AppContextProvider>
</div>
</HashRouter>
);
}
function AppLayout() {
return (
<>
<Sidebar />
<div className="chat-screen drawer-content grow flex flex-col h-screen w-screen mx-auto px-4">
<Header />
<Outlet />
</div>
</>
);
}
export default App;

89
examples/server/webui/src/Config.ts Normal file
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import daisyuiThemes from 'daisyui/src/theming/themes';
import { isNumeric } from './utils/misc';
export const isDev = import.meta.env.MODE === 'development';
// constants
export const BASE_URL = new URL('.', document.baseURI).href
.toString()
.replace(/\/$/, '');
export const CONFIG_DEFAULT = {
// Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
apiKey: '',
systemMessage: 'You are a helpful assistant.',
showTokensPerSecond: false,
showThoughtInProgress: false,
excludeThoughtOnReq: true,
// make sure these default values are in sync with `common.h`
samplers: 'edkypmxt',
temperature: 0.8,
dynatemp_range: 0.0,
dynatemp_exponent: 1.0,
top_k: 40,
top_p: 0.95,
min_p: 0.05,
xtc_probability: 0.0,
xtc_threshold: 0.1,
typical_p: 1.0,
repeat_last_n: 64,
repeat_penalty: 1.0,
presence_penalty: 0.0,
frequency_penalty: 0.0,
dry_multiplier: 0.0,
dry_base: 1.75,
dry_allowed_length: 2,
dry_penalty_last_n: -1,
max_tokens: -1,
custom: '', // custom json-stringified object
};
export const CONFIG_INFO: Record<string, string> = {
apiKey: 'Set the API Key if you are using --api-key option for the server.',
systemMessage: 'The starting message that defines how model should behave.',
samplers:
'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature',
temperature:
'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
dynatemp_range:
'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
dynatemp_exponent:
'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
top_k: 'Keeps only k top tokens.',
top_p:
'Limits tokens to those that together have a cumulative probability of at least p',
min_p:
'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
xtc_probability:
'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
xtc_threshold:
'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
typical_p:
'Sorts and limits tokens based on the difference between log-probability and entropy.',
repeat_last_n: 'Last n tokens to consider for penalizing repetition',
repeat_penalty:
'Controls the repetition of token sequences in the generated text',
presence_penalty:
'Limits tokens based on whether they appear in the output or not.',
frequency_penalty:
'Limits tokens based on how often they appear in the output.',
dry_multiplier:
'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
dry_base:
'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
dry_allowed_length:
'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
dry_penalty_last_n:
'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
max_tokens: 'The maximum number of token per output.',
custom: '', // custom json-stringified object
};
// config keys having numeric value (i.e. temperature, top_k, top_p, etc)
export const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT)
.filter((e) => isNumeric(e[1]))
.map((e) => e[0]);
// list of themes supported by daisyui
export const THEMES = ['light', 'dark']
// make sure light & dark are always at the beginning
.concat(
Object.keys(daisyuiThemes).filter((t) => t !== 'light' && t !== 'dark')
);

229
examples/server/webui/src/components/ChatMessage.tsx Normal file
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import { useMemo, useState } from 'react';
import { useAppContext } from '../utils/app.context';
import { Message, PendingMessage } from '../utils/types';
import { classNames } from '../utils/misc';
import MarkdownDisplay, { CopyButton } from './MarkdownDisplay';
interface SplitMessage {
content: PendingMessage['content'];
thought?: string;
isThinking?: boolean;
}
export default function ChatMessage({
msg,
id,
scrollToBottom,
isPending,
}: {
msg: Message | PendingMessage;
id?: string;
scrollToBottom: (requiresNearBottom: boolean) => void;
isPending?: boolean;
}) {
const { viewingConversation, replaceMessageAndGenerate, config } =
useAppContext();
const [editingContent, setEditingContent] = useState<string | null>(null);
const timings = useMemo(
() =>
msg.timings
? {
...msg.timings,
prompt_per_second:
(msg.timings.prompt_n / msg.timings.prompt_ms) * 1000,
predicted_per_second:
(msg.timings.predicted_n / msg.timings.predicted_ms) * 1000,
}
: null,
[msg.timings]
);
// for reasoning model, we split the message into content and thought
// TODO: implement this as remark/rehype plugin in the future
const { content, thought, isThinking }: SplitMessage = useMemo(() => {
if (msg.content === null || msg.role !== 'assistant') {
return { content: msg.content };
}
let actualContent = '';
let thought = '';
let isThinking = false;
let thinkSplit = msg.content.split('<think>', 2);
actualContent += thinkSplit[0];
while (thinkSplit[1] !== undefined) {
// <think> tag found
thinkSplit = thinkSplit[1].split('</think>', 2);
thought += thinkSplit[0];
isThinking = true;
if (thinkSplit[1] !== undefined) {
// </think> closing tag found
isThinking = false;
thinkSplit = thinkSplit[1].split('<think>', 2);
actualContent += thinkSplit[0];
}
}
return { content: actualContent, thought, isThinking };
}, [msg]);
if (!viewingConversation) return null;
const regenerate = async () => {
replaceMessageAndGenerate(viewingConversation.id, msg.id, undefined, () =>
scrollToBottom(true)
);
};
return (
<div className="group" id={id}>
<div
className={classNames({
chat: true,
'chat-start': msg.role !== 'user',
'chat-end': msg.role === 'user',
})}
>
<div
className={classNames({
'chat-bubble markdown': true,
'chat-bubble-base-300': msg.role !== 'user',
})}
>
{/* textarea for editing message */}
{editingContent !== null && (
<>
<textarea
dir="auto"
className="textarea textarea-bordered bg-base-100 text-base-content max-w-2xl w-[calc(90vw-8em)] h-24"
value={editingContent}
onChange={(e) => setEditingContent(e.target.value)}
></textarea>
<br />
<button
className="btn btn-ghost mt-2 mr-2"
onClick={() => setEditingContent(null)}
>
Cancel
</button>
<button
className="btn mt-2"
onClick={() =>
replaceMessageAndGenerate(
viewingConversation.id,
msg.id,
editingContent
)
}
>
Submit
</button>
</>
)}
{/* not editing content, render message */}
{editingContent === null && (
<>
{content === null ? (
<>
{/* show loading dots for pending message */}
<span className="loading loading-dots loading-md"></span>
</>
) : (
<>
{/* render message as markdown */}
<div dir="auto">
{thought && (
<details
className="collapse bg-base-200 collapse-arrow mb-4"
open={isThinking && config.showThoughtInProgress}
>
<summary className="collapse-title">
{isPending && isThinking ? (
<span>
<span
v-if="isGenerating"
className="loading loading-spinner loading-md mr-2"
style={{ verticalAlign: 'middle' }}
></span>
<b>Thinking</b>
</span>
) : (
<b>Thought Process</b>
)}
</summary>
<div className="collapse-content">
<MarkdownDisplay content={thought} />
</div>
</details>
)}
<MarkdownDisplay content={content} />
</div>
</>
)}
{/* render timings if enabled */}
{timings && config.showTokensPerSecond && (
<div className="dropdown dropdown-hover dropdown-top mt-2">
<div
tabIndex={0}
role="button"
className="cursor-pointer font-semibold text-sm opacity-60"
>
Speed: {timings.predicted_per_second.toFixed(1)} t/s
</div>
<div className="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
<b>Prompt</b>
<br />- Tokens: {timings.prompt_n}
<br />- Time: {timings.prompt_ms} ms
<br />- Speed: {timings.prompt_per_second.toFixed(1)} t/s
<br />
<b>Generation</b>
<br />- Tokens: {timings.predicted_n}
<br />- Time: {timings.predicted_ms} ms
<br />- Speed: {timings.predicted_per_second.toFixed(1)} t/s
<br />
</div>
</div>
)}
</>
)}
</div>
</div>
{/* actions for each message */}
{msg.content !== null && (
<div
className={classNames({
'mx-4 mt-2 mb-2': true,
'text-right': msg.role === 'user',
})}
>
{/* user message */}
{msg.role === 'user' && (
<button
className="badge btn-mini show-on-hover"
onClick={() => setEditingContent(msg.content)}
disabled={msg.content === null}
>
✍️ Edit
</button>
)}
{/* assistant message */}
{msg.role === 'assistant' && (
<>
{!isPending && (
<button
className="badge btn-mini show-on-hover mr-2"
onClick={regenerate}
disabled={msg.content === null}
>
🔄 Regenerate
</button>
)}
<CopyButton
className="badge btn-mini show-on-hover mr-2"
content={msg.content}
/>
</>
)}
</div>
)}
</div>
);
}

123
examples/server/webui/src/components/ChatScreen.tsx Normal file
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@ -0,0 +1,123 @@
import { useEffect, useRef, useState } from 'react';
import { useAppContext } from '../utils/app.context';
import StorageUtils from '../utils/storage';
import { useNavigate } from 'react-router';
import ChatMessage from './ChatMessage';
import { PendingMessage } from '../utils/types';
export default function ChatScreen() {
const {
viewingConversation,
sendMessage,
isGenerating,
stopGenerating,
pendingMessages,
} = useAppContext();
const [inputMsg, setInputMsg] = useState('');
const containerRef = useRef<HTMLDivElement>(null);
const navigate = useNavigate();
const currConvId = viewingConversation?.id ?? '';
const pendingMsg: PendingMessage | undefined = pendingMessages[currConvId];
const scrollToBottom = (requiresNearBottom: boolean) => {
if (!containerRef.current) return;
const msgListElem = containerRef.current;
const spaceToBottom =
msgListElem.scrollHeight -
msgListElem.scrollTop -
msgListElem.clientHeight;
if (!requiresNearBottom || spaceToBottom < 50) {
setTimeout(
() => msgListElem.scrollTo({ top: msgListElem.scrollHeight }),
1
);
}
};
// scroll to bottom when conversation changes
useEffect(() => {
scrollToBottom(false);
}, [viewingConversation?.id]);
const sendNewMessage = async () => {
if (inputMsg.trim().length === 0 || isGenerating(currConvId)) return;
const convId = viewingConversation?.id ?? StorageUtils.getNewConvId();
const lastInpMsg = inputMsg;
setInputMsg('');
if (!viewingConversation) {
// if user is creating a new conversation, redirect to the new conversation
navigate(`/chat/${convId}`);
}
scrollToBottom(false);
// auto scroll as message is being generated
const onChunk = () => scrollToBottom(true);
if (!(await sendMessage(convId, inputMsg, onChunk))) {
// restore the input message if failed
setInputMsg(lastInpMsg);
}
};
return (
<>
{/* chat messages */}
<div
id="messages-list"
className="flex flex-col grow overflow-y-auto"
ref={containerRef}
>
<div className="mt-auto flex justify-center">
{/* placeholder to shift the message to the bottom */}
{viewingConversation ? '' : 'Send a message to start'}
</div>
{viewingConversation?.messages.map((msg) => (
<ChatMessage key={msg.id} msg={msg} scrollToBottom={scrollToBottom} />
))}
{pendingMsg && (
<ChatMessage
msg={pendingMsg}
scrollToBottom={scrollToBottom}
isPending
id="pending-msg"
/>
)}
</div>
{/* chat input */}
<div className="flex flex-row items-center mt-8 mb-6">
<textarea
className="textarea textarea-bordered w-full"
placeholder="Type a message (Shift+Enter to add a new line)"
value={inputMsg}
onChange={(e) => setInputMsg(e.target.value)}
onKeyDown={(e) => {
if (e.key === 'Enter' && e.shiftKey) return;
if (e.key === 'Enter' && !e.shiftKey) {
e.preventDefault();
sendNewMessage();
}
}}
id="msg-input"
dir="auto"
></textarea>
{isGenerating(currConvId) ? (
<button
className="btn btn-neutral ml-2"
onClick={() => stopGenerating(currConvId)}
>
Stop
</button>
) : (
<button
className="btn btn-primary ml-2"
onClick={sendNewMessage}
disabled={inputMsg.trim().length === 0}
>
Send
</button>
)}
</div>
</>
);
}

