diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml index bfa97decdd..6cbac0cb1c 100644 --- a/.github/workflows/build-and-release.yaml +++ b/.github/workflows/build-and-release.yaml @@ -11,7 +11,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-20.04, windows-2019, macos-12] + os: [ubuntu-22.04, windows-2022, macos-14, macos-15] steps: - uses: actions/checkout@v4 @@ -42,10 +42,20 @@ jobs: shell: cmd - name: Build wheels - uses: pypa/cibuildwheel@v2.21.1 + uses: pypa/cibuildwheel@v2.22.0 env: - # disable repair + # Keep repair disabled by default for non-Linux platforms in this job. CIBW_REPAIR_WHEEL_COMMAND: "" + # Linux needs auditwheel repair so manylinux and musllinux wheels are + # published with distinct platform tags instead of generic linux tags. + CIBW_REPAIR_WHEEL_COMMAND_LINUX: "auditwheel repair -w {dest_dir} {wheel}" + # Skip cibuildwheel's default i686 sidecar and keep Linux release + # wheels on a portable x86_64 CPU baseline. + CIBW_ARCHS_LINUX: "auto64" + CIBW_ENVIRONMENT_LINUX: CMAKE_ARGS="-DGGML_NATIVE=off" + # Keep macOS release wheels on a portable CPU baseline instead of + # inheriting the hosted runner's native flags. + CIBW_ENVIRONMENT_MACOS: CMAKE_ARGS="-DGGML_NATIVE=off" with: package-dir: . output-dir: wheelhouse @@ -57,7 +67,53 @@ jobs: build_wheels_arm64: name: Build arm64 wheels + runs-on: ubuntu-24.04-arm + steps: + - uses: actions/checkout@v4 + with: + submodules: "recursive" + + - name: Build wheels + uses: pypa/cibuildwheel@v2.22.0 + env: + CIBW_SKIP: "*musllinux* pp*" + CIBW_REPAIR_WHEEL_COMMAND: "" + CIBW_ARCHS: "aarch64" + # Keep native arm64 builds on a portable CPU baseline instead of + # tuning wheels to the hosted runner. + CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off" + CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-* cp312-*" + with: + output-dir: wheelhouse + + - name: Upload wheels as artifacts + uses: actions/upload-artifact@v4 + with: + name: wheels_arm64 + path: ./wheelhouse/*.whl + + build_wheels_riscv64: + name: Build riscv64 wheels (${{ matrix.shard.name }}) runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + shard: + - name: cp310 + build: "cp310-*" + artifact: wheels_riscv64_cp310 + - name: cp311 + build: "cp311-*" + artifact: wheels_riscv64_cp311 + - name: cp312 + build: "cp312-*" + artifact: wheels_riscv64_cp312 + - name: cp313 + build: "cp313-*" + artifact: wheels_riscv64_cp313 + - name: cp314 + build: "cp314-*" + artifact: wheels_riscv64_cp314 steps: - uses: actions/checkout@v4 with: @@ -66,22 +122,27 @@ jobs: - name: Set up QEMU uses: docker/setup-qemu-action@v3 with: - platforms: linux/arm64 + platforms: linux/riscv64 - name: Build wheels - uses: pypa/cibuildwheel@v2.21.1 + uses: pypa/cibuildwheel@v3.1.2 env: CIBW_SKIP: "*musllinux* pp*" CIBW_REPAIR_WHEEL_COMMAND: "" - CIBW_ARCHS: "aarch64" - CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-* cp312-*" + CIBW_ARCHS: "riscv64" + # Build riscv64 wheels against a conservative baseline instead of + # enabling RVV-related extensions from the build container. + CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_RVV=off -DGGML_RV_ZFH=off -DGGML_RV_ZVFH=off -DGGML_RV_ZICBOP=off -DGGML_RV_ZIHINTPAUSE=off" + # Split the emulated riscv64 build into one Python version per job + # to minimize wall-clock time without changing the release artifacts. + CIBW_BUILD: ${{ matrix.shard.build }} with: output-dir: wheelhouse - name: Upload wheels as artifacts uses: actions/upload-artifact@v4 with: - name: wheels_arm64 + name: ${{ matrix.shard.artifact }} path: ./wheelhouse/*.whl build_sdist: @@ -128,7 +189,8 @@ jobs: release: name: Release - needs: [build_wheels, build_wheels_arm64, build_sdist] + needs: [build_wheels, build_wheels_arm64, build_wheels_riscv64, build_sdist] + if: startsWith(github.ref, 'refs/tags/') runs-on: ubuntu-latest steps: diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml index b5c7346db4..c656958479 100644 --- a/.github/workflows/build-docker.yaml +++ b/.github/workflows/build-docker.yaml @@ -9,13 +9,22 @@ permissions: jobs: docker: name: Build and push Docker image - runs-on: ubuntu-latest + runs-on: ubuntu-22.04 steps: - name: Checkout uses: actions/checkout@v4 with: submodules: "recursive" + - name: Set image tag + run: | + if [[ "${GITHUB_REF_TYPE}" == "tag" ]]; then + image_tag="${GITHUB_REF_NAME}" + else + image_tag="${GITHUB_REF_NAME//\//-}" + fi + echo "IMAGE_TAG=$image_tag" >> "$GITHUB_ENV" + - name: Set up QEMU uses: docker/setup-qemu-action@v3 @@ -40,7 +49,7 @@ jobs: platforms: linux/amd64,linux/arm64 tags: | ghcr.io/abetlen/llama-cpp-python:latest - ghcr.io/abetlen/llama-cpp-python:${{ github.ref_name }} + ghcr.io/abetlen/llama-cpp-python:${{ env.IMAGE_TAG }} build-args: | BUILDKIT_INLINE_CACHE=1 diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml index 647b171e84..17daaa12a2 100644 --- a/.github/workflows/build-wheels-cuda.yaml +++ b/.github/workflows/build-wheels-cuda.yaml @@ -8,7 +8,7 @@ permissions: jobs: define_matrix: name: Define Build Matrix - runs-on: ubuntu-latest + runs-on: ubuntu-22.04 outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} defaults: @@ -20,9 +20,9 @@ jobs: id: set-matrix run: | $matrix = @{ - 'os' = @('ubuntu-latest', 'windows-2019') + 'os' = @('ubuntu-22.04') #, 'windows-2022') 'pyver' = @("3.9", "3.10", "3.11", "3.12") - 'cuda' = @("12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.0") + 'cuda' = @("12.1.1", "12.2.2", "12.3.2", "12.4.1") #, "12.5.1", "12.6.1") 'releasetag' = @("basic") } @@ -59,13 +59,11 @@ jobs: cache: 'pip' - name: Setup Mamba - uses: conda-incubator/setup-miniconda@v3.0.4 + uses: conda-incubator/setup-miniconda@v3.1.0 with: - activate-environment: "build" + activate-environment: "llamacpp" python-version: ${{ matrix.pyver }} - miniforge-variant: Mambaforge miniforge-version: latest - use-mamba: true add-pip-as-python-dependency: true auto-activate-base: false @@ -101,21 +99,64 @@ jobs: MAMBA_NO_LOW_SPEED_LIMIT: "1" run: | $cudaVersion = $env:CUDAVER - mamba install -y 'cuda' -c nvidia/label/cuda-$cudaVersion + $cudaChannel = "nvidia/label/cuda-$cudaVersion" + if ($IsLinux) { + # Keep nvcc, cudart, and headers on the same NVIDIA label so the + # detected toolkit version matches the published wheel tag. + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "$cudaChannel::cuda-toolkit=$cudaVersion" "$cudaChannel::cuda-nvcc_linux-64=$cudaVersion" "$cudaChannel::cuda-cudart" "$cudaChannel::cuda-cudart-dev" + } else { + mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "$cudaChannel::cuda-toolkit=$cudaVersion" + } + if ($LASTEXITCODE -ne 0) { + exit $LASTEXITCODE + } python -m pip install build wheel - name: Build Wheel run: | - $cudaVersion = $env:CUDAVER.Remove($env:CUDAVER.LastIndexOf('.')).Replace('.','') $env:CUDA_PATH = $env:CONDA_PREFIX $env:CUDA_HOME = $env:CONDA_PREFIX $env:CUDA_TOOLKIT_ROOT_DIR = $env:CONDA_PREFIX + $cudaHostCompilerArg = '' + $env:CMAKE_ARGS = '' if ($IsLinux) { - $env:LD_LIBRARY_PATH = $env:CONDA_PREFIX + '/lib:' + $env:LD_LIBRARY_PATH + if (Test-Path '/usr/bin/g++-12') { + $env:CC = '/usr/bin/gcc-12' + $env:CXX = '/usr/bin/g++-12' + $env:CUDAHOSTCXX = '/usr/bin/g++-12' + $cudaHostCompilerArg = " -DCMAKE_CUDA_HOST_COMPILER=$env:CUDAHOSTCXX" + } + if (Test-Path (Join-Path $env:CONDA_PREFIX 'include/cuda_runtime.h')) { + $env:CUDAToolkit_ROOT = $env:CONDA_PREFIX + $env:CUDA_TOOLKIT_ROOT_DIR = $env:CONDA_PREFIX + $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$env:CONDA_PREFIX -DCUDA_TOOLKIT_ROOT_DIR=$env:CONDA_PREFIX$cudaHostCompilerArg" + $env:CPATH = "$env:CONDA_PREFIX/include:$env:CPATH" + $env:CPLUS_INCLUDE_PATH = "$env:CONDA_PREFIX/include:$env:CPLUS_INCLUDE_PATH" + $env:LIBRARY_PATH = "$env:CONDA_PREFIX/lib:$env:LIBRARY_PATH" + $env:LD_LIBRARY_PATH = "$env:CONDA_PREFIX/lib:$env:LD_LIBRARY_PATH" + } else { + $env:CMAKE_ARGS = $cudaHostCompilerArg.Trim() + } + } + $nvccPath = Join-Path $env:CONDA_PREFIX 'bin/nvcc' + if (-not (Test-Path $nvccPath)) { + $nvccPath = Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux/bin/nvcc' + } + if (-not (Test-Path $nvccPath)) { + throw 'Failed to find nvcc in the conda environment' + } + $env:CUDACXX = $nvccPath + $env:PATH = "$(Split-Path $nvccPath):$env:PATH" + $nvccVersion = ((& $nvccPath --version) | Select-String 'release ([0-9]+\.[0-9]+)').Matches[0].Groups[1].Value + if (-not $nvccVersion) { + throw 'Failed to detect the installed CUDA toolkit version' } + $cudaTagVersion = $nvccVersion.Replace('.','') $env:VERBOSE = '1' - $env:CMAKE_ARGS = '-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=all' - $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON $env:CMAKE_ARGS" + # Build real cubins for the supported GPUs, including sm_70, and keep + # one forward-compatible PTX target instead of embedding PTX for every + # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit. + $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=70-real;75-real;80-real;86-real;89-real;90-real;90-virtual -DCMAKE_CUDA_FLAGS=--allow-unsupported-compiler $env:CMAKE_ARGS" # if ($env:AVXVER -eq 'AVX') { $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' # } @@ -126,10 +167,11 @@ jobs: # $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off' # } python -m build --wheel - # write the build tag to the output - Write-Output "CUDA_VERSION=$cudaVersion" >> $env:GITHUB_ENV + # Publish tags that reflect the actual installed toolkit version. + Write-Output "CUDA_VERSION=$cudaTagVersion" >> $env:GITHUB_ENV - uses: softprops/action-gh-release@v2 + if: startsWith(github.ref, 'refs/tags/') with: files: dist/* # Set tag_name to -cu diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml index 8e6fb5cb78..98f511e4a6 100644 --- a/.github/workflows/build-wheels-metal.yaml +++ b/.github/workflows/build-wheels-metal.yaml @@ -11,7 +11,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [macos-12, macos-13, macos-14] + os: [macos-14, macos-15] steps: - uses: actions/checkout@v4 @@ -23,32 +23,21 @@ jobs: with: python-version: "3.12" cache: 'pip' - + - name: Install dependencies (Linux/MacOS) - if: runner.os != 'Windows' run: | python -m pip install --upgrade pip python -m pip install uv RUST_LOG=trace python -m uv pip install -e .[all] --verbose shell: bash - - name: Install dependencies (Windows) - if: runner.os == 'Windows' - env: - RUST_LOG: trace - run: | - python -m pip install --upgrade pip - python -m pip install uv - python -m uv pip install -e .[all] --verbose - shell: cmd - - name: Build wheels - uses: pypa/cibuildwheel@v2.21.1 + uses: pypa/cibuildwheel@v2.22.0 env: # disable repair CIBW_REPAIR_WHEEL_COMMAND: "" CIBW_ARCHS: "arm64" - CIBW_ENVIRONMENT: CMAKE_ARGS="-DCMAKE_OSX_ARCHITECTURES=arm64 -DCMAKE_APPLE_SILICON_PROCESSOR=arm64 -DGGML_METAL=on" + CIBW_ENVIRONMENT: CMAKE_ARGS="-DCMAKE_OSX_ARCHITECTURES=arm64 -DCMAKE_APPLE_SILICON_PROCESSOR=arm64 -DGGML_METAL=on -DCMAKE_CROSSCOMPILING=ON" CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" with: package-dir: . @@ -69,7 +58,7 @@ jobs: with: merge-multiple: true path: dist2 - + - uses: softprops/action-gh-release@v2 with: files: dist2/* diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml index 8d80e7e47f..255ee67d6f 100644 --- a/.github/workflows/generate-index-from-release.yaml +++ b/.github/workflows/generate-index-from-release.yaml @@ -44,7 +44,8 @@ jobs: ./scripts/releases-to-pep-503.sh index/whl/cu122 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu122$' ./scripts/releases-to-pep-503.sh index/whl/cu123 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu123$' ./scripts/releases-to-pep-503.sh index/whl/cu124 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' - ./scripts/releases-to-pep-503.sh index/whl/cu125 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu125$' + # ./scripts/releases-to-pep-503.sh index/whl/cu125 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' + # ./scripts/releases-to-pep-503.sh index/whl/cu126 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$' ./scripts/releases-to-pep-503.sh index/whl/metal '^[v]?[0-9]+\.[0-9]+\.[0-9]+-metal$' - name: Upload artifact uses: actions/upload-pages-artifact@v3 diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 0000000000..8b3e6322dc --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,29 @@ +name: Lint + +on: + pull_request: + branches: + - main + push: + branches: + - main + +jobs: + ruff: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install Ruff + run: python -m pip install "ruff>=0.15.7" + + - name: Lint with Ruff + run: python -m ruff check llama_cpp tests + + - name: Check formatting with Ruff + run: python -m ruff format --check llama_cpp tests diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 8f619cf1f4..8a6845ff24 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -8,8 +8,8 @@ on: - main env: - REPO_ID: Qwen/Qwen2-0.5B-Instruct-GGUF - MODEL_FILE: qwen2-0_5b-instruct-q8_0.gguf + REPO_ID: lmstudio-community/Qwen3.5-0.8B-GGUF + MODEL_FILE: Qwen3.5-0.8B-Q8_0.gguf jobs: download-model: @@ -22,7 +22,7 @@ jobs: - name: Install huggingface-hub run: pip install huggingface-hub - name: Download model - run: huggingface-cli download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }} + run: hf download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }} - name: Cache model uses: actions/cache@v4 with: @@ -51,19 +51,11 @@ jobs: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} - name: Install dependencies (Linux/MacOS) - if: runner.os != 'Windows' run: | python -m pip install --upgrade pip python -m pip install uv python -m uv pip install -e .[all] --verbose shell: bash - - name: Install dependencies (Windows) - if: runner.os == 'Windows' - run: | - python -m pip install --upgrade pip - python -m pip install uv - python -m uv pip install -e .[all] --verbose - shell: cmd - name: Test with pytest run: | python -m pytest @@ -90,22 +82,13 @@ jobs: with: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} - - - name: Install dependencies (Linux/MacOS) - if: runner.os != 'Windows' - run: | - python -m pip install --upgrade pip - python -m pip install uv - python -m uv pip install -e .[all] --verbose - shell: bash - name: Install dependencies (Windows) - if: runner.os == 'Windows' run: | python -m pip install --upgrade pip python -m pip install uv python -m uv pip install -e .[all] --verbose - shell: cmd + shell: cmd - name: Test with pytest run: | @@ -113,7 +96,7 @@ jobs: build-macos: needs: download-model - runs-on: macos-latest + runs-on: macos-15 strategy: matrix: python-version: ["3.9", "3.10", "3.11", "3.12"] @@ -128,6 +111,12 @@ jobs: python-version: ${{ matrix.python-version }} cache: 'pip' + - name: System Info + run: | + uname -a + sysctl -n machdep.cpu.brand_string + python3 -c "import platform; print(platform.machine(), platform.architecture())" + - name: Restore model cache uses: actions/cache@v4 with: @@ -135,28 +124,19 @@ jobs: key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} - name: Install dependencies (Linux/MacOS) - if: runner.os != 'Windows' run: | - python -m pip install --upgrade pip - python -m pip install uv - python -m uv pip install -e .[all] --verbose + python3 -m pip install --upgrade pip + python3 -m pip install uv + CMAKE_ARGS="-DGGML_NATIVE=off" python3 -m uv pip install -e .[all] --verbose shell: bash - - name: Install dependencies (Windows) - if: runner.os == 'Windows' - run: | - python -m pip install --upgrade pip - python -m pip install uv - python -m uv pip install -e .[all] --verbose - shell: cmd - - name: Test with pytest run: | - python -m pytest + python3 -m pytest - build-macos-metal: + build-macos-intel: needs: download-model - runs-on: macos-latest + runs-on: macos-15-intel steps: - uses: actions/checkout@v4 with: @@ -167,25 +147,24 @@ jobs: with: python-version: "3.9" + - name: System Info + run: | + uname -a + sysctl -n machdep.cpu.brand_string + python3 -c "import platform; print(platform.machine(), platform.architecture())" + - name: Restore model cache uses: actions/cache@v4 with: path: ~/.cache/huggingface/hub key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }} - - name: Install dependencies (Linux/MacOS) - if: runner.os != 'Windows' + - name: Install dependencies run: | - python -m pip install --upgrade pip - python -m pip install uv - CMAKE_ARGS="-DLLAMA_METAL=on" python -m uv pip install .[all] --verbose + python3 -m pip install --upgrade pip + python3 -m pip install .[all] --verbose shell: bash - - name: Install dependencies (Windows) - if: runner.os == 'Windows' - run: | - python -m pip install --upgrade pip - CMAKE_ARGS="-DGGML_METAL=on" python -m pip install .[all] --verbose - name: Test with pytest run: | - python -m pytest + python3 -m pytest diff --git a/.gitignore b/.gitignore index 9d68dbcd9d..ff773c6684 100644 --- a/.gitignore +++ b/.gitignore @@ -66,6 +66,7 @@ coverage.xml *.py,cover .hypothesis/ .pytest_cache/ +.ruff_cache/ cover/ # Translations diff --git a/CHANGELOG.md b/CHANGELOG.md index 8a194fecef..9bc8d6654b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,111 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.3.20] + +- refactor: Replace deprecated llama.cpp references in library, docs, and examples by @abetlen in #2170 +- feat: Update llama.cpp to ggerganov/llama.cpp@f49e9178767d557a522618b16ce8694f9ddac628 by @abetlen in #2169 +- feat(server): Add model-load `chat_template_kwargs` support and document the CLI/config usage by @abetlen in #2168 +- ci: Publish release wheels as `py3-none` by @Bing-su in #2166 +- fix(ci): Publish distinct manylinux and musllinux CPU wheels by @abetlen in #2165 + +## [0.3.19] + +- feat: Update llama.cpp to ggerganov/llama.cpp@c0159f9c1f874da15e94f371d136f5920b4b5335 by @abetlen in #2161 +- fix: Handle embedding models without KV memory and test embeddings with a real GGUF embedding model by @abetlen in #2160 +- fix(ci): Shrink CUDA wheel fatbins so CUDA releases stay under GitHub's asset size limit by @abetlen in #2158 + +## [0.3.18] + +- feat: Expose `attention_type` in `Llama.__init__` for non-causal embedding models by @jamesbiederbeck in #2143 +- fix(ci): Build Docker images from the checked-out source and sanitize branch tags by @abetlen in #2156 +- fix(ci): Fix the CUDA wheel workflow and keep release tags aligned with the built toolkit by @abetlen in #2155 +- fix(ci): Speed up release wheel builds by moving arm64 off QEMU and parallelizing riscv64 by @abetlen in #2154 + +## [0.3.17] + +- feat: Update llama.cpp to ggerganov/llama.cpp@49bfddeca18e62fa3d39114a23e9fcbdf8a22388 and sync Python bindings by @abetlen in #2151 +- fix: Handle Qwen 3.5 hybrid prefix reuse by @codavidgarcia and @r-dh in #2152 +- chore(dev): Add Ruff-based formatting and a safe lint baseline, and run it in CI for pull requests and pushes to `main` +- fix(ci): Run macOS CI on supported Apple Silicon and Intel runners by @abetlen in #2150 +- fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149 +- ci: add riscv64 wheel builds to release workflow by @gounthar in #2139 + +## [0.3.16] + +- feat: Update llama.cpp to ggerganov/llama.cpp@4227c9be4268ac844921b90f31595f81236bd317 + +## [0.3.15] + +- feat: Update llama.cpp to ggerganov/llama.cpp@9a96389544a08fd829fccda28142ce2066017fde +- feat: Add gpt-oss chat format support through strftime_now in chat format by @iamlemec in af637928db7351e030011085f818b034c6efc047 +- fix: rename op_offloat to op_offload in llama.py by @sergey21000 in #2046 + +## [0.3.14] + +- feat: Update llama.cpp to ggerganov/llama.cpp@79e0b68c178656bb0632cb8602d2940b755077f8 + +## [0.3.13] + +- feat: Update llama.cpp to ggerganov/llama.cpp@bdca38376f7e8dd928defe01ce6a16218a64b040 +- fix: Better chat format for Qwen2.5-VL by @alcoftTAO in #2040 + +## [0.3.12] + +- feat: Update llama.cpp to ggerganov/llama.cpp@a0374a67e2924f2e845cdc59dd67d9a44065a89c + +## [0.3.11] + +- fix: Update reference to `llama_kv_cache_clear` in Llama.embed. Closes #2037 by @abetlen in 9e5a4eaa84156084ed7bbb91e6efcc91dc6217bc + +## [0.3.10] + +- feat: Update llama.cpp to ggerganov/llama.cpp@8846aace4934ad29651ea61b8c7e3f6b0556e3d2 +- feat: Add support for llama.cpp multimodal, add Qwen2.5-VL chat handler by @abetlen in cd548bd0f14210627798237d5c2ea78acfb88ccb + +## [0.3.9] + +- feat: Update llama.cpp to ggerganov/llama.cpp@8733e0cf6eefc7c7752297cc22d0836706f4222c + +## [0.3.8] + +- feat: Update llama.cpp to ggerganov/llama.cpp@7841fc723e059d1fd9640e5c0ef19050fcc7c698 + +## [0.3.7] + +- feat: Update llama.cpp to ggerganov/llama.cpp@794fe23f29fb40104975c91fe19f23798f7c726e +- fix(ci): Fix the CUDA workflow by @oobabooga in #1894 +- fix: error showing time spent in llama perf context print, adds `no_perf` flag to `Llama` class by @shakalaca in #1898 + +## [0.3.6] + +- feat: Update llama.cpp to ggerganov/llama.cpp@f7cd13301c2a88f97073fd119072b4cc92c08df1 +- fix(server): streaming resource lock by @gjpower in #1879 + +## [0.3.5] + +- feat: Update llama.cpp to ggerganov/llama.cpp@26a8406ba9198eb6fdd8329fa717555b4f77f05f +- fix(ci): Fix release by updating macos runner image to non-deprecated version by @abetlen in afedfc888462f9a6e809dc9455eb3b663764cc3f +- fix(server): add missing await statements for async exit_stack handling by @gjpower in #1858 + +## [0.3.4] + +- fix(ci): Build wheels for macos 13-15, cuda 12.1-12.4 by @abetlen in ca808028bd16b8327bd84128d48015a4b1304690 + +## [0.3.3] + +- feat: Update llama.cpp to ggerganov/llama.cpp@ce8784bdb153ff7794dde5a50b0ebfa51baa6171 +- fix: chat API logprobs format by @domdomegg in #1788 +- feat: Add support for CUDA 12.6, fix CUDA 12.5 by @Smartappli in #1775 +- fix: Make content not required in ChatCompletionRequestAssistantMessage by @feloy in #1807 +- fix: Fix pickling of Llama class by setting seed from _seed member by @abetlen in 2523472c3eccb9ab9277117cc4ff705212b6888a +- fix: Fix logit-bias type hint by @ddh0 in #1802 +- fix(server): Avoid thread starvation on many concurrent requests by making use of asyncio to lock llama_proxy context by @gjpower in #1798 +- fix(server): Added missing exit_stack.close() to /v1/chat/completions by @Ian321 in #1796 +- fix(examples): Refactor Batching notebook to use new sampler chain API by @lukestanley in #1793 +- fix(docs): Update development instructions by @Florents-Tselai in #1833 +- fix(docs): Remove ref to llama_eval in llama_cpp.py docs by @richdougherty in #1819 + ## [0.3.2] - feat: Update llama.cpp to ggerganov/llama.cpp@74d73dc85cc2057446bf63cc37ff649ae7cebd80 diff --git a/CMakeLists.txt b/CMakeLists.txt index c9f3c2b361..9b2744cdce 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -62,21 +62,49 @@ if (LLAMA_BUILD) # Enable building of the common library set(LLAMA_BUILD_COMMON ON CACHE BOOL "Build llama.cpp common library" FORCE) - # Building llama - if (APPLE AND NOT CMAKE_SYSTEM_PROCESSOR MATCHES "arm64") - # Need to disable these llama.cpp flags on Apple x86_64, - # otherwise users may encounter invalid instruction errors - set(GGML_AVX "Off" CACHE BOOL "ggml: enable AVX" FORCE) - set(GGML_AVX2 "Off" CACHE BOOL "ggml: enable AVX2" FORCE) - set(GGML_FMA "Off" CACHE BOOL "gml: enable FMA" FORCE) - set(GGML_F16C "Off" CACHE BOOL "gml: enable F16C" FORCE) - endif() + # Disable building curl support + set(LLAMA_CURL OFF CACHE BOOL "llama.cpp: enable curl" FORCE) + # Architecture detection and settings for Apple platforms if (APPLE) - set(GGML_METAL_EMBED_LIBRARY "On" CACHE BOOL "llama: embed metal library" FORCE) + # Get the target architecture + execute_process( + COMMAND uname -m + OUTPUT_VARIABLE HOST_ARCH + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + + # If CMAKE_OSX_ARCHITECTURES is not set, use the host architecture + if(NOT CMAKE_OSX_ARCHITECTURES) + set(CMAKE_OSX_ARCHITECTURES ${HOST_ARCH} CACHE STRING "Build architecture for macOS" FORCE) + endif() + + message(STATUS "Host architecture: ${HOST_ARCH}") + message(STATUS "Target architecture: ${CMAKE_OSX_ARCHITECTURES}") + + # Configure based on target architecture + if(CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64") + # Intel Mac settings + set(GGML_AVX "OFF" CACHE BOOL "ggml: enable AVX" FORCE) + set(GGML_AVX2 "OFF" CACHE BOOL "ggml: enable AVX2" FORCE) + set(GGML_FMA "OFF" CACHE BOOL "ggml: enable FMA" FORCE) + set(GGML_F16C "OFF" CACHE BOOL "ggml: enable F16C" FORCE) + endif() + + # Metal settings (enable for both architectures) + set(GGML_METAL "ON" CACHE BOOL "ggml: enable Metal" FORCE) + set(GGML_METAL_EMBED_LIBRARY "ON" CACHE BOOL "ggml: embed metal library" FORCE) endif() + add_subdirectory(vendor/llama.cpp) + + if (WIN32) + if (TARGET llama) + set_target_properties(llama PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS ON) + endif() + endif() + llama_cpp_python_install_target(llama) llama_cpp_python_install_target(ggml) @@ -125,36 +153,55 @@ if (LLAMA_BUILD) add_compile_definitions(GGML_USE_METAL) endif() + # Upstream mtmd expects LLAMA_INSTALL_VERSION to be set by llama.cpp's + # top-level CMakeLists.txt. When we include tools/mtmd directly from the + # Python package build, that directory scope is skipped. + if (NOT DEFINED LLAMA_INSTALL_VERSION OR "${LLAMA_INSTALL_VERSION}" STREQUAL "") + set(LLAMA_INSTALL_VERSION 0.0.0) + find_package(Git QUIET) + if (Git_FOUND) + execute_process( + COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp + OUTPUT_VARIABLE LLAMA_MTMD_BUILD_NUMBER + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE LLAMA_MTMD_BUILD_NUMBER_RESULT + ) + if (LLAMA_MTMD_BUILD_NUMBER_RESULT EQUAL 0) + set(LLAMA_INSTALL_VERSION 0.0.${LLAMA_MTMD_BUILD_NUMBER}) + endif() + endif() + endif() + # Building llava - add_subdirectory(vendor/llama.cpp/examples/llava) - set_target_properties(llava_shared PROPERTIES OUTPUT_NAME "llava") - # Set CUDA_ARCHITECTURES to OFF on Windows + add_subdirectory(vendor/llama.cpp/tools/mtmd) + if (WIN32) - set_target_properties(llava_shared PROPERTIES CUDA_ARCHITECTURES OFF) + set_target_properties(mtmd PROPERTIES CUDA_ARCHITECTURES OFF) endif() - llama_cpp_python_install_target(llava_shared) + llama_cpp_python_install_target(mtmd) if (WIN32) install( - FILES $ + FILES $ DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib ) install( - FILES $ + FILES $ DESTINATION ${SKBUILD_PLATLIB_DIR}/llama_cpp/lib ) endif() - # Fix for llava build: Add include directory for llama.h + # Fix for mtmd build: Add include directory for llama.h # Move these commands after the add_subdirectory call - target_include_directories(llava PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) - target_include_directories(llava PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include) + target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) + target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include) if (BUILD_SHARED_LIBS) - target_include_directories(llava_shared PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) - target_include_directories(llava_shared PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include) + target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) + target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include) endif() - target_include_directories(llama-llava-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) - target_include_directories(llama-minicpmv-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) + # target_include_directories(llama-llava-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) + # target_include_directories(llama-minicpmv-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include) endif() endif() diff --git a/Makefile b/Makefile index be9b55a3e7..db45246c79 100644 --- a/Makefile +++ b/Makefile @@ -21,6 +21,15 @@ build.debug: --config-settings=cmake.args="-DCMAKE_BUILD_TYPE=Debug;-DCMAKE_C_FLAGS='-ggdb -O0';-DCMAKE_CXX_FLAGS='-ggdb -O0'" \ --editable . +build.debug.extra: + python3 -m pip install \ + --verbose \ + --config-settings=cmake.verbose=true \ + --config-settings=logging.level=INFO \ + --config-settings=install.strip=false \ + --config-settings=cmake.args="-DCMAKE_BUILD_TYPE=Debug;-DCMAKE_C_FLAGS='-fsanitize=address -ggdb -O0';-DCMAKE_CXX_FLAGS='-fsanitize=address -ggdb -O0'" \ + --editable . + build.cuda: CMAKE_ARGS="-DGGML_CUDA=on" python3 -m pip install --verbose -e . @@ -46,7 +55,7 @@ build.rpc: CMAKE_ARGS="-DGGML_RPC=on" python3 -m pip install --verbose -e . build.sdist: - python3 -m build --sdist + python3 -m build --sdist --verbose deploy.pypi: python3 -m twine upload dist/* @@ -56,23 +65,29 @@ deploy.gh-docs: mkdocs gh-deploy test: - python3 -m pytest + python3 -m pytest --full-trace -v + +lint: + python3 -m ruff check llama_cpp tests + python3 -m ruff format --check llama_cpp tests + +format: + python3 -m ruff check --fix llama_cpp tests + python3 -m ruff format llama_cpp tests docker: docker build -t llama-cpp-python:latest -f docker/simple/Dockerfile . run-server: - uvicorn --factory llama.server:app --host ${HOST} --port ${PORT} + python3 -m llama_cpp.server --model ${MODEL} clean: - - cd vendor/llama.cpp && make clean - - cd vendor/llama.cpp && rm libllama.so - rm -rf _skbuild - - rm llama_cpp/*.so - - rm llama_cpp/*.dylib - - rm llama_cpp/*.metal - - rm llama_cpp/*.dll - - rm llama_cpp/*.lib + - rm llama_cpp/lib/*.so + - rm llama_cpp/lib/*.dylib + - rm llama_cpp/lib/*.metal + - rm llama_cpp/lib/*.dll + - rm llama_cpp/lib/*.lib .PHONY: \ update \ @@ -84,5 +99,7 @@ clean: build.sdist \ deploy.pypi \ deploy.gh-docs \ + lint \ + format \ docker \ clean diff --git a/README.md b/README.md index dbaec50775..69a0f82345 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ [![PyPI](https://img.shields.io/pypi/v/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) [![PyPI - License](https://img.shields.io/pypi/l/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) -[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/) +[![PyPI - Downloads](https://static.pepy.tech/badge/llama-cpp-python/month)](https://pepy.tech/projects/llama-cpp-python) [![Github All Releases](https://img.shields.io/github/downloads/abetlen/llama-cpp-python/total.svg?label=Github%20Downloads)]() Simple Python bindings for **@ggerganov's** [`llama.cpp`](https://github.com/ggerganov/llama.cpp) library. @@ -322,13 +322,13 @@ You'll need to install the `huggingface-hub` package to use this feature (`pip i ```python llm = Llama.from_pretrained( - repo_id="Qwen/Qwen2-0.5B-Instruct-GGUF", - filename="*q8_0.gguf", + repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF", + filename="*Q8_0.gguf", verbose=False ) ``` -By default [`from_pretrained`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.from_pretrained) will download the model to the huggingface cache directory, you can then manage installed model files with the [`huggingface-cli`](https://huggingface.co/docs/huggingface_hub/en/guides/cli) tool. +By default [`from_pretrained`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.from_pretrained) will download the model to the huggingface cache directory, you can then manage installed model files with the [`hf`](https://huggingface.co/docs/huggingface_hub/en/guides/cli) tool. ### Chat Completion @@ -505,6 +505,7 @@ Below are the supported multi-modal models and their respective chat handlers (P | [nanollava](https://huggingface.co/abetlen/nanollava-gguf) | `NanollavaChatHandler` | `nanollava` | | [llama-3-vision-alpha](https://huggingface.co/abetlen/llama-3-vision-alpha-gguf) | `Llama3VisionAlphaChatHandler` | `llama-3-vision-alpha` | | [minicpm-v-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `MiniCPMv26ChatHandler` | `minicpm-v-2.6` | +| [qwen2.5-vl](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | `Qwen25VLChatHandler` | `qwen2.5-vl` | Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images. @@ -684,7 +685,7 @@ For possible options, see [llama_cpp/llama_chat_format.py](llama_cpp/llama_chat_ If you have `huggingface-hub` installed, you can also use the `--hf_model_repo_id` flag to load a model from the Hugging Face Hub. ```bash -python3 -m llama_cpp.server --hf_model_repo_id Qwen/Qwen2-0.5B-Instruct-GGUF --model '*q8_0.gguf' +python3 -m llama_cpp.server --hf_model_repo_id lmstudio-community/Qwen3.5-0.8B-GGUF --model '*Q8_0.gguf' ``` ### Web Server Features @@ -716,16 +717,20 @@ Below is a short example demonstrating how to use the low-level API to tokenize ```python import llama_cpp import ctypes -llama_cpp.llama_backend_init(False) # Must be called once at the start of each program -params = llama_cpp.llama_context_default_params() +llama_cpp.llama_backend_init() # Must be called once at the start of each program +model_params = llama_cpp.llama_model_default_params() +ctx_params = llama_cpp.llama_context_default_params() +prompt = b"Q: Name the planets in the solar system? A: " # use bytes for char * params -model = llama_cpp.llama_load_model_from_file(b"./models/7b/llama-model.gguf", params) -ctx = llama_cpp.llama_new_context_with_model(model, params) -max_tokens = params.n_ctx +model = llama_cpp.llama_model_load_from_file(b"./models/7b/llama-model.gguf", model_params) +ctx = llama_cpp.llama_init_from_model(model, ctx_params) +vocab = llama_cpp.llama_model_get_vocab(model) +max_tokens = ctx_params.n_ctx # use ctypes arrays for array params tokens = (llama_cpp.llama_token * int(max_tokens))() -n_tokens = llama_cpp.llama_tokenize(ctx, b"Q: Name the planets in the solar system? A: ", tokens, max_tokens, llama_cpp.c_bool(True)) +n_tokens = llama_cpp.llama_tokenize(vocab, prompt, len(prompt), tokens, max_tokens, True, False) llama_cpp.llama_free(ctx) +llama_cpp.llama_model_free(model) ``` Check out the [examples folder](examples/low_level_api) for more examples of using the low-level API. @@ -751,16 +756,44 @@ pip install --upgrade pip # Install with pip pip install -e . +# install development tooling (tests, docs, ruff) +pip install -e '.[dev]' + # if you want to use the fastapi / openapi server -pip install -e .[server] +pip install -e '.[server]' # to install all optional dependencies -pip install -e .[all] +pip install -e '.[all]' # to clear the local build cache make clean ``` +Now try running the tests + +```bash +pytest +``` + +And check formatting / linting before opening a PR: + +```bash +python -m ruff check llama_cpp tests +python -m ruff format --check llama_cpp tests + +# or use the Makefile targets +make lint +make format +``` + +There's a `Makefile` available with useful targets. +A typical workflow would look like this: + +```bash +make build +make test +``` + You can also test out specific commits of `llama.cpp` by checking out the desired commit in the `vendor/llama.cpp` submodule and then running `make clean` and `pip install -e .` again. Any changes in the `llama.h` API will require changes to the `llama_cpp/llama_cpp.py` file to match the new API (additional changes may be required elsewhere). diff --git a/docker/simple/Dockerfile b/docker/simple/Dockerfile index 3594df1a50..bad4f456ff 100644 --- a/docker/simple/Dockerfile +++ b/docker/simple/Dockerfile @@ -6,9 +6,11 @@ FROM ${IMAGE} # Re-declare the ARG after FROM ARG IMAGE +ARG CMAKE_ARGS="-DGGML_NATIVE=off" # Update and upgrade the existing packages RUN apt-get update && apt-get upgrade -y && apt-get install -y --no-install-recommends \ + git \ python3 \ python3-pip \ ninja-build \ @@ -25,7 +27,7 @@ RUN python3 -m pip install --upgrade pip RUN python3 -m pip install --upgrade pip pytest cmake scikit-build setuptools fastapi uvicorn sse-starlette pydantic-settings starlette-context -RUN pip install llama-cpp-python --verbose; +RUN CMAKE_ARGS="${CMAKE_ARGS}" pip install . --verbose # Set environment variable for the host ENV HOST=0.0.0.0 diff --git a/docs/server.md b/docs/server.md index cd6f86c513..9c09a1f1cf 100644 --- a/docs/server.md +++ b/docs/server.md @@ -22,6 +22,15 @@ The server can then be started by running the following command: python3 -m llama_cpp.server --model ``` +You can also pass chat-template kwargs at model load time from the CLI: + +```bash +python3 -m llama_cpp.server \ + --model \ + --chat_format chatml \ + --chat_template_kwargs '{"enable_thinking": true}' +``` + ### Server options For a full list of options, run: @@ -147,6 +156,22 @@ The server supports routing requests to multiple models based on the `model` par At the moment only a single model is loaded into memory at, the server will automatically load and unload models as needed. +For a single-model config, `chat_template_kwargs` can be set directly on the model entry: + +```json +{ + "models": [ + { + "model": "models/Qwen3.5-0.8B/qwen3.5-0.8b-q8_0.gguf", + "chat_format": "chatml", + "chat_template_kwargs": { + "enable_thinking": true + } + } + ] +} +``` + ```json { "host": "0.0.0.0", diff --git a/examples/batch-processing/server.py b/examples/batch-processing/server.py index 0b36746f91..2b6fa759e0 100644 --- a/examples/batch-processing/server.py +++ b/examples/batch-processing/server.py @@ -6,14 +6,14 @@ # path = b"../../models/Qwen1.5-0.5B-Chat-GGUF/qwen1_5-0_5b-chat-q8_0.gguf" # model_params = llama_cpp.llama_model_default_params() -# model = llama_cpp.llama_load_model_from_file(path, model_params) +# model = llama_cpp.llama_model_load_from_file(path, model_params) # if model is None: # raise RuntimeError(f"Failed to load model from file: {path}") # ctx_params = llama_cpp.llama_context_default_params() -# ctx = llama_cpp.llama_new_context_with_model(model, ctx_params) +# ctx = llama_cpp.llama_init_from_model(model, ctx_params) # if ctx is None: # raise RuntimeError("Failed to create context") diff --git a/examples/gradio_chat/local.py b/examples/gradio_chat/local.py index e16bf234ab..871d8b09b5 100644 --- a/examples/gradio_chat/local.py +++ b/examples/gradio_chat/local.py @@ -4,10 +4,10 @@ import gradio as gr llama = llama_cpp.Llama.from_pretrained( - repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF", - filename="*q8_0.gguf", + repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF", + filename="*Q8_0.gguf", tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "Qwen/Qwen1.5-0.5B" + "Qwen/Qwen3.5-0.8B" ), verbose=False, ) diff --git a/examples/hf_pull/main.py b/examples/hf_pull/main.py index dfed17516d..a9ca424d19 100644 --- a/examples/hf_pull/main.py +++ b/examples/hf_pull/main.py @@ -3,10 +3,10 @@ llama = llama_cpp.Llama.from_pretrained( - repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF", - filename="*q8_0.gguf", + repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF", + filename="*Q8_0.gguf", tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained( - "Qwen/Qwen1.5-0.5B" + "Qwen/Qwen3.5-0.8B" ), verbose=False, ) diff --git a/examples/low_level_api/low_level_api_chat_cpp.py b/examples/low_level_api/low_level_api_chat_cpp.py index 39081be17a..20f7a158ac 100644 --- a/examples/low_level_api/low_level_api_chat_cpp.py +++ b/examples/low_level_api/low_level_api_chat_cpp.py @@ -79,19 +79,22 @@ def __init__(self, params: GptParams) -> None: self.lparams.use_mlock = self.params.use_mlock self.lparams.use_mmap = self.params.use_mmap - self.model = llama_cpp.llama_load_model_from_file( + self.model = llama_cpp.llama_model_load_from_file( self.params.model.encode("utf8"), self.lparams ) + self.vocab = llama_cpp.llama_model_get_vocab(self.model) # Context Params. self.cparams = llama_cpp.llama_context_default_params() - self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.cparams) + self.ctx = llama_cpp.llama_init_from_model(self.model, self.cparams) if not self.ctx: raise RuntimeError(f"error: failed to load model '{self.params.model}'") if self.params.ignore_eos: - self.params.logit_bias[llama_cpp.llama_token_eos()] = -float("inf") + self.params.logit_bias[llama_cpp.llama_vocab_eos(self.vocab)] = -float( + "inf" + ) if len(self.params.lora_adapter) > 0: if ( @@ -153,7 +156,7 @@ def __init__(self, params: GptParams) -> None: _session_tokens = (llama_cpp.llama_token * (self.params.n_ctx))() _n_token_count_out = llama_cpp.c_size_t() if ( - llama_cpp.llama_load_session_file( + llama_cpp.llama_state_load_file( self.ctx, self.params.path_session.encode("utf8"), _session_tokens, @@ -314,7 +317,7 @@ def __init__(self, params: GptParams) -> None: def _tokenize(self, prompt, bos=True): _arr = (llama_cpp.llama_token * ((len(prompt) + 1) * 4))() _n = llama_cpp.llama_tokenize( - self.model, + self.vocab, prompt.encode("utf8", errors="ignore"), len(prompt), _arr, @@ -406,7 +409,7 @@ def generate(self): if len(self.embd_inp) <= self.input_consumed: # && !is_interacting # out of user input, sample next token top_k = ( - llama_cpp.llama_n_vocab(self.ctx) + llama_cpp.llama_vocab_n_tokens(self.vocab) if self.params.top_k <= 0 else self.params.top_k ) @@ -419,7 +422,7 @@ def generate(self): # optionally save the session on first sample (for faster prompt loading next time) if len(self.params.path_session) > 0 and self.need_to_save_session: self.need_to_save_session = False - llama_cpp.llama_save_session_file( + llama_cpp.llama_state_save_file( self.ctx, self.params.path_session.encode("utf8"), (llama_cpp.llama_token * len(self.session_tokens))( @@ -431,7 +434,7 @@ def generate(self): id = 0 logits = llama_cpp.llama_get_logits(self.ctx) - n_vocab = llama_cpp.llama_n_vocab(self.model) + n_vocab = llama_cpp.llama_vocab_n_tokens(self.vocab) # Apply params.logit_bias map for key, value in self.params.logit_bias.items(): @@ -448,7 +451,7 @@ def generate(self): ) # Apply penalties - nl_logit = logits[llama_cpp.llama_token_nl(self.ctx)] + nl_logit = logits[llama_cpp.llama_vocab_nl(self.vocab)] last_n_repeat = min(len(self.last_n_tokens), repeat_last_n, self.n_ctx) _arr = (llama_cpp.llama_token * last_n_repeat)( @@ -470,7 +473,7 @@ def generate(self): # last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty)) if not self.params.penalize_nl: - logits[llama_cpp.llama_token_nl()] = nl_logit + logits[llama_cpp.llama_vocab_nl(self.vocab)] = nl_logit if self.params.temp <= 0: # Greedy sampling @@ -539,7 +542,7 @@ def generate(self): # replace end of text token with newline token when in interactive mode if ( - id == llama_cpp.llama_token_eos(self.ctx) + id == llama_cpp.llama_vocab_eos(self.vocab) and self.params.interactive and not self.params.instruct ): @@ -599,8 +602,8 @@ def generate(self): break # end of text token - if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos( - self.ctx + if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_vocab_eos( + self.vocab ): if not self.params.instruct: for i in self.llama_token_eot: @@ -636,7 +639,7 @@ def token_to_str(self, token_id: int) -> bytes: size = 32 buffer = (ctypes.c_char * size)() n = llama_cpp.llama_token_to_piece( - self.model, llama_cpp.llama_token(token_id), buffer, size + self.vocab, llama_cpp.llama_token(token_id), buffer, size, 0, False ) assert n <= size return bytes(buffer[:n]) diff --git a/examples/low_level_api/low_level_api_llama_cpp.py b/examples/low_level_api/low_level_api_llama_cpp.py index ba3545771d..9fb3424ec0 100644 --- a/examples/low_level_api/low_level_api_llama_cpp.py +++ b/examples/low_level_api/low_level_api_llama_cpp.py @@ -4,7 +4,7 @@ import llama_cpp -llama_cpp.llama_backend_init(numa=False) +llama_cpp.llama_backend_init() N_THREADS = multiprocessing.cpu_count() MODEL_PATH = os.environ.get("MODEL", "../models/7B/ggml-model.bin") @@ -13,8 +13,9 @@ lparams = llama_cpp.llama_model_default_params() cparams = llama_cpp.llama_context_default_params() -model = llama_cpp.llama_load_model_from_file(MODEL_PATH.encode("utf-8"), lparams) -ctx = llama_cpp.llama_new_context_with_model(model, cparams) +model = llama_cpp.llama_model_load_from_file(MODEL_PATH.encode("utf-8"), lparams) +ctx = llama_cpp.llama_init_from_model(model, cparams) +vocab = llama_cpp.llama_model_get_vocab(model) # determine the required inference memory per token: tmp = [0, 1, 2, 3] @@ -28,13 +29,13 @@ embd_inp = (llama_cpp.llama_token * (len(prompt) + 1))() n_of_tok = llama_cpp.llama_tokenize( - model=model, - text=bytes(str(prompt), "utf-8"), - text_len=len(embd_inp), + vocab=vocab, + text=prompt, + text_len=len(prompt), tokens=embd_inp, - n_max_tokens=len(embd_inp), - add_bos=False, - special=False, + n_tokens_max=len(embd_inp), + add_special=False, + parse_special=False, ) embd_inp = embd_inp[:n_of_tok] @@ -70,7 +71,7 @@ embd = [] if len(embd_inp) <= input_consumed: logits = llama_cpp.llama_get_logits(ctx) - n_vocab = llama_cpp.llama_n_vocab(model) + n_vocab = llama_cpp.llama_vocab_n_tokens(vocab) _arr = (llama_cpp.llama_token_data * n_vocab)( *[ @@ -114,7 +115,7 @@ size = 32 buffer = (ctypes.c_char * size)() n = llama_cpp.llama_token_to_piece( - model, llama_cpp.llama_token(id), buffer, size + vocab, llama_cpp.llama_token(id), buffer, size, 0, False ) assert n <= size print( @@ -123,7 +124,7 @@ flush=True, ) - if len(embd) > 0 and embd[-1] == llama_cpp.llama_token_eos(ctx): + if len(embd) > 0 and embd[-1] == llama_cpp.llama_vocab_eos(vocab): break print() @@ -131,3 +132,4 @@ llama_cpp.llama_print_timings(ctx) llama_cpp.llama_free(ctx) +llama_cpp.llama_model_free(model) diff --git a/examples/notebooks/Batching.ipynb b/examples/notebooks/Batching.ipynb index 73b28c7444..0b36cd9c3a 100644 --- a/examples/notebooks/Batching.ipynb +++ b/examples/notebooks/Batching.ipynb @@ -6,6 +6,7 @@ "metadata": {}, "outputs": [], "source": [ + "import ctypes\n", "import llama_cpp" ] }, @@ -13,18 +14,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no\n", - "ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes\n", - "ggml_init_cublas: found 1 CUDA devices:\n", - " Device 0: NVIDIA GeForce RTX 2060, compute capability 7.5\n" - ] - } - ], + "outputs": [], "source": [ "llama_cpp.llama_backend_init(numa=False)" ] @@ -38,363 +28,104 @@ "name": "stderr", "output_type": "stream", "text": [ - "llama_model_loader: loaded meta data with 16 key-value pairs and 291 tensors from ../../models/mistral-7b-v0.1-GGUF/ggml-model-Q4_K.gguf (version GGUF V2)\n", - "llama_model_loader: - tensor 0: token_embd.weight q4_K [ 4096, 32000, 1, 1 ]\n", - "llama_model_loader: - tensor 1: output_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 2: output.weight q6_K [ 4096, 32000, 1, 1 ]\n", - "llama_model_loader: - tensor 3: blk.0.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 4: blk.0.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 5: blk.0.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 6: blk.0.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 7: blk.0.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 8: blk.0.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 9: blk.0.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 10: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 11: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 12: blk.1.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 13: blk.1.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 14: blk.1.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 15: blk.1.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 16: blk.1.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 17: blk.1.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 18: blk.1.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 19: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 20: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 21: blk.2.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 22: blk.2.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 23: blk.2.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 24: blk.2.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 25: blk.2.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 26: blk.2.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 27: blk.2.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 28: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 29: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 30: blk.3.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 31: blk.3.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 32: blk.3.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 33: blk.3.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 34: blk.3.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 35: blk.3.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 36: blk.3.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 37: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 38: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 39: blk.4.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 40: blk.4.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - 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"llama_model_loader: - tensor 229: blk.25.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 230: blk.25.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 231: blk.25.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 232: blk.25.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 233: blk.25.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 234: blk.25.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 235: blk.25.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 236: blk.25.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 237: blk.26.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 238: blk.26.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 239: blk.26.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 240: blk.26.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 241: blk.26.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 242: blk.26.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 243: blk.26.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 244: blk.26.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 245: blk.26.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 246: blk.27.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 247: blk.27.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 248: blk.27.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 249: blk.27.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 250: blk.27.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 251: blk.27.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 252: blk.27.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 253: blk.27.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 254: blk.27.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 255: blk.28.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 256: blk.28.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 257: blk.28.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 258: blk.28.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 259: blk.28.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 260: blk.28.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 261: blk.28.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 262: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 263: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 264: blk.29.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 265: blk.29.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 266: blk.29.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 267: blk.29.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 268: blk.29.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 269: blk.29.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 270: blk.29.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 271: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 272: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 273: blk.30.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 274: blk.30.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 275: blk.30.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 276: blk.30.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 277: blk.30.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 278: blk.30.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 279: blk.30.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 280: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 281: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 282: blk.31.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 283: blk.31.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 284: blk.31.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n", - "llama_model_loader: - tensor 285: blk.31.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 286: blk.31.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 287: blk.31.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n", - "llama_model_loader: - tensor 288: blk.31.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n", - "llama_model_loader: - tensor 289: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - tensor 290: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n", - "llama_model_loader: - kv 0: general.architecture str \n", - "llama_model_loader: - kv 1: general.name str \n", - "llama_model_loader: - kv 2: llama.context_length u32 \n", - "llama_model_loader: - kv 3: llama.embedding_length u32 \n", - "llama_model_loader: - kv 4: llama.block_count u32 \n", - "llama_model_loader: - kv 5: llama.feed_forward_length u32 \n", - "llama_model_loader: - kv 6: llama.rope.dimension_count u32 \n", - "llama_model_loader: - kv 7: llama.attention.head_count u32 \n", - "llama_model_loader: - kv 8: llama.attention.head_count_kv u32 \n", - "llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 \n", - "llama_model_loader: - kv 10: general.file_type u32 \n", - "llama_model_loader: - kv 11: tokenizer.ggml.model str \n", - "llama_model_loader: - kv 12: tokenizer.ggml.tokens arr \n", - "llama_model_loader: - kv 13: tokenizer.ggml.scores arr \n", - "llama_model_loader: - kv 14: tokenizer.ggml.token_type arr \n", - "llama_model_loader: - kv 15: general.quantization_version u32 \n", + "llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from /workspaces/llama-cpp-python/mistral-7b-v0.1.Q2_K.gguf (version GGUF V2)\n", + "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", + "llama_model_loader: - kv 0: general.architecture str = llama\n", + "llama_model_loader: - kv 1: general.name str = mistralai_mistral-7b-v0.1\n", + "llama_model_loader: - kv 2: llama.context_length u32 = 32768\n", + "llama_model_loader: - kv 3: llama.embedding_length u32 = 4096\n", + "llama_model_loader: - kv 4: llama.block_count u32 = 32\n", + "llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336\n", + "llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128\n", + "llama_model_loader: - kv 7: llama.attention.head_count u32 = 32\n", + "llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8\n", + "llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010\n", + "llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000\n", + "llama_model_loader: - kv 11: general.file_type u32 = 10\n", + "llama_model_loader: - kv 12: tokenizer.ggml.model str = llama\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = [\"\", \"\", \"\", \"<0x00>\", \"<...\n", + "llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...\n", + "llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n", + "llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1\n", + "llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2\n", + "llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0\n", + "llama_model_loader: - kv 19: general.quantization_version u32 = 2\n", "llama_model_loader: - type f32: 65 tensors\n", - "llama_model_loader: - type q4_K: 193 tensors\n", - "llama_model_loader: - type q6_K: 33 tensors\n", - "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n", + "llama_model_loader: - type q2_K: 65 tensors\n", + "llama_model_loader: - type q3_K: 160 tensors\n", + "llama_model_loader: - type q6_K: 1 tensors\n", + "llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", + "llm_load_vocab: special tokens cache size = 3\n", + "llm_load_vocab: token to piece cache size = 0.1637 MB\n", "llm_load_print_meta: format = GGUF V2\n", "llm_load_print_meta: arch = llama\n", "llm_load_print_meta: vocab type = SPM\n", "llm_load_print_meta: n_vocab = 32000\n", "llm_load_print_meta: n_merges = 0\n", - "llm_load_print_meta: n_ctx_train = 4096\n", + "llm_load_print_meta: vocab_only = 0\n", + "llm_load_print_meta: n_ctx_train = 32768\n", "llm_load_print_meta: n_embd = 4096\n", + "llm_load_print_meta: n_layer = 32\n", "llm_load_print_meta: n_head = 32\n", "llm_load_print_meta: n_head_kv = 8\n", - "llm_load_print_meta: n_layer = 32\n", "llm_load_print_meta: n_rot = 128\n", + "llm_load_print_meta: n_swa = 0\n", + "llm_load_print_meta: n_embd_head_k = 128\n", + "llm_load_print_meta: n_embd_head_v = 128\n", "llm_load_print_meta: n_gqa = 4\n", + "llm_load_print_meta: n_embd_k_gqa = 1024\n", + "llm_load_print_meta: n_embd_v_gqa = 1024\n", "llm_load_print_meta: f_norm_eps = 0.0e+00\n", "llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n", "llm_load_print_meta: f_clamp_kqv = 0.0e+00\n", "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n", + "llm_load_print_meta: f_logit_scale = 0.0e+00\n", "llm_load_print_meta: n_ff = 14336\n", + "llm_load_print_meta: n_expert = 0\n", + "llm_load_print_meta: n_expert_used = 0\n", + "llm_load_print_meta: causal attn = 1\n", + "llm_load_print_meta: pooling type = 0\n", + "llm_load_print_meta: rope type = 0\n", + "llm_load_print_meta: rope scaling = linear\n", "llm_load_print_meta: freq_base_train = 10000.0\n", "llm_load_print_meta: freq_scale_train = 1\n", + "llm_load_print_meta: n_ctx_orig_yarn = 32768\n", + "llm_load_print_meta: rope_finetuned = unknown\n", + "llm_load_print_meta: ssm_d_conv = 0\n", + "llm_load_print_meta: ssm_d_inner = 0\n", + "llm_load_print_meta: ssm_d_state = 0\n", + "llm_load_print_meta: ssm_dt_rank = 0\n", + "llm_load_print_meta: ssm_dt_b_c_rms = 0\n", "llm_load_print_meta: model type = 7B\n", - "llm_load_print_meta: model ftype = mostly Q4_K - Medium\n", + "llm_load_print_meta: model ftype = Q2_K - Medium\n", "llm_load_print_meta: model params = 7.24 B\n", - "llm_load_print_meta: model size = 4.07 GiB (4.83 BPW) \n", - "llm_load_print_meta: general.name = LLaMA v2\n", - "llm_load_print_meta: BOS token = 1 ''\n", - "llm_load_print_meta: EOS token = 2 ''\n", - "llm_load_print_meta: UNK token = 0 ''\n", - "llm_load_print_meta: LF token = 13 '<0x0A>'\n", - "llm_load_tensors: ggml ctx size = 0.10 MB\n", - "llm_load_tensors: using CUDA for GPU acceleration\n", - "llm_load_tensors: mem required = 70.41 MB\n", - "llm_load_tensors: offloading 32 repeating layers to GPU\n", - "llm_load_tensors: offloading non-repeating layers to GPU\n", - "llm_load_tensors: offloaded 35/35 layers to GPU\n", - "llm_load_tensors: VRAM used: 4095.05 MB\n", - ".................................................................................................\n" + "llm_load_print_meta: model size = 2.87 GiB (3.41 BPW) \n", + "llm_load_print_meta: general.name = mistralai_mistral-7b-v0.1\n", + "llm_load_print_meta: BOS token = 1 ''\n", + "llm_load_print_meta: EOS token = 2 ''\n", + "llm_load_print_meta: UNK token = 0 ''\n", + "llm_load_print_meta: LF token = 13 '<0x0A>'\n", + "llm_load_print_meta: EOG token = 2 ''\n", + "llm_load_print_meta: max token length = 48\n", + "llm_load_tensors: ggml ctx size = 0.14 MiB\n", + "llm_load_tensors: CPU buffer size = 2939.57 MiB\n", + "..................................................................................................\n" ] } ], "source": [ "params = llama_cpp.llama_model_default_params()\n", "params.n_gpu_layers = 35\n", - "model = llama_cpp.llama_load_model_from_file(\n", - " b\"../../models/mistral-7b-v0.1-GGUF/ggml-model-Q4_K.gguf\", params=params\n", - ") # Update this to whatever" + "model = llama_cpp.llama_model_load_from_file(\n", + " b\"/workspaces/llama-cpp-python/mistral-7b-v0.1.Q2_K.gguf\", params\n", + ") # Update this to whatever\n", + "vocab = llama_cpp.llama_model_get_vocab(model)" ] }, { @@ -406,7 +137,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1, 1014, 2936, 9060, 285, 1142]\n", + "[1, 415, 2936, 9060, 285, 1142]\n", "58\n" ] } @@ -419,7 +150,7 @@ "\n", "tokens = (llama_cpp.llama_token * n_ctx)()\n", "tokens_len = llama_cpp.llama_tokenize(\n", - " model, prompt, len(prompt), tokens, len(tokens), True, True\n", + " vocab, prompt, len(prompt), tokens, len(tokens), True, True\n", ")\n", "print(tokens[:tokens_len])\n", "\n", @@ -436,17 +167,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "llama_new_context_with_model: n_ctx = 58\n", + "llama_new_context_with_model: n_ctx = 64\n", + "llama_new_context_with_model: n_batch = 32\n", + "llama_new_context_with_model: n_ubatch = 32\n", + "llama_new_context_with_model: flash_attn = 0\n", "llama_new_context_with_model: freq_base = 10000.0\n", "llama_new_context_with_model: freq_scale = 1\n", - "llama_kv_cache_init: offloading v cache to GPU\n", - "llama_kv_cache_init: offloading k cache to GPU\n", - "llama_kv_cache_init: VRAM kv self = 7.25 MB\n", - "llama_new_context_with_model: kv self size = 7.25 MB\n", - "llama_build_graph: non-view tensors processed: 740/740\n", - "llama_new_context_with_model: compute buffer total size = 10.63 MB\n", - "llama_new_context_with_model: VRAM scratch buffer: 4.51 MB\n", - "llama_new_context_with_model: total VRAM used: 4106.81 MB (model: 4095.05 MB, context: 11.76 MB)\n" + "llama_kv_cache_init: CPU KV buffer size = 8.00 MiB\n", + "llama_new_context_with_model: KV self size = 8.00 MiB, K (f16): 4.00 MiB, V (f16): 4.00 MiB\n", + "llama_new_context_with_model: CPU output buffer size = 0.12 MiB\n", + "llama_new_context_with_model: CPU compute buffer size = 5.01 MiB\n", + "llama_new_context_with_model: graph nodes = 1030\n", + "llama_new_context_with_model: graph splits = 1\n" ] } ], @@ -457,7 +189,7 @@ "ctx_params.n_batch = max(n_len, n_parallel)\n", "ctx_params.n_threads = 1\n", "ctx_params.n_threads_batch = 1\n", - "ctx = llama_cpp.llama_new_context_with_model(model, ctx_params)" + "ctx = llama_cpp.llama_init_from_model(model, ctx_params)" ] }, { @@ -476,7 +208,7 @@ "metadata": {}, "outputs": [], "source": [ - "import ctypes\n", + "\n", "\n", "batch.n_tokens = tokens_len\n", "for i in range(tokens_len):\n", @@ -502,10 +234,35 @@ " llama_cpp.llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Initialize sampler chain with default parameters\n", + "sparams = llama_cpp.llama_sampler_chain_default_params()\n", + "sampler_chain = llama_cpp.llama_sampler_chain_init(sparams)\n", + "\n", + "# Add top_k, top_p, temperature, and final distribution-based sampler\n", + "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_top_k(40))\n", + "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_top_p(0.9, 1))\n", + "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_temp(0.4))\n", + "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_dist(1234)) # Final \"dist\" sampler" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -514,61 +271,59 @@ "7\n", "[' j', ' jumped']\n", "8\n", - "[' jumps', ' jumped over']\n", + "[' j over', ' jumped over']\n", "9\n", - "[' jumps over', ' jumped over the']\n", + "[' j over the', ' jumped over the']\n", "10\n", - "[' jumps over the', ' jumped over the lazy']\n", + "[' j over the lazy', ' jumped over the lazy']\n", "11\n", - "[' jumps over the lazy', ' jumped over the lazy dog']\n", + "[' j over the lazy dog', ' jumped over the lazy dog']\n", "12\n", - "[' jumps over the lazy dog', ' jumped over the lazy dog.']\n", + "[' j over the lazy dog.', ' jumped over the lazy dog\\n']\n", "13\n", - "[' jumps over the lazy dog.', ' jumped over the lazy dog.\\n']\n", + "[' j over the lazy dog. También', ' jumped over the lazy dog\\nGroupLayout']\n", "14\n", - "[' jumps over the lazy dog.\\n', ' jumped over the lazy dog.\\n\\n']\n", + "[' j over the lazy dog. También:', ' jumped over the lazy dog\\nGroupLayouting']\n", "15\n", - "[' jumps over the lazy dog.\\n\\n', ' jumped over the lazy dog.\\n\\nThe']\n", + "[' j over the lazy dog. También: is', ' jumped over the lazy dog\\nGroupLayouting is']\n", "16\n", - "[' jumps over the lazy dog.\\n\\nI', ' jumped over the lazy dog.\\n\\nThe quick']\n", + "[' j over the lazy dog. También: is a', ' jumped over the lazy dog\\nGroupLayouting is a']\n", "17\n", - "[' jumps over the lazy dog.\\n\\nI’', ' jumped over the lazy dog.\\n\\nThe quick brown']\n", + "[' j over the lazy dog. También: is a technique', ' jumped over the lazy dog\\nGroupLayouting is a common']\n", "18\n", - "[' jumps over the lazy dog.\\n\\nI’m', ' jumped over the lazy dog.\\n\\nThe quick brown f']\n", + "[' j over the lazy dog. También: is a technique practice', ' jumped over the lazy dog\\nGroupLayouting is a common practice']\n", "19\n", - "[' jumps over the lazy dog.\\n\\nI’m not', ' jumped over the lazy dog.\\n\\nThe quick brown fox']\n", + "[' j over the lazy dog. También: is a technique practice in', ' jumped over the lazy dog\\nGroupLayouting is a common practice in']\n", "20\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped']\n", + "[' j over the lazy dog. También: is a technique practice in the', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the']\n", "21\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over']\n", + "[' j over the lazy dog. También: is a technique practice in the real', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media']\n", "22\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the']\n", + "[' j over the lazy dog. También: is a technique practice in the real-', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry']\n", "23\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy']\n", + "[' j over the lazy dog. También: is a technique practice in the real-.', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry.']