182
examples/server/webui/src/components/Header.tsx Normal file
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@ -0,0 +1,182 @@
import { useEffect, useState } from 'react';
import StorageUtils from '../utils/storage';
import { useAppContext } from '../utils/app.context';
import { classNames } from '../utils/misc';
import daisyuiThemes from 'daisyui/src/theming/themes';
import { THEMES } from '../Config';
import { useNavigate } from 'react-router';
import SettingDialog from './SettingDialog';
export default function Header() {
const navigate = useNavigate();
const [selectedTheme, setSelectedTheme] = useState(StorageUtils.getTheme());
const [showSettingDialog, setShowSettingDialog] = useState(false);
const setTheme = (theme: string) => {
StorageUtils.setTheme(theme);
setSelectedTheme(theme);
};
useEffect(() => {
document.body.setAttribute('data-theme', selectedTheme);
document.body.setAttribute(
'data-color-scheme',
// @ts-expect-error daisyuiThemes complains about index type, but it should work
daisyuiThemes[selectedTheme]?.['color-scheme'] ?? 'auto'
);
}, [selectedTheme]);
const { isGenerating, viewingConversation } = useAppContext();
const isCurrConvGenerating = isGenerating(viewingConversation?.id ?? '');
const removeConversation = () => {
if (isCurrConvGenerating || !viewingConversation) return;
const convId = viewingConversation.id;
if (window.confirm('Are you sure to delete this conversation?')) {
StorageUtils.remove(convId);
navigate('/');
}
};
const downloadConversation = () => {
if (isCurrConvGenerating || !viewingConversation) return;
const convId = viewingConversation.id;
const conversationJson = JSON.stringify(viewingConversation, null, 2);
const blob = new Blob([conversationJson], { type: 'application/json' });
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = `conversation_${convId}.json`;
document.body.appendChild(a);
a.click();
document.body.removeChild(a);
URL.revokeObjectURL(url);
};
return (
<div className="flex flex-row items-center mt-6 mb-6">
{/* open sidebar button */}
<label htmlFor="toggle-drawer" className="btn btn-ghost lg:hidden">
<svg
xmlns="http://www.w3.org/2000/svg"
width="16"
height="16"
fill="currentColor"
className="bi bi-list"
viewBox="0 0 16 16"
>
<path
fillRule="evenodd"
d="M2.5 12a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5"
/>
</svg>
</label>
<div className="grow text-2xl font-bold ml-2">llama.cpp</div>
{/* action buttons (top right) */}
<div className="flex items-center">
<div v-if="messages.length > 0" className="dropdown dropdown-end">
{/* "..." button */}
<button
tabIndex={0}
role="button"
className="btn m-1"
disabled={isCurrConvGenerating}
>
<svg
xmlns="http://www.w3.org/2000/svg"
width="16"
height="16"
fill="currentColor"
className="bi bi-three-dots-vertical"
viewBox="0 0 16 16"
>
<path d="M9.5 13a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0" />
</svg>
</button>
{/* dropdown menu */}
<ul
tabIndex={0}
className="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow"
>
<li onClick={downloadConversation}>
<a>Download</a>
</li>
<li className="text-error" onClick={removeConversation}>
<a>Delete</a>
</li>
</ul>
</div>
<div className="tooltip tooltip-bottom" data-tip="Settings">
<button className="btn" onClick={() => setShowSettingDialog(true)}>
{/* settings button */}
<svg
xmlns="http://www.w3.org/2000/svg"
width="16"
height="16"
fill="currentColor"
className="bi bi-gear"
viewBox="0 0 16 16"
>
<path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0" />
<path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z" />
</svg>
</button>
</div>
{/* theme controller is copied from https://daisyui.com/components/theme-controller/ */}
<div className="tooltip tooltip-bottom" data-tip="Themes">
<div className="dropdown dropdown-end dropdown-bottom">
<div tabIndex={0} role="button" className="btn m-1">
<svg
xmlns="http://www.w3.org/2000/svg"
width="16"
height="16"
fill="currentColor"
className="bi bi-palette2"
viewBox="0 0 16 16"
>
<path d="M0 .5A.5.5 0 0 1 .5 0h5a.5.5 0 0 1 .5.5v5.277l4.147-4.131a.5.5 0 0 1 .707 0l3.535 3.536a.5.5 0 0 1 0 .708L10.261 10H15.5a.5.5 0 0 1 .5.5v5a.5.5 0 0 1-.5.5H3a3 3 0 0 1-2.121-.879A3 3 0 0 1 0 13.044m6-.21 7.328-7.3-2.829-2.828L6 7.188zM4.5 13a1.5 1.5 0 1 0-3 0 1.5 1.5 0 0 0 3 0M15 15v-4H9.258l-4.015 4zM0 .5v12.495zm0 12.495V13z" />
</svg>
</div>
<ul
tabIndex={0}
className="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto"
>
<li>
<button
className={classNames({
'btn btn-sm btn-block btn-ghost justify-start': true,
'btn-active': selectedTheme === 'auto',
})}
onClick={() => setTheme('auto')}
>
auto
</button>
</li>
{THEMES.map((theme) => (
<li key={theme}>
<input
type="radio"
name="theme-dropdown"
className="theme-controller btn btn-sm btn-block btn-ghost justify-start"
aria-label={theme}
value={theme}
checked={selectedTheme === theme}
onChange={(e) => e.target.checked && setTheme(theme)}
/>
</li>
))}
</ul>
</div>
</div>
</div>
<SettingDialog
show={showSettingDialog}
onClose={() => setShowSettingDialog(false)}
/>
</div>
);
}

255
examples/server/webui/src/components/MarkdownDisplay.tsx Normal file
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@ -0,0 +1,255 @@
import React, { useMemo, useState } from 'react';
import Markdown, { ExtraProps } from 'react-markdown';
import remarkGfm from 'remark-gfm';
import rehypeHightlight from 'rehype-highlight';
import rehypeKatex from 'rehype-katex';
import remarkMath from 'remark-math';
import remarkBreaks from 'remark-breaks';
import 'katex/dist/katex.min.css';
import { classNames, copyStr } from '../utils/misc';
import { ElementContent, Root } from 'hast';
import { visit } from 'unist-util-visit';
export default function MarkdownDisplay({ content }: { content: string }) {
const preprocessedContent = useMemo(
() => preprocessLaTeX(content),
[content]
);
return (
<Markdown
remarkPlugins={[remarkGfm, remarkMath, remarkBreaks]}
rehypePlugins={[rehypeHightlight, rehypeKatex, rehypeCustomCopyButton]}
components={{
button: (props) => (
<CopyCodeButton {...props} origContent={preprocessedContent} />
),
// note: do not use "pre", "p" or other basic html elements here, it will cause the node to re-render when the message is being generated (this should be a bug with react-markdown, not sure how to fix it)
}}
>
{preprocessedContent}
</Markdown>
);
}
const CopyCodeButton: React.ElementType<
React.ClassAttributes<HTMLButtonElement> &
React.HTMLAttributes<HTMLButtonElement> &
ExtraProps & { origContent: string }
> = ({ node, origContent }) => {
const startOffset = node?.position?.start.offset ?? 0;
const endOffset = node?.position?.end.offset ?? 0;
const copiedContent = useMemo(
() =>
origContent
.substring(startOffset, endOffset)
.replace(/^```[^\n]+\n/g, '')
.replace(/```$/g, ''),
[origContent, startOffset, endOffset]
);
return (
<div
className={classNames({
'text-right sticky top-4 mb-2 mr-2 h-0': true,
'display-none': !node?.position,
})}
>
<CopyButton className="badge btn-mini" content={copiedContent} />
</div>
);
};
export const CopyButton = ({
content,
className,
}: {
content: string;
className?: string;
}) => {
const [copied, setCopied] = useState(false);
return (
<button
className={className}
onClick={() => {
copyStr(content);
setCopied(true);
}}
onMouseLeave={() => setCopied(false)}
>
{copied ? 'Copied!' : '📋 Copy'}
</button>
);
};
/**
* This injects the "button" element before each "pre" element.
* The actual button will be replaced with a react component in the MarkdownDisplay.
* We don't replace "pre" node directly because it will cause the node to re-render, which causes this bug: https://github.com/ggerganov/llama.cpp/issues/9608
*/
function rehypeCustomCopyButton() {
return function (tree: Root) {
visit(tree, 'element', function (node) {
if (node.tagName === 'pre' && !node.properties.visited) {
const preNode = { ...node };
// replace current node
preNode.properties.visited = 'true';
node.tagName = 'div';
node.properties = {
className: 'relative my-4',
};
// add node for button
const btnNode: ElementContent = {
type: 'element',
tagName: 'button',
properties: {},
children: [],
position: node.position,
};
node.children = [btnNode, preNode];
}
});
};
}
/**
* The part below is copied and adapted from:
* https://github.com/danny-avila/LibreChat/blob/main/client/src/utils/latex.ts
* (MIT License)
*/
// Regex to check if the processed content contains any potential LaTeX patterns
const containsLatexRegex =
/\\\(.*?\\\)|\\\[.*?\\\]|\$.*?\$|\\begin\{equation\}.*?\\end\{equation\}/;
// Regex for inline and block LaTeX expressions
const inlineLatex = new RegExp(/\\\((.+?)\\\)/, 'g');
const blockLatex = new RegExp(/\\\[(.*?[^\\])\\\]/, 'gs');
// Function to restore code blocks
const restoreCodeBlocks = (content: string, codeBlocks: string[]) => {
return content.replace(
/<<CODE_BLOCK_(\d+)>>/g,
(_, index) => codeBlocks[index]
);
};
// Regex to identify code blocks and inline code
const codeBlockRegex = /(```[\s\S]*?```|`.*?`)/g;
export const processLaTeX = (_content: string) => {
let content = _content;
// Temporarily replace code blocks and inline code with placeholders
const codeBlocks: string[] = [];
let index = 0;
content = content.replace(codeBlockRegex, (match) => {
codeBlocks[index] = match;
return `<<CODE_BLOCK_${index++}>>`;
});
// Escape dollar signs followed by a digit or space and digit
let processedContent = content.replace(/(\$)(?=\s?\d)/g, '\\$');
// If no LaTeX patterns are found, restore code blocks and return the processed content
if (!containsLatexRegex.test(processedContent)) {
return restoreCodeBlocks(processedContent, codeBlocks);
}
// Convert LaTeX expressions to a markdown compatible format
processedContent = processedContent
.replace(inlineLatex, (_: string, equation: string) => `$${equation}$`) // Convert inline LaTeX
.replace(blockLatex, (_: string, equation: string) => `$$${equation}$$`); // Convert block LaTeX
// Restore code blocks
return restoreCodeBlocks(processedContent, codeBlocks);
};
/**
* Preprocesses LaTeX content by replacing delimiters and escaping certain characters.
*
* @param content The input string containing LaTeX expressions.
* @returns The processed string with replaced delimiters and escaped characters.
*/
export function preprocessLaTeX(content: string): string {
// Step 1: Protect code blocks
const codeBlocks: string[] = [];
content = content.replace(/(```[\s\S]*?```|`[^`\n]+`)/g, (_, code) => {
codeBlocks.push(code);
return `<<CODE_BLOCK_${codeBlocks.length - 1}>>`;
});
// Step 2: Protect existing LaTeX expressions
const latexExpressions: string[] = [];
// Protect block math ($$...$$), \[...\], and \(...\) as before.
content = content.replace(
/(\$\$[\s\S]*?\$\$|\\\[[\s\S]*?\\\]|\\\(.*?\\\))/g,
(match) => {
latexExpressions.push(match);
return `<<LATEX_${latexExpressions.length - 1}>>`;
}
);
// Protect inline math ($...$) only if it does NOT match a currency pattern.
// We assume a currency pattern is one where the inner content is purely numeric (with optional decimals).
content = content.replace(/\$([^$]+)\$/g, (match, inner) => {
if (/^\s*\d+(?:\.\d+)?\s*$/.test(inner)) {
// This looks like a currency value (e.g. "$123" or "$12.34"),
// so don't protect it.
return match;
} else {
// Otherwise, treat it as a LaTeX expression.
latexExpressions.push(match);
return `<<LATEX_${latexExpressions.length - 1}>>`;
}
});
// Step 3: Escape dollar signs that are likely currency indicators.
// (Now that inline math is protected, this will only escape dollars not already protected)
content = content.replace(/\$(?=\d)/g, '\\$');
// Step 4: Restore LaTeX expressions
content = content.replace(
/<<LATEX_(\d+)>>/g,
(_, index) => latexExpressions[parseInt(index)]
);
// Step 5: Restore code blocks
content = content.replace(
/<<CODE_BLOCK_(\d+)>>/g,
(_, index) => codeBlocks[parseInt(index)]
);
// Step 6: Apply additional escaping functions
content = escapeBrackets(content);
content = escapeMhchem(content);
return content;
}
export function escapeBrackets(text: string): string {
const pattern =
/(```[\S\s]*?```|`.*?`)|\\\[([\S\s]*?[^\\])\\]|\\\((.*?)\\\)/g;
return text.replace(
pattern,
(
match: string,
codeBlock: string | undefined,
squareBracket: string | undefined,
roundBracket: string | undefined
): string => {
if (codeBlock != null) {
return codeBlock;
} else if (squareBracket != null) {
return `$$${squareBracket}$$`;
} else if (roundBracket != null) {
return `$${roundBracket}$`;
}
return match;
}
);
}
export function escapeMhchem(text: string) {
return text.replaceAll('$\\ce{', '$\\\\ce{').replaceAll('$\\pu{', '$\\\\pu{');
}