\n", "24\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However']\n", "25\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We,', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However,']\n", "26\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We, when', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there']\n", "27\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\n']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We, when is', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has']\n", "28\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been']\n", "29\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little']\n", "30\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little research', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little emp']\n", "31\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown f']\n", + "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little researchirical', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little empirical']\n", "32\n", - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English language', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown fox']\n" + "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little researchirical research', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little empirical research']\n" ] } ], "source": [ - "import ctypes\n", - "\n", "streams = [\"\"] * n_parallel\n", "i_batch = [batch.n_tokens - 1] * n_parallel\n", "\n", @@ -581,36 +336,17 @@ " if i_batch[i] < 0:\n", " continue\n", "\n", - " n_vocab = llama_cpp.llama_n_vocab(model)\n", - " logits = llama_cpp.llama_get_logits_ith(ctx, i_batch[i])\n", - "\n", - " candidates = (llama_cpp.llama_token_data * n_vocab)()\n", - "\n", - " for token_id in range(n_vocab):\n", - " candidates[token_id].id = token_id\n", - " candidates[token_id].logit = logits[token_id]\n", - " candidates[token_id].p = 0.0\n", + " # Sample the next token using the sampler chain\n", + " new_token_id = llama_cpp.llama_sampler_sample(sampler_chain, ctx, -1)\n", "\n", - " candidates_p = llama_cpp.llama_token_data_array(\n", - " candidates, len(candidates), False\n", - " )\n", - "\n", - " top_k = 40\n", - " top_p = 0.9\n", - " temp = 0.4\n", - "\n", - " llama_cpp.llama_sample_top_k(ctx, ctypes.byref(candidates_p), top_k, 1)\n", - " llama_cpp.llama_sample_top_p(ctx, ctypes.byref(candidates_p), top_p, 1)\n", - " llama_cpp.llama_sample_temp(ctx, ctypes.byref(candidates_p), temp)\n", - "\n", - " new_token_id = llama_cpp.llama_sample_token(ctx, ctypes.byref(candidates_p))\n", - "\n", - " if new_token_id == llama_cpp.llama_token_eos(ctx) or n_cur == n_len:\n", + " if new_token_id == llama_cpp.llama_vocab_eos(vocab) or n_cur == n_len:\n", " i_batch[i] = -1\n", " continue\n", "\n", " buf = (ctypes.c_char * 32)()\n", - " outlen = llama_cpp.llama_token_to_piece(model, new_token_id, buf, len(buf))\n", + " \n", + " # Convert token ID to text\n", + " outlen = llama_cpp.llama_token_to_piece(vocab, new_token_id, buf, len(buf), 0, False)\n", " streams[i] += bytes(buf[:outlen]).decode(\"utf-8\")\n", "\n", " batch.token[batch.n_tokens] = new_token_id\n", @@ -637,14 +373,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English language', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown fox']\n" + "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little researchirical research', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little empirical research']\n" ] } ], @@ -654,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -663,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -672,16 +408,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "llama_cpp.llama_free_model(model)" + "llama_cpp.llama_model_free(model)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -705,7 +441,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -719,7 +455,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5+" + "version": "3.12.1" }, "orig_nbformat": 4 }, diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py index cda38a841f..83177c065d 100644 --- a/llama_cpp/__init__.py +++ b/llama_cpp/__init__.py @@ -1,4 +1,4 @@ from .llama_cpp import * from .llama import * -__version__ = "0.3.2" +__version__ = "0.3.20" diff --git a/llama_cpp/_ggml.py b/llama_cpp/_ggml.py index 5bee8a93b5..5ece01e032 100644 --- a/llama_cpp/_ggml.py +++ b/llama_cpp/_ggml.py @@ -2,6 +2,7 @@ This module provides a minimal interface for working with ggml tensors from llama-cpp-python """ + import os import pathlib @@ -9,4 +10,3 @@ libggml_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" libggml = ctypes_ext.load_shared_library("ggml", libggml_base_path) - diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py index 994d5f1497..cde52c8c8d 100644 --- a/llama_cpp/_internals.py +++ b/llama_cpp/_internals.py @@ -2,6 +2,7 @@ import os import ctypes +import warnings from typing import ( Dict, @@ -9,6 +10,8 @@ Tuple, Optional, Sequence, + Callable, + Union, ) from dataclasses import dataclass, field from contextlib import ExitStack @@ -48,43 +51,55 @@ def __init__( raise ValueError(f"Model path does not exist: {path_model}") with suppress_stdout_stderr(disable=verbose): - model = llama_cpp.llama_load_model_from_file( + model = llama_cpp.llama_model_load_from_file( self.path_model.encode("utf-8"), self.params ) if model is None: raise ValueError(f"Failed to load model from file: {path_model}") + vocab = llama_cpp.llama_model_get_vocab(model) + + if vocab is None: + raise ValueError(f"Failed to get vocab from model: {path_model}") + self.model = model + self.vocab = vocab + self.sampler = None # LlamaModel doesn't use samplers, but some cleanup code expects this attribute def free_model(): if self.model is None: return - llama_cpp.llama_free_model(self.model) + llama_cpp.llama_model_free(self.model) self.model = None self._exit_stack.callback(free_model) def close(self): + if self.sampler is not None: + # NOTE: Must remove custom samplers before free or llama.cpp will try to free them + for i, _ in reversed(self.custom_samplers): + llama_cpp.llama_sampler_chain_remove(self.sampler, i) + self.custom_samplers.clear() self._exit_stack.close() def __del__(self): self.close() def vocab_type(self) -> int: - return llama_cpp.llama_vocab_type(self.model) + return llama_cpp.llama_vocab_type(self.vocab) def n_vocab(self) -> int: - return llama_cpp.llama_n_vocab(self.model) + return llama_cpp.llama_vocab_n_tokens(self.vocab) def n_ctx_train(self) -> int: - return llama_cpp.llama_n_ctx_train(self.model) + return llama_cpp.llama_model_n_ctx_train(self.model) def n_embd(self) -> int: - return llama_cpp.llama_n_embd(self.model) + return llama_cpp.llama_model_n_embd(self.model) def rope_freq_scale_train(self) -> float: - return llama_cpp.llama_rope_freq_scale_train(self.model) + return llama_cpp.llama_model_rope_freq_scale_train(self.model) def desc(self) -> str: buf = ctypes.create_string_buffer(1024) @@ -98,53 +113,53 @@ def n_params(self) -> int: return llama_cpp.llama_model_n_params(self.model) def get_tensor(self, name: str) -> ctypes.c_void_p: - return llama_cpp.llama_get_model_tensor(self.model, name.encode("utf-8")) + raise NotImplementedError("get_tensor is not implemented in llama.cpp") # Vocab def token_get_text(self, token: int) -> str: - return llama_cpp.llama_token_get_text(self.model, token).decode("utf-8") + return llama_cpp.llama_vocab_get_text(self.vocab, token).decode("utf-8") def token_get_score(self, token: int) -> float: - return llama_cpp.llama_token_get_score(self.model, token) + return llama_cpp.llama_vocab_get_score(self.vocab, token) def token_get_attr(self, token: int) -> int: - return llama_cpp.llama_token_get_attr(self.model, token) + return llama_cpp.llama_vocab_get_attr(self.vocab, token) # Special tokens def token_bos(self) -> int: - return llama_cpp.llama_token_bos(self.model) + return llama_cpp.llama_vocab_bos(self.vocab) def token_eos(self) -> int: - return llama_cpp.llama_token_eos(self.model) + return llama_cpp.llama_vocab_eos(self.vocab) def token_cls(self) -> int: - return llama_cpp.llama_token_cls(self.model) + return llama_cpp.llama_vocab_bos(self.vocab) def token_sep(self) -> int: - return llama_cpp.llama_token_sep(self.model) + return llama_cpp.llama_vocab_sep(self.vocab) def token_nl(self) -> int: - return llama_cpp.llama_token_nl(self.model) + return llama_cpp.llama_vocab_nl(self.vocab) def token_prefix(self) -> int: - return llama_cpp.llama_token_prefix(self.model) + return llama_cpp.llama_vocab_fim_pre(self.vocab) def token_middle(self) -> int: - return llama_cpp.llama_token_middle(self.model) + return llama_cpp.llama_vocab_fim_mid(self.vocab) def token_suffix(self) -> int: - return llama_cpp.llama_token_suffix(self.model) + return llama_cpp.llama_vocab_fim_suf(self.vocab) def token_eot(self) -> int: - return llama_cpp.llama_token_eot(self.model) + return llama_cpp.llama_vocab_eot(self.vocab) def add_bos_token(self) -> bool: - return llama_cpp.llama_add_bos_token(self.model) + return llama_cpp.llama_vocab_get_add_bos(self.vocab) def add_eos_token(self) -> bool: - return llama_cpp.llama_add_eos_token(self.model) + return llama_cpp.llama_vocab_get_add_eos(self.vocab) # Tokenization @@ -152,13 +167,13 @@ def tokenize(self, text: bytes, add_bos: bool, special: bool): n_ctx = self.n_ctx_train() tokens = (llama_cpp.llama_token * n_ctx)() n_tokens = llama_cpp.llama_tokenize( - self.model, text, len(text), tokens, n_ctx, add_bos, special + self.vocab, text, len(text), tokens, n_ctx, add_bos, special ) if n_tokens < 0: n_tokens = abs(n_tokens) tokens = (llama_cpp.llama_token * n_tokens)() n_tokens = llama_cpp.llama_tokenize( - self.model, text, len(text), tokens, n_tokens, add_bos, special + self.vocab, text, len(text), tokens, n_tokens, add_bos, special ) if n_tokens < 0: raise RuntimeError( @@ -168,7 +183,7 @@ def tokenize(self, text: bytes, add_bos: bool, special: bool): def token_to_piece(self, token: int, special: bool = False) -> bytes: buf = ctypes.create_string_buffer(32) - llama_cpp.llama_token_to_piece(self.model, token, buf, 32, 0, special) + llama_cpp.llama_token_to_piece(self.vocab, token, buf, 32, 0, special) return bytes(buf) def detokenize(self, tokens: List[int], special: bool = False) -> bytes: @@ -177,7 +192,7 @@ def detokenize(self, tokens: List[int], special: bool = False) -> bytes: buffer = (ctypes.c_char * size)() for token in tokens: n = llama_cpp.llama_token_to_piece( - self.model, llama_cpp.llama_token(token), buffer, size, 0, special + self.vocab, llama_cpp.llama_token(token), buffer, size, 0, special ) assert n <= size output += bytes(buffer[:n]) @@ -243,12 +258,14 @@ def __init__( self.verbose = verbose self._exit_stack = ExitStack() - ctx = llama_cpp.llama_new_context_with_model(self.model.model, self.params) + ctx = llama_cpp.llama_init_from_model(self.model.model, self.params) if ctx is None: raise ValueError("Failed to create llama_context") self.ctx = ctx + self.memory = llama_cpp.llama_get_memory(self.ctx) + self.sampler = None # LlamaContext doesn't manage samplers directly, but some cleanup code expects this attribute def free_ctx(): if self.ctx is None: @@ -271,30 +288,38 @@ def pooling_type(self) -> int: return llama_cpp.llama_pooling_type(self.ctx) def kv_cache_clear(self): - llama_cpp.llama_kv_cache_clear(self.ctx) + # Embedding models with non-causal attention may not allocate memory. + if self.memory is None: + return + llama_cpp.llama_memory_clear(self.memory, True) - def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int): - llama_cpp.llama_kv_cache_seq_rm(self.ctx, seq_id, p0, p1) + def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int) -> bool: + assert self.memory is not None, "Memory is not initialized" + seq_id = seq_id if seq_id >= 0 else 0 + return llama_cpp.llama_memory_seq_rm(self.memory, seq_id, p0, p1) def kv_cache_seq_cp(self, seq_id_src: int, seq_id_dst: int, p0: int, p1: int): - llama_cpp.llama_kv_cache_seq_cp(self.ctx, seq_id_src, seq_id_dst, p0, p1) + assert self.memory is not None, "Memory is not initialized" + llama_cpp.llama_memory_seq_cp(self.memory, seq_id_src, seq_id_dst, p0, p1) def kv_cache_seq_keep(self, seq_id: int): - llama_cpp.llama_kv_cache_seq_keep(self.ctx, seq_id) + assert self.memory is not None, "Memory is not initialized" + llama_cpp.llama_memory_seq_keep(self.memory, seq_id) def kv_cache_seq_shift(self, seq_id: int, p0: int, p1: int, shift: int): - llama_cpp.llama_kv_cache_seq_add(self.ctx, seq_id, p0, p1, shift) + assert self.memory is not None, "Memory is not initialized" + llama_cpp.llama_memory_seq_add(self.memory, seq_id, p0, p1, shift) def get_state_size(self) -> int: - return llama_cpp.llama_get_state_size(self.ctx) + return llama_cpp.llama_state_get_size(self.ctx) # TODO: copy_state_data # TODO: set_state_data - # TODO: llama_load_session_file + # TODO: llama_state_load_file - # TODO: llama_save_session_file + # TODO: llama_state_save_file def decode(self, batch: LlamaBatch): return_code = llama_cpp.llama_decode( @@ -304,6 +329,14 @@ def decode(self, batch: LlamaBatch): if return_code != 0: raise RuntimeError(f"llama_decode returned {return_code}") + def encode(self, batch: LlamaBatch): + return_code = llama_cpp.llama_encode( + self.ctx, + batch.batch, + ) + if return_code != 0: + raise RuntimeError(f"llama_encode returned {return_code}") + def set_n_threads(self, n_threads: int, n_threads_batch: int): llama_cpp.llama_set_n_threads(self.ctx, n_threads, n_threads_batch) @@ -316,11 +349,18 @@ def get_logits_ith(self, i: int): def get_embeddings(self): return llama_cpp.llama_get_embeddings(self.ctx) - # Sampling functions + def get_embeddings_ith(self, i: int): + return llama_cpp.llama_get_embeddings_ith(self.ctx, i) + + def get_embeddings_seq(self, seq_id: int): + return llama_cpp.llama_get_embeddings_seq(self.ctx, seq_id) + + # Sampling functions - deprecated, use LlamaSampler instead def set_rng_seed(self, seed: int): - # TODO: Fix - llama_cpp.llama_set_rng_seed(self.ctx, seed) + raise NotImplementedError( + "set_rng_seed is deprecated, use LlamaSampler instead" + ) def sample_repetition_penalties( self, @@ -331,54 +371,43 @@ def sample_repetition_penalties( penalty_freq: float, penalty_present: float, ): - llama_cpp.llama_sample_repetition_penalties( - self.ctx, - llama_cpp.byref(candidates.candidates), - last_tokens_data, - penalty_last_n, - penalty_repeat, - penalty_freq, - penalty_present, + raise NotImplementedError( + "sample_repetition_penalties is deprecated, use LlamaSampler instead" ) def sample_softmax(self, candidates: "_LlamaTokenDataArray"): - llama_cpp.llama_sample_softmax( - self.ctx, - llama_cpp.byref(candidates.candidates), + raise NotImplementedError( + "sample_softmax is deprecated, use LlamaSampler instead" ) def sample_top_k(self, candidates: "_LlamaTokenDataArray", k: int, min_keep: int): - llama_cpp.llama_sample_top_k( - self.ctx, llama_cpp.byref(candidates.candidates), k, min_keep + raise NotImplementedError( + "sample_top_k is deprecated, use LlamaSampler instead" ) def sample_top_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int): - llama_cpp.llama_sample_top_p( - self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep + raise NotImplementedError( + "sample_top_p is deprecated, use LlamaSampler instead" ) def sample_min_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int): - llama_cpp.llama_sample_min_p( - self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep + raise NotImplementedError( + "sample_min_p is deprecated, use LlamaSampler instead" ) def sample_typical( self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int ): - llama_cpp.llama_sample_typical( - self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep + raise NotImplementedError( + "sample_typical is deprecated, use LlamaSampler instead" ) def sample_temp(self, candidates: "_LlamaTokenDataArray", temp: float): - llama_cpp.llama_sample_temp( - self.ctx, llama_cpp.byref(candidates.candidates), temp - ) + raise NotImplementedError("sample_temp is deprecated, use LlamaSampler instead") def sample_grammar(self, candidates: "_LlamaTokenDataArray", grammar: LlamaGrammar): - llama_cpp.llama_sample_grammar( - self.ctx, - llama_cpp.byref(candidates.candidates), - grammar.grammar, + raise NotImplementedError( + "sample_grammar is deprecated, use LlamaSampler instead" ) def sample_token_mirostat( @@ -389,13 +418,8 @@ def sample_token_mirostat( m: int, mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float], ) -> int: - return llama_cpp.llama_sample_token_mirostat( - self.ctx, - llama_cpp.byref(candidates.candidates), - tau, - eta, - m, - mu, + raise NotImplementedError( + "sample_token_mirostat is deprecated, use LlamaSampler instead" ) def sample_token_mirostat_v2( @@ -405,29 +429,25 @@ def sample_token_mirostat_v2( eta: float, mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float], ) -> int: - return llama_cpp.llama_sample_token_mirostat_v2( - self.ctx, - llama_cpp.byref(candidates.candidates), - tau, - eta, - mu, + raise NotImplementedError( + "sample_token_mirostat_v2 is deprecated, use LlamaSampler instead" ) def sample_token_greedy(self, candidates: "_LlamaTokenDataArray") -> int: - return llama_cpp.llama_sample_token_greedy( - self.ctx, - llama_cpp.byref(candidates.candidates), + raise NotImplementedError( + "sample_token_greedy is deprecated, use LlamaSampler instead" ) def sample_token(self, candidates: "_LlamaTokenDataArray") -> int: - return llama_cpp.llama_sample_token( - self.ctx, - llama_cpp.byref(candidates.candidates), + raise NotImplementedError( + "sample_token is deprecated, use LlamaSampler instead" ) # Grammar def grammar_accept_token(self, grammar: LlamaGrammar, token: int): - llama_cpp.llama_grammar_accept_token(grammar.grammar, self.ctx, token) + raise NotImplementedError( + "grammar_accept_token is deprecated, use LlamaSampler instead" + ) def reset_timings(self): llama_cpp.llama_perf_context_reset(self.ctx) @@ -458,6 +478,7 @@ def __init__( raise ValueError("Failed to create llama_batch") self.batch = batch + self.sampler = None # LlamaBatch doesn't use samplers, but some cleanup code expects this attribute def free_batch(): if self.batch is None: @@ -520,6 +541,7 @@ def __init__(self, *, n_vocab: int): ) self.default_candidates_data_id = np.arange(self.n_vocab, dtype=np.intc) # type: ignore self.default_candidates_data_p = np.zeros(self.n_vocab, dtype=np.single) + self.sampler = None # LlamaTokenDataArray doesn't use samplers, but some cleanup code expects this attribute def copy_logits(self, logits: npt.NDArray[np.single]): self.candidates_data.id[:] = self.default_candidates_data_id @@ -608,104 +630,17 @@ def sample( idx: int = 0, logits_array: Optional[npt.NDArray[np.single]] = None, ): - n_vocab = ctx_main.model.n_vocab() - id: int = 0 - - if logits_array is None: - logits = ctx_main.get_logits_ith(idx) - logits_array = np.array( - ctypes.cast(logits, ctypes.POINTER(ctypes.c_float * n_vocab)).contents, - dtype=np.single, - ) - - # apply logit_bias - for token, logit_bias in self.params.logit_bias.items(): - logits_array[token] += logit_bias - - token_data_array = LlamaTokenDataArray( - n_vocab=n_vocab - ) # TODO: Only create this once - token_data_array.copy_logits(logits_array) - - # apply penalties - if len(self.prev) > 0: - nl_token = ctx_main.model.token_nl() - nl_logit = logits_array[nl_token] - last_tokens = self.prev[-self.params.penalty_last_n :] - last_tokens_size = min(len(last_tokens), self.params.penalty_last_n) - if last_tokens_size > 0: - last_tokens_p = (llama_cpp.llama_token * len(last_tokens))(*last_tokens) - ctx_main.sample_repetition_penalties( - token_data_array, - last_tokens_p, - last_tokens_size, - self.params.penalty_repeat, - self.params.penalty_freq, - self.params.penalty_present, - ) - if not self.params.penalize_nl: - token_data_array.candidates_data.logit[nl_token] = nl_logit - - if self.grammar is not None: - ctx_main.sample_grammar(token_data_array, self.grammar) - - if self.params.temp < 0: - ctx_main.sample_softmax(token_data_array) - id = token_data_array.candidates_data.id[0] - elif self.params.temp == 0: - id = ctx_main.sample_token_greedy(token_data_array) - else: - if self.params.mirostat == 1: - mirostat_m = 100 - ctx_main.sample_temp(token_data_array, self.params.temp) - id = ctx_main.sample_token_mirostat( - token_data_array, - self.params.mirostat_tau, - self.params.mirostat_eta, - mirostat_m, - ctypes.pointer(self.mirostat_mu), - ) - elif self.params.mirostat == 2: - ctx_main.sample_temp(token_data_array, self.params.temp) - id = ctx_main.sample_token_mirostat_v2( - token_data_array, - self.params.mirostat_tau, - self.params.mirostat_eta, - ctypes.pointer(self.mirostat_mu), - ) - else: - min_keep = max(1, self.params.n_probs) - ctx_main.sample_top_k( - token_data_array, self.params.top_k, min_keep=min_keep - ) - ctx_main.sample_typical( - token_data_array, self.params.typical_p, min_keep=min_keep - ) - ctx_main.sample_top_p( - token_data_array, self.params.top_p, min_keep=min_keep - ) - ctx_main.sample_min_p( - token_data_array, self.params.min_p, min_keep=min_keep - ) - ctx_main.sample_temp(token_data_array, self.params.temp) - id = ctx_main.sample_token(token_data_array) - return id + # This method is deprecated in favor of using LlamaSampler directly + raise NotImplementedError( + "LlamaSamplingContext.sample is deprecated, use LlamaSampler instead" + ) def accept(self, ctx_main: LlamaContext, id: int, apply_grammar: bool): - if apply_grammar and self.grammar is not None: - ctx_main.grammar_accept_token(self.grammar, id) self.prev.append(id) -from typing import List, Callable, Optional, Union -import ctypes -import llama_cpp - - class CustomSampler: - def __init__( - self, apply_func: typing.Callable[[llama_cpp.llama_token_data_array], None] - ): + def __init__(self, apply_func: Callable[[llama_cpp.llama_token_data_array], None]): self.apply_func = apply_func def apply_wrapper( @@ -737,127 +672,213 @@ def get_sampler(self) -> llama_cpp.llama_sampler_p: class LlamaSampler: def __init__(self): - params = llama_cpp.llama_sampler_chain_params() + params = llama_cpp.llama_sampler_chain_default_params() self.sampler = llama_cpp.llama_sampler_chain_init(params) - self.samplers: List[llama_cpp.llama_sampler_p] = [] self.custom_samplers: List[Tuple[int, CustomSampler]] = [] + self._exit_stack = ExitStack() + + def free_sampler(): + if self.sampler is not None: + # NOTE: Must remove custom samplers before free or llama.cpp will try to free them + for i, _ in reversed(self.custom_samplers): + llama_cpp.llama_sampler_chain_remove(self.sampler, i) + llama_cpp.llama_sampler_free(self.sampler) + self.sampler = None + + self._exit_stack.callback(free_sampler) + + def close(self): + self._exit_stack.close() + + def __del__(self): + self.close() def add_greedy(self): sampler = llama_cpp.llama_sampler_init_greedy() - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_dist(self, seed: int): sampler = llama_cpp.llama_sampler_init_dist(seed) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_softmax(self): - sampler = llama_cpp.llama_sampler_init_softmax() - self._add_sampler(sampler) + warnings.warn( + "add_softmax is deprecated; llama_sampler_init_dist now samples directly from logits", + DeprecationWarning, + stacklevel=2, + ) def add_top_k(self, k: int): sampler = llama_cpp.llama_sampler_init_top_k(k) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) - def add_top_p(self, p: float, min_keep: int): + def add_top_p(self, p: float, min_keep: int = 1): sampler = llama_cpp.llama_sampler_init_top_p(p, min_keep) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) - def add_min_p(self, p: float, min_keep: int): + def add_min_p(self, p: float, min_keep: int = 1): sampler = llama_cpp.llama_sampler_init_min_p(p, min_keep) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) - def add_typical(self, p: float, min_keep: int): + def add_typical(self, p: float, min_keep: int = 1): sampler = llama_cpp.llama_sampler_init_typical(p, min_keep) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_temp(self, temp: float): sampler = llama_cpp.llama_sampler_init_temp(temp) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_temp_ext(self, t: float, delta: float, exponent: float): sampler = llama_cpp.llama_sampler_init_temp_ext(t, delta, exponent) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) + + def add_xtc(self, p: float, t: float, min_keep: int, seed: int): + sampler = llama_cpp.llama_sampler_init_xtc(p, t, min_keep, seed) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) + + def add_top_n_sigma(self, n: float): + sampler = llama_cpp.llama_sampler_init_top_n_sigma(n) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_mirostat(self, n_vocab: int, seed: int, tau: float, eta: float, m: int): sampler = llama_cpp.llama_sampler_init_mirostat(n_vocab, seed, tau, eta, m) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_mirostat_v2(self, seed: int, tau: float, eta: float): sampler = llama_cpp.llama_sampler_init_mirostat_v2(seed, tau, eta) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_grammar(self, model: LlamaModel, grammar: LlamaGrammar): sampler = llama_cpp.llama_sampler_init_grammar( - model.model, grammar._grammar.encode("utf-8"), grammar._root.encode("utf-8") + model.vocab, grammar._grammar.encode("utf-8"), grammar._root.encode("utf-8") + ) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) + + def add_grammar_lazy_patterns( + self, + model: LlamaModel, + grammar: LlamaGrammar, + trigger_patterns: List[str], + trigger_tokens: List[int], + ): + # Convert patterns to C array + pattern_ptrs = (ctypes.c_char_p * len(trigger_patterns))() + for i, pattern in enumerate(trigger_patterns): + pattern_ptrs[i] = pattern.encode("utf-8") + + # Convert tokens to C array + token_array = (llama_cpp.llama_token * len(trigger_tokens))(*trigger_tokens) + + sampler = llama_cpp.llama_sampler_init_grammar_lazy_patterns( + model.vocab, + grammar._grammar.encode("utf-8"), + grammar._root.encode("utf-8"), + pattern_ptrs, + len(trigger_patterns), + token_array, + len(trigger_tokens), ) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_penalties( self, - n_vocab: int, - special_eos_id: int, - linefeed_id: int, penalty_last_n: int, penalty_repeat: float, penalty_freq: float, penalty_present: float, - penalize_nl: bool, - ignore_eos: bool, ): sampler = llama_cpp.llama_sampler_init_penalties( - n_vocab, - special_eos_id, - linefeed_id, penalty_last_n, penalty_repeat, penalty_freq, penalty_present, - penalize_nl, - ignore_eos, ) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) - def init_logit_bias( - self, n_vocab: int, n_logit_bias, logit_bias: llama_cpp.llama_logit_bias_p + def add_dry( + self, + model: LlamaModel, + n_ctx_train: int, + dry_multiplier: float, + dry_base: float, + dry_allowed_length: int, + dry_penalty_last_n: int, + seq_breakers: List[str], ): + # Convert seq_breakers to C array + breaker_ptrs = (ctypes.c_char_p * len(seq_breakers))() + for i, breaker in enumerate(seq_breakers): + breaker_ptrs[i] = breaker.encode("utf-8") + + sampler = llama_cpp.llama_sampler_init_dry( + model.vocab, + n_ctx_train, + dry_multiplier, + dry_base, + dry_allowed_length, + dry_penalty_last_n, + breaker_ptrs, + len(seq_breakers), + ) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) + + def add_logit_bias(self, n_vocab: int, logit_bias: Dict[int, float]): + # Convert logit_bias dict to C array + bias_array = (llama_cpp.llama_logit_bias * len(logit_bias))() + for i, (token, bias) in enumerate(logit_bias.items()): + bias_array[i].token = token + bias_array[i].bias = bias + sampler = llama_cpp.llama_sampler_init_logit_bias( - n_vocab, n_logit_bias, logit_bias + n_vocab, len(logit_bias), bias_array ) - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) + + def add_infill(self, model: LlamaModel): + sampler = llama_cpp.llama_sampler_init_infill(model.vocab) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) def add_custom( self, apply_func: Callable[[llama_cpp.llama_token_data_array], None] ): custom_sampler = CustomSampler(apply_func) sampler = custom_sampler.get_sampler() - self._add_sampler(sampler) + llama_cpp.llama_sampler_chain_add(self.sampler, sampler) # NOTE: Must remove custom samplers before free or llama.cpp will try to free them self.custom_samplers.append( (llama_cpp.llama_sampler_chain_n(self.sampler) - 1, custom_sampler) ) - def _add_sampler(self, sampler: llama_cpp.llama_sampler_p): - assert self.sampler is not None - llama_cpp.llama_sampler_chain_add(self.sampler, sampler) - self.samplers.append(sampler) - def get_seed(self) -> int: - assert self.sampler is not None return llama_cpp.llama_sampler_get_seed(self.sampler) - def sample(self, ctx: LlamaContext, idx: int) -> int: - assert self.sampler is not None + def sample(self, ctx: LlamaContext, idx: int = -1) -> int: return llama_cpp.llama_sampler_sample(self.sampler, ctx.ctx, idx) - def close(self): - if self.sampler: - # NOTE: Must remove custom samplers before free or llama.cpp will try to free them - for i, _ in reversed(self.custom_samplers): - llama_cpp.llama_sampler_chain_remove(self.sampler, i) - llama_cpp.llama_sampler_free(self.sampler) - self.sampler = None - self.samplers.clear() - self.custom_samplers.clear() + def accept(self, token: int): + llama_cpp.llama_sampler_accept(self.sampler, token) - def __del__(self): - self.close() + def reset(self): + llama_cpp.llama_sampler_reset(self.sampler) + + def clone(self): + # NOTE: Custom samplers cannot be cloned due to Python callback limitations + if self.custom_samplers: + raise NotImplementedError( + "Cannot clone LlamaSampler that contains custom samplers" + ) + + cloned_sampler = llama_cpp.llama_sampler_clone(self.sampler) + # Create a new wrapper around the cloned sampler + new_sampler = LlamaSampler.__new__(LlamaSampler) + new_sampler.sampler = cloned_sampler + new_sampler.custom_samplers = [] + new_sampler._exit_stack = ExitStack() + + def free_sampler(): + if new_sampler.sampler is not None: + llama_cpp.llama_sampler_free(new_sampler.sampler) + new_sampler.sampler = None + + new_sampler._exit_stack.callback(free_sampler) + return new_sampler diff --git a/llama_cpp/_logger.py b/llama_cpp/_logger.py index 787b3f108e..31d89d0995 100644 --- a/llama_cpp/_logger.py +++ b/llama_cpp/_logger.py @@ -25,6 +25,7 @@ _last_log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[0] + # typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data); @llama_cpp.llama_log_callback def llama_log_callback( @@ -34,7 +35,9 @@ def llama_log_callback( ): # TODO: Correctly implement continue previous log global _last_log_level - log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level] if level != 5 else _last_log_level + log_level = ( + GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level] if level != 5 else _last_log_level + ) if logger.level <= GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level]: print(text.decode("utf-8"), end="", flush=True, file=sys.stderr) _last_log_level = log_level diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py index d15a88b00e..11fe169cf5 100644 --- a/llama_cpp/llama.py +++ b/llama_cpp/llama.py @@ -66,7 +66,6 @@ def __init__( split_mode: int = llama_cpp.LLAMA_SPLIT_MODE_LAYER, main_gpu: int = 0, tensor_split: Optional[List[float]] = None, - rpc_servers: Optional[str] = None, vocab_only: bool = False, use_mmap: bool = True, use_mlock: bool = False, @@ -82,6 +81,7 @@ def __init__( int ] = llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED, pooling_type: int = llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED, + attention_type: int = llama_cpp.LLAMA_ATTENTION_TYPE_UNSPECIFIED, rope_freq_base: float = 0.0, rope_freq_scale: float = 0.0, yarn_ext_factor: float = -1.0, @@ -93,7 +93,10 @@ def __init__( embedding: bool = False, offload_kqv: bool = True, flash_attn: bool = False, + op_offload: Optional[bool] = None, + swa_full: Optional[bool] = None, # Sampling Params + no_perf: bool = False, last_n_tokens_size: int = 64, # LoRA Params lora_base: Optional[str] = None, @@ -149,7 +152,6 @@ def __init__( split_mode: How to split the model across GPUs. See llama_cpp.LLAMA_SPLIT_* for options. main_gpu: main_gpu interpretation depends on split_mode: LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model. LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results. LLAMA_SPLIT_MODE_LAYER: ignored tensor_split: How split tensors should be distributed across GPUs. If None, the model is not split. - rpc_servers: Comma separated list of RPC servers to use for offloading vocab_only: Only load the vocabulary no weights. use_mmap: Use mmap if possible. use_mlock: Force the system to keep the model in RAM. @@ -162,6 +164,7 @@ def __init__( n_threads_batch: Number of threads to use for batch processing rope_scaling_type: RoPE scaling type, from `enum llama_rope_scaling_type`. ref: https://github.com/ggerganov/llama.cpp/pull/2054 pooling_type: Pooling type, from `enum llama_pooling_type`. + attention_type: Attention type, from `enum llama_attention_type`. rope_freq_base: RoPE base frequency, 0 = from model rope_freq_scale: RoPE frequency scaling factor, 0 = from model yarn_ext_factor: YaRN extrapolation mix factor, negative = from model @@ -173,6 +176,9 @@ def __init__( embedding: Embedding mode only. offload_kqv: Offload K, Q, V to GPU. flash_attn: Use flash attention. + op_offload: offload host tensor operations to device + swa_full: use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) + no_perf: Measure performance timings. last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque. lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model. lora_path: Path to a LoRA file to apply to the model. @@ -224,11 +230,6 @@ def __init__( ) # 0x7FFFFFFF is INT32 max, will be auto set to all layers self.model_params.split_mode = split_mode self.model_params.main_gpu = main_gpu - if rpc_servers is not None: - self.model_params.rpc_servers = rpc_servers.encode("utf-8") - self._rpc_servers = rpc_servers - else: - self._rpc_servers = None self.tensor_split = tensor_split self._c_tensor_split = None if self.tensor_split is not None: @@ -320,6 +321,7 @@ def __init__( else llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED ) self.context_params.pooling_type = pooling_type + self.context_params.attention_type = attention_type self.context_params.rope_freq_base = ( rope_freq_base if rope_freq_base != 0.0 else 0 ) @@ -339,18 +341,28 @@ def __init__( yarn_beta_slow if yarn_beta_slow != 0.0 else 0 ) self.context_params.yarn_orig_ctx = yarn_orig_ctx if yarn_orig_ctx != 0 else 0 - self.context_params.logits_all = ( - logits_all if draft_model is None else True - ) # Must be set to True for speculative decoding + self._logits_all = logits_all if draft_model is None else True self.context_params.embeddings = embedding # TODO: Rename to embeddings self.context_params.offload_kqv = offload_kqv - self.context_params.flash_attn = flash_attn + self.context_params.flash_attn_type = ( + llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + if flash_attn + else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED + ) + + if op_offload is not None: + self.context_params.op_offload = op_offload + + if swa_full is not None: + self.context_params.swa_full = swa_full + # KV cache quantization if type_k is not None: self.context_params.type_k = type_k if type_v is not None: self.context_params.type_v = type_v # Sampling Params + self.context_params.no_perf = no_perf self.last_n_tokens_size = last_n_tokens_size self.cache: Optional[BaseLlamaCache] = None @@ -406,10 +418,10 @@ def __init__( ) ) - self._lora_adapter: Optional[llama_cpp.llama_lora_adapter_p] = None + self._lora_adapter: Optional[llama_cpp.llama_adapter_lora_p] = None if self.lora_path: - self._lora_adapter = llama_cpp.llama_lora_adapter_init( + self._lora_adapter = llama_cpp.llama_adapter_lora_init( self._model.model, self.lora_path.encode("utf-8"), ) @@ -421,14 +433,14 @@ def __init__( def free_lora_adapter(): if self._lora_adapter is None: return - llama_cpp.llama_lora_adapter_free(self._lora_adapter) + llama_cpp.llama_adapter_lora_free(self._lora_adapter) self._lora_adapter = None self._stack.callback(free_lora_adapter) - if llama_cpp.llama_lora_adapter_set( - self._ctx.ctx, self._lora_adapter, self.lora_scale - ): + adapters = (llama_cpp.llama_adapter_lora_p_ctypes * 1)(self._lora_adapter) + scales = (ctypes.c_float * 1)(self.lora_scale) + if llama_cpp.llama_set_adapters_lora(self._ctx.ctx, adapters, 1, scales): raise RuntimeError( f"Failed to set LoRA adapter from lora path: {self.lora_path}" ) @@ -565,7 +577,7 @@ def eval_tokens(self) -> Deque[int]: def eval_logits(self) -> Deque[List[float]]: return deque( self.scores[: self.n_tokens, :].tolist(), - maxlen=self._n_ctx if self.context_params.logits_all else 1, + maxlen=self._n_ctx if self._logits_all else 1, ) def tokenize( @@ -638,13 +650,13 @@ def eval(self, tokens: Sequence[int]): n_past = self.n_tokens n_tokens = len(batch) self._batch.set_batch( - batch=batch, n_past=n_past, logits_all=self.context_params.logits_all + batch=batch, n_past=n_past, logits_all=self._logits_all ) self._ctx.decode(self._batch) # Save tokens self.input_ids[n_past : n_past + n_tokens] = batch # Save logits - if self.context_params.logits_all: + if self._logits_all: rows = n_tokens cols = self._n_vocab logits = np.ctypeslib.as_array( @@ -706,22 +718,21 @@ def apply_func(token_data_array: llama_cpp.llama_token_data_array_p): sampler.add_custom(apply_func) sampler.add_penalties( - n_vocab=self._n_vocab, - special_eos_id=self._token_eos, - linefeed_id=self._token_nl, + # n_vocab=self._n_vocab, + # special_eos_id=self._token_eos, + # linefeed_id=self._token_nl, penalty_last_n=self.last_n_tokens_size, penalty_repeat=repeat_penalty, penalty_freq=frequency_penalty, penalty_present=presence_penalty, - penalize_nl=penalize_nl, - ignore_eos=False, + # penalize_nl=penalize_nl, + # ignore_eos=False, ) if grammar is not None: sampler.add_grammar(self._model, grammar) if temp < 0.0: - sampler.add_softmax() sampler.add_dist(self._seed) elif temp == 0.0: sampler.add_greedy() @@ -883,13 +894,20 @@ def generate( else: break if longest_prefix > 0: - reset = False - tokens = tokens[longest_prefix:] - self.n_tokens = longest_prefix - if self.verbose: + if self._ctx.kv_cache_seq_rm(-1, longest_prefix, -1): + reset = False + tokens = tokens[longest_prefix:] + self.n_tokens = longest_prefix + if self.verbose: + print( + f"Llama.generate: {longest_prefix} prefix-match hit, " + f"remaining {len(tokens)} prompt tokens to eval", + file=sys.stderr, + ) + elif self.verbose: print( - f"Llama.generate: {longest_prefix} prefix-match hit, " - f"remaining {len(tokens)} prompt tokens to eval", + f"Llama.generate: {longest_prefix} prefix-match found " + f"but partial kv removal not supported, re-evaluating full prompt", file=sys.stderr, ) @@ -929,7 +947,8 @@ def generate( sample_idx += 1 if stopping_criteria is not None and stopping_criteria( - self._input_ids[: sample_idx], self._scores[sample_idx - self.n_tokens, :] + self._input_ids[:sample_idx], + self._scores[sample_idx - self.n_tokens, :], ): return tokens_or_none = yield token @@ -1036,7 +1055,7 @@ def embed( data: Union[List[List[float]], List[List[List[float]]]] = [] def decode_batch(seq_sizes: List[int]): - llama_cpp.llama_kv_cache_clear(self._ctx.ctx) + self._ctx.kv_cache_clear() self._ctx.decode(self._batch) self._batch.reset() @@ -1107,7 +1126,7 @@ def decode_batch(seq_sizes: List[int]): output = data[0] if isinstance(input, str) else data - llama_cpp.llama_kv_cache_clear(self._ctx.ctx) + self._ctx.kv_cache_clear() self.reset() if return_count: @@ -1141,7 +1160,7 @@ def _create_completion( stopping_criteria: Optional[StoppingCriteriaList] = None, logits_processor: Optional[LogitsProcessorList] = None, grammar: Optional[LlamaGrammar] = None, - logit_bias: Optional[Dict[str, float]] = None, + logit_bias: Optional[Dict[int, float]] = None, ) -> Union[ Iterator[CreateCompletionResponse], Iterator[CreateCompletionStreamResponse] ]: @@ -1152,9 +1171,9 @@ def _create_completion( bos_token_id: int = self.token_bos() cls_token_id: int = self._model.token_cls() sep_token_id: int = self._model.token_sep() - prefix_token_id: int = self._model.token_prefix() - middle_token_id: int = self._model.token_middle() - suffix_token_id: int = self._model.token_suffix() + prefix_token_id: int = 0 # self._model.token_prefix() # TODO: Fix + middle_token_id: int = 0 # self._model.token_middle() # TODO: Fix + suffix_token_id: int = 0 # self._model.token_suffix() # TODO: Fix add_space_prefix: bool = ( self.metadata.get("tokenizer.ggml.add_space_prefix", "true") == "true" ) @@ -1285,7 +1304,7 @@ def logit_bias_processor( else: stop_sequences = [] - if logprobs is not None and self.context_params.logits_all is False: + if logprobs is not None and self._logits_all is False: raise ValueError( "logprobs is not supported for models created with logits_all=False" ) @@ -1310,7 +1329,7 @@ def logit_bias_processor( if seed is not None: self.set_seed(seed) else: - self.set_seed(random.Random(self._seed).randint(0, 2 ** 32)) + self.set_seed(random.Random(self._seed).randint(0, 2**32)) finish_reason = "length" multibyte_fix = 0 @@ -1332,7 +1351,7 @@ def logit_bias_processor( logits_processor=logits_processor, grammar=grammar, ): - if llama_cpp.llama_token_is_eog(self._model.model, token): + if llama_cpp.llama_vocab_is_eog(self._model.vocab, token): text = self.detokenize(completion_tokens, prev_tokens=prompt_tokens) finish_reason = "stop" break @@ -1761,7 +1780,7 @@ def create_completion( stopping_criteria: Optional[StoppingCriteriaList] = None, logits_processor: Optional[LogitsProcessorList] = None, grammar: Optional[LlamaGrammar] = None, - logit_bias: Optional[Dict[str, float]] = None, + logit_bias: Optional[Dict[int, float]] = None, ) -> Union[CreateCompletionResponse, Iterator[CreateCompletionStreamResponse]]: """Generate text from a prompt. @@ -1858,7 +1877,7 @@ def __call__( stopping_criteria: Optional[StoppingCriteriaList] = None, logits_processor: Optional[LogitsProcessorList] = None, grammar: Optional[LlamaGrammar] = None, - logit_bias: Optional[Dict[str, float]] = None, + logit_bias: Optional[Dict[int, float]] = None, ) -> Union[CreateCompletionResponse, Iterator[CreateCompletionStreamResponse]]: """Generate text from a prompt. @@ -1951,7 +1970,7 @@ def create_chat_completion( model: Optional[str] = None, logits_processor: Optional[LogitsProcessorList] = None, grammar: Optional[LlamaGrammar] = None, - logit_bias: Optional[Dict[str, float]] = None, + logit_bias: Optional[Dict[int, float]] = None, logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, ) -> Union[ @@ -2051,7 +2070,10 @@ def create_chat_completion_openai_v1( stream = kwargs.get("stream", False) # type: ignore assert isinstance(stream, bool) if stream: - return (ChatCompletionChunk(**chunk) for chunk in self.create_chat_completion(*args, **kwargs)) # type: ignore + return ( + ChatCompletionChunk(**chunk) + for chunk in self.create_chat_completion(*args, **kwargs) + ) # type: ignore else: return ChatCompletion(**self.create_chat_completion(*args, **kwargs)) # type: ignore except ImportError: @@ -2073,7 +2095,7 @@ def __getstate__(self): use_mlock=self.model_params.use_mlock, kv_overrides=self.kv_overrides, # Context Params - seed=self.context_params.seed, + seed=self._seed, n_ctx=self.context_params.n_ctx, n_batch=self.n_batch, n_ubatch=self.context_params.n_ubatch, @@ -2081,6 +2103,7 @@ def __getstate__(self): n_threads_batch=self.context_params.n_threads_batch, rope_scaling_type=self.context_params.rope_scaling_type, pooling_type=self.context_params.pooling_type, + attention_type=self.context_params.attention_type, rope_freq_base=self.context_params.rope_freq_base, rope_freq_scale=self.context_params.rope_freq_scale, yarn_ext_factor=self.context_params.yarn_ext_factor, @@ -2088,11 +2111,17 @@ def __getstate__(self): yarn_beta_fast=self.context_params.yarn_beta_fast, yarn_beta_slow=self.context_params.yarn_beta_slow, yarn_orig_ctx=self.context_params.yarn_orig_ctx, - logits_all=self.context_params.logits_all, + logits_all=self._logits_all, embedding=self.context_params.embeddings, offload_kqv=self.context_params.offload_kqv, - flash_attn=self.context_params.flash_attn, + flash_attn=( + self.context_params.flash_attn_type + == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + ), + op_offload=self.context_params.op_offload, + swa_full=self.context_params.swa_full, # Sampling Params + no_perf=self.context_params.no_perf, last_n_tokens_size=self.last_n_tokens_size, # LoRA Params lora_base=self.lora_base, @@ -2119,13 +2148,13 @@ def __setstate__(self, state): def save_state(self) -> LlamaState: if self.verbose: print("Llama.save_state: saving llama state", file=sys.stderr) - state_size = llama_cpp.llama_get_state_size(self._ctx.ctx) + state_size = llama_cpp.llama_state_get_size(self._ctx.ctx) if self.verbose: print(f"Llama.save_state: got state size: {state_size}", file=sys.stderr) llama_state = (ctypes.c_uint8 * int(state_size))() if self.verbose: print("Llama.save_state: allocated state", file=sys.stderr) - n_bytes = llama_cpp.llama_copy_state_data(self._ctx.ctx, llama_state) + n_bytes = llama_cpp.llama_state_get_data(self._ctx.ctx, llama_state, state_size) if self.verbose: print(f"Llama.save_state: copied llama state: {n_bytes}", file=sys.stderr) if int(n_bytes) > int(state_size): @@ -2158,7 +2187,10 @@ def load_state(self, state: LlamaState) -> None: LLamaStateArrayType = ctypes.c_uint8 * state_size llama_state = LLamaStateArrayType.from_buffer_copy(state.llama_state) - if llama_cpp.llama_set_state_data(self._ctx.ctx, llama_state) != state_size: + if ( + llama_cpp.llama_state_set_data(self._ctx.ctx, llama_state, state_size) + != state_size + ): raise RuntimeError("Failed to set llama state data") def n_ctx(self) -> int: @@ -2310,7 +2342,11 @@ def from_pretrained( if additional_files: for additonal_file_name in additional_files: # find the additional shard file: - matching_additional_files = [file for file in file_list if fnmatch.fnmatch(file, additonal_file_name)] + matching_additional_files = [ + file + for file in file_list + if fnmatch.fnmatch(file, additonal_file_name) + ] if len(matching_additional_files) == 0: raise ValueError( diff --git a/llama_cpp/llama_cache.py b/llama_cpp/llama_cache.py index e059e98e1b..5220c79337 100644 --- a/llama_cpp/llama_cache.py +++ b/llama_cpp/llama_cache.py @@ -52,9 +52,9 @@ class LlamaRAMCache(BaseLlamaCache): def __init__(self, capacity_bytes: int = (2 << 30)): super().__init__(capacity_bytes) self.capacity_bytes = capacity_bytes - self.cache_state: OrderedDict[ - Tuple[int, ...], "llama_cpp.llama.LlamaState" - ] = OrderedDict() + self.cache_state: OrderedDict[Tuple[int, ...], "llama_cpp.llama.LlamaState"] = ( + OrderedDict() + ) @property def cache_size(self): diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py index dfb0af65e6..1024fb85b9 100644 --- a/llama_cpp/llama_chat_format.py +++ b/llama_cpp/llama_chat_format.py @@ -8,6 +8,7 @@ import random import string +from datetime import datetime from contextlib import ExitStack from typing import ( Any, @@ -28,6 +29,7 @@ import numpy as np import numpy.typing as npt +import llama_cpp.llama_cpp as llama_cpp import llama_cpp.llama as llama import llama_cpp.llama_types as llama_types import llama_cpp.llama_grammar as llama_grammar @@ -213,6 +215,10 @@ def __init__( lstrip_blocks=True, ).from_string(self.template) + @staticmethod + def strftime_now(f: str) -> str: + return datetime.now().strftime(f) + def __call__( self, *, @@ -236,6 +242,8 @@ def raise_exception(message: str): function_call=function_call, tools=tools, tool_choice=tool_choice, + strftime_now=self.strftime_now, + **kwargs, ) stopping_criteria = None @@ -259,6 +267,35 @@ def to_chat_handler(self) -> LlamaChatCompletionHandler: return chat_formatter_to_chat_completion_handler(self) +def _convert_text_completion_logprobs_to_chat( + logprobs: Optional[llama_types.CompletionLogprobs], +) -> llama_types.ChatCompletionLogprobs: + if logprobs is None: + return None + + return { + "content": [ + { + "token": token, + "bytes": None, + "logprob": logprob, + "top_logprobs": [ + { + "token": top_token, + "logprob": top_logprob, + "bytes": None, + } + for top_token, top_logprob in top_logprobs.items() + ], + } + for (token, logprob, top_logprobs) in zip( + logprobs["tokens"], logprobs["token_logprobs"], logprobs["top_logprobs"] + ) + ], + "refusal": None, + } + + def _convert_text_completion_to_chat( completion: llama_types.Completion, ) -> llama_types.ChatCompletion: @@ -275,7 +312,9 @@ def _convert_text_completion_to_chat( "role": "assistant", "content": completion["choices"][0]["text"], }, - "logprobs": completion["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "finish_reason": completion["choices"][0]["finish_reason"], } ], @@ -319,7 +358,9 @@ def _convert_text_completion_chunks_to_chat( if chunk["choices"][0]["finish_reason"] is None else {} ), - "logprobs": chunk["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "finish_reason": chunk["choices"][0]["finish_reason"], } ], @@ -336,7 +377,9 @@ def _convert_completion_to_chat( llama_types.CreateChatCompletionResponse, Iterator[llama_types.ChatCompletionChunk] ]: if stream: - chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore + chunks: Iterator[llama_types.CreateCompletionStreamResponse] = ( + completion_or_chunks # type: ignore + ) return _convert_text_completion_chunks_to_chat(chunks) else: completion: llama_types.Completion = completion_or_chunks # type: ignore @@ -382,7 +425,9 @@ def _convert_completion_to_chat_function( } ], }, - "logprobs": completion["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "finish_reason": "tool_calls", } ], @@ -390,7 +435,9 @@ def _convert_completion_to_chat_function( } return chat_completion else: - chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore + chunks: Iterator[llama_types.CreateCompletionStreamResponse] = ( + completion_or_chunks # type: ignore + ) def _stream_response_to_function_stream( chunks: Iterator[llama_types.CreateCompletionStreamResponse], @@ -435,7 +482,9 @@ def _stream_response_to_function_stream( { "index": 0, "finish_reason": None, - "logprobs": chunk["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -472,7 +521,9 @@ def _stream_response_to_function_stream( { "index": 0, "finish_reason": None, - "logprobs": chunk["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -567,6 +618,7 @@ def chat_completion_handler( function_call=function_call, tools=tools, tool_choice=tool_choice, + **kwargs, ) prompt = llama.tokenize( result.prompt.encode("utf-8"), @@ -670,7 +722,7 @@ def chat_completion_handler( def hf_autotokenizer_to_chat_formatter( - pretrained_model_name_or_path: Union[str, os.PathLike[str]] + pretrained_model_name_or_path: Union[str, os.PathLike[str]], ) -> ChatFormatter: # https://huggingface.co/docs/transformers/main/chat_templating # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format @@ -684,7 +736,9 @@ def format_autotokenizer( **kwargs: Any, ) -> ChatFormatterResponse: tokenizer.use_default_system_prompt = False # type: ignore - prompt: str = tokenizer.apply_chat_template(messages, tokenize=False) # type: ignore + prompt: str = tokenizer.apply_chat_template( # type: ignore + messages, tokenize=False, **kwargs + ) assert isinstance(prompt, str) # Return formatted prompt and eos token by default return ChatFormatterResponse( @@ -695,7 +749,7 @@ def format_autotokenizer( def hf_autotokenizer_to_chat_completion_handler( - pretrained_model_name_or_path: Union[str, os.PathLike[str]] + pretrained_model_name_or_path: Union[str, os.PathLike[str]], ) -> LlamaChatCompletionHandler: chat_formatter = hf_autotokenizer_to_chat_formatter(pretrained_model_name_or_path) return chat_formatter_to_chat_completion_handler(chat_formatter) @@ -741,6 +795,7 @@ def format_tokenizer_config( messages=messages, bos_token=bos_token, eos_token=eos_token, + **kwargs, ) return ChatFormatterResponse( prompt=prompt, stop=[eos_token, bos_token], added_special=True @@ -1520,9 +1575,9 @@ def prepare_messages_for_inference( message["name"] = f"functions.{message['name']}" # Function call requests by assistant if "function_call" in message: - message["function_call"][ - "name" - ] = f"functions.{message['function_call']['name']}" + message["function_call"]["name"] = ( + f"functions.{message['function_call']['name']}" + ) all_messages.append(message) all_messages.append( @@ -1600,7 +1655,9 @@ def message_to_str(msg: llama_types.ChatCompletionRequestMessage): logits_processor=logits_processor, grammar=grammar, ) - return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore + return _convert_completion_to_chat( + completion_or_completion_chunks, stream=stream + ) # type: ignore if function_call is None or ( isinstance(function_call, str) and function_call == "auto" @@ -1716,7 +1773,9 @@ def message_to_str(msg: llama_types.ChatCompletionRequestMessage): } ], }, - "logprobs": completion["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "finish_reason": "tool_calls", } ], @@ -1757,9 +1816,9 @@ def functionary_v1_v2_chat_handler( SYSTEM_MESSAGE = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary""" tokenizer = llama.tokenizer_ - assert hasattr( - tokenizer, "hf_tokenizer" - ), "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class" + assert hasattr(tokenizer, "hf_tokenizer"), ( + "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class" + ) from transformers import AutoTokenizer if "<|START_OF_FUNCTION_CALL|>" in tokenizer.hf_tokenizer.additional_special_tokens: @@ -1909,9 +1968,9 @@ def prepare_messages_for_inference( message["name"] = f"functions.{message['name']}" # Function call requests by assistant if "function_call" in message: - message["function_call"][ - "name" - ] = f"functions.{message['function_call']['name']}" + message["function_call"]["name"] = ( + f"functions.{message['function_call']['name']}" + ) all_messages.append(message) if version == "v1": @@ -1973,7 +2032,9 @@ def prepare_messages_for_inference( completion_or_completion_chunks["choices"][0]["text"] = ( completion_or_completion_chunks["choices"][0]["text"].lstrip() ) - return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore + return _convert_completion_to_chat( + completion_or_completion_chunks, stream=stream + ) # type: ignore def get_grammar(function_call): function_body = None @@ -2128,7 +2189,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": chunk["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -2230,7 +2293,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": chunk["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": "assistant", "content": None, @@ -2268,9 +2333,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": chunk["choices"][0][ - "logprobs" - ], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": "assistant", "content": buffer.pop(0), @@ -2293,7 +2358,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": chunk["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": "assistant", "content": ( @@ -2379,7 +2446,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": chunk["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + chunk["choices"][0]["logprobs"] + ), "delta": { "role": None, "content": None, @@ -2613,7 +2682,9 @@ def generate_streaming(tools, functions, function_call, prompt): choices=[ { "index": 0, - "logprobs": completion["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "message": { "role": "assistant", "content": None if content == "" else content, @@ -2628,7 +2699,7 @@ def generate_streaming(tools, functions, function_call, prompt): class Llava15ChatHandler: DEFAULT_SYSTEM_MESSAGE: Optional[str] = ( - "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions." + "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions." ) CHAT_FORMAT = ( @@ -2667,70 +2738,79 @@ class Llava15ChatHandler: ) def __init__(self, clip_model_path: str, verbose: bool = True): - import llama_cpp.llava_cpp as llava_cpp + import llama_cpp.mtmd_cpp as mtmd_cpp self.clip_model_path = clip_model_path self.verbose = verbose - - self._llava_cpp = llava_cpp # TODO: Fix + self._mtmd_cpp = mtmd_cpp self._exit_stack = ExitStack() - self._last_image_embed: Optional[ - llava_cpp.CtypesPointer[llava_cpp.llava_image_embed] - ] = None - self._last_image_hash: Optional[int] = None + self.mtmd_ctx: Optional[mtmd_cpp.mtmd_context_p] = None if not os.path.exists(clip_model_path): raise ValueError(f"Clip model path does not exist: {clip_model_path}") - with suppress_stdout_stderr(disable=self.verbose): - clip_ctx = self._llava_cpp.clip_model_load(self.clip_model_path.encode(), 0) + def _init_mtmd_context(self, llama_model: llama.Llama): + """Initialize mtmd context with the llama model.""" + if self.mtmd_ctx is not None: + return # Already initialized - if clip_ctx is None: - raise ValueError(f"Failed to load clip model: {clip_model_path}") + with suppress_stdout_stderr(disable=self.verbose): + # Get default parameters + ctx_params = self._mtmd_cpp.mtmd_context_params_default() + ctx_params.use_gpu = True # TODO: Make this configurable + ctx_params.print_timings = self.verbose + ctx_params.n_threads = llama_model.n_threads + ctx_params.flash_attn_type = ( + llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + if ( + llama_model.context_params.flash_attn_type + == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED + ) + else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED + ) - self.clip_ctx = clip_ctx + # Initialize mtmd context + self.mtmd_ctx = self._mtmd_cpp.mtmd_init_from_file( + self.clip_model_path.encode(), llama_model.model, ctx_params + ) - def clip_free(): - with suppress_stdout_stderr(disable=self.verbose): - self._llava_cpp.clip_free(self.clip_ctx) + if self.mtmd_ctx is None: + raise ValueError( + f"Failed to load mtmd context from: {self.clip_model_path}" + ) - self._exit_stack.callback(clip_free) + # Check if vision is supported + if not self._mtmd_cpp.mtmd_support_vision(self.mtmd_ctx): + raise ValueError("Vision is not supported by this model") - def last_image_embed_free(): - with suppress_stdout_stderr(disable=self.verbose): - if self._last_image_embed is not None: - self._llava_cpp.llava_image_embed_free(self._last_image_embed) - self._last_image_embed = None + def mtmd_free(): + with suppress_stdout_stderr(disable=self.verbose): + if self.mtmd_ctx is not None: + self._mtmd_cpp.mtmd_free(self.mtmd_ctx) + self.mtmd_ctx = None - self._exit_stack.callback(last_image_embed_free) + self._exit_stack.callback(mtmd_free) def load_image(self, image_url: str) -> bytes: return self._load_image(image_url) - def _embed_image_bytes(self, image_bytes: bytes, n_threads_batch: int = 1): - if ( - self._last_image_embed is not None - and self._last_image_hash is not None - and hash(image_bytes) == self._last_image_hash - ): - return self._last_image_embed + def _create_bitmap_from_bytes(self, image_bytes: bytes): + """Create mtmd_bitmap from image bytes.""" + if self.mtmd_ctx is None: + raise ValueError("mtmd context not initialized") + with suppress_stdout_stderr(disable=self.verbose): - # Free the previous image embed - if self._last_image_embed is not None: - self._llava_cpp.llava_image_embed_free(self._last_image_embed) - self._last_image_embed = None - self._last_image_hash = None - embed = self._llava_cpp.llava_image_embed_make_with_bytes( - self.clip_ctx, - n_threads_batch, - (ctypes.c_uint8 * len(image_bytes)).from_buffer( - bytearray(image_bytes) - ), + # Create bitmap from buffer using helper function + bitmap = self._mtmd_cpp.mtmd_helper_bitmap_init_from_buf( + self.mtmd_ctx, + (ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)), len(image_bytes), ) - self._last_image_embed = embed - self._last_image_hash = hash(image_bytes) - return embed + + if bitmap is None: + raise ValueError("Failed to create bitmap from image bytes") + + return bitmap def __call__( self, @@ -2771,7 +2851,9 @@ def __call__( llama_types.CreateChatCompletionResponse, Iterator[llama_types.CreateChatCompletionStreamResponse], ]: - assert self.clip_ctx is not None + # Initialize mtmd context + self._init_mtmd_context(llama) + assert self.mtmd_ctx is not None system_prompt = _get_system_message(messages) if system_prompt == "" and self.DEFAULT_SYSTEM_MESSAGE is not None: @@ -2786,54 +2868,140 @@ def __call__( trim_blocks=True, lstrip_blocks=True, ).from_string(self.CHAT_FORMAT) + + # Get the default media marker + media_marker = self._mtmd_cpp.mtmd_default_marker().decode("utf-8") + + # Replace image URLs with media markers in the template text = template.render( messages=messages, add_generation_prompt=True, eos_token=llama.detokenize([llama.token_eos()]), bos_token=llama.detokenize([llama.token_bos()]), ) - split_text = self.split_text_on_image_urls(text, image_urls) + + # Replace image URLs in text with media markers + for image_url in image_urls: + text = text.replace(image_url, media_marker) if self.verbose: print(text, file=sys.stderr) + # Create bitmaps from images + bitmaps = [] + bitmap_cleanup = [] + try: + for image_url in image_urls: + image_bytes = self.load_image(image_url) + bitmap = self._create_bitmap_from_bytes(image_bytes) + bitmaps.append(bitmap) + bitmap_cleanup.append(bitmap) + + # Create input text structure + input_text = self._mtmd_cpp.mtmd_input_text() + input_text.text = text.encode("utf-8") + input_text.add_special = True + input_text.parse_special = True + + # Create input chunks + chunks = self._mtmd_cpp.mtmd_input_chunks_init() + if chunks is None: + raise ValueError("Failed to create input chunks") - # Evaluate prompt - llama.reset() - llama._ctx.kv_cache_clear() - for type_, value in split_text: - if type_ == "text": - tokens = llama.tokenize( - value.encode("utf8"), add_bos=False, special=True + try: + # Tokenize text and images together + bitmap_array = (self._mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))( + *bitmaps ) - if llama.n_tokens + len(tokens) > llama.n_ctx(): - raise ValueError( - f"Prompt exceeds n_ctx: {llama.n_tokens + len(tokens)} > {llama.n_ctx()}" - ) - llama.eval(tokens) - else: - image_bytes = self.load_image(value) - embed = self._embed_image_bytes(image_bytes, llama.context_params.n_threads_batch) - if llama.n_tokens + embed.contents.n_image_pos > llama.n_ctx(): - raise ValueError( - f"Prompt exceeds n_ctx: {llama.n_tokens + embed.contents.n_image_pos} > {llama.n_ctx()}" - ) - n_past = ctypes.c_int(llama.n_tokens) - n_past_p = ctypes.pointer(n_past) - with suppress_stdout_stderr(disable=self.verbose): - self._llava_cpp.llava_eval_image_embed( - llama.ctx, - embed, - llama.n_batch, - n_past_p, - ) - # Required to avoid issues with hf tokenizer - llama.input_ids[llama.n_tokens : n_past.value] = -1 - llama.n_tokens = n_past.value + result = self._mtmd_cpp.mtmd_tokenize( + self.mtmd_ctx, + chunks, + ctypes.byref(input_text), + bitmap_array, + len(bitmaps), + ) + + if result != 0: + raise ValueError(f"Failed to tokenize input: error code {result}") + + # Reset llama context + llama.reset() + llama._ctx.kv_cache_clear() + + # Process each chunk + n_past = llama_cpp.llama_pos(0) + n_chunks = self._mtmd_cpp.mtmd_input_chunks_size(chunks) + + for i in range(n_chunks): + chunk = self._mtmd_cpp.mtmd_input_chunks_get(chunks, i) + if chunk is None: + continue - # Get prompt tokens to avoid a cache miss - prompt = llama.input_ids[: llama.n_tokens].tolist() + chunk_type = self._mtmd_cpp.mtmd_input_chunk_get_type(chunk) + if chunk_type == self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_TEXT: + # Handle text chunk + n_tokens_out = ctypes.c_size_t() + tokens_ptr = self._mtmd_cpp.mtmd_input_chunk_get_tokens_text( + chunk, ctypes.byref(n_tokens_out) + ) + + if tokens_ptr and n_tokens_out.value > 0: + # Convert ctypes array to Python list + tokens = [tokens_ptr[j] for j in range(n_tokens_out.value)] + + if llama.n_tokens + len(tokens) > llama.n_ctx(): + raise ValueError( + f"Prompt exceeds n_ctx: {llama.n_tokens + len(tokens)} > {llama.n_ctx()}" + ) + llama.eval(tokens) + + elif chunk_type in [ + self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE, + self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO, + ]: + # Handle image/audio chunk using helper + chunk_n_tokens = self._mtmd_cpp.mtmd_input_chunk_get_n_tokens( + chunk + ) + + if llama.n_tokens + chunk_n_tokens > llama.n_ctx(): + raise ValueError( + f"Prompt exceeds n_ctx: {llama.n_tokens + chunk_n_tokens} > {llama.n_ctx()}" + ) + + new_n_past = llama_cpp.llama_pos(0) + result = self._mtmd_cpp.mtmd_helper_eval_chunk_single( + self.mtmd_ctx, + llama._ctx.ctx, + chunk, + llama_cpp.llama_pos(llama.n_tokens), + llama_cpp.llama_seq_id(0), + llama.n_batch, + False, # logits_last + ctypes.byref(new_n_past), + ) + + if result != 0: + raise ValueError( + f"Failed to evaluate chunk: error code {result}" + ) + + # Update llama's token count + llama.n_tokens = new_n_past.value + + # Get prompt tokens to avoid a cache miss + prompt = llama.input_ids[: llama.n_tokens].tolist() + + finally: + self._mtmd_cpp.mtmd_input_chunks_free(chunks) + + finally: + # Cleanup bitmaps + for bitmap in bitmap_cleanup: + self._mtmd_cpp.mtmd_bitmap_free(bitmap) + + # Handle response format and tools (same as before) if response_format is not None and response_format["type"] == "json_object": grammar = _grammar_for_response_format(response_format) @@ -2908,6 +3076,7 @@ def __call__( grammar=grammar, logit_bias=logit_bias, ) + if tool is not None: tool_name = tool["function"]["name"] return _convert_completion_to_chat_function( @@ -2951,6 +3120,8 @@ def get_image_urls(messages: List[llama_types.ChatCompletionRequestMessage]): @staticmethod def split_text_on_image_urls(text: str, image_urls: List[str]): + """This method is no longer used in the new implementation.""" + def find_first(s: str, substrs: List[str]): for i, substr in enumerate(substrs): pos = s.find(substr) @@ -3332,7 +3503,6 @@ class MiniCPMv26ChatHandler(Llava15ChatHandler): "{% endif %}" "{% endif %}" "{% endfor %}" - "{% for content in message['content'] %}" "{% if content.type == 'text' %}" "{{ content.text }}" @@ -3350,6 +3520,67 @@ class MiniCPMv26ChatHandler(Llava15ChatHandler): ) +class Qwen25VLChatHandler(Llava15ChatHandler): + DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant." + + CHAT_FORMAT = ( + # "{% set image_count = namespace(value=0) %}" + # "{% set video_count = namespace(value=0) %}" + "{% for message in messages %}" + "{% if loop.first and message['role'] != 'system' %}" + "<|im_start|>system\n" + "{{ self.DEFAULT_SYSTEM_MESSAGE }}<|im_end|>\n" + "{% endif %}" + "<|im_start|>{{ message['role'] }}\n" + "{% if message['content'] is string %}" + "{{ message['content'] }}<|im_end|>\n" + "{% else %}" + "{% for content in message['content'] %}" + "{% if content['type'] == 'image_url' %}" + "{% if content.image_url is string %}" + "{{ content.image_url }}" + "{% else %}" + "{{ content.image_url.url }}" + "{% endif %}" + # "{% set image_count.value = image_count.value + 1 %}" + "{% elif content['type'] == 'text' %}" + "{{ content['text'] }}" + "{% endif %}" + "{% endfor %}" + "<|im_end|>\n" + "{% endif %}" + "{% endfor %}" + "<|im_start|>assistant\n" + ) + + def __call__(self, **kwargs): + llama = kwargs["llama"] + + # Clear state for multiple runs + llama.reset() + llama._ctx.kv_cache_clear() + llama.n_tokens = 0 + + if hasattr(llama, "input_ids"): + llama.input_ids.fill(0) + + # Clear any handler state + if hasattr(self, "_last_image_embed"): + self._last_image_embed = None + self._last_image_hash = None + + if self.verbose: + messages = kwargs.get("messages", []) + image_count = len(self.get_image_urls(messages)) + print( + f"Minimal - Cleared state, processing {image_count} images", + file=sys.stderr, + ) + + # Use parent implementation + return super().__call__(**kwargs) + + @register_chat_completion_handler("chatml-function-calling") def chatml_function_calling( llama: llama.Llama, @@ -3467,7 +3698,9 @@ def chatml_function_calling( stop = ( [stop, "<|im_end|>"] if isinstance(stop, str) - else stop + ["<|im_end|>"] if stop else ["<|im_end|>"] + else stop + ["<|im_end|>"] + if stop + else ["<|im_end|>"] ) # Case 1: No tool choice by user @@ -3569,7 +3802,7 @@ def chatml_function_calling( # Case 3: Automatic tool choice assert isinstance(tool_choice, str) and tool_choice == "auto" function_names = " | ".join( - [f'''"functions.{tool['function']['name']}:"''' for tool in tools] + [f'''"functions.