321
examples/server/webui/src/components/SettingDialog.tsx Normal file
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@ -0,0 +1,321 @@
import { useState } from 'react';
import { useAppContext } from '../utils/app.context';
import { CONFIG_DEFAULT, CONFIG_INFO } from '../Config';
import { isDev } from '../Config';
import StorageUtils from '../utils/storage';
import { isBoolean, isNumeric, isString } from '../utils/misc';
type SettKey = keyof typeof CONFIG_DEFAULT;
const COMMON_SAMPLER_KEYS: SettKey[] = [
'temperature',
'top_k',
'top_p',
'min_p',
'max_tokens',
];
const OTHER_SAMPLER_KEYS: SettKey[] = [
'dynatemp_range',
'dynatemp_exponent',
'typical_p',
'xtc_probability',
'xtc_threshold',
];
const PENALTY_KEYS: SettKey[] = [
'repeat_last_n',
'repeat_penalty',
'presence_penalty',
'frequency_penalty',
'dry_multiplier',
'dry_base',
'dry_allowed_length',
'dry_penalty_last_n',
];
export default function SettingDialog({
show,
onClose,
}: {
show: boolean;
onClose: () => void;
}) {
const { config, saveConfig } = useAppContext();
// clone the config object to prevent direct mutation
const [localConfig, setLocalConfig] = useState<typeof CONFIG_DEFAULT>(
JSON.parse(JSON.stringify(config))
);
const resetConfig = () => {
if (window.confirm('Are you sure to reset all settings?')) {
setLocalConfig(CONFIG_DEFAULT);
}
};
const handleSave = () => {
// copy the local config to prevent direct mutation
const newConfig: typeof CONFIG_DEFAULT = JSON.parse(
JSON.stringify(localConfig)
);
// validate the config
for (const key in newConfig) {
const value = newConfig[key as SettKey];
const mustBeBoolean = isBoolean(CONFIG_DEFAULT[key as SettKey]);
const mustBeString = isString(CONFIG_DEFAULT[key as SettKey]);
const mustBeNumeric = isNumeric(CONFIG_DEFAULT[key as SettKey]);
if (mustBeString) {
if (!isString(value)) {
alert(`Value for ${key} must be string`);
return;
}
} else if (mustBeNumeric) {
const trimedValue = value.toString().trim();
const numVal = Number(trimedValue);
if (isNaN(numVal) || !isNumeric(numVal) || trimedValue.length === 0) {
alert(`Value for ${key} must be numeric`);
return;
}
// force conversion to number
// @ts-expect-error this is safe
newConfig[key] = numVal;
} else if (mustBeBoolean) {
if (!isBoolean(value)) {
alert(`Value for ${key} must be boolean`);
return;
}
} else {
console.error(`Unknown default type for key ${key}`);
}
}
if (isDev) console.log('Saving config', newConfig);
saveConfig(newConfig);
onClose();
};
const debugImportDemoConv = async () => {
const res = await fetch('/demo-conversation.json');
const demoConv = await res.json();
StorageUtils.remove(demoConv.id);
for (const msg of demoConv.messages) {
StorageUtils.appendMsg(demoConv.id, msg);
}
onClose();
};
const onChange = (key: SettKey) => (value: string | boolean) => {
// note: we do not perform validation here, because we may get incomplete value as user is still typing it
setLocalConfig({ ...localConfig, [key]: value });
};
return (
<dialog className={`modal ${show ? 'modal-open' : ''}`}>
<div className="modal-box">
<h3 className="text-lg font-bold mb-6">Settings</h3>
<div className="h-[calc(90vh-12rem)] overflow-y-auto">
<p className="opacity-40 mb-6">
Settings below are saved in browser's localStorage
</p>
<SettingsModalShortInput
configKey="apiKey"
configDefault={CONFIG_DEFAULT}
value={localConfig.apiKey}
onChange={onChange('apiKey')}
/>
<label className="form-control mb-2">
<div className="label">
System Message (will be disabled if left empty)
</div>
<textarea
className="textarea textarea-bordered h-24"
placeholder={`Default: ${CONFIG_DEFAULT.systemMessage}`}
value={localConfig.systemMessage}
onChange={(e) => onChange('systemMessage')(e.target.value)}
/>
</label>
{COMMON_SAMPLER_KEYS.map((key) => (
<SettingsModalShortInput
key={key}
configKey={key}
configDefault={CONFIG_DEFAULT}
value={localConfig[key]}
onChange={onChange(key)}
/>
))}
<details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary className="collapse-title font-bold">
Other sampler settings
</summary>
<div className="collapse-content">
<SettingsModalShortInput
label="Samplers queue"
configKey="samplers"
configDefault={CONFIG_DEFAULT}
value={localConfig.samplers}
onChange={onChange('samplers')}
/>
{OTHER_SAMPLER_KEYS.map((key) => (
<SettingsModalShortInput
key={key}
configKey={key}
configDefault={CONFIG_DEFAULT}
value={localConfig[key]}
onChange={onChange(key)}
/>
))}
</div>
</details>
<details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary className="collapse-title font-bold">
Penalties settings
</summary>
<div className="collapse-content">
{PENALTY_KEYS.map((key) => (
<SettingsModalShortInput
key={key}
configKey={key}
configDefault={CONFIG_DEFAULT}
value={localConfig[key]}
onChange={onChange(key)}
/>
))}
</div>
</details>
<details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary className="collapse-title font-bold">
Reasoning models
</summary>
<div className="collapse-content">
<div className="flex flex-row items-center mb-2">
<input
type="checkbox"
className="checkbox"
checked={localConfig.showThoughtInProgress}
onChange={(e) =>
onChange('showThoughtInProgress')(e.target.checked)
}
/>
<span className="ml-4">
Expand though process by default for generating message
</span>
</div>
<div className="flex flex-row items-center mb-2">
<input
type="checkbox"
className="checkbox"
checked={localConfig.excludeThoughtOnReq}
onChange={(e) =>
onChange('excludeThoughtOnReq')(e.target.checked)
}
/>
<span className="ml-4">
Exclude thought process when sending request to API
(Recommended for DeepSeek-R1)
</span>
</div>
</div>
</details>
<details className="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
<summary className="collapse-title font-bold">
Advanced config
</summary>
<div className="collapse-content">
{/* this button only shows in dev mode, used to import a demo conversation to test message rendering */}
{isDev && (
<div className="flex flex-row items-center mb-2">
<button className="btn" onClick={debugImportDemoConv}>
(debug) Import demo conversation
</button>
</div>
)}
<div className="flex flex-row items-center mb-2">
<input
type="checkbox"
className="checkbox"
checked={localConfig.showTokensPerSecond}
onChange={(e) =>
onChange('showTokensPerSecond')(e.target.checked)
}
/>
<span className="ml-4">Show tokens per second</span>
</div>
<label className="form-control mb-2">
<div className="label inline">
Custom JSON config (For more info, refer to{' '}
<a
className="underline"
href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md"
target="_blank"
rel="noopener noreferrer"
>
server documentation
</a>
)
</div>
<textarea
className="textarea textarea-bordered h-24"
placeholder='Example: { "mirostat": 1, "min_p": 0.1 }'
value={localConfig.custom}
onChange={(e) => onChange('custom')(e.target.value)}
/>
</label>
</div>
</details>
</div>
<div className="modal-action">
<button className="btn" onClick={resetConfig}>
Reset to default
</button>
<button className="btn" onClick={onClose}>
Close
</button>
<button className="btn btn-primary" onClick={handleSave}>
Save
</button>
</div>
</div>
</dialog>
);
}
function SettingsModalShortInput({
configKey,
configDefault,
value,
onChange,
label,
}: {
configKey: SettKey;
configDefault: typeof CONFIG_DEFAULT;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
value: any;
onChange: (value: string) => void;
label?: string;
}) {
return (
<label className="input input-bordered join-item grow flex items-center gap-2 mb-2">
<div className="dropdown dropdown-hover">
<div tabIndex={0} role="button" className="font-bold">
{label || configKey}
</div>
<div className="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
{CONFIG_INFO[configKey] ?? '(no help message available)'}
</div>
</div>
<input
type="text"
className="grow"
placeholder={`Default: ${configDefault[configKey] || 'none'}`}
value={value}
onChange={(e) => onChange(e.target.value)}
/>
</label>
);
}

95
examples/server/webui/src/components/Sidebar.tsx Normal file
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@ -0,0 +1,95 @@
import { useEffect, useMemo, useState } from 'react';
import { classNames } from '../utils/misc';
import { Conversation } from '../utils/types';
import StorageUtils from '../utils/storage';
import { useNavigate, useParams } from 'react-router';
export default function Sidebar() {
const params = useParams();
const navigate = useNavigate();
const currConv = useMemo(
() => StorageUtils.getOneConversation(params.convId ?? ''),
[params.convId]
);
const [conversations, setConversations] = useState<Conversation[]>([]);
useEffect(() => {
const handleConversationChange = () => {
setConversations(StorageUtils.getAllConversations());
};
StorageUtils.onConversationChanged(handleConversationChange);
handleConversationChange();
return () => {
StorageUtils.offConversationChanged(handleConversationChange);
};
}, []);
return (
<>
<input
id="toggle-drawer"
type="checkbox"
className="drawer-toggle"
defaultChecked
/>
<div className="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
<label
htmlFor="toggle-drawer"
aria-label="close sidebar"
className="drawer-overlay"
></label>
<div className="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
<div className="flex flex-row items-center justify-between mb-4 mt-4">
<h2 className="font-bold ml-4">Conversations</h2>
{/* close sidebar button */}
<label htmlFor="toggle-drawer" className="btn btn-ghost lg:hidden">
<svg
xmlns="http://www.w3.org/2000/svg"
width="16"
height="16"
fill="currentColor"
className="bi bi-arrow-bar-left"
viewBox="0 0 16 16"
>
<path
fillRule="evenodd"
d="M12.5 15a.5.5 0 0 1-.5-.5v-13a.5.5 0 0 1 1 0v13a.5.5 0 0 1-.5.5M10 8a.5.5 0 0 1-.5.5H3.707l2.147 2.146a.5.5 0 0 1-.708.708l-3-3a.5.5 0 0 1 0-.708l3-3a.5.5 0 1 1 .708.708L3.707 7.5H9.5a.5.5 0 0 1 .5.5"
/>
</svg>
</label>
</div>
{/* list of conversations */}
<div
className={classNames({
'btn btn-ghost justify-start': true,
'btn-active': !currConv,
})}
onClick={() => navigate('/')}
>
+ New conversation
</div>
{conversations.map((conv) => (
<div
key={conv.id}
className={classNames({
'btn btn-ghost justify-start font-normal': true,
'btn-active': conv.id === currConv?.id,
})}
onClick={() => navigate(`/chat/${conv.id}`)}
dir="auto"
>
<span className="truncate">{conv.messages[0].content}</span>
</div>
))}
<div className="text-center text-xs opacity-40 mt-auto mx-4">
Conversations are saved to browser's localStorage
</div>
</div>
</div>
</>
);
}

60
examples/server/webui/src/highlight-config.js
View file

@ -1,60 +0,0 @@
import hljs from 'highlight.js/lib/core';
// only import commonly used languages to reduce bundle size
import python from 'highlight.js/lib/languages/python';
import javascript from 'highlight.js/lib/languages/javascript';
import json from 'highlight.js/lib/languages/json';
import bash from 'highlight.js/lib/languages/bash';
import yaml from 'highlight.js/lib/languages/yaml';
import markdown from 'highlight.js/lib/languages/markdown';
import scss from 'highlight.js/lib/languages/scss';
import xml from 'highlight.js/lib/languages/xml';
import ruby from 'highlight.js/lib/languages/ruby';
import go from 'highlight.js/lib/languages/go';
import java from 'highlight.js/lib/languages/java';
import rust from 'highlight.js/lib/languages/rust';
import scala from 'highlight.js/lib/languages/scala';
import cpp from 'highlight.js/lib/languages/cpp';
import csharp from 'highlight.js/lib/languages/csharp';
import swift from 'highlight.js/lib/languages/swift';
import dart from 'highlight.js/lib/languages/dart';
import elixir from 'highlight.js/lib/languages/elixir';
import kotlin from 'highlight.js/lib/languages/kotlin';
import lua from 'highlight.js/lib/languages/lua';
import php from 'highlight.js/lib/languages/php';
import latex from 'highlight.js/lib/languages/latex';
hljs.registerLanguage('python', python);
hljs.registerLanguage('javascript', javascript);
hljs.registerLanguage('json', json);
hljs.registerLanguage('yaml', yaml);
hljs.registerLanguage('markdown', markdown);
hljs.registerLanguage('xml', xml);
hljs.registerLanguage('ruby', ruby);
hljs.registerLanguage('go', go);
hljs.registerLanguage('java', java);
hljs.registerLanguage('rust', rust);
hljs.registerLanguage('scala', scala);
hljs.registerLanguage('csharp', csharp);
hljs.registerLanguage('swift', swift);
hljs.registerLanguage('dart', dart);
hljs.registerLanguage('elixir', elixir);
hljs.registerLanguage('kotlin', kotlin);
hljs.registerLanguage('lua', lua);
hljs.registerLanguage('php', php);
hljs.registerLanguage('latex', latex);
// reuse some languages to further reduce bundle size
hljs.registerLanguage('shell', bash);
hljs.registerLanguage('bash', bash);
hljs.registerLanguage('sh', bash);
hljs.registerLanguage('css', scss);
hljs.registerLanguage('scss', scss);
hljs.registerLanguage('c', cpp);
hljs.registerLanguage('cpp', cpp);
export default hljs;

25
examples/server/webui/src/styles.scss → examples/server/webui/src/index.scss
View file

@ -1,15 +1,28 @@
@use "sass:meta";
@use 'sass:meta';
@tailwind base;
@tailwind components;
@tailwind utilities;
.markdown {
h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
h1,
h2,
h3,
h4,
h5,
h6,
ul,
ol,
li {
all: revert;
}
pre {
@apply whitespace-pre-wrap rounded-lg p-2;
border: 1px solid currentColor;
}
p {
@apply mb-2;
}
/* TODO: fix markdown table */
}
@ -19,7 +32,9 @@
.btn-mini {
@apply cursor-pointer hover:shadow-md;
}
.chat-screen { max-width: 900px; }
.chat-screen {
max-width: 900px;
}
.chat-bubble-base-300 {
--tw-bg-opacity: 1;
@ -46,3 +61,7 @@
background: transparent !important;
padding: 0.5em !important;
}
.katex-display {
margin: 0 0 !important;
}

66
examples/server/webui/src/katex-gpt.js
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@ -1,66 +0,0 @@
import katex from 'katex';
// Adapted from https://github.com/SchneeHertz/markdown-it-katex-gpt
// MIT license
const defaultOptions = {
delimiters: [
{ left: '\\[', right: '\\]', display: true },
{ left: '\\(', right: '\\)', display: false },
],
};
export function renderLatexHTML(content, display = false) {
return katex.renderToString(content, {
throwOnError: false,
output: 'mathml',
displayMode: display,
});
}
function escapedBracketRule(options) {
return (state, silent) => {
const max = state.posMax;
const start = state.pos;
for (const { left, right, display } of options.delimiters) {
// Check if it starts with the left delimiter
if (!state.src.slice(start).startsWith(left)) continue;
// Skip the length of the left delimiter
let pos = start + left.length;
// Find the matching right delimiter
while (pos < max) {
if (state.src.slice(pos).startsWith(right)) {
break;
}
pos++;
}
// No matching right delimiter found, skip to the next match
if (pos >= max) continue;
// If not in silent mode, convert LaTeX formula to MathML
if (!silent) {
const content = state.src.slice(start + left.length, pos);
try {
const renderedContent = renderLatexHTML(content, display);
const token = state.push('html_inline', '', 0);
token.content = renderedContent;
} catch (e) {
console.error(e);
}
}
// Update position, skip the length of the right delimiter
state.pos = pos + right.length;
return true;
}
}
}
export default function (md, options = defaultOptions) {
md.inline.ruler.after('text', 'escaped_bracket', escapedBracketRule(options));
}