{tool["function"]["name"]}:"''' for tool in tools] ) initial_gbnf_tool_grammar = ( """root ::= functions | "message:"\n""" @@ -3745,7 +3978,9 @@ def chatml_function_calling( { "finish_reason": "tool_calls", "index": 0, - "logprobs": completion["choices"][0]["logprobs"], + "logprobs": _convert_text_completion_logprobs_to_chat( + completion["choices"][0]["logprobs"] + ), "message": { "role": "assistant", "content": None, diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py index bb2ba1993c..204f1e6262 100644 --- a/llama_cpp/llama_cpp.py +++ b/llama_cpp/llama_cpp.py @@ -3,6 +3,7 @@ import os import ctypes import pathlib +import warnings from typing import ( Callable, @@ -33,13 +34,25 @@ # Specify the base name of the shared library to load _lib_base_name = "llama" _override_base_path = os.environ.get("LLAMA_CPP_LIB_PATH") -_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _override_base_path is None else pathlib.Path(_override_base_path) +_base_path = ( + pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" + if _override_base_path is None + else pathlib.Path(_override_base_path) +) # Load the library _lib = load_shared_library(_lib_base_name, _base_path) ctypes_function = ctypes_function_for_shared_library(_lib) +def _warn_deprecated(symbol: str, hint: str) -> None: + warnings.warn( + f"{symbol} is deprecated; {hint}", + DeprecationWarning, + stacklevel=2, + ) + + # from ggml.h # // NOTE: always add types at the end of the enum to keep backward compatibility # enum ggml_type { @@ -73,6 +86,8 @@ # GGML_TYPE_I64 = 27, # GGML_TYPE_F64 = 28, # GGML_TYPE_IQ1_M = 29, +# GGML_TYPE_MXFP4 = 39, +# GGML_TYPE_NVFP4 = 40, # GGML_TYPE_COUNT, # }; GGML_TYPE_F32 = 0 @@ -103,7 +118,9 @@ GGML_TYPE_I64 = 27 GGML_TYPE_F64 = 28 GGML_TYPE_IQ1_M = 29 -GGML_TYPE_COUNT = 30 +GGML_TYPE_MXFP4 = 39 +GGML_TYPE_NVFP4 = 40 +GGML_TYPE_COUNT = 41 # from ggml-backend.h # typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); @@ -149,6 +166,10 @@ # define LLAMA_STATE_SEQ_VERSION 2 LLAMA_STATE_SEQ_VERSION = 2 +# struct llama_vocab; +llama_vocab_p = NewType("llama_vocab_p", int) +llama_vocab_p_ctypes = ctypes.c_void_p + # struct llama_model; llama_model_p = NewType("llama_model_p", int) llama_model_p_ctypes = ctypes.c_void_p @@ -157,9 +178,17 @@ llama_context_p = NewType("llama_context_p", int) llama_context_p_ctypes = ctypes.c_void_p -# # struct llama_sampler; -# llama_sampler_p = NewType("llama_sampler_p", int) -# llama_sampler_p_ctypes = ctypes.c_void_p +# typedef struct llama_memory_i * llama_memory_t; +llama_memory_t = NewType("llama_memory_t", int) +llama_memory_t_ctypes = ctypes.c_void_p + +# struct llama_kv_cache; (DEPRECATED) +llama_kv_cache_p = NewType("llama_kv_cache_p", int) +llama_kv_cache_p_ctypes = ctypes.c_void_p + +# struct gguf_context; +gguf_context_p = NewType("gguf_context_p", int) +gguf_context_p_ctypes = ctypes.c_void_p # typedef int32_t llama_pos; llama_pos = ctypes.c_int32 @@ -171,12 +200,13 @@ # enum llama_vocab_type { -# LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab -# LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback -# LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE -# LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece -# LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram -# LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization +# LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab +# LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback +# LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE +# LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece +# LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram +# LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization +# LLAMA_VOCAB_TYPE_PLAMO2 = 6, // PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming # }; LLAMA_VOCAB_TYPE_NONE = 0 """For models without vocab""" @@ -190,8 +220,11 @@ """T5 tokenizer based on Unigram""" LLAMA_VOCAB_TYPE_RWKV = 5 """RWKV tokenizer based on greedy tokenization""" +LLAMA_VOCAB_TYPE_PLAMO2 = 6 +"""PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming""" +# NOTE: Deprecated and will be removed in the future. (already gone in llama.cpp) # // pre-tokenization types # enum llama_vocab_pre_type { # LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0, @@ -221,6 +254,15 @@ # LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24, # LLAMA_VOCAB_PRE_TYPE_EXAONE = 25, # LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26, +# LLAMA_VOCAB_PRE_TYPE_MINERVA = 27, +# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28, +# LLAMA_VOCAB_PRE_TYPE_GPT4O = 29, +# LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30, +# LLAMA_VOCAB_PRE_TYPE_TRILLION = 31, +# LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32, +# LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33, +# LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34, +# LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35, # }; LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0 LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1 @@ -238,7 +280,7 @@ LLAMA_VOCAB_PRE_TYPE_DBRX = 13 LLAMA_VOCAB_PRE_TYPE_SMAUG = 14 LLAMA_VOCAB_PRE_TYPE_PORO = 15 -LLAMA_VOCAV_PRE_TYPE_CHATGLM3 = 16 +LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16 LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17 LLAMA_VOCAB_PRE_TYPE_VIKING = 18 LLAMA_VOCAB_PRE_TYPE_JAIS = 19 @@ -249,18 +291,33 @@ LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24 LLAMA_VOCAB_PRE_TYPE_EXAONE = 25 LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26 +LLAMA_VOCAB_PRE_TYPE_MINERVA = 27 +LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28 +LLAMA_VOCAB_PRE_TYPE_GPT4O = 29 +LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30 +LLAMA_VOCAB_PRE_TYPE_TRILLION = 31 +LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32 +LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33 +LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34 +LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35 # // note: these values should be synchronized with ggml_rope # // TODO: maybe move this enum to ggml.h (ggml_rope_type) # enum llama_rope_type { -# LLAMA_ROPE_TYPE_NONE = -1, -# LLAMA_ROPE_TYPE_NORM = 0, -# LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX, +# LLAMA_ROPE_TYPE_NONE = -1, +# LLAMA_ROPE_TYPE_NORM = 0, +# LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX, +# LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE, +# LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE, +# LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION, # }; LLAMA_ROPE_TYPE_NONE = -1 LLAMA_ROPE_TYPE_NORM = 0 LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX = 2 +LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE = 8 +LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE = 40 +LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION = 24 # enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file @@ -342,11 +399,13 @@ # LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors # LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors # LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors -# LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // except 1d tensors -# LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // except 1d tensors -# LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // except 1d tensors +# //LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // removed from gguf files, use Q4_0 and runtime repack +# //LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // removed from gguf files, use Q4_0 and runtime repack +# //LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // removed from gguf files, use Q4_0 and runtime repack # LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors # LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors +# LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38, // except 1d tensors +# LLAMA_FTYPE_MOSTLY_NVFP4 = 39, // except 1d tensors # # LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file # }; @@ -380,11 +439,13 @@ LLAMA_FTYPE_MOSTLY_IQ4_XS = 30 LLAMA_FTYPE_MOSTLY_IQ1_M = 31 LLAMA_FTYPE_MOSTLY_BF16 = 32 -LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33 -LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34 -LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35 +# LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33 +# LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34 +# LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35 LLAMA_FTYPE_MOSTLY_TQ1_0 = 36 LLAMA_FTYPE_MOSTLY_TQ2_0 = 37 +LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38 +LLAMA_FTYPE_MOSTLY_NVFP4 = 39 LLAMA_FTYPE_GUESSED = 1024 # enum llama_rope_scaling_type { @@ -392,13 +453,15 @@ # LLAMA_ROPE_SCALING_TYPE_NONE = 0, # LLAMA_ROPE_SCALING_TYPE_LINEAR = 1, # LLAMA_ROPE_SCALING_TYPE_YARN = 2, -# LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN, +# LLAMA_ROPE_SCALING_TYPE_LONGROPE = 3, +# LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_LONGROPE, # }; LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1 LLAMA_ROPE_SCALING_TYPE_NONE = 0 LLAMA_ROPE_SCALING_TYPE_LINEAR = 1 LLAMA_ROPE_SCALING_TYPE_YARN = 2 -LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN +LLAMA_ROPE_SCALING_TYPE_LONGROPE = 3 +LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_LONGROPE # enum llama_pooling_type { # LLAMA_POOLING_TYPE_UNSPECIFIED = -1, @@ -425,10 +488,20 @@ LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1 +# enum llama_flash_attn_type { +# LLAMA_FLASH_ATTN_TYPE_AUTO = -1, +# LLAMA_FLASH_ATTN_TYPE_DISABLED = 0, +# LLAMA_FLASH_ATTN_TYPE_ENABLED = 1, +# }; +LLAMA_FLASH_ATTN_TYPE_AUTO = -1 +LLAMA_FLASH_ATTN_TYPE_DISABLED = 0 +LLAMA_FLASH_ATTN_TYPE_ENABLED = 1 + + # enum llama_split_mode { # LLAMA_SPLIT_MODE_NONE = 0, // single GPU # LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs -# LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs +# LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported # }; LLAMA_SPLIT_MODE_NONE = 0 LLAMA_SPLIT_MODE_LAYER = 1 @@ -502,31 +575,35 @@ class llama_token_data_array(ctypes.Structure): ) -# // Input data for llama_decode +# // Input data for llama_encode/llama_decode # // A llama_batch object can contain input about one or many sequences # // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens # // # // - token : the token ids of the input (used when embd is NULL) # // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) # // - pos : the positions of the respective token in the sequence -# // (if set to NULL, the token position will be tracked automatically by llama_decode) +# // (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode) # // - seq_id : the sequence to which the respective token belongs # // (if set to NULL, the sequence ID will be assumed to be 0) # // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output -# // (if set to NULL, only the logits for last token will be returned) +# // (if set to NULL: +# // - if embeddings: all tokens are output +# // - if not: only the last token is output +# // ) # // # typedef struct llama_batch { # int32_t n_tokens; + # llama_token * token; # float * embd; # llama_pos * pos; # int32_t * n_seq_id; # llama_seq_id ** seq_id; -# int8_t * logits; // TODO: rename this to "output" +# int8_t * logits; // TODO: rename this to "output" # } llama_batch; class llama_batch(ctypes.Structure): - """Input data for llama_decode + """Input data for llama_encode/llama_decode A llama_batch object can contain input about one or many sequences @@ -573,6 +650,34 @@ class llama_batch(ctypes.Structure): LLAMA_KV_OVERRIDE_TYPE_STR = 3 +# enum llama_model_meta_key { +# LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE, +# LLAMA_MODEL_META_KEY_SAMPLING_TOP_K, +# LLAMA_MODEL_META_KEY_SAMPLING_TOP_P, +# LLAMA_MODEL_META_KEY_SAMPLING_MIN_P, +# LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY, +# LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD, +# LLAMA_MODEL_META_KEY_SAMPLING_TEMP, +# LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N, +# LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT, +# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT, +# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU, +# LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA, +# }; +LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE = 0 +LLAMA_MODEL_META_KEY_SAMPLING_TOP_K = 1 +LLAMA_MODEL_META_KEY_SAMPLING_TOP_P = 2 +LLAMA_MODEL_META_KEY_SAMPLING_MIN_P = 3 +LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY = 4 +LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD = 5 +LLAMA_MODEL_META_KEY_SAMPLING_TEMP = 6 +LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N = 7 +LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT = 8 +LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT = 9 +LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU = 10 +LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA = 11 + + # struct llama_model_kv_override { # enum llama_model_kv_override_type tag; @@ -614,25 +719,65 @@ class llama_model_kv_override(ctypes.Structure): value: Union[int, float, bool, bytes] +# struct llama_model_tensor_override { +# const char * pattern; +# enum ggml_type type; +# }; +class llama_model_tensor_override(ctypes.Structure): + """Override the quantization type for tensors matching a pattern.""" + + _fields_ = [ + ("pattern", ctypes.c_char_p), + ("type", ctypes.c_int), + ] + + if TYPE_CHECKING: + pattern: Optional[bytes] + type: int + + +# struct llama_model_imatrix_data { +# const char * name; +# const float * data; +# size_t size; +# }; +class llama_model_imatrix_data(ctypes.Structure): + """Importance matrix data for a tensor used during quantization.""" + + _fields_ = [ + ("name", ctypes.c_char_p), + ("data", ctypes.POINTER(ctypes.c_float)), + ("size", ctypes.c_size_t), + ] + + if TYPE_CHECKING: + name: Optional[bytes] + data: CtypesPointer[ctypes.c_float] + size: int + + +# struct llama_model_tensor_buft_override { +# const char * pattern; +# ggml_backend_buffer_type_t buft; +# }; + + # struct llama_model_params { # // NULL-terminated list of devices to use for offloading (if NULL, all available devices are used) # ggml_backend_dev_t * devices; +# // NULL-terminated list of buffer types to use for tensors that match a pattern +# const struct llama_model_tensor_buft_override * tensor_buft_overrides; + # int32_t n_gpu_layers; // number of layers to store in VRAM # enum llama_split_mode split_mode; // how to split the model across multiple GPUs -# // main_gpu interpretation depends on split_mode: -# // LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model -# // LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results -# // LLAMA_SPLIT_MODE_LAYER: ignored +# // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE # int32_t main_gpu; # // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() # const float * tensor_split; -# // comma separated list of RPC servers to use for offloading -# const char * rpc_servers; - # // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. # // If the provided progress_callback returns true, model loading continues. # // If it returns false, model loading is immediately aborted. @@ -646,61 +791,96 @@ class llama_model_kv_override(ctypes.Structure): # // Keep the booleans together to avoid misalignment during copy-by-value. -# bool vocab_only; // only load the vocabulary, no weights -# bool use_mmap; // use mmap if possible -# bool use_mlock; // force system to keep model in RAM -# bool check_tensors; // validate model tensor data +# bool vocab_only; // only load the vocabulary, no weights +# bool use_mmap; // use mmap if possible +# bool use_direct_io; // use direct io, takes precedence over use_mmap when supported +# bool use_mlock; // force system to keep model in RAM +# bool check_tensors; // validate model tensor data +# bool use_extra_bufts; // use extra buffer types (used for weight repacking) +# bool no_host; // bypass host buffer allowing extra buffers to be used +# bool no_alloc; // only load metadata and simulate memory allocations # }; class llama_model_params(ctypes.Structure): """Parameters for llama_model Attributes: + devices (ctypes.Array[ggml_backend_dev_t]): NULL-terminated list of devices to use for offloading (if NULL, all available devices are used) + tensor_buft_overrides (ctypes.Array[llama_model_tensor_buft_override]): NULL-terminated list of buffer types to use for tensors that match a pattern n_gpu_layers (int): number of layers to store in VRAM split_mode (int): how to split the model across multiple GPUs - main_gpu (int): the GPU that is used for the entire model. main_gpu interpretation depends on split_mode: LLAMA_SPLIT_NONE: the GPU that is used for the entire model LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results LLAMA_SPLIT_LAYER: ignored + main_gpu (int): the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE tensor_split (ctypes.Array[ctypes.ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() - rpc_servers (ctypes.c_char_p): comma separated list of RPC servers to use for offloading progress_callback (llama_progress_callback): called with a progress value between 0.0 and 1.0. Pass NULL to disable. If the provided progress_callback returns true, model loading continues. If it returns false, model loading is immediately aborted. progress_callback_user_data (ctypes.ctypes.c_void_p): context pointer passed to the progress callback kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data vocab_only (bool): only load the vocabulary, no weights use_mmap (bool): use mmap if possible + use_direct_io (bool): use direct io, takes precedence over use_mmap when supported use_mlock (bool): force system to keep model in RAM - check_tensors (bool): validate model tensor data""" + check_tensors (bool): validate model tensor data + use_extra_bufts (bool): use extra buffer types (used for weight repacking) + no_host (bool): bypass host buffer allowing extra buffers to be used + no_alloc (bool): only load metadata and simulate memory allocations""" if TYPE_CHECKING: + devices: CtypesArray[ctypes.c_void_p] # NOTE: unused + tensor_buft_overrides: CtypesArray[ + llama_model_tensor_buft_override + ] # NOTE: unused n_gpu_layers: int split_mode: int main_gpu: int tensor_split: CtypesArray[ctypes.c_float] - rpc_servers: ctypes.c_char_p progress_callback: Callable[[float, ctypes.c_void_p], bool] progress_callback_user_data: ctypes.c_void_p kv_overrides: CtypesArray[llama_model_kv_override] vocab_only: bool use_mmap: bool + use_direct_io: bool use_mlock: bool check_tensors: bool + use_extra_bufts: bool + no_host: bool + no_alloc: bool _fields_ = [ - ("devices", ctypes.c_void_p), # NOTE: unnused + ("devices", ctypes.c_void_p), # NOTE: unnused + ("tensor_buft_overrides", ctypes.c_void_p), # NOTE: unused ("n_gpu_layers", ctypes.c_int32), ("split_mode", ctypes.c_int), ("main_gpu", ctypes.c_int32), ("tensor_split", ctypes.POINTER(ctypes.c_float)), - ("rpc_servers", ctypes.c_char_p), ("progress_callback", llama_progress_callback), ("progress_callback_user_data", ctypes.c_void_p), ("kv_overrides", ctypes.POINTER(llama_model_kv_override)), ("vocab_only", ctypes.c_bool), ("use_mmap", ctypes.c_bool), + ("use_direct_io", ctypes.c_bool), ("use_mlock", ctypes.c_bool), ("check_tensors", ctypes.c_bool), + ("use_extra_bufts", ctypes.c_bool), + ("no_host", ctypes.c_bool), + ("no_alloc", ctypes.c_bool), + ] + + +# struct llama_sampler_seq_config { +# llama_seq_id seq_id; +# struct llama_sampler * sampler; +# }; +class llama_sampler_seq_config(ctypes.Structure): + if TYPE_CHECKING: + seq_id: int + sampler: ctypes.c_void_p + + _fields_ = [ + ("seq_id", llama_seq_id), + ("sampler", ctypes.c_void_p), ] # // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations -# // https://github.com/ggerganov/llama.cpp/pull/7544 +# // https://github.com/ggml-org/llama.cpp/pull/7544 # struct llama_context_params { # uint32_t n_ctx; // text context, 0 = from model # uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode @@ -712,8 +892,9 @@ class llama_model_params(ctypes.Structure): # enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` # enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id # enum llama_attention_type attention_type; // attention type to use for embeddings +# enum llama_flash_attn_type flash_attn_type; // when to enable Flash Attention -# // ref: https://github.com/ggerganov/llama.cpp/pull/2054 +# // ref: https://github.com/ggml-org/llama.cpp/pull/2054 # float rope_freq_base; // RoPE base frequency, 0 = from model # float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model # float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model @@ -721,7 +902,7 @@ class llama_model_params(ctypes.Structure): # float yarn_beta_fast; // YaRN low correction dim # float yarn_beta_slow; // YaRN high correction dim # uint32_t yarn_orig_ctx; // YaRN original context size -# float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default) +# float defrag_thold; // defragment the KV cache if holes/size > thold, <= 0 disabled (default) # ggml_backend_sched_eval_callback cb_eval; # void * cb_eval_user_data; @@ -729,19 +910,26 @@ class llama_model_params(ctypes.Structure): # enum ggml_type type_k; // data type for K cache [EXPERIMENTAL] # enum ggml_type type_v; // data type for V cache [EXPERIMENTAL] -# // Keep the booleans together to avoid misalignment during copy-by-value. -# bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) -# bool embeddings; // if true, extract embeddings (together with logits) -# bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU -# bool flash_attn; // whether to use flash attention [EXPERIMENTAL] -# bool no_perf; // whether to measure performance timings - - # // Abort callback # // if it returns true, execution of llama_decode() will be aborted # // currently works only with CPU execution # ggml_abort_callback abort_callback; # void * abort_callback_data; + + +# // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value. +# bool embeddings; // if true, extract embeddings (together with logits) +# bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU +# bool no_perf; // measure performance timings +# bool op_offload; // offload host tensor operations to device +# bool swa_full; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) +# // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases +# // ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573 +# bool kv_unified; // use a unified buffer across the input sequences when computing the attention +# // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix +# // ref: https://github.com/ggml-org/llama.cpp/pull/14363 +# struct llama_sampler_seq_config * samplers; +# size_t n_samplers; # }; class llama_context_params(ctypes.Structure): """Parameters for llama_context @@ -756,6 +944,7 @@ class llama_context_params(ctypes.Structure): rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type` pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer) attention_type (int): attention type to use for embeddings + flash_attn_type (int): when to enable flash attention rope_freq_base (float): RoPE base frequency, 0 = from model rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model @@ -763,17 +952,21 @@ class llama_context_params(ctypes.Structure): yarn_beta_fast (float): YaRN low correction dim yarn_beta_slow (float): YaRN high correction dim yarn_orig_ctx (int): YaRN original context size - defrag_thold (float): defragment the KV cache if holes/size > thold, < 0 disabled (default) + defrag_thold (float): defragment the KV cache if holes/size > thold, <= 0 disabled (default) cb_eval (ggml_backend_sched_eval_callback): callback for scheduling eval cb_eval_user_data (ctypes.ctypes.c_void_p): user data for cb_eval type_k (int): data type for K cache type_v (int): data type for V cache - logits_all (bool): the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) - embeddings (bool): if true, extract embeddings (together with logits) - offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU - flash_attn (bool): whether to use flash attention abort_callback (ggml_abort_callback): abort callback if it returns true, execution of llama_decode() will be aborted abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback + embeddings (bool): if true, extract embeddings (together with logits) + offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU + no_perf (bool): whether to measure performance timings + op_offload (bool): offload host tensor operations to device + swa_full (bool): use full-size SWA cache + kv_unified (bool): use a unified buffer across the input sequences when computing the attention + samplers (ctypes.POINTER(llama_sampler_seq_config)): backend sampler chain configuration + n_samplers (int): number of backend sampler chain configurations """ if TYPE_CHECKING: @@ -786,6 +979,7 @@ class llama_context_params(ctypes.Structure): rope_scaling_type: int pooling_type: int attention_type: int + flash_attn_type: int rope_freq_base: float rope_freq_scale: float yarn_ext_factor: float @@ -798,12 +992,16 @@ class llama_context_params(ctypes.Structure): cb_eval_user_data: ctypes.c_void_p type_k: int type_v: int - logits_all: bool - embeddings: bool - offload_kqv: bool - flash_attn: bool abort_callback: Callable[[ctypes.c_void_p], bool] abort_callback_data: ctypes.c_void_p + embeddings: bool + offload_kqv: bool + no_perf: bool + op_offload: bool + swa_full: bool + kv_unified: bool + samplers: ctypes.POINTER(llama_sampler_seq_config) + n_samplers: int _fields_ = [ ("n_ctx", ctypes.c_uint32), @@ -815,6 +1013,7 @@ class llama_context_params(ctypes.Structure): ("rope_scaling_type", ctypes.c_int), ("pooling_type", ctypes.c_int), ("attention_type", ctypes.c_int), + ("flash_attn_type", ctypes.c_int), ("rope_freq_base", ctypes.c_float), ("rope_freq_scale", ctypes.c_float), ("yarn_ext_factor", ctypes.c_float), @@ -827,12 +1026,16 @@ class llama_context_params(ctypes.Structure): ("cb_eval_user_data", ctypes.c_void_p), ("type_k", ctypes.c_int), ("type_v", ctypes.c_int), - ("logits_all", ctypes.c_bool), - ("embeddings", ctypes.c_bool), - ("offload_kqv", ctypes.c_bool), - ("flash_attn", ctypes.c_bool), ("abort_callback", ggml_abort_callback), ("abort_callback_data", ctypes.c_void_p), + ("embeddings", ctypes.c_bool), + ("offload_kqv", ctypes.c_bool), + ("no_perf", ctypes.c_bool), + ("op_offload", ctypes.c_bool), + ("swa_full", ctypes.c_bool), + ("kv_unified", ctypes.c_bool), + ("samplers", ctypes.POINTER(llama_sampler_seq_config)), + ("n_samplers", ctypes.c_size_t), ] @@ -854,17 +1057,20 @@ class llama_context_params(ctypes.Structure): # // model quantization parameters # typedef struct llama_model_quantize_params { -# int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() -# enum llama_ftype ftype; // quantize to this llama_ftype -# enum ggml_type output_tensor_type; // output tensor type -# enum ggml_type token_embedding_type; // token embeddings tensor type -# bool allow_requantize; // allow quantizing non-f32/f16 tensors -# bool quantize_output_tensor; // quantize output.weight -# bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored -# bool pure; // quantize all tensors to the default type -# bool keep_split; // quantize to the same number of shards -# void * imatrix; // pointer to importance matrix data -# void * kv_overrides; // pointer to vector containing overrides +# int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() +# enum llama_ftype ftype; // quantize to this llama_ftype +# enum ggml_type output_tensor_type; // output tensor type +# enum ggml_type token_embedding_type; // token embeddings tensor type +# bool allow_requantize; // allow quantizing non-f32/f16 tensors +# bool quantize_output_tensor; // quantize output.weight +# bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored +# bool pure; // quantize all tensors to the default type +# bool keep_split; // quantize to the same number of shards +# bool dry_run; // calculate and show the final quantization size without performing quantization +# const struct llama_model_imatrix_data * imatrix; // pointer to importance matrix data +# const struct llama_model_kv_override * kv_overrides; // pointer to kv overrides +# const struct llama_model_tensor_override * tt_overrides; // pointer to tensor overrides +# const int32_t * prune_layers; // pointer to layer indices to prune # } llama_model_quantize_params; class llama_model_quantize_params(ctypes.Structure): """Parameters for llama_model_quantize @@ -879,8 +1085,11 @@ class llama_model_quantize_params(ctypes.Structure): only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored pure (bool): quantize all tensors to the default type keep_split (bool): quantize to the same number of shards - imatrix (ctypes.c_void_p): pointer to importance matrix data - kv_overrides (ctypes.c_void_p): pointer to vector containing overrides + dry_run (bool): calculate and show the final quantization size without performing quantization + imatrix (ctypes.Array[llama_model_imatrix_data]): pointer to importance matrix data + kv_overrides (ctypes.Array[llama_model_kv_override]): pointer to kv overrides + tt_overrides (ctypes.Array[llama_model_tensor_override]): pointer to tensor overrides + prune_layers (ctypes.Array[ctypes.c_int32]): pointer to layer indices to prune """ if TYPE_CHECKING: @@ -893,8 +1102,11 @@ class llama_model_quantize_params(ctypes.Structure): only_copy: bool pure: bool keep_split: bool - imatrix: ctypes.c_void_p - kv_overrides: ctypes.c_void_p + dry_run: bool + imatrix: CtypesPointer[llama_model_imatrix_data] + kv_overrides: CtypesPointer[llama_model_kv_override] + tt_overrides: CtypesPointer[llama_model_tensor_override] + prune_layers: CtypesPointer[ctypes.c_int32] _fields_ = [ ("nthread", ctypes.c_int32), @@ -906,8 +1118,11 @@ class llama_model_quantize_params(ctypes.Structure): ("only_copy", ctypes.c_bool), ("pure", ctypes.c_bool), ("keep_split", ctypes.c_bool), - ("imatrix", ctypes.c_void_p), - ("kv_overrides", ctypes.c_void_p), + ("dry_run", ctypes.c_bool), + ("imatrix", ctypes.POINTER(llama_model_imatrix_data)), + ("kv_overrides", ctypes.POINTER(llama_model_kv_override)), + ("tt_overrides", ctypes.POINTER(llama_model_tensor_override)), + ("prune_layers", ctypes.POINTER(ctypes.c_int32)), ] @@ -965,9 +1180,9 @@ class llama_chat_message(ctypes.Structure): # // lora adapter -# struct llama_lora_adapter; -llama_lora_adapter_p = ctypes.c_void_p -llama_lora_adapter_p_ctypes = ctypes.POINTER(ctypes.c_void_p) +# struct llama_adapter_lora; +llama_adapter_lora_p = ctypes.c_void_p +llama_adapter_lora_p_ctypes = ctypes.POINTER(ctypes.c_void_p) # // Helpers for getting default parameters @@ -1015,10 +1230,16 @@ def llama_model_quantize_default_params() -> llama_model_quantize_params: ... +# LLAMA_API const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type); +@ctypes_function("llama_flash_attn_type_name", [ctypes.c_int], ctypes.c_char_p) +def llama_flash_attn_type_name(flash_attn_type: int, /) -> Optional[bytes]: + """Get the flash attention type name.""" + ... + + # // Initialize the llama + ggml backend # // If numa is true, use NUMA optimizations # // Call once at the start of the program -# LLAMA_API void llama_backend_init(bool numa); # LLAMA_API void llama_backend_init(void); @ctypes_function( "llama_backend_init", @@ -1027,7 +1248,6 @@ def llama_model_quantize_default_params() -> llama_model_quantize_params: ) def llama_backend_init(): """Initialize the llama + ggml backend - If numa is true, use NUMA optimizations Call once at the start of the program""" ... @@ -1049,6 +1269,18 @@ def llama_backend_init(): GGML_NUMA_STRATEGY_COUNT = 5 +# // Call once at the end of the program - currently only used for MPI +# LLAMA_API void llama_backend_free(void); +@ctypes_function( + "llama_backend_free", + [], + None, +) +def llama_backend_free(): + """Call once at the end of the program - currently only used for MPI""" + ... + + # //optional: # LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa); @ctypes_function( @@ -1056,68 +1288,214 @@ def llama_backend_init(): [ctypes.c_int], None, ) -def llama_numa_init(numa: int, /): - ... +def llama_numa_init(numa: int, /): ... # // Optional: an auto threadpool gets created in ggml if not passed explicitly # LLAMA_API void llama_attach_threadpool( -# struct llama_context * ctx, -# ggml_threadpool_t threadpool, -# ggml_threadpool_t threadpool_batch); +# struct llama_context * ctx, +# ggml_threadpool_t threadpool, +# ggml_threadpool_t threadpool_batch); +# TODO: Add llama_attach_threadpool # LLAMA_API void llama_detach_threadpool(struct llama_context * ctx); +# TODO: Add llama_detach_threadpool -# // Call once at the end of the program - currently only used for MPI -# LLAMA_API void llama_backend_free(void); +# DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file( +# const char * path_model, +# struct llama_model_params params), +# "use llama_model_load_from_file instead"); @ctypes_function( - "llama_backend_free", - [], - None, + "llama_load_model_from_file", + [ctypes.c_char_p, llama_model_params], + llama_model_p_ctypes, ) -def llama_backend_free(): - """Call once at the end of the program - currently only used for MPI""" - ... +def llama_load_model_from_file( + path_model: bytes, params: llama_model_params, / +) -> Optional[llama_model_p]: ... + + +_llama_load_model_from_file = llama_load_model_from_file + + +def llama_load_model_from_file( + path_model: bytes, params: llama_model_params, / +) -> Optional[llama_model_p]: + _warn_deprecated( + "llama_load_model_from_file", + "use llama_model_load_from_file instead", + ) + return _llama_load_model_from_file(path_model, params) -# LLAMA_API struct llama_model * llama_load_model_from_file( +# // Load the model from a file +# // If the file is split into multiple parts, the file name must follow this pattern: -%05d-of-%05d.gguf +# // If the split file name does not follow this pattern, use llama_model_load_from_splits +# LLAMA_API struct llama_model * llama_model_load_from_file( # const char * path_model, # struct llama_model_params params); @ctypes_function( - "llama_load_model_from_file", + "llama_model_load_from_file", [ctypes.c_char_p, llama_model_params], llama_model_p_ctypes, ) -def llama_load_model_from_file( +def llama_model_load_from_file( path_model: bytes, params: llama_model_params, / ) -> Optional[llama_model_p]: + """Load the model from a file + + If the file is split into multiple parts, the file name must follow this pattern: -%05d-of-%05d.gguf + + If the split file name does not follow this pattern, use llama_model_load_from_splits""" + ... + + +# // Load the model from