701
examples/server/webui/src/main.js
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import './styles.scss';
import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js';
import MarkdownIt from 'markdown-it';
import TextLineStream from 'textlinestream';
// math formula rendering
import 'katex/dist/katex.min.css';
import markdownItKatexGpt from './katex-gpt';
import markdownItKatexNormal from '@vscode/markdown-it-katex';
// code highlighting
import hljs from './highlight-config';
import daisyuiThemes from 'daisyui/src/theming/themes';
// ponyfill for missing ReadableStream asyncIterator on Safari
import { asyncIterator } from '@sec-ant/readable-stream/ponyfill/asyncIterator';
const isDev = import.meta.env.MODE === 'development';
// types
/** @typedef {{ id: number, role: 'user' | 'assistant', content: string, timings: any }} Message */
/** @typedef {{ role: 'user' | 'assistant', content: string }} APIMessage */
/** @typedef {{ id: string, lastModified: number, messages: Array<Message> }} Conversation */
// utility functions
const isString = (x) => !!x.toLowerCase;
const isBoolean = (x) => x === true || x === false;
const isNumeric = (n) => !isString(n) && !isNaN(n) && !isBoolean(n);
const escapeAttr = (str) => str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
const copyStr = (textToCopy) => {
// Navigator clipboard api needs a secure context (https)
if (navigator.clipboard && window.isSecureContext) {
navigator.clipboard.writeText(textToCopy);
} else {
// Use the 'out of viewport hidden text area' trick
const textArea = document.createElement('textarea');
textArea.value = textToCopy;
// Move textarea out of the viewport so it's not visible
textArea.style.position = 'absolute';
textArea.style.left = '-999999px';
document.body.prepend(textArea);
textArea.select();
document.execCommand('copy');
}
};
// constants
const BASE_URL = isDev
? (localStorage.getItem('base') || 'https://localhost:8080') // for debugging
: (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
console.log({ BASE_URL });
const CONFIG_DEFAULT = {
// Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
apiKey: '',
systemMessage: 'You are a helpful assistant.',
showTokensPerSecond: false,
showThoughtInProgress: false,
excludeThoughtOnReq: true,
// make sure these default values are in sync with `common.h`
samplers: 'edkypmxt',
temperature: 0.8,
dynatemp_range: 0.0,
dynatemp_exponent: 1.0,
top_k: 40,
top_p: 0.95,
min_p: 0.05,
xtc_probability: 0.0,
xtc_threshold: 0.1,
typical_p: 1.0,
repeat_last_n: 64,
repeat_penalty: 1.0,
presence_penalty: 0.0,
frequency_penalty: 0.0,
dry_multiplier: 0.0,
dry_base: 1.75,
dry_allowed_length: 2,
dry_penalty_last_n: -1,
max_tokens: -1,
custom: '', // custom json-stringified object
};
const CONFIG_INFO = {
apiKey: 'Set the API Key if you are using --api-key option for the server.',
systemMessage: 'The starting message that defines how model should behave.',
samplers: 'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature',
temperature: 'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
dynatemp_range: 'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
dynatemp_exponent: 'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
top_k: 'Keeps only k top tokens.',
top_p: 'Limits tokens to those that together have a cumulative probability of at least p',
min_p: 'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
xtc_probability: 'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
xtc_threshold: 'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
typical_p: 'Sorts and limits tokens based on the difference between log-probability and entropy.',
repeat_last_n: 'Last n tokens to consider for penalizing repetition',
repeat_penalty: 'Controls the repetition of token sequences in the generated text',
presence_penalty: 'Limits tokens based on whether they appear in the output or not.',
frequency_penalty: 'Limits tokens based on how often they appear in the output.',
dry_multiplier: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
dry_base: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
dry_allowed_length: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
dry_penalty_last_n: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
max_tokens: 'The maximum number of token per output.',
custom: '', // custom json-stringified object
};
// config keys having numeric value (i.e. temperature, top_k, top_p, etc)
const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]);
// list of themes supported by daisyui
const THEMES = ['light', 'dark']
// make sure light & dark are always at the beginning
.concat(Object.keys(daisyuiThemes).filter(t => t !== 'light' && t !== 'dark'));
// markdown support
const VueMarkdown = defineComponent(
(props) => {
const md = shallowRef(new MarkdownIt({
breaks: true,
highlight: function (str, lang) { // Add highlight.js
if (lang && hljs.getLanguage(lang)) {
try {
return '<pre dir="auto"><code class="hljs">' +
hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
'</code></pre>';
} catch (__) {}
}
return '<pre dir="auto"><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
}
}));
// support latex with double dollar sign and square brackets
md.value.use(markdownItKatexGpt, {
delimiters: [
{ left: '\\[', right: '\\]', display: true },
{ left: '\\(', right: '\\)', display: false },
{ left: '$$', right: '$$', display: false },
// do not add single dollar sign here, other wise it will confused with dollar used for money symbol
],
throwOnError: false,
});
// support latex with single dollar sign
md.value.use(markdownItKatexNormal, { throwOnError: false });
// add copy button to code blocks
const origFenchRenderer = md.value.renderer.rules.fence;
md.value.renderer.rules.fence = (tokens, idx, ...args) => {
const content = tokens[idx].content;
const origRendered = origFenchRenderer(tokens, idx, ...args);
return `<div class="relative my-4">
<div class="text-right sticky top-4 mb-2 mr-2 h-0">
<button class="badge btn-mini" onclick="copyStr(${escapeAttr(JSON.stringify(content))})">📋 Copy</button>
</div>
${origRendered}
</div>`;
};
window.copyStr = copyStr;
const content = computed(() => md.value.render(props.source));
return () => h('div', { innerHTML: content.value });
},
{ props: ['source'] }
);
// input field to be used by settings modal
const SettingsModalShortInput = defineComponent({
template: document.getElementById('settings-modal-short-input').innerHTML,
props: {
label: { type: String, required: false },
configKey: String,
configDefault: Object,
configInfo: Object,
modelValue: [Object, String, Number],
},
});
// message bubble component
const MessageBubble = defineComponent({
components: {
VueMarkdown
},
template: document.getElementById('message-bubble').innerHTML,
props: {
config: Object,
msg: Object,
isGenerating: Boolean,
showThoughtInProgress: Boolean,
editUserMsgAndRegenerate: Function,
regenerateMsg: Function,
},
data() {
return {
editingContent: null,
};
},
computed: {
timings() {
if (!this.msg.timings) return null;
return {
...this.msg.timings,
prompt_per_second: this.msg.timings.prompt_n / (this.msg.timings.prompt_ms / 1000),
predicted_per_second: this.msg.timings.predicted_n / (this.msg.timings.predicted_ms / 1000),
};
},
splitMsgContent() {
const content = this.msg.content;
if (this.msg.role !== 'assistant') {
return { content };
}
let actualContent = '';
let cot = '';
let isThinking = false;
let thinkSplit = content.split('<think>', 2);
actualContent += thinkSplit[0];
while (thinkSplit[1] !== undefined) {
// <think> tag found
thinkSplit = thinkSplit[1].split('</think>', 2);
cot += thinkSplit[0];
isThinking = true;
if (thinkSplit[1] !== undefined) {
// </think> closing tag found
isThinking = false;
thinkSplit = thinkSplit[1].split('<think>', 2);
actualContent += thinkSplit[0];
}
}
return { content: actualContent, cot, isThinking };
},
},
methods: {
copyMsg() {
copyStr(this.msg.content);
},
editMsg() {
this.editUserMsgAndRegenerate({
...this.msg,
content: this.editingContent,
});
this.editingContent = null;
},
},
});
// coversations is stored in localStorage
// format: { [convId]: { id: string, lastModified: number, messages: [...] } }
// convId is a string prefixed with 'conv-'
const StorageUtils = {
/**
* manage conversations
* @returns {Array<Conversation>}
*/
getAllConversations() {
const res = [];
for (const key in localStorage) {
if (key.startsWith('conv-')) {
res.push(JSON.parse(localStorage.getItem(key)));
}
}
res.sort((a, b) => b.lastModified - a.lastModified);
return res;
},
/**
* can return null if convId does not exist
* @param {string} convId
* @returns {Conversation | null}
*/
getOneConversation(convId) {
return JSON.parse(localStorage.getItem(convId) || 'null');
},
/**
* if convId does not exist, create one
* @param {string} convId
* @param {Message} msg
*/
appendMsg(convId, msg) {
if (msg.content === null) return;
const conv = StorageUtils.getOneConversation(convId) || {
id: convId,
lastModified: Date.now(),
messages: [],
};
conv.messages.push(msg);
conv.lastModified = Date.now();
localStorage.setItem(convId, JSON.stringify(conv));
},
/**
* Get new conversation id
* @returns {string}
*/
getNewConvId() {
return `conv-${Date.now()}`;
},
/**
* remove conversation by id
* @param {string} convId
*/
remove(convId) {
localStorage.removeItem(convId);
},
/**
* remove all conversations
* @param {string} convId
*/
filterAndKeepMsgs(convId, predicate) {
const conv = StorageUtils.getOneConversation(convId);
if (!conv) return;
conv.messages = conv.messages.filter(predicate);
conv.lastModified = Date.now();
localStorage.setItem(convId, JSON.stringify(conv));
},
/**
* remove last message from conversation
* @param {string} convId
* @returns {Message | undefined}
*/
popMsg(convId) {
const conv = StorageUtils.getOneConversation(convId);
if (!conv) return;
const msg = conv.messages.pop();
conv.lastModified = Date.now();
if (conv.messages.length === 0) {
StorageUtils.remove(convId);
} else {
localStorage.setItem(convId, JSON.stringify(conv));
}
return msg;
},
// manage config
getConfig() {
const savedVal = JSON.parse(localStorage.getItem('config') || '{}');
// to prevent breaking changes in the future, we always provide default value for missing keys
return {
...CONFIG_DEFAULT,
...savedVal,
};
},
setConfig(config) {
localStorage.setItem('config', JSON.stringify(config));
},
getTheme() {
return localStorage.getItem('theme') || 'auto';
},
setTheme(theme) {
if (theme === 'auto') {
localStorage.removeItem('theme');
} else {
localStorage.setItem('theme', theme);
}
},
};
// scroll to bottom of chat messages
// if requiresNearBottom is true, only auto-scroll if user is near bottom
const chatScrollToBottom = (requiresNearBottom) => {
const msgListElem = document.getElementById('messages-list');
const spaceToBottom = msgListElem.scrollHeight - msgListElem.scrollTop - msgListElem.clientHeight;
if (!requiresNearBottom || (spaceToBottom < 100)) {
setTimeout(() => msgListElem.scrollTo({ top: msgListElem.scrollHeight }), 1);
}
};
// wrapper for SSE
async function* sendSSEPostRequest(url, fetchOptions) {
const res = await fetch(url, fetchOptions);
const lines = res.body
.pipeThrough(new TextDecoderStream())
.pipeThrough(new TextLineStream());
for await (const line of asyncIterator(lines)) {
if (isDev) console.log({line});
if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
const data = JSON.parse(line.slice(5));
yield data;
} else if (line.startsWith('error:')) {
const data = JSON.parse(line.slice(6));
throw new Error(data.message || 'Unknown error');
}
}
};
const mainApp = createApp({
components: {
VueMarkdown,
SettingsModalShortInput,
MessageBubble,
},
data() {
return {
conversations: StorageUtils.getAllConversations(),
/** @type {Array<Message>} */
messages: [],
viewingConvId: StorageUtils.getNewConvId(),
inputMsg: '',
isGenerating: false,
/** @type {Array<Message> | null} */
pendingMsg: null, // the on-going message from assistant
stopGeneration: () => {},
selectedTheme: StorageUtils.getTheme(),
config: StorageUtils.getConfig(),
showConfigDialog: false,
// const
themes: THEMES,
/** @type {CONFIG_DEFAULT} */
configDefault: {...CONFIG_DEFAULT},
configInfo: {...CONFIG_INFO},
isDev,
}
},
computed: {},
mounted() {
document.getElementById('app').classList.remove('opacity-0'); // show app
// scroll to the bottom when the pending message height is updated
const pendingMsgElem = document.getElementById('pending-msg');
const resizeObserver = new ResizeObserver(() => {
if (this.isGenerating) chatScrollToBottom(true);
});
resizeObserver.observe(pendingMsgElem);
this.setSelectedTheme(this.selectedTheme);
},
watch: {
viewingConvId: function(val, oldVal) {
if (val != oldVal) {
this.fetchMessages();
chatScrollToBottom();
this.hideSidebar();
}
}
},
methods: {
hideSidebar() {
document.getElementById('toggle-drawer').checked = false;
},
setSelectedTheme(theme) {
this.selectedTheme = theme;
document.body.setAttribute('data-theme', theme);
document.body.setAttribute('data-color-scheme', daisyuiThemes[theme]?.['color-scheme'] ?? 'auto');
StorageUtils.setTheme(theme);
},
newConversation() {
if (this.isGenerating) return;
this.viewingConvId = StorageUtils.getNewConvId();
},
setViewingConv(convId) {
if (this.isGenerating) return;
this.viewingConvId = convId;
},
deleteConv(convId) {
if (this.isGenerating) return;
if (window.confirm('Are you sure to delete this conversation?')) {
StorageUtils.remove(convId);
if (this.viewingConvId === convId) {
this.viewingConvId = StorageUtils.getNewConvId();
}
this.fetchConversation();
this.fetchMessages();
}
},
downloadConv(convId) {
const conversation = StorageUtils.getOneConversation(convId);
if (!conversation) {
alert('Conversation not found.');
return;
}
const conversationJson = JSON.stringify(conversation, null, 2);
const blob = new Blob([conversationJson], { type: 'application/json' });
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = `conversation_${convId}.json`;
document.body.appendChild(a);
a.click();
document.body.removeChild(a);
URL.revokeObjectURL(url);
},
async sendMessage() {
if (!this.inputMsg) return;
const currConvId = this.viewingConvId;
StorageUtils.appendMsg(currConvId, {
id: Date.now(),
role: 'user',
content: this.inputMsg,
});
this.fetchConversation();
this.fetchMessages();
this.inputMsg = '';
this.generateMessage(currConvId);
chatScrollToBottom();
},
async generateMessage(currConvId) {
if (this.isGenerating) return;
this.pendingMsg = { id: Date.now()+1, role: 'assistant', content: null };
this.isGenerating = true;
try {
/** @type {CONFIG_DEFAULT} */
const config = this.config;
const abortController = new AbortController();
this.stopGeneration = () => abortController.abort();
/** @type {Array<APIMessage>} */
let messages = [
{ role: 'system', content: config.systemMessage },
...normalizeMsgsForAPI(this.messages),
];
if (config.excludeThoughtOnReq) {
messages = filterThoughtFromMsgs(messages);
}
if (isDev) console.log({messages});
const params = {
messages,
stream: true,
cache_prompt: true,
samplers: config.samplers,
temperature: config.temperature,
dynatemp_range: config.dynatemp_range,
dynatemp_exponent: config.dynatemp_exponent,
top_k: config.top_k,
top_p: config.top_p,
min_p: config.min_p,
typical_p: config.typical_p,
xtc_probability: config.xtc_probability,
xtc_threshold: config.xtc_threshold,
repeat_last_n: config.repeat_last_n,
repeat_penalty: config.repeat_penalty,
presence_penalty: config.presence_penalty,
frequency_penalty: config.frequency_penalty,
dry_multiplier: config.dry_multiplier,
dry_base: config.dry_base,
dry_allowed_length: config.dry_allowed_length,
dry_penalty_last_n: config.dry_penalty_last_n,
max_tokens: config.max_tokens,
timings_per_token: !!config.showTokensPerSecond,
...(config.custom.length ? JSON.parse(config.custom) : {}),
};
const chunks = sendSSEPostRequest(`${BASE_URL}/v1/chat/completions`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...(config.apiKey ? {'Authorization': `Bearer ${config.apiKey}`} : {})
},
body: JSON.stringify(params),
signal: abortController.signal,
});
for await (const chunk of chunks) {
const stop = chunk.stop;
const addedContent = chunk.choices[0].delta.content;
const lastContent = this.pendingMsg.content || '';
if (addedContent) {
this.pendingMsg = {
id: this.pendingMsg.id,
role: 'assistant',
content: lastContent + addedContent,
};
}
const timings = chunk.timings;
if (timings && config.showTokensPerSecond) {
// only extract what's really needed, to save some space
this.pendingMsg.timings = {
prompt_n: timings.prompt_n,
prompt_ms: timings.prompt_ms,
predicted_n: timings.predicted_n,
predicted_ms: timings.predicted_ms,
};
}
}
StorageUtils.appendMsg(currConvId, this.pendingMsg);
this.fetchConversation();
this.fetchMessages();
setTimeout(() => document.getElementById('msg-input').focus(), 1);
} catch (error) {
if (error.name === 'AbortError') {
// user stopped the generation via stopGeneration() function
StorageUtils.appendMsg(currConvId, this.pendingMsg);
this.fetchConversation();
this.fetchMessages();
} else {
console.error(error);
alert(error);
// pop last user message
const lastUserMsg = StorageUtils.popMsg(currConvId);
this.inputMsg = lastUserMsg ? lastUserMsg.content : '';
}
}
this.pendingMsg = null;
this.isGenerating = false;
this.stopGeneration = () => {};
this.fetchMessages();
chatScrollToBottom();
},
// message actions
regenerateMsg(msg) {
if (this.isGenerating) return;
// TODO: somehow keep old history (like how ChatGPT has different "tree"). This can be done by adding "sub-conversations" with "subconv-" prefix, and new message will have a list of subconvIds
const currConvId = this.viewingConvId;
StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
this.fetchConversation();
this.fetchMessages();
this.generateMessage(currConvId);
},
editUserMsgAndRegenerate(msg) {
if (this.isGenerating) return;
const currConvId = this.viewingConvId;
const newContent = msg.content;
StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
StorageUtils.appendMsg(currConvId, {
id: Date.now(),
role: 'user',
content: newContent,
});
this.fetchConversation();
this.fetchMessages();
this.generateMessage(currConvId);
},
// settings dialog methods
closeAndSaveConfigDialog() {
try {
if (this.config.custom.length) JSON.parse(this.config.custom);
} catch (error) {
alert('Invalid JSON for custom config. Please either fix it or leave it empty.');
return;
}
for (const key of CONFIG_NUMERIC_KEYS) {
if (isNaN(this.config[key]) || this.config[key].toString().trim().length === 0) {
alert(`Invalid number for ${key} (expected an integer or a float)`);
return;
}
this.config[key] = parseFloat(this.config[key]);
}
this.showConfigDialog = false;
StorageUtils.setConfig(this.config);
},
closeAndDiscardConfigDialog() {
this.showConfigDialog = false;
this.config = StorageUtils.getConfig();
},
resetConfigDialog() {
if (window.confirm('Are you sure to reset all settings?')) {
this.config = {...CONFIG_DEFAULT};
}
},
// sync state functions
fetchConversation() {
this.conversations = StorageUtils.getAllConversations();
},
fetchMessages() {
this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? [];
},
// debug functions
async debugImportDemoConv() {
const res = await fetch('/demo-conversation.json');
const demoConv = await res.json();
StorageUtils.remove(demoConv.id);
for (const msg of demoConv.messages) {
StorageUtils.appendMsg(demoConv.id, msg);
}
this.fetchConversation();
}
},
});
mainApp.config.errorHandler = alert;
try {
mainApp.mount('#app');
} catch (err) {
console.error(err);
document.getElementById('app').innerHTML = `<div style="margin:2em auto">
Failed to start app. Please try clearing localStorage and try again.<br/>
<br/>
<button class="btn" onClick="localStorage.clear(); window.location.reload();">Clear localStorage</button>
</div>`;
}
/**
* filter out redundant fields upon sending to API
* @param {Array<APIMessage>} messages
* @returns {Array<APIMessage>}
*/
function normalizeMsgsForAPI(messages) {
return messages.map((msg) => {
return {
role: msg.role,
content: msg.content,
};
});
}
/**
* recommended for DeepsSeek-R1, filter out content between <think> and </think> tags
* @param {Array<APIMessage>} messages
* @returns {Array<APIMessage>}
*/
function filterThoughtFromMsgs(messages) {
return messages.map((msg) => {
return {
role: msg.role,
content: msg.role === 'assistant'
? msg.content.split('</think>').at(-1).trim()
: msg.content,
};
});
}

10
examples/server/webui/src/main.tsx Normal file
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@ -0,0 +1,10 @@
import { StrictMode } from 'react';
import { createRoot } from 'react-dom/client';
import './index.scss';
import App from './App.tsx';
createRoot(document.getElementById('root')!).render(
<StrictMode>
<App />
</StrictMode>
);

304
examples/server/webui/src/utils/app.context.tsx Normal file
View file

@ -0,0 +1,304 @@
import React, { createContext, useContext, useEffect, useState } from 'react';
import { APIMessage, Conversation, Message, PendingMessage } from './types';
import StorageUtils from './storage';
import {
filterThoughtFromMsgs,
normalizeMsgsForAPI,
getSSEStreamAsync,
} from './misc';
import { BASE_URL, CONFIG_DEFAULT, isDev } from '../Config';
import { matchPath, useLocation } from 'react-router';
interface AppContextValue {
viewingConversation: Conversation | null;
pendingMessages: Record<Conversation['id'], PendingMessage>;
isGenerating: (convId: string) => boolean;
sendMessage: (
convId: string,
content: string,
onChunk?: CallbackGeneratedChunk
) => Promise<boolean>;
stopGenerating: (convId: string) => void;
replaceMessageAndGenerate: (
convId: string,
origMsgId: Message['id'],
content?: string,
onChunk?: CallbackGeneratedChunk
) => Promise<void>;
config: typeof CONFIG_DEFAULT;
saveConfig: (config: typeof CONFIG_DEFAULT) => void;
}
// for now, this callback is only used for scrolling to the bottom of the chat
type CallbackGeneratedChunk = () => void;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const AppContext = createContext<AppContextValue>({} as any);
export const AppContextProvider = ({
children,
}: {
children: React.ReactElement;
}) => {
const { pathname } = useLocation();
const params = matchPath('/chat/:convId', pathname);
const convId = params?.params?.convId;
const [viewingConversation, setViewingConversation] =
useState<Conversation | null>(null);
const [pendingMessages, setPendingMessages] = useState<
Record<Conversation['id'], PendingMessage>
>({});
const [aborts, setAborts] = useState<
Record<Conversation['id'], AbortController>
>({});
const [config, setConfig] = useState(StorageUtils.getConfig());
useEffect(() => {
const handleConversationChange = (changedConvId: string) => {
if (changedConvId !== convId) return;
setViewingConversation(StorageUtils.getOneConversation(convId));
};
StorageUtils.onConversationChanged(handleConversationChange);
setViewingConversation(StorageUtils.getOneConversation(convId ?? ''));
return () => {
StorageUtils.offConversationChanged(handleConversationChange);
};
}, [convId]);
const setPending = (convId: string, pendingMsg: PendingMessage | null) => {
// if pendingMsg is null, remove the key from the object
if (!pendingMsg) {
setPendingMessages((prev) => {
const newState = { ...prev };
delete newState[convId];
return newState;
});
} else {
setPendingMessages((prev) => ({ ...prev, [convId]: pendingMsg }));
}
};
const setAbort = (convId: string, controller: AbortController | null) => {
if (!controller) {
setAborts((prev) => {
const newState = { ...prev };
delete newState[convId];
return newState;
});
} else {
setAborts((prev) => ({ ...prev, [convId]: controller }));
}
};
////////////////////////////////////////////////////////////////////////
// public functions
const isGenerating = (convId: string) => !!pendingMessages[convId];
const generateMessage = async (
convId: string,
onChunk?: CallbackGeneratedChunk
) => {
if (isGenerating(convId)) return;
const config = StorageUtils.getConfig();
const currConversation = StorageUtils.getOneConversation(convId);
if (!currConversation) {
throw new Error('Current conversation is not found');
}
const abortController = new AbortController();
setAbort(convId, abortController);
let pendingMsg: PendingMessage = {
id: Date.now() + 1,
role: 'assistant',
content: null,
};
setPending(convId, pendingMsg);
try {
// prepare messages for API
let messages: APIMessage[] = [
...(config.systemMessage.length === 0
? []
: [{ role: 'system', content: config.systemMessage } as APIMessage]),
...normalizeMsgsForAPI(currConversation?.messages ?? []),
];
if (config.excludeThoughtOnReq) {
messages = filterThoughtFromMsgs(messages);
}
if (isDev) console.log({ messages });
// prepare params
const params = {
messages,
stream: true,
cache_prompt: true,
samplers: config.samplers,
temperature: config.temperature,
dynatemp_range: config.dynatemp_range,
dynatemp_exponent: config.dynatemp_exponent,
top_k: config.top_k,
top_p: config.top_p,
min_p: config.min_p,
typical_p: config.typical_p,
xtc_probability: config.xtc_probability,
xtc_threshold: config.xtc_threshold,
repeat_last_n: config.repeat_last_n,
repeat_penalty: config.repeat_penalty,
presence_penalty: config.presence_penalty,
frequency_penalty: config.frequency_penalty,
dry_multiplier: config.dry_multiplier,
dry_base: config.dry_base,
dry_allowed_length: config.dry_allowed_length,
dry_penalty_last_n: config.dry_penalty_last_n,
max_tokens: config.max_tokens,
timings_per_token: !!config.showTokensPerSecond,
...(config.custom.length ? JSON.parse(config.custom) : {}),
};
// send request
const fetchResponse = await fetch(`${BASE_URL}/v1/chat/completions`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...(config.apiKey
? { Authorization: `Bearer ${config.apiKey}` }
: {}),
},
body: JSON.stringify(params),
signal: abortController.signal,
});
if (fetchResponse.status !== 200) {
const body = await fetchResponse.json();
throw new Error(body?.error?.message || 'Unknown error');
}
const chunks = getSSEStreamAsync(fetchResponse);
for await (const chunk of chunks) {
// const stop = chunk.stop;
if (chunk.error) {
throw new Error(chunk.error?.message || 'Unknown error');
}
const addedContent = chunk.choices[0].delta.content;
const lastContent = pendingMsg.content || '';
if (addedContent) {
pendingMsg = {
id: pendingMsg.id,
role: 'assistant',
content: lastContent + addedContent,
};
}
const timings = chunk.timings;
if (timings && config.showTokensPerSecond) {
// only extract what's really needed, to save some space
pendingMsg.timings = {
prompt_n: timings.prompt_n,
prompt_ms: timings.prompt_ms,
predicted_n: timings.predicted_n,
predicted_ms: timings.predicted_ms,
};
}
setPending(convId, pendingMsg);
onChunk?.();
}
} catch (err) {
setPending(convId, null);
if ((err as Error).name === 'AbortError') {
// user stopped the generation via stopGeneration() function
// we can safely ignore this error
} else {
console.error(err);
// eslint-disable-next-line @typescript-eslint/no-explicit-any
alert((err as any)?.message ?? 'Unknown error');
throw err; // rethrow
}
}
if (pendingMsg.content) {
StorageUtils.appendMsg(currConversation.id, {
id: pendingMsg.id,
content: pendingMsg.content,
role: pendingMsg.role,
timings: pendingMsg.timings,
});
}
setPending(convId, null);
onChunk?.(); // trigger scroll to bottom
};
const sendMessage = async (
convId: string,
content: string,
onChunk?: CallbackGeneratedChunk
): Promise<boolean> => {
if (isGenerating(convId) || content.trim().length === 0) return false;
StorageUtils.appendMsg(convId, {
id: Date.now(),
role: 'user',
content,
});
try {
await generateMessage(convId, onChunk);
return true;
} catch (_) {
// rollback
StorageUtils.popMsg(convId);
}
return false;
};
const stopGenerating = (convId: string) => {
setPending(convId, null);
aborts[convId]?.abort();
};
// if content is undefined, we remove last assistant message
const replaceMessageAndGenerate = async (
convId: string,
origMsgId: Message['id'],
content?: string,
onChunk?: CallbackGeneratedChunk
) => {
if (isGenerating(convId)) return;
StorageUtils.filterAndKeepMsgs(convId, (msg) => msg.id < origMsgId);
if (content) {
StorageUtils.appendMsg(convId, {
id: Date.now(),
role: 'user',
content,
});
}
await generateMessage(convId, onChunk);
};
const saveConfig = (config: typeof CONFIG_DEFAULT) => {
StorageUtils.setConfig(config);
setConfig(config);
};
return (
<AppContext.Provider
value={{
isGenerating,
viewingConversation,
pendingMessages,
sendMessage,
stopGenerating,
replaceMessageAndGenerate,
config,
saveConfig,
}}
>
{children}
</AppContext.Provider>
);
};
export const useAppContext = () => useContext(AppContext);

87
examples/server/webui/src/utils/misc.ts Normal file
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@ -0,0 +1,87 @@
// @ts-expect-error this package does not have typing
import TextLineStream from 'textlinestream';
import { APIMessage, Message } from './types';
// ponyfill for missing ReadableStream asyncIterator on Safari
import { asyncIterator } from '@sec-ant/readable-stream/ponyfill/asyncIterator';
import { isDev } from '../Config';
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export const isString = (x: any) => !!x.toLowerCase;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export const isBoolean = (x: any) => x === true || x === false;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export const isNumeric = (n: any) => !isString(n) && !isNaN(n) && !isBoolean(n);
export const escapeAttr = (str: string) =>
str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
// wrapper for SSE
export async function* getSSEStreamAsync(fetchResponse: Response) {
if (!fetchResponse.body) throw new Error('Response body is empty');
const lines: ReadableStream<string> = fetchResponse.body
.pipeThrough(new TextDecoderStream())
.pipeThrough(new TextLineStream());
// @ts-expect-error asyncIterator complains about type, but it should work
for await (const line of asyncIterator(lines)) {
if (isDev) console.log({ line });
if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
const data = JSON.parse(line.slice(5));
yield data;
} else if (line.startsWith('error:')) {
const data = JSON.parse(line.slice(6));
throw new Error(data.message || 'Unknown error');
}
}
}
// copy text to clipboard
export const copyStr = (textToCopy: string) => {
// Navigator clipboard api needs a secure context (https)
if (navigator.clipboard && window.isSecureContext) {
navigator.clipboard.writeText(textToCopy);
} else {
// Use the 'out of viewport hidden text area' trick
const textArea = document.createElement('textarea');
textArea.value = textToCopy;
// Move textarea out of the viewport so it's not visible
textArea.style.position = 'absolute';
textArea.style.left = '-999999px';
document.body.prepend(textArea);
textArea.select();
document.execCommand('copy');
}
};
/**
* filter out redundant fields upon sending to API
*/
export function normalizeMsgsForAPI(messages: Message[]) {
return messages.map((msg) => {
return {
role: msg.role,
content: msg.content,
};
}) as APIMessage[];
}
/**
* recommended for DeepsSeek-R1, filter out content between <think> and </think> tags
*/
export function filterThoughtFromMsgs(messages: APIMessage[]) {
return messages.map((msg) => {
return {
role: msg.role,
content:
msg.role === 'assistant'
? msg.content.split('</think>').at(-1)!.trim()
: msg.content,
} as APIMessage;
});
}
export function classNames(classes: Record<string, boolean>): string {
return Object.entries(classes)
.filter(([_, value]) => value)
.map(([key, _]) => key)
.join(' ');
}

138
examples/server/webui/src/utils/storage.ts Normal file
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@ -0,0 +1,138 @@
// coversations is stored in localStorage
// format: { [convId]: { id: string, lastModified: number, messages: [...] } }
import { CONFIG_DEFAULT } from '../Config';
import { Conversation, Message } from './types';
const event = new EventTarget();
type CallbackConversationChanged = (convId: string) => void;
let onConversationChangedHandlers: [
CallbackConversationChanged,
EventListener,
][] = [];
const dispatchConversationChange = (convId: string) => {
event.dispatchEvent(
new CustomEvent('conversationChange', { detail: { convId } })
);
};
// convId is a string prefixed with 'conv-'
const StorageUtils = {
/**
* manage conversations
*/
getAllConversations(): Conversation[] {
const res = [];
for (const key in localStorage) {
if (key.startsWith('conv-')) {
res.push(JSON.parse(localStorage.getItem(key) ?? '{}'));
}
}
res.sort((a, b) => b.lastModified - a.lastModified);
return res;
},
/**
* can return null if convId does not exist
*/
getOneConversation(convId: string): Conversation | null {
return JSON.parse(localStorage.getItem(convId) || 'null');
},
/**
* if convId does not exist, create one
*/
appendMsg(convId: string, msg: Message): void {
if (msg.content === null) return;
const conv = StorageUtils.getOneConversation(convId) || {
id: convId,
lastModified: Date.now(),
messages: [],
};
conv.messages.push(msg);
conv.lastModified = Date.now();
localStorage.setItem(convId, JSON.stringify(conv));
dispatchConversationChange(convId);
},
/**
* Get new conversation id
*/
getNewConvId(): string {
return `conv-${Date.now()}`;
},
/**
* remove conversation by id
*/
remove(convId: string): void {
localStorage.removeItem(convId);
dispatchConversationChange(convId);
},
/**
* remove all conversations
*/
filterAndKeepMsgs(
convId: string,
predicate: (msg: Message) => boolean
): void {
const conv = StorageUtils.getOneConversation(convId);
if (!conv) return;
conv.messages = conv.messages.filter(predicate);
conv.lastModified = Date.now();
localStorage.setItem(convId, JSON.stringify(conv));
dispatchConversationChange(convId);
},
/**
* remove last message from conversation
*/
popMsg(convId: string): Message | undefined {
const conv = StorageUtils.getOneConversation(convId);
if (!conv) return;
const msg = conv.messages.pop();
conv.lastModified = Date.now();
if (conv.messages.length === 0) {
StorageUtils.remove(convId);
} else {
localStorage.setItem(convId, JSON.stringify(conv));
}
dispatchConversationChange(convId);
return msg;
},
// event listeners
onConversationChanged(callback: CallbackConversationChanged) {
const fn = (e: Event) => callback((e as CustomEvent).detail.convId);
onConversationChangedHandlers.push([callback, fn]);
event.addEventListener('conversationChange', fn);
},
offConversationChanged(callback: CallbackConversationChanged) {
const fn = onConversationChangedHandlers.find(([cb, _]) => cb === callback);
if (fn) {
event.removeEventListener('conversationChange', fn[1]);
}
onConversationChangedHandlers = [];
},
// manage config
getConfig(): typeof CONFIG_DEFAULT {
const savedVal = JSON.parse(localStorage.getItem('config') || '{}');
// to prevent breaking changes in the future, we always provide default value for missing keys
return {
...CONFIG_DEFAULT,
...savedVal,
};
},
setConfig(config: typeof CONFIG_DEFAULT) {
localStorage.setItem('config', JSON.stringify(config));
},
getTheme(): string {
return localStorage.getItem('theme') || 'auto';
},
setTheme(theme: string) {
if (theme === 'auto') {
localStorage.removeItem('theme');
} else {
localStorage.setItem('theme', theme);
}
},
};
export default StorageUtils;

25
examples/server/webui/src/utils/types.ts Normal file
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@ -0,0 +1,25 @@
export interface TimingReport {
prompt_n: number;
prompt_ms: number;
predicted_n: number;
predicted_ms: number;
}
export interface Message {
id: number;
role: 'user' | 'assistant' | 'system';
content: string;
timings?: TimingReport;
}
export type APIMessage = Pick<Message, 'role' | 'content'>;
export interface Conversation {
id: string; // format: `conv-{timestamp}`
lastModified: number; // timestamp from Date.now()
messages: Message[];
}
export type PendingMessage = Omit<Message, 'content'> & {
content: string | null;
};

1
examples/server/webui/src/vite-env.d.ts vendored Normal file
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@ -0,0 +1 @@
/// <reference types="vite/client" />

26
examples/server/webui/tsconfig.app.json Normal file
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@ -0,0 +1,26 @@
{
"compilerOptions": {
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
"target": "ES2021",
"useDefineForClassFields": true,
"lib": ["ES2021", "DOM", "DOM.Iterable"],
"module": "ESNext",
"skipLibCheck": true,
/* Bundler mode */
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"isolatedModules": true,
"moduleDetection": "force",
"noEmit": true,
"jsx": "react-jsx",
/* Linting */
"strict": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"noFallthroughCasesInSwitch": true,
"noUncheckedSideEffectImports": true
},
"include": ["src"]
}

7
examples/server/webui/tsconfig.json Normal file
View file

@ -0,0 +1,7 @@
{
"files": [],
"references": [
{ "path": "./tsconfig.app.json" },
{ "path": "./tsconfig.node.json" }
]
}

24
examples/server/webui/tsconfig.node.json Normal file
View file

@ -0,0 +1,24 @@
{
"compilerOptions": {
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
"target": "ES2022",
"lib": ["ES2023"],
"module": "ESNext",
"skipLibCheck": true,
/* Bundler mode */
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"isolatedModules": true,
"moduleDetection": "force",
"noEmit": true,
/* Linting */
"strict": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"noFallthroughCasesInSwitch": true,
"noUncheckedSideEffectImports": true
},
"include": ["vite.config.ts"]
}

49
examples/server/webui/vite.config.js → examples/server/webui/vite.config.ts
View file

@ -1,8 +1,11 @@
import { defineConfig, PluginOption } from 'vite';
import react from '@vitejs/plugin-react';
import { viteSingleFile } from 'vite-plugin-singlefile';
import path from 'path';
import fs from 'fs';
import zlib from 'zlib';
import path from 'node:path';
import fs from 'node:fs';
import zlib from 'node:zlib';
/* eslint-disable */
const MAX_BUNDLE_SIZE = 1.5 * 1024 * 1024; // only increase when absolutely necessary
@ -15,20 +18,26 @@ const GUIDE_FOR_FRONTEND = `
-->
`.trim();
const FRONTEND_PLUGINS = [react()];
const BUILD_PLUGINS = [
...FRONTEND_PLUGINS,
viteSingleFile(),
(function llamaCppPlugin() {
let config;
let config: any;
return {
name: 'llamacpp:build',
apply: 'build',
async configResolved(_config) {
async configResolved(_config: any) {
config = _config;
},
writeBundle() {
const outputIndexHtml = path.join(config.build.outDir, 'index.html');
const content = GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), { level: 9 });
const content =
GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), {
level: 9,
});
// because gzip header contains machine-specific info, we must remove these data from the header
// timestamp
@ -42,18 +51,26 @@ const BUILD_PLUGINS = [
if (compressed.byteLength > MAX_BUNDLE_SIZE) {
throw new Error(
`Bundle size is too large (${Math.ceil(compressed.byteLength / 1024)} KB).\n` +
`Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`,
`Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`
);
}
const targetOutputFile = path.join(config.build.outDir, '../../public/index.html.gz');
const targetOutputFile = path.join(
config.build.outDir,
'../../public/index.html.gz'
);
fs.writeFileSync(targetOutputFile, compressed);
}
}
},
} satisfies PluginOption;
})(),
];
/** @type {import('vite').UserConfig} */
export default {
plugins: process.env.ANALYZE ? [] : BUILD_PLUGINS,
};
export default defineConfig({
// @ts-ignore
plugins: process.env.ANALYZE ? FRONTEND_PLUGINS : BUILD_PLUGINS,
server: {
proxy: {
'/v1': 'http://localhost:8080',
},
},
});

29
ggml/CMakeLists.txt
View file

@ -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
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE GGML_BUILD_NUMBER
OUTPUT_STRIP_TRAILING_WHITESPACE
)
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)
message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
if(GGML_BUILD_NUMBER EQUAL 1)
message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
endif()
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()
execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE GGML_BUILD_COMMIT
OUTPUT_STRIP_TRAILING_WHITESPACE
)
# Capture variables prefixed with GGML_.

2
ggml/include/ggml.h
View file

@ -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]

14
ggml/src/CMakeLists.txt
View file

@ -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 ()
@ -308,7 +314,7 @@ if (GGML_CPU_ALL_VARIANTS)
# MSVC doesn't support AMX
ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
endif()
else ()
elseif (GGML_CPU)
ggml_add_cpu_backend_variant_impl("")
endif()

14
ggml/src/ggml-alloc.c
View file

@ -989,19 +989,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
}
if (this_size > max_size) {
GGML_LOG_ERROR("%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
__func__, t->name,
ggml_backend_buft_name(buft),
this_size, max_size);
for (size_t i = 0; i < n_buffers; i++) {
ggml_backend_buffer_free(buffers[i]);
}
free(buffers);
return NULL;
}
if ((cur_buf_size + this_size) > max_size) {
if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) {
// allocate tensors in the current buffer
if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
return NULL;

18
ggml/src/ggml-cpu/ggml-cpu-impl.h
View file

@ -360,21 +360,15 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)
#endif
#if defined(__loongarch_asx)
typedef union {
int32_t i;
float f;
} ft_union;
/* float type data load instructions */
static __m128 __lsx_vreplfr2vr_s(float val) {
ft_union fi_tmpval = {.f = val};
return (__m128)__lsx_vreplgr2vr_w(fi_tmpval.i);
static __m128 __lsx_vreplfr2vr_s(const float val) {
v4f32 res = {val, val, val, val};
return (__m128)res;
}
static __m256 __lasx_xvreplfr2vr_s(float val) {
ft_union fi_tmpval = {.f = val};
return (__m256)__lasx_xvreplgr2vr_w(fi_tmpval.i);
static __m256 __lasx_xvreplfr2vr_s(const float val) {
v8f32 res = {val, val, val, val, val, val, val, val};
return (__m256)res;
}
#endif

206
ggml/src/ggml-cpu/ggml-cpu-quants.c
View file

@ -297,6 +297,90 @@ static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4
static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4
#endif
#if defined(__loongarch_sx)
static __m128i lsx_packs_w(__m128i a, __m128i b) {
__m128i tmp, tmp1;
tmp = __lsx_vsat_w(a, 15);
tmp1 = __lsx_vsat_w(b, 15);
return __lsx_vpickev_h(tmp1, tmp);
}
static __m128i lsx_packs_h(__m128i a, __m128i b) {
__m128i tmp, tmp1;
tmp = __lsx_vsat_h(a, 7);
tmp1 = __lsx_vsat_h(b, 7);
return __lsx_vpickev_b(tmp1, tmp);
}
static __m128i lsx_packus_h(__m128i a, __m128i b) {
__m128i tmp, tmp1;
tmp = __lsx_vsat_hu(a, 7);
tmp1 = __lsx_vsat_hu(b, 7);
return __lsx_vpickev_b(tmp1, tmp);
}
static __m128i lsx_maddubs_h(__m128i a, __m128i b) {
__m128i tmp1, tmp2;
tmp1 = __lsx_vmulwev_h_b(a, b);
tmp2 = __lsx_vmulwod_h_b(a, b);
return __lsx_vsadd_h(tmp1, tmp2);
}
static __m128i lsx_madd_h(__m128i a, __m128i b) {
__m128i tmp1, tmp2;
tmp1 = __lsx_vmulwev_w_h(a, b);
tmp2 = __lsx_vmulwod_w_h(a, b);
return __lsx_vadd_w(tmp1, tmp2);
}
static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) {
v4i32 __ret = {d, c, b, a};
return (__m128i)__ret;
}
static __m128i lsx_shuffle_b(__m128i a, __m128i b) {
__m128i mask_f, zero, tmp0, tmp2, mask;
int f = 0x8f;
mask_f = __lsx_vreplgr2vr_b(f);
zero = __lsx_vldi(0);
tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits
tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive
mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask
tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones
return __lsx_vshuf_b(a, zero, tmp2);
}
static __m128i lsx_hadd_h(__m128i a, __m128i b) {
__m128i tmp1 = __lsx_vpickev_h(b, a);
__m128i tmp2 = __lsx_vpickod_h(b, a);
return __lsx_vadd_h(tmp1, tmp2);
}
static __m128i lsx_hadd_w(__m128i a, __m128i b) {
__m128i tmp1 = __lsx_vpickev_w(b, a);
__m128i tmp2 = __lsx_vpickod_w(b, a);
return __lsx_vadd_w(tmp1, tmp2);
}
static __m128 lsx_hadd_s(__m128 a, __m128 b) {
__m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a);
__m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a);
return __lsx_vfadd_s(tmp1, tmp2);
}
static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) {
__m128 res_0 =lsx_hadd_s(a, b);
__m128 res_1 =lsx_hadd_s(c, d);
__m128 res =lsx_hadd_s(res_0, res_1);
res =lsx_hadd_s(res, res);
res =lsx_hadd_s(res, res);
return ((v4f32)res)[0];
}
#endif
#if defined(__loongarch_asx)
#ifdef __clang__
@ -395,11 +479,6 @@ static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1
return (__m256i)__ret;
}
static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) {
v4i32 __ret = {d, c, b, a};
return (__m128i)__ret;
}
static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) {
v4i64 __ret = {d, c, b, a};
return (__m256i)__ret;
@ -409,18 +488,6 @@ static __m256i lasx_insertf128( __m128i x, __m128i y) {
return lasx_set_q(x, y);
}
static __m128i lsx_shuffle_b(__m128i a, __m128i b) {
__m128i mask_f, zero, tmp0, tmp2, mask;
int f = 0x8f;
mask_f = __lsx_vreplgr2vr_b(f);
zero = __lsx_vldi(0);
tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits
tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive
mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask
tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones
return __lsx_vshuf_b(a, zero, tmp2);
}
static __m256i lasx_shuffle_b(__m256i a, __m256i b) {
__m256i mask_f, zero, tmp0, tmp2, mask;
int f = 0x8f;
@ -434,30 +501,15 @@ static __m256i lasx_shuffle_b(__m256i a, __m256i b) {
}
static __m256i lasx_extu8_16(__m128i a) {
__m128i zero = __lsx_vldi(0);
__m128i vlo = __lsx_vilvl_b(zero, a);
__m128i vhi = __lsx_vilvh_b(zero, a);
return lasx_set_q(vhi, vlo);
return __lasx_vext2xv_hu_bu(____m256i(a));
}
static __m256i lasx_ext8_16(__m128i a) {
__m128i sign = __lsx_vslti_b(a, 0);
__m128i vlo = __lsx_vilvl_b(sign, a);
__m128i vhi = __lsx_vilvh_b(sign, a);
return lasx_set_q(vhi, vlo);
return __lasx_vext2xv_h_b(____m256i(a));
}
static __m256i lasx_ext16_32(__m128i a) {
__m256i tmp1;
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0);
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1);
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2);
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3);
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4);
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5);
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6);
tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7);
return tmp1;
return __lasx_vext2xv_w_h(____m256i(a));
}
static __m128i lasx_extracti128( __m256i a, int pos) {
@ -482,25 +534,6 @@ static __m128 lasx_extractf128( __m256 a, int pos) {
return ret;
}
static __m128i lsx_hadd_h(__m128i a, __m128i b) {
__m128i tmp1 = __lsx_vpickev_h(b, a);
__m128i tmp2 = __lsx_vpickod_h(b, a);
return __lsx_vadd_h(tmp1, tmp2);
}
static __m128i lsx_hadd_w(__m128i a, __m128i b) {
__m128i tmp1 = __lsx_vpickev_w(b, a);
__m128i tmp2 = __lsx_vpickod_w(b, a);
return __lsx_vadd_w(tmp1, tmp2);
}
static __m128 lsx_hadd_s(__m128 a, __m128 b) {
__m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a);
__m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a);
return __lsx_vfadd_s(tmp1, tmp2);
}
static __m256i lasx_maddubs_h(__m256i a, __m256i b) {
__m256i tmp1, tmp2;
tmp1 = __lasx_xvmulwev_h_b(a, b);
@ -529,42 +562,6 @@ static __m256i lasx_packs_h(__m256i a, __m256i b) {
return __lasx_xvpickev_b(tmp1, tmp);
}
static __m128i lsx_packs_w(__m128i a, __m128i b) {
__m128i tmp, tmp1;
tmp = __lsx_vsat_w(a, 15);
tmp1 = __lsx_vsat_w(b, 15);
return __lsx_vpickev_h(tmp1, tmp);
}
static __m128i lsx_packs_h(__m128i a, __m128i b) {
__m128i tmp, tmp1;
tmp = __lsx_vsat_h(a, 7);
tmp1 = __lsx_vsat_h(b, 7);
return __lsx_vpickev_b(tmp1, tmp);
}
static __m128i lsx_packus_h(__m128i a, __m128i b) {
__m128i tmp, tmp1;
tmp = __lsx_vsat_hu(a, 7);
tmp1 = __lsx_vsat_hu(b, 7);
return __lsx_vpickev_b(tmp1, tmp);
}
static __m128i lsx_maddubs_h(__m128i a, __m128i b) {
__m128i tmp1, tmp2;
tmp1 = __lsx_vmulwev_h_b(a, b);
tmp2 = __lsx_vmulwod_h_b(a, b);
return __lsx_vsadd_h(tmp1, tmp2);
}
static __m128i lsx_madd_h(__m128i a, __m128i b) {
__m128i tmp1, tmp2;
tmp1 = __lsx_vmulwev_w_h(a, b);
tmp2 = __lsx_vmulwod_w_h(a, b);
return __lsx_vadd_w(tmp1, tmp2);
}
// multiply int8_t, add results pairwise twice
static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
// Get absolute values of x vectors
@ -580,12 +577,10 @@ static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
// horizontally add 8 floats
static inline float hsum_float_8(const __m256 x) {
__m128 res = lasx_extractf128(x, 1);
ft_union tmp;
res = __lsx_vfadd_s(res, lasx_extractf128(x, 0));
res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res));
res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0));
tmp.i = __lsx_vpickve2gr_w(res, 0);
return tmp.f;
return ((v4f32)res)[0];
}
// horizontally add 8 int32_t
@ -927,7 +922,6 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
#elif defined(__loongarch_asx)
for (int i = 0; i < nb; i++) {
ft_union fi;
__m256 v0 = (__m256)__lasx_xvld( x , 0);
__m256 v1 = (__m256)__lasx_xvld( x , 32);
__m256 v2 = (__m256)__lasx_xvld( x , 64);
@ -945,8 +939,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) );
__m128 tmp = max4;
max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 ));
fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 );
const float max_scalar = fi.f;
const float max_scalar = ((v4f32)max4)[0];
// Quantize these floats
const float d = max_scalar / 127.f;
@ -1251,7 +1244,6 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
#elif defined(__loongarch_asx)
for (int i = 0; i < nb; i++) {
ft_union ft;
__m256 v0 = (__m256)__lasx_xvld( x , 0 );
__m256 v1 = (__m256)__lasx_xvld( x , 32 );
__m256 v2 = (__m256)__lasx_xvld( x , 64 );
@ -1269,8 +1261,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) );
__m128 tmp = max4;
max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 ));
ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 );
const float max_scalar = ft.f;
const float max_scalar = ((v4f32)max4)[0];
// Quantize these floats
const float d = max_scalar / 127.f;
@ -2401,21 +2392,22 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
}
sumf = hsum_float_8(acc);
#elif defined(__loongarch_sx)
// set constants
const __m128i low_mask = __lsx_vreplgr2vr_b(0xF);
const __m128i off = __lsx_vreplgr2vr_b(8);
// Initialize accumulator with zeros
__m128 acc_0 = __lsx_vldi(0);
__m128 acc_1 = __lsx_vldi(0);
__m128 acc_2 = __lsx_vldi(0);
__m128 acc_3 = __lsx_vldi(0);
__m128 acc_0 = (__m128)__lsx_vldi(0);
__m128 acc_1 = (__m128)__lsx_vldi(0);
__m128 acc_2 = (__m128)__lsx_vldi(0);
__m128 acc_3 = (__m128)__lsx_vldi(0);
for (; ib + 1 < nb; ib += 2) {
// Compute combined scale for the block 0 and 1
const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
const __m128 d_0_1 = (__m128)__lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0);
@ -2433,7 +2425,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
//_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
// Compute combined scale for the block 2 and 3
const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
const __m128 d_2_3 = (__m128)__lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0);
@ -6638,9 +6630,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1);
ft_union fi;
fi.i = __lsx_vpickve2gr_w(acc_m, 0);
*s = hsum_float_8(acc) + fi.f ;
*s = hsum_float_8(acc) + ((v4f32)acc_m)[0];
#else
const uint8_t * scales = (const uint8_t*)&utmp[0];

40
ggml/src/ggml-cpu/ggml-cpu.c
View file

@ -1078,29 +1078,23 @@ do { \
#define GGML_F16_STEP 32
#define GGML_F16_EPR 8
// F16 arithmetic is not supported by AVX, so we use F32 instead
// F16 arithmetic is not supported by LASX, so we use F32 instead
#define GGML_F32Cx8 __m256
#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0)
#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x))
static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) {
float tmp[8];
for (int i = 0; i < 8; i++) {
tmp[i] = GGML_FP16_TO_FP32(x[i]);
}
return (__m256)__lasx_xvld(tmp, 0);
__m256i a;
memcpy(&a, x, sizeof(ggml_fp16_t) * 8);
a = __lasx_xvpermi_d(a, 0 | (1 << 4));
return __lasx_xvfcvtl_s_h(a);
}
static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
float arr[8];
__lasx_xvst(y, arr, 0);
for (int i = 0; i < 8; i++) {
x[i] = GGML_FP32_TO_FP16(arr[i]);
}
__m256i a = __lasx_xvfcvt_h_s(y, y);
a = __lasx_xvpermi_d(a, 0 | (2 << 2));
memcpy(x, &a, sizeof(ggml_fp16_t) * 8);
}
#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x)
#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y)
@ -1302,7 +1296,7 @@ struct ggml_threadpool {
// these are atomic as an annotation for thread-sanitizer
atomic_bool stop; // Used for stopping the threadpool altogether
atomic_bool pause; // Used for pausing the threadpool or individual threads
atomic_bool abort; // Used for aborting processing of a graph
atomic_int abort; // Used for aborting processing of a graph
struct ggml_compute_state * workers; // per thread state
int n_threads_max; // number of threads in the pool
@ -13851,20 +13845,24 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
/*.threadpool=*/ tp,
};
for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) {
for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) {
struct ggml_tensor * node = cgraph->nodes[node_n];
ggml_compute_forward(&params, node);
if (state->ith == 0 && cplan->abort_callback &&
cplan->abort_callback(cplan->abort_callback_data)) {
tp->abort = true;
atomic_store_explicit(&tp->abort, node_n + 1, memory_order_relaxed);
tp->ec = GGML_STATUS_ABORTED;
}
ggml_barrier(state->threadpool);
if (node_n + 1 < cgraph->n_nodes) {
ggml_barrier(state->threadpool);
}
}
ggml_barrier(state->threadpool);
return 0;
}
@ -14031,7 +14029,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
threadpool->current_chunk = 0;
threadpool->stop = false;
threadpool->pause = tpp->paused;
threadpool->abort = false;
threadpool->abort = -1;
threadpool->workers = NULL;
threadpool->n_threads_max = tpp->n_threads;
threadpool->n_threads_cur = tpp->n_threads;
@ -14110,7 +14108,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
threadpool->cgraph = cgraph;
threadpool->cplan = cplan;
threadpool->current_chunk = 0;
threadpool->abort = false;
threadpool->abort = -1;
threadpool->ec = GGML_STATUS_SUCCESS;
}

2
ggml/src/ggml-cuda/CMakeLists.txt
View file

@ -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})

101
ggml/src/ggml-cuda/common.cuh
View file

@ -46,20 +46,27 @@
#define GGML_CUDA_CC_VOLTA 700
#define GGML_CUDA_CC_TURING 750
#define GGML_CUDA_CC_AMPERE 800
#define GGML_CUDA_CC_OFFSET_AMD 1000000
#define GGML_CUDA_CC_OFFSET_AMD 0x1000000
// GCN/CNDA, wave size is 64
#define GGML_CUDA_CC_GCN4 (GGML_CUDA_CC_OFFSET_AMD + 803) // Tonga, Fiji, Polaris, minimum for fast fp16
#define GGML_CUDA_CC_VEGA (GGML_CUDA_CC_OFFSET_AMD + 900) // Vega56/64, minimum for fp16 dual issue
#define GGML_CUDA_CC_VEGA20 (GGML_CUDA_CC_OFFSET_AMD + 906) // MI50/Radeon VII, minimum for dp4a
#define GGML_CUDA_CC_CDNA (GGML_CUDA_CC_OFFSET_AMD + 908) // MI100, minimum for MFMA, acc registers
#define GGML_CUDA_CC_CDNA2 (GGML_CUDA_CC_OFFSET_AMD + 910) // MI210, minimum acc register renameing
#define GGML_CUDA_CC_CDNA3 (GGML_CUDA_CC_OFFSET_AMD + 942) // MI300
#define GGML_CUDA_CC_GCN4 (GGML_CUDA_CC_OFFSET_AMD + 0x803) // Tonga, Fiji, Polaris, minimum for fast fp16
#define GGML_CUDA_CC_VEGA (GGML_CUDA_CC_OFFSET_AMD + 0x900) // Vega56/64, minimum for fp16 dual issue
#define GGML_CUDA_CC_VEGA20 (GGML_CUDA_CC_OFFSET_AMD + 0x906) // MI50/Radeon VII, minimum for dp4a
#define GGML_CUDA_CC_CDNA (GGML_CUDA_CC_OFFSET_AMD + 0x908) // MI100, minimum for MFMA, acc registers
#define GGML_CUDA_CC_CDNA2 (GGML_CUDA_CC_OFFSET_AMD + 0x910) // MI210, minimum acc register renameing
#define GGML_CUDA_CC_CDNA3 (GGML_CUDA_CC_OFFSET_AMD + 0x942) // MI300
// RNDA removes MFMA, dp4a, xnack, acc registers, wave size is 32
#define GGML_CUDA_CC_RDNA1 (GGML_CUDA_CC_OFFSET_AMD + 1010) // RX 5000
#define GGML_CUDA_CC_RDNA2 (GGML_CUDA_CC_OFFSET_AMD + 1030) // RX 6000, minimum for dp4a
#define GGML_CUDA_CC_RDNA3 (GGML_CUDA_CC_OFFSET_AMD + 1100) // RX 7000, minimum for WMMA
#define GGML_CUDA_CC_RDNA1 (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000
#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] = {};

191
ggml/src/ggml-cuda/fattn-common.cuh
View file

@ -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;
}
if (parallel_blocks > 1) {
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
}
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);
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;
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));
}
}
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);
flash_attn_combine_results<D, parallel_blocks>
<<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
}
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>>>
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
CUDA_CHECK(cudaGetLastError());
}

637
ggml/src/ggml-cuda/fattn-mma-f16.cuh Normal file
View 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);

24
ggml/src/ggml-cuda/fattn-tile-f16.cu
View file

@ -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;
@ -288,16 +298,18 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
case 64: {
constexpr int D = 64;
constexpr int nwarps = 8;
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 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.");

19
ggml/src/ggml-cuda/fattn-tile-f32.cu
View file

@ -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;
@ -287,16 +292,18 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor *
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
case 64: {
constexpr int D = 64;
constexpr int nwarps = 8;
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 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.");

9
ggml/src/ggml-cuda/fattn-vec-f16.cuh
View file

@ -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>

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