multiple splits (support custom naming scheme) +# // The paths must be in the correct order +# LLAMA_API struct llama_model * llama_model_load_from_splits( +# const char ** paths, +# size_t n_paths, +# struct llama_model_params params); +@ctypes_function( + "llama_model_load_from_splits", + [ctypes.POINTER(ctypes.c_char_p), ctypes.c_size_t, llama_model_params], + llama_model_p_ctypes, +) +def llama_model_load_from_splits( + paths: List[bytes], n_paths: int, params: llama_model_params, / +) -> Optional[llama_model_p]: + """Load the model from multiple splits (support custom naming scheme) + + The paths must be in the correct order""" + ... + + +# // Load a model from an open FILE pointer +# LLAMA_API struct llama_model * llama_model_load_from_file_ptr( +# FILE * file, +# struct llama_model_params params); +@ctypes_function( + "llama_model_load_from_file_ptr", + [ctypes.c_void_p, llama_model_params], + llama_model_p_ctypes, +) +def llama_model_load_from_file_ptr( + file: ctypes.c_void_p, params: llama_model_params, / +) -> Optional[llama_model_p]: + """Load a model from an open FILE pointer.""" + ... + + +# LLAMA_API void llama_model_save_to_file( +# const struct llama_model * model, +# const char * path_model); +@ctypes_function( + "llama_model_save_to_file", + [llama_model_p_ctypes, ctypes.c_char_p], + None, +) +def llama_model_save_to_file(model: llama_model_p, path_model: bytes, /): + """Save the model to a file""" ... -# LLAMA_API void llama_free_model(struct llama_model * model); +# DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model), +# "use llama_model_free instead"); @ctypes_function( "llama_free_model", [llama_model_p_ctypes], None, ) +def llama_free_model(model: llama_model_p, /): ... + + +_llama_free_model = llama_free_model + + def llama_free_model(model: llama_model_p, /): + _warn_deprecated("llama_free_model", "use llama_model_free instead") + return _llama_free_model(model) + + +# LLAMA_API void llama_model_free(struct llama_model * model); +@ctypes_function( + "llama_model_free", + [llama_model_p_ctypes], + None, +) +def llama_model_free(model: llama_model_p, /): ... + + +# typedef void (*llama_model_set_tensor_data_t)(struct ggml_tensor * tensor, void * userdata); +llama_model_set_tensor_data_t = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p) + + +# LLAMA_API struct llama_model * llama_model_init_from_user( +# struct gguf_context * metadata, +# llama_model_set_tensor_data_t set_tensor_data, +# void * set_tensor_data_ud, +# struct llama_model_params params); +@ctypes_function( + "llama_model_init_from_user", + [ + gguf_context_p_ctypes, + llama_model_set_tensor_data_t, + ctypes.c_void_p, + llama_model_params, + ], + llama_model_p_ctypes, +) +def llama_model_init_from_user( + metadata: gguf_context_p, + set_tensor_data: llama_model_set_tensor_data_t, + set_tensor_data_ud: ctypes.c_void_p, + params: llama_model_params, + /, +) -> Optional[llama_model_p]: + """Initialize a model from user-provided metadata and tensor data.""" ... -# LLAMA_API struct llama_context * llama_new_context_with_model( +# LLAMA_API struct llama_context * llama_init_from_model( # struct llama_model * model, # struct llama_context_params params); +@ctypes_function( + "llama_init_from_model", + [llama_model_p_ctypes, llama_context_params], + llama_context_p_ctypes, +) +def llama_init_from_model( + model: llama_model_p, params: llama_context_params, / +) -> Optional[llama_context_p]: ... + + +# DEPRECATED(LLAMA_API struct llama_context * llama_new_context_with_model( +# struct llama_model * model, +# struct llama_context_params params), +# "use llama_init_from_model instead"); @ctypes_function( "llama_new_context_with_model", [llama_model_p_ctypes, llama_context_params], llama_context_p_ctypes, ) +def llama_new_context_with_model( + model: llama_model_p, params: llama_context_params, / +) -> Optional[llama_context_p]: ... + + +_llama_new_context_with_model = llama_new_context_with_model + + def llama_new_context_with_model( model: llama_model_p, params: llama_context_params, / ) -> Optional[llama_context_p]: - ... + _warn_deprecated( + "llama_new_context_with_model", + "use llama_init_from_model instead", + ) + return _llama_new_context_with_model(model, params) # // Frees all allocated memory @@ -1132,142 +1510,311 @@ def llama_free(ctx: llama_context_p, /): ... +# enum llama_params_fit_status { +# LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, +# LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, +# LLAMA_PARAMS_FIT_STATUS_ERROR = 2, +# }; +LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0 +LLAMA_PARAMS_FIT_STATUS_FAILURE = 1 +LLAMA_PARAMS_FIT_STATUS_ERROR = 2 + + +# LLAMA_API enum llama_params_fit_status llama_params_fit( +# const char * path_model, +# struct llama_model_params * mparams, +# struct llama_context_params * cparams, +# float * tensor_split, +# struct llama_model_tensor_buft_override * tensor_buft_overrides, +# size_t * margins, +# uint32_t n_ctx_min, +# enum ggml_log_level log_level); +@ctypes_function( + "llama_params_fit", + [ + ctypes.c_char_p, + ctypes.POINTER(llama_model_params), + ctypes.POINTER(llama_context_params), + ctypes.POINTER(ctypes.c_float), + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_size_t), + ctypes.c_uint32, + ctypes.c_int, + ], + ctypes.c_int, +) +def llama_params_fit( + path_model: bytes, + mparams: CtypesPointerOrRef[llama_model_params], + cparams: CtypesPointerOrRef[llama_context_params], + tensor_split: Optional[CtypesPointer[ctypes.c_float]], + tensor_buft_overrides: ctypes.c_void_p, + margins: Optional[CtypesPointer[ctypes.c_size_t]], + n_ctx_min: int, + log_level: int, + /, +) -> int: + """Fit model and context parameters for a model path.""" + ... + + # LLAMA_API int64_t llama_time_us(void); @ctypes_function( "llama_time_us", [], ctypes.c_int64, ) -def llama_time_us() -> int: - ... +def llama_time_us() -> int: ... # LLAMA_API size_t llama_max_devices(void); @ctypes_function("llama_max_devices", [], ctypes.c_size_t) -def llama_max_devices() -> int: +def llama_max_devices() -> int: ... + + +# LLAMA_API size_t llama_max_parallel_sequences(void); +@ctypes_function("llama_max_parallel_sequences", [], ctypes.c_size_t) +def llama_max_parallel_sequences() -> int: ... + + +# LLAMA_API size_t llama_max_tensor_buft_overrides(void); +@ctypes_function("llama_max_tensor_buft_overrides", [], ctypes.c_size_t) +def llama_max_tensor_buft_overrides() -> int: + """Get the maximum number of tensor buffer type overrides.""" ... # LLAMA_API bool llama_supports_mmap (void); @ctypes_function("llama_supports_mmap", [], ctypes.c_bool) -def llama_supports_mmap() -> bool: - ... +def llama_supports_mmap() -> bool: ... # LLAMA_API bool llama_supports_mlock (void); @ctypes_function("llama_supports_mlock", [], ctypes.c_bool) -def llama_supports_mlock() -> bool: - ... +def llama_supports_mlock() -> bool: ... # LLAMA_API bool llama_supports_gpu_offload(void); @ctypes_function("llama_supports_gpu_offload", [], ctypes.c_bool) -def llama_supports_gpu_offload() -> bool: - ... +def llama_supports_gpu_offload() -> bool: ... # LLAMA_API bool llama_supports_rpc (void); @ctypes_function("llama_supports_rpc", [], ctypes.c_bool) -def llama_supports_rpc() -> bool: - ... +def llama_supports_rpc() -> bool: ... # LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); @ctypes_function("llama_n_ctx", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_ctx(ctx: llama_context_p, /) -> int: +def llama_n_ctx(ctx: llama_context_p, /) -> int: ... + + +# LLAMA_API uint32_t llama_n_ctx_seq (const struct llama_context * ctx); +@ctypes_function("llama_n_ctx_seq", [llama_context_p_ctypes], ctypes.c_uint32) +def llama_n_ctx_seq(ctx: llama_context_p, /) -> int: + """Get the context size per sequence.""" ... # LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); @ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_batch(ctx: llama_context_p, /) -> int: - ... +def llama_n_batch(ctx: llama_context_p, /) -> int: ... # LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx); @ctypes_function("llama_n_ubatch", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_ubatch(ctx: llama_context_p, /) -> int: - ... +def llama_n_ubatch(ctx: llama_context_p, /) -> int: ... # LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx); @ctypes_function("llama_n_seq_max", [llama_context_p_ctypes], ctypes.c_uint32) -def llama_n_seq_max(ctx: llama_context_p, /) -> int: - ... +def llama_n_seq_max(ctx: llama_context_p, /) -> int: ... -# LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); -@ctypes_function("llama_n_vocab", [llama_model_p_ctypes], ctypes.c_int32) -def llama_n_vocab(model: llama_model_p, /) -> int: - ... +# DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead"); +@ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32) +def llama_n_ctx_train(model: llama_model_p, /) -> int: ... + + +_llama_n_ctx_train = llama_n_ctx_train -# LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); -@ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32) def llama_n_ctx_train(model: llama_model_p, /) -> int: - ... + _warn_deprecated("llama_n_ctx_train", "use llama_model_n_ctx_train instead") + return _llama_n_ctx_train(model) -# LLAMA_API int32_t llama_n_embd (const struct llama_model * model); +# DEPRECATED(LLAMA_API int32_t llama_n_embd (const struct llama_model * model), "use llama_model_n_embd instead"); @ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32) +def llama_n_embd(model: llama_model_p, /) -> int: ... + + +_llama_n_embd = llama_n_embd + + def llama_n_embd(model: llama_model_p, /) -> int: - ... + _warn_deprecated("llama_n_embd", "use llama_model_n_embd instead") + return _llama_n_embd(model) -# LLAMA_API int32_t llama_n_layer (const struct llama_model * model); +# DEPRECATED(LLAMA_API int32_t llama_n_layer (const struct llama_model * model), "use llama_model_n_layer instead"); @ctypes_function("llama_n_layer", [llama_model_p_ctypes], ctypes.c_int32) +def llama_n_layer(model: llama_model_p, /) -> int: ... + + +_llama_n_layer = llama_n_layer + + def llama_n_layer(model: llama_model_p, /) -> int: - ... + _warn_deprecated("llama_n_layer", "use llama_model_n_layer instead") + return _llama_n_layer(model) -# LLAMA_API int32_t llama_n_head (const struct llama_model * model); +# DEPRECATED(LLAMA_API int32_t llama_n_head (const struct llama_model * model), "use llama_model_n_head instead"); @ctypes_function("llama_n_head", [llama_model_p_ctypes], ctypes.c_int32) +def llama_n_head(model: llama_model_p, /) -> int: ... + + +_llama_n_head = llama_n_head + + def llama_n_head(model: llama_model_p, /) -> int: - ... + _warn_deprecated("llama_n_head", "use llama_model_n_head instead") + return _llama_n_head(model) -# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); +# DEPRECATED(LLAMA_API int32_t llama_n_vocab (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead"); +@ctypes_function("llama_n_vocab", [llama_vocab_p_ctypes], ctypes.c_int32) +def llama_n_vocab(model: llama_vocab_p, /) -> int: ... + + +_llama_n_vocab = llama_n_vocab + + +def llama_n_vocab(model: llama_vocab_p, /) -> int: + _warn_deprecated("llama_n_vocab", "use llama_vocab_n_tokens instead") + return _llama_n_vocab(model) + + +# LLAMA_API const struct llama_model * llama_get_model (const struct llama_context * ctx); @ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes) -def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: +def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: ... + + +# LLAMA_API llama_memory_t llama_get_memory (const struct llama_context * ctx); +@ctypes_function("llama_get_memory", [llama_context_p_ctypes], llama_memory_t_ctypes) +def llama_get_memory(ctx: llama_context_p, /) -> Optional[llama_memory_t]: + """Get the memory for the context""" ... -# LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); +# LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); @ctypes_function("llama_pooling_type", [llama_context_p_ctypes], ctypes.c_int) -def llama_pooling_type(ctx: llama_context_p, /) -> int: - ... +def llama_pooling_type(ctx: llama_context_p, /) -> int: ... -# LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); -@ctypes_function("llama_vocab_type", [llama_model_p_ctypes], ctypes.c_int) -def llama_vocab_type(model: llama_model_p, /) -> int: - ... +# LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model); +@ctypes_function("llama_model_get_vocab", [llama_model_p_ctypes], llama_vocab_p_ctypes) +def llama_model_get_vocab(model: llama_model_p, /) -> Optional[llama_vocab_p]: ... + + +# LLAMA_API enum llama_rope_type llama_model_rope_type(const struct llama_model * model); +@ctypes_function("llama_model_rope_type", [llama_model_p_ctypes], ctypes.c_int) +def llama_model_rope_type(model: llama_model_p, /) -> int: ... + + +# LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model); +@ctypes_function("llama_model_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_ctx_train(model: llama_model_p, /) -> int: ... + +# LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model); +@ctypes_function("llama_model_n_embd", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_embd(model: llama_model_p, /) -> int: ... -# LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); -@ctypes_function("llama_rope_type", [llama_model_p_ctypes], ctypes.c_int) -def llama_rope_type(model: llama_model_p, /) -> int: + +# LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model); +@ctypes_function("llama_model_n_embd_inp", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_embd_inp(model: llama_model_p, /) -> int: + """Get the model input embedding size.""" ... -# // Get the model's RoPE frequency scaling factor -# LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model); -@ctypes_function("llama_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float) -def llama_rope_freq_scale_train(model: llama_model_p, /) -> float: - """Get the model's RoPE frequency scaling factor""" +# LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model); +@ctypes_function("llama_model_n_embd_out", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_embd_out(model: llama_model_p, /) -> int: + """Get the model output embedding size.""" ... -# // Functions to access the model's GGUF metadata scalar values -# // - The functions return the length of the string on success, or -1 on failure -# // - The output string is always null-terminated and cleared on failure -# // - GGUF array values are not supported by these functions +# LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model); +@ctypes_function("llama_model_n_layer", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_layer(model: llama_model_p, /) -> int: ... -# // Get metadata value as a string by key name -# LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size); +# LLAMA_API int32_t llama_model_n_head (const struct llama_model * model); +@ctypes_function("llama_model_n_head", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_head(model: llama_model_p, /) -> int: ... + + +# LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model); +@ctypes_function("llama_model_n_head_kv", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_head_kv(model: llama_model_p, /) -> int: ... + + +# LLAMA_API int32_t llama_model_n_swa (const struct llama_model * model); +@ctypes_function("llama_model_n_swa", [llama_model_p_ctypes], ctypes.c_int32) +def llama_model_n_swa(model: llama_model_p, /) -> int: ... + + +# // Get the model's RoPE frequency scaling factor +# LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model); @ctypes_function( - "llama_model_meta_val_str", + "llama_model_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float +) +def llama_model_rope_freq_scale_train(model: llama_model_p, /) -> float: ... + + +# // Returns the number of classifier outputs (only valid for classifier models) +# // Undefined behavior for non-classifier models +# LLAMA_API uint32_t llama_model_n_cls_out(const struct llama_model * model); +@ctypes_function("llama_model_n_cls_out", [llama_model_p_ctypes], ctypes.c_uint32) +def llama_model_n_cls_out(model: llama_model_p, /) -> int: + """Returns the number of classifier outputs (only valid for classifier models)""" + ... + + +# // Returns label of classifier output by index ( Optional[bytes]: + """Returns label of classifier output by index. Returns None if no label provided""" + ... + + +# LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); +@ctypes_function("llama_vocab_type", [llama_vocab_p_ctypes], ctypes.c_int) +def llama_vocab_type(vocab: llama_vocab_p, /) -> int: ... + + +# LLAMA_API int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab); +@ctypes_function("llama_vocab_n_tokens", [llama_vocab_p_ctypes], ctypes.c_int32) +def llama_vocab_n_tokens(vocab: llama_vocab_p, /) -> int: ... + + +# // Functions to access the model's GGUF metadata scalar values +# // - The functions return the length of the string on success, or -1 on failure +# // - The output string is always null-terminated and cleared on failure +# // - When retrieving a string, an extra byte must be allocated to account for the null terminator +# // - GGUF array values are not supported by these functions + + +# // Get metadata value as a string by key name +# LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size); +@ctypes_function( + "llama_model_meta_val_str", [ llama_model_p_ctypes, ctypes.c_char_p, @@ -1295,6 +1842,14 @@ def llama_model_meta_count(model: llama_model_p, /) -> int: ... +# // Get sampling metadata key name. Returns nullptr if the key is invalid +# LLAMA_API const char * llama_model_meta_key_str(enum llama_model_meta_key key); +@ctypes_function("llama_model_meta_key_str", [ctypes.c_int], ctypes.c_char_p) +def llama_model_meta_key_str(key: int, /) -> Optional[bytes]: + """Get sampling metadata key name. Returns None if the key is invalid.""" + ... + + # // Get metadata key name by index # LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size); @ctypes_function( @@ -1366,6 +1921,22 @@ def llama_model_size(model: llama_model_p, /) -> int: ... +# // Get the default chat template. Returns nullptr if not available +# // If name is NULL, returns the default chat template +# LLAMA_API const char * llama_model_chat_template(const struct llama_model * model, const char * name); +@ctypes_function( + "llama_model_chat_template", + [llama_model_p_ctypes, ctypes.c_char_p], + ctypes.c_char_p, +) +def llama_model_chat_template( + model: llama_model_p, name: Optional[bytes], / +) -> Optional[bytes]: + """Get the default chat template. Returns None if not available + If name is None, returns the default chat template""" + ... + + # // Returns the total number of parameters in the model # LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model); @ctypes_function("llama_model_n_params", [llama_model_p_ctypes], ctypes.c_uint64) @@ -1374,18 +1945,6 @@ def llama_model_n_params(model: llama_model_p, /) -> int: ... -# // Get a llama model tensor -# LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name); -@ctypes_function( - "llama_get_model_tensor", [llama_model_p_ctypes, ctypes.c_char_p], ctypes.c_void_p -) -def llama_get_model_tensor( - model: llama_model_p, name: Union[ctypes.c_char_p, bytes], / -) -> ctypes.c_void_p: - """Get a llama model tensor""" - ... - - # // Returns true if the model contains an encoder that requires llama_encode() call # LLAMA_API bool llama_model_has_encoder(const struct llama_model * model); @ctypes_function("llama_model_has_encoder", [llama_model_p_ctypes], ctypes.c_bool) @@ -1423,6 +1982,22 @@ def llama_model_is_recurrent(model: llama_model_p, /) -> bool: ... +# // Returns true if the model is hybrid (like Jamba, Granite, etc.) +# LLAMA_API bool llama_model_is_hybrid(const struct llama_model * model); +@ctypes_function("llama_model_is_hybrid", [llama_model_p_ctypes], ctypes.c_bool) +def llama_model_is_hybrid(model: llama_model_p, /) -> bool: + """Returns true if the model is hybrid (like Jamba, Granite, etc.)""" + ... + + +# // Returns true if the model is diffusion-based (like LLaDA, Dream, etc.) +# LLAMA_API bool llama_model_is_diffusion(const struct llama_model * model); +@ctypes_function("llama_model_is_diffusion", [llama_model_p_ctypes], ctypes.c_bool) +def llama_model_is_diffusion(model: llama_model_p, /) -> bool: + """Returns true if the model is diffusion-based (like LLaDA, Dream, etc.)""" + ... + + # // Returns 0 on success # LLAMA_API uint32_t llama_model_quantize( # const char * fname_inp, @@ -1447,104 +2022,201 @@ def llama_model_quantize( ... +# // +# // Adapters +# // + + # // Load a LoRA adapter from file -# // The loaded adapter will be associated to the given model, and will be free when the model is deleted -# LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init( +# LLAMA_API struct llama_adapter_lora * llama_adapter_lora_init( # struct llama_model * model, # const char * path_lora); @ctypes_function( - "llama_lora_adapter_init", + "llama_adapter_lora_init", [llama_model_p_ctypes, ctypes.c_char_p], - llama_lora_adapter_p_ctypes, + llama_adapter_lora_p_ctypes, ) -def llama_lora_adapter_init( +def llama_adapter_lora_init( model: llama_model_p, path_lora: bytes, / -) -> Optional[llama_lora_adapter_p]: - """Load a LoRA adapter from file - The loaded adapter will be associated to the given model, and will be free when the model is deleted - """ - ... +) -> Optional[llama_adapter_lora_p]: ... -# // Add a loaded LoRA adapter to given context -# // This will not modify model's weight -# LLAMA_API int32_t llama_lora_adapter_set( -# struct llama_context * ctx, -# struct llama_lora_adapter * adapter, -# float scale); +# // Get metadata value as a string by key name +# LLAMA_API int32_t llama_adapter_meta_val_str(const struct llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size); @ctypes_function( - "llama_lora_adapter_set", - [llama_context_p_ctypes, llama_lora_adapter_p_ctypes, ctypes.c_float], + "llama_adapter_meta_val_str", + [ + llama_adapter_lora_p_ctypes, + ctypes.c_char_p, + ctypes.c_char_p, + ctypes.c_size_t, + ], ctypes.c_int32, ) -def llama_lora_adapter_set( - ctx: llama_context_p, adapter: llama_lora_adapter_p, scale: float, / +def llama_adapter_meta_val_str( + adapter: llama_adapter_lora_p, + key: bytes, + buf: Union[bytes, CtypesArray[ctypes.c_char]], + buf_size: int, + /, ) -> int: - """Add a loaded LoRA adapter to given context - This will not modify model's weight""" + """Get adapter metadata value as a string by key name.""" ... -# // Remove a specific LoRA adapter from given context -# // Return -1 if the adapter is not present in the context -# LLAMA_API int32_t llama_lora_adapter_remove( -# struct llama_context * ctx, -# struct llama_lora_adapter * adapter); +# // Get the number of metadata key/value pairs +# LLAMA_API int32_t llama_adapter_meta_count(const struct llama_adapter_lora * adapter); +@ctypes_function( + "llama_adapter_meta_count", [llama_adapter_lora_p_ctypes], ctypes.c_int32 +) +def llama_adapter_meta_count(adapter: llama_adapter_lora_p, /) -> int: + """Get the number of adapter metadata key/value pairs.""" + ... + + +# // Get metadata key name by index +# LLAMA_API int32_t llama_adapter_meta_key_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size); @ctypes_function( - "llama_lora_adapter_remove", - [llama_context_p_ctypes, llama_lora_adapter_p_ctypes], + "llama_adapter_meta_key_by_index", + [ + llama_adapter_lora_p_ctypes, + ctypes.c_int32, + ctypes.c_char_p, + ctypes.c_size_t, + ], ctypes.c_int32, ) -def llama_lora_adapter_remove( - ctx: llama_context_p, adapter: llama_lora_adapter_p, / +def llama_adapter_meta_key_by_index( + adapter: llama_adapter_lora_p, + i: int, + buf: Union[bytes, CtypesArray[ctypes.c_char]], + buf_size: int, + /, ) -> int: - """Remove a LoRA adapter from given context - Return -1 if the adapter is not present in the context""" + """Get adapter metadata key name by index.""" ... -# // Remove all LoRA adapters from given context -# LLAMA_API void llama_lora_adapter_clear( -# struct llama_context * ctx); +# // Get metadata value as a string by index +# LLAMA_API int32_t llama_adapter_meta_val_str_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size); @ctypes_function( - "llama_lora_adapter_clear", - [llama_context_p_ctypes], - None, + "llama_adapter_meta_val_str_by_index", + [ + llama_adapter_lora_p_ctypes, + ctypes.c_int32, + ctypes.c_char_p, + ctypes.c_size_t, + ], + ctypes.c_int32, ) -def llama_lora_adapter_clear(ctx: llama_context_p, /): - """Remove all LoRA adapters from given context""" +def llama_adapter_meta_val_str_by_index( + adapter: llama_adapter_lora_p, + i: int, + buf: Union[bytes, CtypesArray[ctypes.c_char]], + buf_size: int, + /, +) -> int: + """Get adapter metadata value as a string by index.""" ... # // Manually free a LoRA adapter # // Note: loaded adapters will be free when the associated model is deleted -# LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter); +# LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter); @ctypes_function( - "llama_lora_adapter_free", - [llama_lora_adapter_p_ctypes], + "llama_adapter_lora_free", + [llama_adapter_lora_p_ctypes], None, ) -def llama_lora_adapter_free(adapter: llama_lora_adapter_p, /): - """Manually free a LoRA adapter - Note: loaded adapters will be free when the associated model is deleted""" +def llama_adapter_lora_free(adapter: llama_adapter_lora_p, /): ... + + +# // Get the invocation tokens if the current lora is an alora +# LLAMA_API uint64_t llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter); +@ctypes_function( + "llama_adapter_get_alora_n_invocation_tokens", + [llama_adapter_lora_p_ctypes], + ctypes.c_uint64, +) +def llama_adapter_get_alora_n_invocation_tokens( + adapter: llama_adapter_lora_p, / +) -> int: + """Get the invocation token count if the current LoRA is an aLoRA.""" + ... + + +# LLAMA_API const llama_token * llama_adapter_get_alora_invocation_tokens (const struct llama_adapter_lora * adapter); +@ctypes_function( + "llama_adapter_get_alora_invocation_tokens", + [llama_adapter_lora_p_ctypes], + ctypes.POINTER(llama_token), +) +def llama_adapter_get_alora_invocation_tokens( + adapter: llama_adapter_lora_p, / +) -> Optional[CtypesPointer[llama_token]]: + """Get the invocation tokens if the current LoRA is an aLoRA.""" + ... + + +# // The following functions operate on a llama_context, hence the naming: llama_verb_... + + +# // Set LoRa adapters on the context. Will only modify if the adapters currently in context are different. +# LLAMA_API int32_t llama_set_adapters_lora( +# struct llama_context * ctx, +# struct llama_adapter_lora ** adapters, +# size_t n_adapters, +# float * scales); +@ctypes_function( + "llama_set_adapters_lora", + [ + llama_context_p_ctypes, + ctypes.POINTER(llama_adapter_lora_p_ctypes), + ctypes.c_size_t, + ctypes.POINTER(ctypes.c_float), + ], + ctypes.c_int32, +) +def llama_set_adapters_lora( + ctx: llama_context_p, + adapters: Optional[CtypesArray[llama_adapter_lora_p_ctypes]], + n_adapters: int, + scales: Optional[CtypesArray[ctypes.c_float]], + /, +) -> int: + """Set LoRA adapters on the context if they differ from the current adapters.""" ... +# Deprecated compatibility wrapper for the renamed llama_set_adapters_lora(). +def llama_set_adapter_lora( + ctx: llama_context_p, adapter: llama_adapter_lora_p, scale: float, / +) -> int: + warnings.warn( + "llama_set_adapter_lora is deprecated; use llama_set_adapters_lora instead", + DeprecationWarning, + stacklevel=2, + ) + adapters = (llama_adapter_lora_p_ctypes * 1)(adapter) + scales = (ctypes.c_float * 1)(scale) + return llama_set_adapters_lora(ctx, adapters, 1, scales) + + # // Apply a loaded control vector to a llama_context, or if data is NULL, clear # // the currently loaded vector. # // n_embd should be the size of a single layer's control, and data should point # // to an n_embd x n_layers buffer starting from layer 1. # // il_start and il_end are the layer range the vector should apply to (both inclusive) # // See llama_control_vector_load in common to load a control vector. -# LLAMA_API int32_t llama_control_vector_apply( -# struct llama_context * lctx, +# LLAMA_API int32_t llama_set_adapter_cvec( +# struct llama_context * ctx, # const float * data, # size_t len, # int32_t n_embd, # int32_t il_start, # int32_t il_end); @ctypes_function( - "llama_control_vector_apply", + "llama_set_adapter_cvec", [ llama_context_p_ctypes, ctypes.POINTER(ctypes.c_float), @@ -1555,8 +2227,8 @@ def llama_lora_adapter_free(adapter: llama_lora_adapter_p, /): ], ctypes.c_int32, ) -def llama_control_vector_apply( - lctx: llama_context_p, +def llama_set_adapter_cvec( + ctx: llama_context_p, data: CtypesPointerOrRef[ctypes.c_float], len: int, n_embd: int, @@ -1573,149 +2245,42 @@ def llama_control_vector_apply( ... -# // -# // KV cache -# // - - -# // Information associated with an individual cell in the KV cache view. -# struct llama_kv_cache_view_cell { -# // The position for this cell. Takes KV cache shifts into account. -# // May be negative if the cell is not populated. -# llama_pos pos; -# }; -class llama_kv_cache_view_cell(ctypes.Structure): - """Information associated with an individual cell in the KV cache view. - - Attributes: - pos (llama_pos): The position for this cell. Takes KV cache shifts into account. - May be negative if the cell is not populated.""" - - if TYPE_CHECKING: - pos: llama_pos - - _fields_ = [("pos", llama_pos)] - - -# // An updateable view of the KV cache. -# struct llama_kv_cache_view { -# // Number of KV cache cells. This will be the same as the context size. -# int32_t n_cells; - -# // Maximum number of sequences that can exist in a cell. It's not an error -# // if there are more sequences in a cell than this value, however they will -# // not be visible in the view cells_sequences. -# int32_t n_seq_max; - -# // Number of tokens in the cache. For example, if there are two populated -# // cells, the first with 1 sequence id in it and the second with 2 sequence -# // ids then you'll have 3 tokens. -# int32_t token_count; - -# // Number of populated cache cells. -# int32_t used_cells; - -# // Maximum contiguous empty slots in the cache. -# int32_t max_contiguous; - -# // Index to the start of the max_contiguous slot range. Can be negative -# // when cache is full. -# int32_t max_contiguous_idx; - -# // Information for an individual cell. -# struct llama_kv_cache_view_cell * cells; - - -# // The sequences for each cell. There will be n_seq_max items per cell. -# llama_seq_id * cells_sequences; -# }; -class llama_kv_cache_view(ctypes.Structure): - if TYPE_CHECKING: - n_cells: int - n_max_seq: int - token_count: int - used_cells: int - max_contiguous: int - max_contiguous_idx: int - cells: CtypesArray[llama_kv_cache_view_cell] - cells_sequences: CtypesArray[llama_seq_id] - - _fields_ = [ - ("n_cells", ctypes.c_int32), - ("n_max_seq", ctypes.c_int32), - ("token_count", ctypes.c_int32), - ("used_cells", ctypes.c_int32), - ("max_contiguous", ctypes.c_int32), - ("max_contiguous_idx", ctypes.c_int32), - ("cells", ctypes.POINTER(llama_kv_cache_view_cell)), - ("cells_sequences", ctypes.POINTER(llama_seq_id)), - ] - - -llama_kv_cache_view_p = ctypes.POINTER(llama_kv_cache_view) - - -# // Create an empty KV cache view. (use only for debugging purposes) -# LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max); -@ctypes_function( - "llama_kv_cache_view_init", - [llama_context_p_ctypes, ctypes.c_int32], - llama_kv_cache_view, -) -def llama_kv_cache_view_init( - ctx: llama_context_p, n_seq_max: Union[ctypes.c_int32, int], / -) -> llama_kv_cache_view: - """Create an empty KV cache view. (use only for debugging purposes)""" - ... - - -# // Free a KV cache view. (use only for debugging purposes) -# LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view); -@ctypes_function("llama_kv_cache_view_free", [llama_kv_cache_view_p], None) -def llama_kv_cache_view_free(view: "ctypes.pointer[llama_kv_cache_view]", /): # type: ignore - """Free a KV cache view. (use only for debugging purposes)""" - ... - - -# // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes) -# LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view); -@ctypes_function( - "llama_kv_cache_view_update", [llama_context_p_ctypes, llama_kv_cache_view_p], None -) -def llama_kv_cache_view_update(ctx: llama_context_p, view: CtypesPointerOrRef[llama_kv_cache_view], /): # type: ignore - """Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)""" - ... +# Deprecated compatibility wrapper for the renamed llama_set_adapter_cvec(). +def llama_apply_adapter_cvec( + ctx: llama_context_p, + data: CtypesPointerOrRef[ctypes.c_float], + len: int, + n_embd: int, + il_start: int, + il_end: int, + /, +) -> int: + warnings.warn( + "llama_apply_adapter_cvec is deprecated; use llama_set_adapter_cvec instead", + DeprecationWarning, + stacklevel=2, + ) + return llama_set_adapter_cvec(ctx, data, len, n_embd, il_start, il_end) -# // Returns the number of tokens in the KV cache (slow, use only for debug) -# // If a KV cell has multiple sequences assigned to it, it will be counted multiple times -# LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx); -@ctypes_function( - "llama_get_kv_cache_token_count", [llama_context_p_ctypes], ctypes.c_int32 -) -def llama_get_kv_cache_token_count(ctx: llama_context_p, /) -> int: - """Returns the number of tokens in the KV cache (slow, use only for debug) - If a KV cell has multiple sequences assigned to it, it will be counted multiple times - """ - ... +# // +# // Memory +# // -# // Returns the number of used KV cells (i.e. have at least one sequence assigned to them) -# LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx); +# // Clear the memory contents +# // If data == true, the data buffers will also be cleared together with the metadata +# LLAMA_API void llama_memory_clear( +# llama_memory_t mem, +# bool data); @ctypes_function( - "llama_get_kv_cache_used_cells", [llama_context_p_ctypes], ctypes.c_int32 + "llama_memory_clear", + [llama_memory_t_ctypes, ctypes.c_bool], + None, ) -def llama_get_kv_cache_used_cells(ctx: llama_context_p, /) -> int: - """Returns the number of used KV cells (i.e. have at least one sequence assigned to them)""" - ... - - -# // Clear the KV cache - both cell info is erased and KV data is zeroed -# LLAMA_API void llama_kv_cache_clear( -# struct llama_context * ctx); -@ctypes_function("llama_kv_cache_clear", [llama_context_p_ctypes], None) -def llama_kv_cache_clear(ctx: llama_context_p, /): - """Clear the KV cache""" +def llama_memory_clear(mem: llama_memory_t, data: bool, /): + """Clear the memory contents + If data == true, the data buffers will also be cleared together with the metadata""" ... @@ -1724,23 +2289,23 @@ def llama_kv_cache_clear(ctx: llama_context_p, /): # // seq_id < 0 : match any sequence # // p0 < 0 : [0, p1] # // p1 < 0 : [p0, inf) -# LLAMA_API bool llama_kv_cache_seq_rm( -# struct llama_context * ctx, -# llama_seq_id seq_id, -# llama_pos p0, -# llama_pos p1); +# LLAMA_API bool llama_memory_seq_rm( +# llama_memory_t mem, +# llama_seq_id seq_id, +# llama_pos p0, +# llama_pos p1); @ctypes_function( - "llama_kv_cache_seq_rm", + "llama_memory_seq_rm", [ - llama_context_p_ctypes, + llama_memory_t_ctypes, llama_seq_id, llama_pos, llama_pos, ], ctypes.c_bool, ) -def llama_kv_cache_seq_rm( - ctx: llama_context_p, +def llama_memory_seq_rm( + mem: llama_memory_t, seq_id: Union[llama_seq_id, int], p0: Union[llama_pos, int], p1: Union[llama_pos, int], @@ -1757,19 +2322,18 @@ def llama_kv_cache_seq_rm( # // Copy all tokens that belong to the specified sequence to another sequence -# // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence # // p0 < 0 : [0, p1] # // p1 < 0 : [p0, inf) -# LLAMA_API void llama_kv_cache_seq_cp( -# struct llama_context * ctx, -# llama_seq_id seq_id_src, -# llama_seq_id seq_id_dst, -# llama_pos p0, -# llama_pos p1); +# LLAMA_API void llama_memory_seq_cp( +# llama_memory_t mem, +# llama_seq_id seq_id_src, +# llama_seq_id seq_id_dst, +# llama_pos p0, +# llama_pos p1); @ctypes_function( - "llama_kv_cache_seq_cp", + "llama_memory_seq_cp", [ - llama_context_p_ctypes, + llama_memory_t_ctypes, llama_seq_id, llama_seq_id, llama_pos, @@ -1777,8 +2341,8 @@ def llama_kv_cache_seq_rm( ], None, ) -def llama_kv_cache_seq_cp( - ctx: llama_context_p, +def llama_memory_seq_cp( + mem: llama_memory_t, seq_id_src: Union[llama_seq_id, int], seq_id_dst: Union[llama_seq_id, int], p0: Union[llama_pos, int], @@ -1786,40 +2350,34 @@ def llama_kv_cache_seq_cp( /, ): """Copy all tokens that belong to the specified sequence to another sequence - Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence p0 < 0 : [0, p1] p1 < 0 : [p0, inf)""" ... # // Removes all tokens that do not belong to the specified sequence -# LLAMA_API void llama_kv_cache_seq_keep( -# struct llama_context * ctx, -# llama_seq_id seq_id); -@ctypes_function( - "llama_kv_cache_seq_keep", [llama_context_p_ctypes, llama_seq_id], None -) -def llama_kv_cache_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /): +# LLAMA_API void llama_memory_seq_keep( +# llama_memory_t mem, +# llama_seq_id seq_id); +@ctypes_function("llama_memory_seq_keep", [llama_memory_t_ctypes, llama_seq_id], None) +def llama_memory_seq_keep(mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /): """Removes all tokens that do not belong to the specified sequence""" ... # // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1) -# // If the KV cache is RoPEd, the KV data is updated accordingly: -# // - lazily on next llama_decode() -# // - explicitly with llama_kv_cache_update() # // p0 < 0 : [0, p1] # // p1 < 0 : [p0, inf) -# LLAMA_API void llama_kv_cache_seq_add( -# struct llama_context * ctx, -# llama_seq_id seq_id, -# llama_pos p0, -# llama_pos p1, -# llama_pos delta); +# LLAMA_API void llama_memory_seq_add( +# llama_memory_t mem, +# llama_seq_id seq_id, +# llama_pos p0, +# llama_pos p1, +# llama_pos delta); @ctypes_function( - "llama_kv_cache_seq_add", + "llama_memory_seq_add", [ - llama_context_p_ctypes, + llama_memory_t_ctypes, llama_seq_id, llama_pos, llama_pos, @@ -1827,8 +2385,8 @@ def llama_kv_cache_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, in ], None, ) -def llama_kv_cache_seq_add( - ctx: llama_context_p, +def llama_memory_seq_add( + mem: llama_memory_t, seq_id: Union[llama_seq_id, int], p0: Union[llama_pos, int], p1: Union[llama_pos, int], @@ -1836,28 +2394,24 @@ def llama_kv_cache_seq_add( /, ): """Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1) - If the KV cache is RoPEd, the KV data is updated accordingly: - - lazily on next llama_decode() - - explicitly with llama_kv_cache_update() p0 < 0 : [0, p1] p1 < 0 : [p0, inf)""" ... # // Integer division of the positions by factor of `d > 1` -# // If the KV cache is RoPEd, the KV data is updated accordingly # // p0 < 0 : [0, p1] # // p1 < 0 : [p0, inf) -# LLAMA_API void llama_kv_cache_seq_div( -# struct llama_context * ctx, -# llama_seq_id seq_id, -# llama_pos p0, -# llama_pos p1, -# int d); +# LLAMA_API void llama_memory_seq_div( +# llama_memory_t mem, +# llama_seq_id seq_id, +# llama_pos p0, +# llama_pos p1, +# int d); @ctypes_function( - "llama_kv_cache_seq_div", + "llama_memory_seq_div", [ - llama_context_p_ctypes, + llama_memory_t_ctypes, llama_seq_id, llama_pos, llama_pos, @@ -1865,8 +2419,8 @@ def llama_kv_cache_seq_add( ], None, ) -def llama_kv_cache_seq_div( - ctx: llama_context_p, +def llama_memory_seq_div( + mem: llama_memory_t, seq_id: Union[llama_seq_id, int], p0: Union[llama_pos, int], p1: Union[llama_pos, int], @@ -1874,39 +2428,52 @@ def llama_kv_cache_seq_div( /, ): """Integer division of the positions by factor of `d > 1` - If the KV cache is RoPEd, the KV data is updated accordingly p0 < 0 : [0, p1] p1 < 0 : [p0, inf)""" ... -# // Defragment the KV cache -# // This will be applied: -# // - lazily on next llama_decode() -# // - explicitly with llama_kv_cache_update() -# LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx); -@ctypes_function("llama_kv_cache_defrag", [llama_context_p_ctypes], None) -def llama_kv_cache_defrag(ctx: llama_context_p, /): - """Defragment the KV cache - This will be applied: - - lazily on next llama_decode() - - explicitly with llama_kv_cache_update()""" +# // Returns the smallest position present in the memory for the specified sequence +# // This is typically non-zero only for SWA caches +# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory +# // Return -1 if the sequence is empty +# LLAMA_API llama_pos llama_memory_seq_pos_min( +# llama_memory_t mem, +# llama_seq_id seq_id); +@ctypes_function( + "llama_memory_seq_pos_min", [llama_memory_t_ctypes, llama_seq_id], llama_pos +) +def llama_memory_seq_pos_min( + mem: llama_memory_t, seq_id: Union[llama_seq_id, int], / +) -> int: + """Returns the smallest position present in the memory for the specified sequence + This is typically non-zero only for SWA caches + Return -1 if the sequence is empty""" ... -# // Apply the KV cache updates (such as K-shifts, defragmentation, etc.) -# LLAMA_API void llama_kv_cache_update(struct llama_context * ctx); -@ctypes_function("llama_kv_cache_update", [llama_context_p_ctypes], None) -def llama_kv_cache_update(ctx: llama_context_p, /): - """Apply the KV cache updates (such as K-shifts, defragmentation, etc.)""" +# // Returns the largest position present in the memory for the specified sequence +# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory +# // Return -1 if the sequence is empty +# LLAMA_API llama_pos llama_memory_seq_pos_max( +# llama_memory_t mem, +# llama_seq_id seq_id); +@ctypes_function( + "llama_memory_seq_pos_max", [llama_memory_t_ctypes, llama_seq_id], llama_pos +) +def llama_memory_seq_pos_max( + mem: llama_memory_t, seq_id: Union[llama_seq_id, int], / +) -> int: + """Returns the largest position present in the memory for the specified sequence + Return -1 if the sequence is empty""" ... -# // Check if the context supports KV cache shifting -# LLAMA_API bool llama_kv_cache_can_shift(struct llama_context * ctx); -@ctypes_function("llama_kv_cache_can_shift", [llama_context_p_ctypes], ctypes.c_bool) -def llama_kv_cache_can_shift(ctx: llama_context_p, /) -> bool: - """Check if the context supports KV cache shifting""" +# // Check if the memory supports shifting +# LLAMA_API bool llama_memory_can_shift(llama_memory_t mem); +@ctypes_function("llama_memory_can_shift", [llama_memory_t_ctypes], ctypes.c_bool) +def llama_memory_can_shift(mem: llama_memory_t, /) -> bool: + """Check if the memory supports shifting""" ... @@ -1916,12 +2483,12 @@ def llama_kv_cache_can_shift(ctx: llama_context_p, /) -> bool: # // Returns the *actual* size in bytes of the state -# // (logits, embedding and kv_cache) +# // (logits, embedding and memory) # // Only use when saving the state, not when restoring it, otherwise the size may be too small. # LLAMA_API size_t llama_state_get_size(struct llama_context * ctx); @ctypes_function("llama_state_get_size", [llama_context_p_ctypes], ctypes.c_size_t) def llama_state_get_size(ctx: llama_context_p, /) -> int: - """Returns the *actual* size in bytes of the state (rng, logits, embedding and kv_cache) - will often be smaller after compacting tokens""" + """Returns the *actual* size in bytes of the state (logits, embedding and memory)""" ... @@ -1929,11 +2496,18 @@ def llama_state_get_size(ctx: llama_context_p, /) -> int: # "use llama_state_get_size instead"); @ctypes_function("llama_get_state_size", [llama_context_p_ctypes], ctypes.c_size_t) def llama_get_state_size(ctx: llama_context_p, /) -> int: - """Returns the maximum size in bytes of the state (rng, logits, embedding - and kv_cache) - will often be smaller after compacting tokens""" + """Returns the size in bytes of the state (DEPRECATED)""" ... +_llama_get_state_size = llama_get_state_size + + +def llama_get_state_size(ctx: llama_context_p, /) -> int: + _warn_deprecated("llama_get_state_size", "use llama_state_get_size instead") + return _llama_get_state_size(ctx) + + # // Copies the state to the specified destination address. # // Destination needs to have allocated enough memory. # // Returns the number of bytes copied @@ -1977,12 +2551,20 @@ def llama_state_get_data( def llama_copy_state_data( ctx: llama_context_p, dst: CtypesArray[ctypes.c_uint8], / ) -> int: - """Copies the state to the specified destination address. - Destination needs to have allocated enough memory. - Returns the number of bytes copied""" + """Copies the state to the specified destination address (DEPRECATED)""" ... +_llama_copy_state_data = llama_copy_state_data + + +def llama_copy_state_data( + ctx: llama_context_p, dst: CtypesArray[ctypes.c_uint8], / +) -> int: + _warn_deprecated("llama_copy_state_data", "use llama_state_get_data instead") + return _llama_copy_state_data(ctx, dst) + + # // Set the state reading from the specified address # // Returns the number of bytes read # LLAMA_API size_t llama_state_set_data( @@ -2017,10 +2599,20 @@ def llama_state_set_data( def llama_set_state_data( ctx: llama_context_p, src: CtypesArray[ctypes.c_uint8], / ) -> int: - """Set the state reading from the specified address""" + """Set the state reading from the specified address (DEPRECATED)""" ... +_llama_set_state_data = llama_set_state_data + + +def llama_set_state_data( + ctx: llama_context_p, src: CtypesArray[ctypes.c_uint8], / +) -> int: + _warn_deprecated("llama_set_state_data", "use llama_state_set_data instead") + return _llama_set_state_data(ctx, src) + + # Save/load session file # LLAMA_API bool llama_state_load_file( # struct llama_context * ctx, @@ -2046,8 +2638,7 @@ def llama_state_load_file( n_token_capacity: Union[ctypes.c_size_t, int], n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], /, -) -> bool: - ... +) -> bool: ... # LLAMA_API DEPRECATED(bool llama_load_session_file( @@ -2066,7 +2657,7 @@ def llama_state_load_file( ctypes.c_size_t, ctypes.POINTER(ctypes.c_size_t), ], - ctypes.c_size_t, + ctypes.c_bool, ) def llama_load_session_file( ctx: llama_context_p, @@ -2075,8 +2666,24 @@ def llama_load_session_file( n_token_capacity: Union[ctypes.c_size_t, int], n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], /, -) -> int: - ... +) -> bool: ... + + +_llama_load_session_file = llama_load_session_file + + +def llama_load_session_file( + ctx: llama_context_p, + path_session: bytes, + tokens_out: CtypesArray[llama_token], + n_token_capacity: Union[ctypes.c_size_t, int], + n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], + /, +) -> bool: + _warn_deprecated("llama_load_session_file", "use llama_state_load_file instead") + return _llama_load_session_file( + ctx, path_session, tokens_out, n_token_capacity, n_token_count_out + ) # LLAMA_API bool llama_state_save_file( @@ -2100,8 +2707,7 @@ def llama_state_save_file( tokens: CtypesArray[llama_token], n_token_count: Union[ctypes.c_size_t, int], /, -) -> bool: - ... +) -> bool: ... # LLAMA_API DEPRECATED(bool llama_save_session_file( @@ -2118,7 +2724,7 @@ def llama_state_save_file( llama_token_p, ctypes.c_size_t, ], - ctypes.c_size_t, + ctypes.c_bool, ) def llama_save_session_file( ctx: llama_context_p, @@ -2126,11 +2732,24 @@ def llama_save_session_file( tokens: CtypesArray[llama_token], n_token_count: Union[ctypes.c_size_t, int], /, -) -> int: - ... +) -> bool: ... + +_llama_save_session_file = llama_save_session_file -# // Get the exact size needed to copy the KV cache of a single sequence + +def llama_save_session_file( + ctx: llama_context_p, + path_session: bytes, + tokens: CtypesArray[llama_token], + n_token_count: Union[ctypes.c_size_t, int], + /, +) -> bool: + _warn_deprecated("llama_save_session_file", "use llama_state_save_file instead") + return _llama_save_session_file(ctx, path_session, tokens, n_token_count) + + +# // Get the exact size needed to copy the state of a single sequence # LLAMA_API size_t llama_state_seq_get_size( # struct llama_context * ctx, # llama_seq_id seq_id); @@ -2140,11 +2759,11 @@ def llama_save_session_file( ctypes.c_size_t, ) def llama_state_seq_get_size(ctx: llama_context_p, seq_id: llama_seq_id, /) -> int: - """Get the exact size needed to copy the KV cache of a single sequence""" + """Get the exact size needed to copy the state of a single sequence""" ... -# // Copy the KV cache of a single sequence into the specified buffer +# // Copy the state of a single sequence into the specified buffer # LLAMA_API size_t llama_state_seq_get_data( # struct llama_context * ctx, # uint8_t * dst, @@ -2167,7 +2786,7 @@ def llama_state_seq_get_data( seq_id: llama_seq_id, /, ) -> int: - """Copy the KV cache of a single sequence into the specified buffer""" + """Copy the state of a single sequence into the specified buffer""" ... @@ -2197,7 +2816,7 @@ def llama_state_seq_set_data( dest_seq_id: llama_seq_id, /, ) -> int: - """Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence""" + """Copy the sequence data into the specified sequence""" ... @@ -2225,8 +2844,7 @@ def llama_state_seq_save_file( tokens: CtypesArray[llama_token], n_token_count: Union[ctypes.c_size_t, int], /, -) -> int: - ... +) -> int: ... # LLAMA_API size_t llama_state_seq_load_file( @@ -2256,8 +2874,7 @@ def llama_state_seq_load_file( n_token_capacity: Union[ctypes.c_size_t, int], n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t], /, -) -> int: - ... +) -> int: ... # // @@ -2287,7 +2904,7 @@ def llama_batch_get_one( n_tokens: Union[ctypes.c_int, int], /, ) -> llama_batch: - """Return batch for single sequence of tokens starting at pos_0 + """Return batch for single sequence of tokens NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it """ @@ -2332,35 +2949,46 @@ def llama_batch_free(batch: llama_batch, /): ... -# // Processes a batch of tokens with the ecoder part of the encoder-decoder model. -# // Stores the encoder output internally for later use by the decoder cross-attention layers. +# // Process a batch of tokens. +# // In contrast to llama_decode() - this call does not use KV cache. +# // For encode-decoder contexts, processes the batch using the encoder. +# // Can store the encoder output internally for later use by the decoder's cross-attention layers. # // 0 - success -# // < 0 - error +# // < 0 - error. the memory state is restored to the state before this call # LLAMA_API int32_t llama_encode( # struct llama_context * ctx, # struct llama_batch batch); @ctypes_function("llama_encode", [llama_context_p_ctypes, llama_batch], ctypes.c_int32) def llama_encode(ctx: llama_context_p, batch: llama_batch, /) -> int: - """Processes a batch of tokens with the ecoder part of the encoder-decoder model. - Stores the encoder output internally for later use by the decoder cross-attention layers. + """Process a batch of tokens using the encoder. 0 - success < 0 - error""" ... +# // Process a batch of tokens. +# // Requires the context to have a memory. +# // For encode-decoder contexts, processes the batch using the decoder. # // Positive return values does not mean a fatal error, but rather a warning. -# // 0 - success -# // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) -# // < 0 - error +# // Upon fatal-error or abort, the ubatches that managed to be been processed will remain in the memory state of the context +# // To handle this correctly, query the memory state using llama_memory_seq_pos_min() and llama_memory_seq_pos_max() +# // Upon other return values, the memory state is restored to the state before this call +# // 0 - success +# // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) +# // 2 - aborted (processed ubatches will remain in the context's memory) +# // -1 - invalid input batch +# // < -1 - fatal error (processed ubatches will remain in the context's memory) # LLAMA_API int32_t llama_decode( # struct llama_context * ctx, # struct llama_batch batch); @ctypes_function("llama_decode", [llama_context_p_ctypes, llama_batch], ctypes.c_int32) def llama_decode(ctx: llama_context_p, batch: llama_batch, /) -> int: - """Positive return values does not mean a fatal error, but rather a warning. + """Process a batch of tokens. 0 - success 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) - < 0 - error""" + 2 - aborted (processed ubatches will remain in the context's memory) + -1 - invalid input batch + < -1 - fatal error (processed ubatches will remain in the context's memory)""" ... @@ -2406,13 +3034,12 @@ def llama_n_threads_batch(ctx: llama_context_p, /) -> int: ... -# // Set whether the model is in embeddings mode or not -# // If true, embeddings will be returned but logits will not +# // Set whether the context outputs embeddings or not +# // TODO: rename to avoid confusion with llama_get_embeddings() # LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings); @ctypes_function("llama_set_embeddings", [llama_context_p_ctypes, ctypes.c_bool], None) def llama_set_embeddings(ctx: llama_context_p, embeddings: bool, /): - """Set whether the model is in embeddings model or not - If true, embeddings will be returned but logits will not""" + """Set whether the context outputs embeddings or not""" ... @@ -2426,6 +3053,16 @@ def llama_set_causal_attn(ctx: llama_context_p, causal_attn: bool, /): ... +# // Set whether the model is in warmup mode or not +# // If true, all model tensors are activated during llama_decode() to load and cache their weights. +# LLAMA_API void llama_set_warmup(struct llama_context * ctx, bool warmup); +@ctypes_function("llama_set_warmup", [llama_context_p_ctypes, ctypes.c_bool], None) +def llama_set_warmup(ctx: llama_context_p, warmup: bool, /): + """Set whether the model is in warmup mode or not + If true, all model tensors are activated during llama_decode() to load and cache their weights.""" + ... + + # // Set abort callback # LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data); @ctypes_function( @@ -2460,15 +3097,16 @@ def llama_synchronize(ctx: llama_context_p, /): # // in the order they have appeared in the batch. # // Rows: number of tokens for which llama_batch.logits[i] != 0 # // Cols: n_vocab +# // TODO: deprecate in favor of llama_get_logits_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522) # LLAMA_API float * llama_get_logits(struct llama_context * ctx); @ctypes_function( "llama_get_logits", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float) ) def llama_get_logits(ctx: llama_context_p, /) -> CtypesArray[ctypes.c_float]: - """Token logits obtained from the last call to llama_eval() - The logits for the last token are stored in the last row - Logits for which llama_batch.logits[i] == 0 are undefined - Rows: n_tokens provided with llama_batch + """Token logits obtained from the last call to llama_decode() + The logits for which llama_batch.logits[i] != 0 are stored contiguously + in the order they have appeared in the batch. + Rows: number of tokens for which llama_batch.logits[i] != 0 Cols: n_vocab Returns: @@ -2500,6 +3138,7 @@ def llama_get_logits_ith( # // in the order they have appeared in the batch. # // shape: [n_outputs*n_embd] # // Otherwise, returns NULL. +# // TODO: deprecate in favor of llama_get_embeddings_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522) # LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); @ctypes_function( "llama_get_embeddings", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float) @@ -2531,7 +3170,7 @@ def llama_get_embeddings_ith( # // Get the embeddings for a sequence id # // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE -# // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[1] with the rank of the sequence +# // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[n_cls_out] with the rank(s) of the sequence # // otherwise: float[n_embd] (1-dimensional) # LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id); @ctypes_function( @@ -2548,169 +3187,665 @@ def llama_get_embeddings_seq( ... -# // -# // Vocab -# // +# // Get the backend sampled token for the ith token. +# // Returns LLAMA_TOKEN_NULL if no token was sampled. +# LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_token_ith", [llama_context_p_ctypes, ctypes.c_int32], llama_token +) +def llama_get_sampled_token_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> int: + """Get the backend sampled token for the ith token.""" + ... -# LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token); +# // Get the backend sampled probabilities for the ith token +# LLAMA_API float * llama_get_sampled_probs_ith (struct llama_context * ctx, int32_t i); @ctypes_function( - "llama_token_get_text", [llama_model_p_ctypes, llama_token], ctypes.c_char_p + "llama_get_sampled_probs_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.POINTER(ctypes.c_float), ) -def llama_token_get_text( - model: llama_model_p, token: Union[llama_token, int], / -) -> bytes: +def llama_get_sampled_probs_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> Optional[CtypesPointer[ctypes.c_float]]: + """Get the backend sampled probabilities for the ith token.""" ... -# LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token); +# LLAMA_API uint32_t llama_get_sampled_probs_count_ith(struct llama_context * ctx, int32_t i); @ctypes_function( - "llama_token_get_score", [llama_model_p_ctypes, llama_token], ctypes.c_float + "llama_get_sampled_probs_count_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.c_uint32, ) -def llama_token_get_score( - model: llama_model_p, token: Union[llama_token, int], / -) -> float: +def llama_get_sampled_probs_count_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> int: + """Get the backend sampled probability count for the ith token.""" ... -# LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token); +# // Get the backend sampled logits for the ith token +# LLAMA_API float * llama_get_sampled_logits_ith (struct llama_context * ctx, int32_t i); @ctypes_function( - "llama_token_get_attr", [llama_model_p_ctypes, llama_token], ctypes.c_int + "llama_get_sampled_logits_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.POINTER(ctypes.c_float), ) -def llama_token_get_attr( - model: llama_model_p, token: Union[llama_token, int], / +def llama_get_sampled_logits_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> Optional[CtypesPointer[ctypes.c_float]]: + """Get the backend sampled logits for the ith token.""" + ... + + +# LLAMA_API uint32_t llama_get_sampled_logits_count_ith(struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_logits_count_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.c_uint32, +) +def llama_get_sampled_logits_count_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / ) -> int: + """Get the backend sampled logit count for the ith token.""" + ... + + +# // Get the backend sampled candidates for the ith token +# LLAMA_API llama_token * llama_get_sampled_candidates_ith (struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_candidates_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.POINTER(llama_token), +) +def llama_get_sampled_candidates_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> Optional[CtypesPointer[llama_token]]: + """Get the backend sampled candidates for the ith token.""" ... +# LLAMA_API uint32_t llama_get_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i); +@ctypes_function( + "llama_get_sampled_candidates_count_ith", + [llama_context_p_ctypes, ctypes.c_int32], + ctypes.c_uint32, +) +def llama_get_sampled_candidates_count_ith( + ctx: llama_context_p, i: Union[ctypes.c_int32, int], / +) -> int: + """Get the backend sampled candidate count for the ith token.""" + ... + + +# // +# // Vocab +# // + + +# LLAMA_API const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token); +@ctypes_function( + "llama_vocab_get_text", [llama_vocab_p_ctypes, llama_token], ctypes.c_char_p +) +def llama_vocab_get_text( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bytes: ... + + +# LLAMA_API float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token); +@ctypes_function( + "llama_vocab_get_score", [llama_vocab_p_ctypes, llama_token], ctypes.c_float +) +def llama_vocab_get_score( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> float: ... + + +# LLAMA_API enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token); +@ctypes_function( + "llama_vocab_get_attr", [llama_vocab_p_ctypes, llama_token], ctypes.c_int +) +def llama_vocab_get_attr( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> int: ... + + # // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.) -# LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token); +# LLAMA_API bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token); @ctypes_function( - "llama_token_is_eog", [llama_model_p_ctypes, llama_token], ctypes.c_bool + "llama_vocab_is_eog", [llama_vocab_p_ctypes, llama_token], ctypes.c_bool ) -def llama_token_is_eog(model: llama_model_p, token: Union[llama_token, int], /) -> bool: +def llama_vocab_is_eog(vocab: llama_vocab_p, token: Union[llama_token, int], /) -> bool: """Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)""" ... # // Identify if Token Id is a control token or a render-able token -# LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token); +# LLAMA_API bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token); @ctypes_function( - "llama_token_is_control", [llama_model_p_ctypes, llama_token], ctypes.c_bool + "llama_vocab_is_control", [llama_vocab_p_ctypes, llama_token], ctypes.c_bool ) -def llama_token_is_control( - model: llama_model_p, token: Union[llama_token, int], / +def llama_vocab_is_control( + vocab: llama_vocab_p, token: Union[llama_token, int], / ) -> bool: """Identify if Token Id is a control token or a render-able token""" ... # // Special tokens - - -# LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence -@ctypes_function("llama_token_bos", [llama_model_p_ctypes], llama_token) -def llama_token_bos(model: llama_model_p, /) -> int: +# LLAMA_API llama_token llama_vocab_bos(const struct llama_vocab * vocab); // beginning-of-sentence +@ctypes_function("llama_vocab_bos", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_bos(vocab: llama_vocab_p, /) -> llama_token: """beginning-of-sentence""" ... -# LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence -@ctypes_function("llama_token_eos", [llama_model_p_ctypes], llama_token) -def llama_token_eos(model: llama_model_p, /) -> int: +# LLAMA_API llama_token llama_vocab_eos(const struct llama_vocab * vocab); // end-of-sentence +@ctypes_function("llama_vocab_eos", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_eos(vocab: llama_vocab_p, /) -> llama_token: """end-of-sentence""" ... -# LLAMA_API llama_token llama_token_eot(const struct llama_model * model); // end-of-turn -@ctypes_function("llama_token_eot", [llama_model_p_ctypes], llama_token) -def llama_token_eot(model: llama_model_p, /) -> int: +# LLAMA_API llama_token llama_vocab_eot(const struct llama_vocab * vocab); // end-of-turn +@ctypes_function("llama_vocab_eot", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_eot(vocab: llama_vocab_p, /) -> llama_token: """end-of-turn""" ... -# LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification -@ctypes_function("llama_token_cls", [llama_model_p_ctypes], llama_token) -def llama_token_cls(model: llama_model_p, /) -> int: - """classification""" - ... - - -# LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator -@ctypes_function("llama_token_sep", [llama_model_p_ctypes], llama_token) -def llama_token_sep(model: llama_model_p, /) -> int: +# LLAMA_API llama_token llama_vocab_sep(const struct llama_vocab * vocab); // sentence separator +@ctypes_function("llama_vocab_sep", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_sep(vocab: llama_vocab_p, /) -> llama_token: """sentence separator""" ... -# LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line -@ctypes_function("llama_token_nl", [llama_model_p_ctypes], llama_token) -def llama_token_nl(model: llama_model_p, /) -> int: +# LLAMA_API llama_token llama_vocab_nl (const struct llama_vocab * vocab); // next-line +@ctypes_function("llama_vocab_nl", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_nl(vocab: llama_vocab_p, /) -> llama_token: """next-line""" ... -# LLAMA_API bool llama_add_bos_token(const struct llama_model * model); -@ctypes_function("llama_add_bos_token", [llama_model_p_ctypes], ctypes.c_bool) -def llama_add_bos_token(model: llama_model_p, /) -> bool: +# LLAMA_API llama_token llama_vocab_pad(const struct llama_vocab * vocab); // padding +@ctypes_function("llama_vocab_pad", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_pad(vocab: llama_vocab_p, /) -> llama_token: + """padding""" ... -# LLAMA_API bool llama_add_eos_token(const struct llama_model * model); -@ctypes_function("llama_add_eos_token", [llama_model_p_ctypes], ctypes.c_bool) -def llama_add_eos_token(model: llama_model_p, /) -> bool: +# LLAMA_API llama_token llama_vocab_mask(const struct llama_vocab * vocab); // mask +@ctypes_function("llama_vocab_mask", [llama_vocab_p_ctypes], llama_token) +def llama_vocab_mask(vocab: llama_vocab_p, /) -> llama_token: + """mask""" ... -# // Codellama infill tokens -# DEPRECATED(LLAMA_API llama_token llama_token_prefix(const struct llama_model * model), "use llama_token_fim_pre instead"); -@ctypes_function("llama_token_prefix", [llama_model_p_ctypes], llama_token) -def llama_token_prefix(model: llama_model_p) -> int: - """codellama infill tokens""" - ... +# LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_get_add_bos", + [llama_vocab_p_ctypes], + ctypes.c_bool, +) +def llama_vocab_get_add_bos(vocab: llama_vocab_p, /) -> bool: ... -# DEPRECATED(LLAMA_API llama_token llama_token_middle(const struct llama_model * model), "use llama_token_fim_mid instead"); -@ctypes_function("llama_token_middle", [llama_model_p_ctypes], llama_token) -def llama_token_middle(model: llama_model_p, /) -> int: - ... +# LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_get_add_eos", + [llama_vocab_p_ctypes], + ctypes.c_bool, +) +def llama_vocab_get_add_eos(vocab: llama_vocab_p, /) -> bool: ... -# DEPRECATED(LLAMA_API llama_token llama_token_suffix(const struct llama_model * model), "use llama_token_fim_suf instead"); -@ctypes_function("llama_token_suffix", [llama_model_p_ctypes], llama_token) -def llama_token_suffix(model: llama_model_p, /) -> int: - ... +# LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_get_add_sep", + [llama_vocab_p_ctypes], + ctypes.c_bool, +) +def llama_vocab_get_add_sep(vocab: llama_vocab_p, /) -> bool: ... -# LLAMA_API llama_token llama_token_fim_pre(const struct llama_model * model); -@ctypes_function("llama_token_fim_pre", [llama_model_p_ctypes], llama_token) -def llama_token_fim_pre(model: llama_model_p, /) -> int: - ... +# LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_fim_pre", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_vocab_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ... + + +# LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_fim_suf", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_vocab_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ... + + +# LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_fim_mid", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_vocab_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ... + + +# LLAMA_API llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_fim_pad", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_vocab_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ... + + +# LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_fim_rep", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_vocab_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ... + + +# LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab); +@ctypes_function( + "llama_vocab_fim_sep", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_vocab_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ... + + +# DEPRECATED functions +# DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_text instead"); +@ctypes_function( + "llama_token_get_text", + [llama_vocab_p_ctypes, llama_token], + ctypes.c_char_p, +) +def llama_token_get_text( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bytes: ... + + +_llama_token_get_text = llama_token_get_text + + +def llama_token_get_text( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bytes: + _warn_deprecated("llama_token_get_text", "use llama_vocab_get_text instead") + return _llama_token_get_text(vocab, token) + + +# DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead"); +@ctypes_function( + "llama_token_get_score", + [llama_vocab_p_ctypes, llama_token], + ctypes.c_float, +) +def llama_token_get_score( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> float: ... + + +_llama_token_get_score = llama_token_get_score + + +def llama_token_get_score( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> float: + _warn_deprecated("llama_token_get_score", "use llama_vocab_get_score instead") + return _llama_token_get_score(vocab, token) + + +# DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead"); +@ctypes_function( + "llama_token_get_attr", + [llama_vocab_p_ctypes, llama_token], + ctypes.c_int, +) +def llama_token_get_attr( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> int: ... + + +_llama_token_get_attr = llama_token_get_attr + + +def llama_token_get_attr( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> int: + _warn_deprecated("llama_token_get_attr", "use llama_vocab_get_attr instead") + return _llama_token_get_attr(vocab, token) + + +# DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead"); +@ctypes_function( + "llama_token_is_eog", + [llama_vocab_p_ctypes, llama_token], + ctypes.c_bool, +) +def llama_token_is_eog( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bool: ... + + +_llama_token_is_eog = llama_token_is_eog + + +def llama_token_is_eog(vocab: llama_vocab_p, token: Union[llama_token, int], /) -> bool: + _warn_deprecated("llama_token_is_eog", "use llama_vocab_is_eog instead") + return _llama_token_is_eog(vocab, token) + + +# DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead"); +@ctypes_function( + "llama_token_is_control", + [llama_vocab_p_ctypes, llama_token], + ctypes.c_bool, +) +def llama_token_is_control( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bool: ... + + +_llama_token_is_control = llama_token_is_control + + +def llama_token_is_control( + vocab: llama_vocab_p, token: Union[llama_token, int], / +) -> bool: + _warn_deprecated( + "llama_token_is_control", + "use llama_vocab_is_control instead", + ) + return _llama_token_is_control(vocab, token) + + +# DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead"); +@ctypes_function( + "llama_token_bos", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_bos(vocab: llama_vocab_p, /) -> int: ... + + +_llama_token_bos = llama_token_bos + + +def llama_token_bos(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_bos", "use llama_vocab_bos instead") + return _llama_token_bos(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead"); +@ctypes_function( + "llama_token_eos", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_eos(vocab: llama_vocab_p, /) -> int: ... + + +_llama_token_eos = llama_token_eos + + +def llama_token_eos(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_eos", "use llama_vocab_eos instead") + return _llama_token_eos(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead"); +@ctypes_function( + "llama_token_eot", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_eot(vocab: llama_vocab_p, /) -> int: ... + + +_llama_token_eot = llama_token_eot + + +def llama_token_eot(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_eot", "use llama_vocab_eot instead") + return _llama_token_eot(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead"); +@ctypes_function( + "llama_token_cls", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_cls(vocab: llama_vocab_p, /) -> int: ... + + +_llama_token_cls = llama_token_cls + + +def llama_token_cls(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_cls", "use llama_vocab_cls instead") + return _llama_token_cls(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead"); +@ctypes_function( + "llama_token_sep", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_sep(vocab: llama_vocab_p, /) -> int: ... + + +_llama_token_sep = llama_token_sep + + +def llama_token_sep(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_sep", "use llama_vocab_sep instead") + return _llama_token_sep(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead"); +@ctypes_function( + "llama_token_nl", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_nl(vocab: llama_vocab_p, /) -> int: ... + + +_llama_token_nl = llama_token_nl + + +def llama_token_nl(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_nl", "use llama_vocab_nl instead") + return _llama_token_nl(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead"); +@ctypes_function( + "llama_token_pad", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_pad(vocab: llama_vocab_p, /) -> int: ... + + +_llama_token_pad = llama_token_pad + + +def llama_token_pad(vocab: llama_vocab_p, /) -> int: + _warn_deprecated("llama_token_pad", "use llama_vocab_pad instead") + return _llama_token_pad(vocab) + + +# DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead"); +@ctypes_function( + "llama_add_bos_token", + [llama_vocab_p_ctypes], + ctypes.c_bool, +) +def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: ... + + +_llama_add_bos_token = llama_add_bos_token + + +def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: + _warn_deprecated("llama_add_bos_token", "use llama_vocab_get_add_bos instead") + return _llama_add_bos_token(vocab) + + +# DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead"); +@ctypes_function( + "llama_add_eos_token", + [llama_vocab_p_ctypes], + ctypes.c_bool, +) +def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: ... + + +_llama_add_eos_token = llama_add_eos_token + + +def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: + _warn_deprecated("llama_add_eos_token", "use llama_vocab_get_add_eos instead") + return _llama_add_eos_token(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead"); +@ctypes_function( + "llama_token_fim_pre", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ... + + +_llama_token_fim_pre = llama_token_fim_pre + + +def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_pre", "use llama_vocab_fim_pre instead") + return _llama_token_fim_pre(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead"); +@ctypes_function( + "llama_token_fim_suf", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ... + + +_llama_token_fim_suf = llama_token_fim_suf + + +def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_suf", "use llama_vocab_fim_suf instead") + return _llama_token_fim_suf(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead"); +@ctypes_function( + "llama_token_fim_mid", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ... + + +_llama_token_fim_mid = llama_token_fim_mid + + +def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_mid", "use llama_vocab_fim_mid instead") + return _llama_token_fim_mid(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead"); +@ctypes_function( + "llama_token_fim_pad", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ... + + +_llama_token_fim_pad = llama_token_fim_pad + + +def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_pad", "use llama_vocab_fim_pad instead") + return _llama_token_fim_pad(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead"); +@ctypes_function( + "llama_token_fim_rep", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ... + + +_llama_token_fim_rep = llama_token_fim_rep + + +def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_rep", "use llama_vocab_fim_rep instead") + return _llama_token_fim_rep(vocab) + + +# DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead"); +@ctypes_function( + "llama_token_fim_sep", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ... + + +_llama_token_fim_sep = llama_token_fim_sep + + +def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_token_fim_sep", "use llama_vocab_fim_sep instead") + return _llama_token_fim_sep(vocab) + + +# // CLS is equivalent to BOS +# DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification +# "use llama_vocab_bos instead"); +@ctypes_function( + "llama_vocab_cls", + [llama_vocab_p_ctypes], + llama_token, +) +def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: ... -# LLAMA_API llama_token llama_token_fim_suf(const struct llama_model * model); -@ctypes_function("llama_token_fim_suf", [llama_model_p_ctypes], llama_token) -def llama_token_fim_suf(model: llama_model_p, /) -> int: - ... -# LLAMA_API llama_token llama_token_fim_mid(const struct llama_model * model); -@ctypes_function("llama_token_fim_mid", [llama_model_p_ctypes], llama_token) -def llama_token_fim_mid(model: llama_model_p, /) -> int: - ... +_llama_vocab_cls = llama_vocab_cls -# LLAMA_API llama_token llama_token_fim_pad(const struct llama_model * model); -@ctypes_function("llama_token_fim_pad", [llama_model_p_ctypes], llama_token) -def llama_token_fim_pad(model: llama_model_p, /) -> int: - ... -# LLAMA_API llama_token llama_token_fim_rep(const struct llama_model * model); -@ctypes_function("llama_token_fim_rep", [llama_model_p_ctypes], llama_token) -def llama_token_fim_rep(model: llama_model_p, /) -> int: - ... +def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: + _warn_deprecated("llama_vocab_cls", "use llama_vocab_bos instead") + return _llama_vocab_cls(vocab) -# LLAMA_API llama_token llama_token_fim_sep(const struct llama_model * model); -@ctypes_function("llama_token_fim_sep", [llama_model_p_ctypes], llama_token) -def llama_token_fim_sep(model: llama_model_p, /) -> int: - ... # // # // Tokenization @@ -2723,11 +3858,12 @@ def llama_token_fim_sep(model: llama_model_p, /) -> int: # /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. # /// @return Returns the number of tokens on success, no more than n_tokens_max # /// @return Returns a negative number on failure - the number of tokens that would have been returned +# /// @return Returns INT32_MIN on overflow (e.g., tokenization result size exceeds int32_t limit) # /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so. # /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated # /// as plaintext. Does not insert a leading space. # LLAMA_API int32_t llama_tokenize( -# const struct llama_model * model, +# const struct llama_vocab * vocab, # const char * text, # int32_t text_len, # llama_token * tokens, @@ -2737,7 +3873,7 @@ def llama_token_fim_sep(model: llama_model_p, /) -> int: @ctypes_function( "llama_tokenize", [ - llama_model_p_ctypes, + llama_vocab_p_ctypes, ctypes.c_char_p, ctypes.c_int32, llama_token_p, @@ -2748,7 +3884,7 @@ def llama_token_fim_sep(model: llama_model_p, /) -> int: ctypes.c_int32, ) def llama_tokenize( - model: llama_model_p, + vocab: llama_vocab_p, text: bytes, text_len: Union[ctypes.c_int, int], tokens: CtypesArray[llama_token], @@ -2760,12 +3896,12 @@ def llama_tokenize( """Convert the provided text into tokens. Args: - model: The model to use for tokenization. + vocab: The vocabulary to use for tokenization. text: The text to tokenize. text_len: The length of the text. tokens: The tokens pointer must be large enough to hold the resulting tokens. n_max_tokens: The maximum number of tokens to return. - add_special: Allow adding special tokenns if the model is configured to do so. + add_special: Allow adding special tokens if the model is configured to do so. parse_special: Allow parsing special tokens. Returns: @@ -2781,7 +3917,7 @@ def llama_tokenize( # // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix') # // @param special If true, special tokens are rendered in the output. # LLAMA_API int32_t llama_token_to_piece( -# const struct llama_model * model, +# const struct llama_vocab * vocab, # llama_token token, # char * buf, # int32_t length, @@ -2790,7 +3926,7 @@ def llama_tokenize( @ctypes_function( "llama_token_to_piece", [ - llama_model_p_ctypes, + llama_vocab_p_ctypes, llama_token, ctypes.c_char_p, ctypes.c_int32, @@ -2800,7 +3936,7 @@ def llama_tokenize( ctypes.c_int32, ) def llama_token_to_piece( - model: llama_model_p, + vocab: llama_vocab_p, token: Union[llama_token, int], buf: Union[ctypes.c_char_p, bytes, CtypesArray[ctypes.c_char]], length: Union[ctypes.c_int, int], @@ -2814,7 +3950,7 @@ def llama_token_to_piece( User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens. Args: - model: The model to use for tokenization. + vocab: The vocabulary to use for tokenization. token: The token to convert. buf: The buffer to write the token to. length: The length of the buffer. @@ -2823,23 +3959,6 @@ def llama_token_to_piece( ... -# # // check if token0 is contained as a prefix in token1 -# # LLAMA_API bool llama_token_is_prefix( -# # const struct llama_model * model, -# # llama_token token0, -# # llama_token token1); -# @ctypes_function( -# "llama_token_is_prefix", -# [llama_model_p_ctypes, llama_token, llama_token], -# ctypes.c_bool, -# ) -# def llama_token_is_prefix( -# model: llama_model_p, token0: Union[llama_token, int], token1: Union[llama_token, int], / -# ) -> bool: -# """Check if token0 is contained as a prefix in token1""" -# ... - - # /// @details Convert the provided tokens into text (inverse of llama_tokenize()). # /// @param text The char pointer must be large enough to hold the resulting text. # /// @return Returns the number of chars/bytes on success, no more than text_len_max. @@ -2847,7 +3966,7 @@ def llama_token_to_piece( # /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so. # /// @param unparse_special If true, special tokens are rendered in the output. # LLAMA_API int32_t llama_detokenize( -# const struct llama_model * model, +# const struct llama_vocab * vocab, # const llama_token * tokens, # int32_t n_tokens, # char * text, @@ -2857,7 +3976,7 @@ def llama_token_to_piece( @ctypes_function( "llama_detokenize", [ - llama_model_p_ctypes, + llama_vocab_p_ctypes, ctypes.POINTER(llama_token), ctypes.c_int32, ctypes.c_char_p, @@ -2868,7 +3987,7 @@ def llama_token_to_piece( ctypes.c_int32, ) def llama_detokenize( - model: llama_model_p, + vocab: llama_vocab_p, tokens: CtypesArray[llama_token], n_tokens: Union[ctypes.c_int, int], text: bytes, @@ -2880,7 +3999,7 @@ def llama_detokenize( """Convert the provided tokens into text (inverse of llama_tokenize()). Args: - model: The model to use for tokenization. + vocab: The vocabulary to use for tokenization. tokens: The tokens to convert. n_tokens: The number of tokens. text: The buffer to write the text to. @@ -2897,8 +4016,8 @@ def llama_detokenize( # /// Apply chat template. Inspired by hf apply_chat_template() on python. # /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model" -# /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template -# /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead. +# /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template +# /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model's default chat template will be used instead. # /// @param chat Pointer to a list of multiple llama_chat_message # /// @param n_msg Number of llama_chat_message in this chat # /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message. @@ -2906,7 +4025,6 @@ def llama_detokenize( # /// @param length The size of the allocated buffer # /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template. # LLAMA_API int32_t llama_chat_apply_template( -# const struct llama_model * model, # const char * tmpl, # const struct llama_chat_message * chat, # size_t n_msg, @@ -2916,67 +4034,91 @@ def llama_detokenize( @ctypes_function( "llama_chat_apply_template", [ - ctypes.c_void_p, - ctypes.c_char_p, - ctypes.POINTER(llama_chat_message), - ctypes.c_size_t, + ctypes.c_char_p, # tmpl + ctypes.POINTER(llama_chat_message), # chat + ctypes.c_size_t, # n_msg + ctypes.c_bool, # add_ass (added) + ctypes.c_char_p, # buf + ctypes.c_int32, # length ], ctypes.c_int32, ) def llama_chat_apply_template( - model: llama_model_p, tmpl: bytes, chat: CtypesArray[llama_chat_message], n_msg: int, + add_ass: bool, # Added parameter + buf: bytes, + length: int, + /, +) -> int: + """Apply chat template. + + Args: + tmpl: Template to use. If None, uses model's default + chat: Array of chat messages + n_msg: Number of messages + add_ass: Whether to end prompt with assistant token + buf: Output buffer + length: Buffer length + + Returns: + Number of bytes written, or needed if buffer too small + """ + ... + + +# // Get list of built-in chat templates +# LLAMA_API int32_t llama_chat_builtin_templates(const char ** output, size_t len); +@ctypes_function( + "llama_chat_builtin_templates", + [ + ctypes.POINTER(ctypes.c_char_p), + ctypes.c_size_t, + ], + ctypes.c_int32, +) +def llama_chat_builtin_templates( + output: CtypesArray[bytes], + len: Union[ctypes.c_size_t, int], /, ) -> int: + """Get list of built-in chat templates. + + Args: + output: Output buffer to store template names. + len: Length of the output buffer. + + Returns: + Number of templates available. + Returns a negative number on error. + """ ... # // # // Sampling API # // -# // Sample usage: -# // -# // // prepare the sampling chain at the start -# // auto sparams = llama_sampler_chain_default_params(); -# // -# // llama_sampler * smpl = llama_sampler_chain_init(sparams); -# // -# // llama_sampler_chain_add(smpl, llama_sampler_init_top_k(50)); -# // llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9, 1)); -# // llama_sampler_chain_add(smpl, llama_sampler_init_temp (0.8)); -# // -# // // typically, the chain should end with a sampler such as "greedy", "dist" or "mirostat" -# // // this sampler will be responsible to select the actual token -# // llama_sampler_chain_add(smpl, llama_sampler_init_dist(seed)); -# // -# // ... -# // -# // // decoding loop: -# // while (...) { -# // ... -# // -# // llama_decode(ctx, batch); -# // -# // // sample from the logits of the last token in the batch -# // const llama_token id = llama_sampler_sample(smpl, ctx, -1); -# // -# // // accepting the token updates the internal state of certain samplers (e.g. grammar, repetition, etc.) -# // llama_sampler_accept(smpl, id); -# // ... -# // } -# // -# // llama_sampler_free(smpl); -# // -# // TODO: In the future, llama_sampler will be utilized to offload the sampling to the backends (e.g. GPU). -# // TODO: in the future, the entire sampling API that uses llama_model should start using llama_vocab -# // # typedef void * llama_sampler_context_t; llama_sampler_context_t = ctypes.c_void_p +# struct llama_sampler_data { +# struct ggml_tensor * logits; +# struct ggml_tensor * probs; +# struct ggml_tensor * sampled; +# struct ggml_tensor * candidates; +# }; +class llama_sampler_data(ctypes.Structure): + _fields_ = [ + ("logits", ctypes.c_void_p), + ("probs", ctypes.c_void_p), + ("sampled", ctypes.c_void_p), + ("candidates", ctypes.c_void_p), + ] + + # // user code can implement the interface below in order to create custom llama_sampler # struct llama_sampler_i { # const char * (*name) (const struct llama_sampler * smpl); // can be NULL @@ -2985,17 +4127,17 @@ def llama_chat_apply_template( # void (*reset) ( struct llama_sampler * smpl); // can be NULL # struct llama_sampler * (*clone) (const struct llama_sampler * smpl); // can be NULL if ctx is NULL # void (*free) ( struct llama_sampler * smpl); // can be NULL if ctx is NULL -# + + # // TODO: API for internal libllama usage for appending the sampling to an existing ggml_cgraph # //void (*apply_ggml) (struct llama_sampler * smpl, ...); # }; -class llama_sampler_i(ctypes.Structure): - ... +class llama_sampler_i(ctypes.Structure): ... # struct llama_sampler { -# struct llama_sampler_i * iface; -# llama_sampler_context_t ctx; +# const struct llama_sampler_i * iface; +# llama_sampler_context_t ctx; # }; class llama_sampler(ctypes.Structure): _fields_ = [ @@ -3017,6 +4159,24 @@ class llama_sampler(ctypes.Structure): llama_sampler_i_reset = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes) llama_sampler_i_clone = ctypes.CFUNCTYPE(llama_sampler_p_ctypes, llama_sampler_p_ctypes) llama_sampler_i_free = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes) +llama_sampler_i_backend_init = ctypes.CFUNCTYPE( + ctypes.c_bool, llama_sampler_p_ctypes, ctypes.c_void_p +) +llama_sampler_i_backend_accept = ctypes.CFUNCTYPE( + None, + llama_sampler_p_ctypes, + ctypes.c_void_p, + ctypes.c_void_p, + ctypes.c_void_p, +) +llama_sampler_i_backend_apply = ctypes.CFUNCTYPE( + None, + llama_sampler_p_ctypes, + ctypes.c_void_p, + ctypes.c_void_p, + ctypes.POINTER(llama_sampler_data), +) +llama_sampler_i_backend_set_input = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes) llama_sampler_i._fields_ = [ ("name", llama_sampler_i_name), @@ -3025,18 +4185,46 @@ class llama_sampler(ctypes.Structure): ("reset", llama_sampler_i_reset), ("clone", llama_sampler_i_clone), ("free", llama_sampler_i_free), + ("backend_init", llama_sampler_i_backend_init), + ("backend_accept", llama_sampler_i_backend_accept), + ("backend_apply", llama_sampler_i_backend_apply), + ("backend_set_input", llama_sampler_i_backend_set_input), ] +# // attach a sampler to the context +# LLAMA_API bool llama_set_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl); +@ctypes_function( + "llama_set_sampler", + [llama_context_p_ctypes, llama_seq_id, llama_sampler_p_ctypes], + ctypes.c_bool, +) +def llama_set_sampler( + ctx: llama_context_p, seq_id: Union[llama_seq_id, int], smpl: llama_sampler_p, / +) -> bool: + """Attach a sampler to the context.""" + ... + + # // mirror of llama_sampler_i: +# LLAMA_API struct llama_sampler * llama_sampler_init (const struct llama_sampler_i * iface, llama_sampler_context_t ctx); +@ctypes_function( + "llama_sampler_init", + [ctypes.POINTER(llama_sampler_i), llama_sampler_context_t], + llama_sampler_p_ctypes, +) +def llama_sampler_init( + iface: ctypes.POINTER(llama_sampler_i), ctx: llama_sampler_context_t, / +) -> llama_sampler_p: ... + + # LLAMA_API const char * llama_sampler_name (const struct llama_sampler * smpl); @ctypes_function( "llama_sampler_name", [llama_sampler_p_ctypes], ctypes.c_char_p, ) -def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes: - ... +def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes: ... # LLAMA_API void llama_sampler_accept( struct llama_sampler * smpl, llama_token token); @@ -3045,8 +4233,7 @@ def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes: [llama_sampler_p_ctypes, llama_token], None, ) -def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], /): - ... +def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], /): ... # LLAMA_API void llama_sampler_apply ( struct llama_sampler * smpl, llama_token_data_array * cur_p); @@ -3057,8 +4244,7 @@ def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], ) def llama_sampler_apply( smpl: llama_sampler_p, cur_p: CtypesArray[llama_token_data_array], / -): - ... +): ... # LLAMA_API void llama_sampler_reset ( struct llama_sampler * smpl); @@ -3067,8 +4253,7 @@ def llama_sampler_apply( [llama_sampler_p_ctypes], None, ) -def llama_sampler_reset(smpl: llama_sampler_p, /): - ... +def llama_sampler_reset(smpl: llama_sampler_p, /): ... # LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl); @@ -3077,8 +4262,7 @@ def llama_sampler_reset(smpl: llama_sampler_p, /): [llama_sampler_p_ctypes], llama_sampler_p_ctypes, ) -def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p: - ... +def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p: ... # // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add) @@ -3088,21 +4272,22 @@ def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p: [llama_sampler_p_ctypes], None, ) -def llama_sampler_free(smpl: llama_sampler_p, /): - ... +def llama_sampler_free(smpl: llama_sampler_p, /): ... # // llama_sampler_chain # // a type of llama_sampler that can chain multiple samplers one after another -# + + # LLAMA_API struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params); @ctypes_function( "llama_sampler_chain_init", [llama_sampler_chain_params], llama_sampler_p_ctypes, ) -def llama_sampler_chain_init(params: llama_sampler_chain_params, /) -> llama_sampler_p: - ... +def llama_sampler_chain_init( + params: llama_sampler_chain_params, / +) -> llama_sampler_p: ... # // important: takes ownership of the sampler object and will free it when llama_sampler_free is called @@ -3112,8 +4297,7 @@ def llama_sampler_chain_init(params: llama_sampler_chain_params, /) -> llama_sam [llama_sampler_p_ctypes, llama_sampler_p_ctypes], None, ) -def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /): - ... +def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /): ... # LLAMA_API struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i); @@ -3124,8 +4308,7 @@ def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /): ) def llama_sampler_chain_get( chain: llama_sampler_p, i: Union[ctypes.c_int32, int], / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # LLAMA_API int llama_sampler_chain_n (const struct llama_sampler * chain); @@ -3134,8 +4317,7 @@ def llama_sampler_chain_get( [llama_sampler_p_ctypes], ctypes.c_int, ) -def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int: - ... +def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int: ... # // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed @@ -3147,38 +4329,27 @@ def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int: ) def llama_sampler_chain_remove( chain: llama_sampler_p, i: Union[ctypes.c_int32, int], / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # // available samplers: -# + + # LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void); @ctypes_function("llama_sampler_init_greedy", [], llama_sampler_p_ctypes) -def llama_sampler_init_greedy() -> llama_sampler_p: - ... +def llama_sampler_init_greedy() -> llama_sampler_p: ... # LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed); @ctypes_function("llama_sampler_init_dist", [ctypes.c_uint32], llama_sampler_p_ctypes) -def llama_sampler_init_dist(seed: int) -> llama_sampler_p: - ... - - -# /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. -# /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first. -# DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void), -# "will be removed in the future (see https://github.com/ggerganov/llama.cpp/pull/9896#discussion_r1800920915)"); -@ctypes_function("llama_sampler_init_softmax", [], llama_sampler_p_ctypes) -def llama_sampler_init_softmax() -> llama_sampler_p: - ... +def llama_sampler_init_dist(seed: int) -> llama_sampler_p: ... # /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 +# /// Setting k <= 0 makes this a noop # LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k); @ctypes_function("llama_sampler_init_top_k", [ctypes.c_int32], llama_sampler_p_ctypes) -def llama_sampler_init_top_k(k: int) -> llama_sampler_p: - ... +def llama_sampler_init_top_k(k: int) -> llama_sampler_p: ... # /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 @@ -3188,19 +4359,17 @@ def llama_sampler_init_top_k(k: int) -> llama_sampler_p: [ctypes.c_float, ctypes.c_size_t], llama_sampler_p_ctypes, ) -def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p: - ... +def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p: ... -# /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841 +# /// @details Minimum P sampling as described in https://github.com/ggml-org/llama.cpp/pull/3841 # LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep); @ctypes_function( "llama_sampler_init_min_p", [ctypes.c_float, ctypes.c_size_t], llama_sampler_p_ctypes, ) -def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p: - ... +def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p: ... # /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. @@ -3210,14 +4379,13 @@ def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p: [ctypes.c_float, ctypes.c_size_t], llama_sampler_p_ctypes, ) -def llama_sampler_init_typical(p: float, min_keep: int) -> llama_sampler_p: - ... +def llama_sampler_init_typical(p: float, min_keep: int) -> llama_sampler_p: ... +# /// #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it's original value, the rest are set to -inf # LLAMA_API struct llama_sampler * llama_sampler_init_temp (float t); @ctypes_function("llama_sampler_init_temp", [ctypes.c_float], llama_sampler_p_ctypes) -def llama_sampler_init_temp(t: float) -> llama_sampler_p: - ... +def llama_sampler_init_temp(t: float) -> llama_sampler_p: ... # /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772. @@ -3229,8 +4397,7 @@ def llama_sampler_init_temp(t: float) -> llama_sampler_p: ) def llama_sampler_init_temp_ext( t: float, delta: float, exponent: float -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335 @@ -3242,16 +4409,20 @@ def llama_sampler_init_temp_ext( ) def llama_sampler_init_xtc( p: float, t: float, min_keep: int, seed: int, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... + + +# /// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641 +# LLAMA_API struct llama_sampler * llama_sampler_init_top_n_sigma(float n); +@ctypes_function( + "llama_sampler_init_top_n_sigma", + [ctypes.c_float], + llama_sampler_p_ctypes, +) +def llama_sampler_init_top_n_sigma(n: float, /) -> llama_sampler_p: ... # /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. -# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. -# /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. -# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. -# /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm. -# /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. # LLAMA_API struct llama_sampler * llama_sampler_init_mirostat( # int32_t n_vocab, # uint32_t seed, @@ -3265,15 +4436,10 @@ def llama_sampler_init_xtc( ) def llama_sampler_init_mirostat( n_vocab: int, seed: int, tau: float, eta: float, m: int, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. -# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. -# /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. -# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. -# /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. # LLAMA_API struct llama_sampler * llama_sampler_init_mirostat_v2( # uint32_t seed, # float tau, @@ -3285,68 +4451,144 @@ def llama_sampler_init_mirostat( ) def llama_sampler_init_mirostat_v2( seed: int, tau: float, eta: float, / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... +# /// @details Intializes a GBNF grammar, see grammars/README.md for details. # LLAMA_API struct llama_sampler * llama_sampler_init_grammar( -# const struct llama_model * model, +# const struct llama_vocab * vocab, # const char * grammar_str, # const char * grammar_root); @ctypes_function( "llama_sampler_init_grammar", - [llama_model_p_ctypes, ctypes.c_char_p, ctypes.c_char_p], + [llama_vocab_p_ctypes, ctypes.c_char_p, ctypes.c_char_p], llama_sampler_p_ctypes, ) def llama_sampler_init_grammar( - model: llama_model_p, grammar_str: bytes, grammar_root: bytes, / + vocab: llama_vocab_p, grammar_str: bytes, grammar_root: bytes, / +) -> llama_sampler_p: ... + + +# DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy( +# const struct llama_vocab * vocab, +# const char * grammar_str, +# const char * grammar_root, +# const char ** trigger_words, +# size_t num_trigger_words, +# const llama_token * trigger_tokens, +# size_t num_trigger_tokens), +# "use llama_sampler_init_grammar_lazy_patterns instead"); +@ctypes_function( + "llama_sampler_init_grammar_lazy", + [ + llama_vocab_p_ctypes, + ctypes.c_char_p, + ctypes.c_char_p, + ctypes.POINTER(ctypes.c_char_p), + ctypes.c_size_t, + ctypes.POINTER(llama_token), + ctypes.c_size_t, + ], + llama_sampler_p_ctypes, +) +def llama_sampler_init_grammar_lazy( + vocab: llama_vocab_p, + grammar_str: bytes, + grammar_root: bytes, + trigger_words: CtypesArray[bytes], + num_trigger_words: int, + trigger_tokens: CtypesArray[llama_token], + num_trigger_tokens: int, + /, +) -> llama_sampler_p: ... + + +_llama_sampler_init_grammar_lazy = llama_sampler_init_grammar_lazy + + +def llama_sampler_init_grammar_lazy( + vocab: llama_vocab_p, + grammar_str: bytes, + grammar_root: bytes, + trigger_words: CtypesArray[bytes], + num_trigger_words: int, + trigger_tokens: CtypesArray[llama_token], + num_trigger_tokens: int, + /, ) -> llama_sampler_p: - ... + _warn_deprecated( + "llama_sampler_init_grammar_lazy", + "use llama_sampler_init_grammar_lazy_patterns instead", + ) + return _llama_sampler_init_grammar_lazy( + vocab, + grammar_str, + grammar_root, + trigger_words, + num_trigger_words, + trigger_tokens, + num_trigger_tokens, + ) -# LLAMA_API struct llama_sampler * llama_sampler_init_penalties( -# int32_t n_vocab, // llama_n_vocab() -# llama_token special_eos_id, // llama_token_eos() -# llama_token linefeed_id, // llama_token_nl() -# int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size) -# float penalty_repeat, // 1.0 = disabled -# float penalty_freq, // 0.0 = disabled -# float penalty_present, // 0.0 = disabled -# bool penalize_nl, // consider newlines as a repeatable token -# bool ignore_eos); // ignore the end-of-sequence token +# /// @details Lazy grammar sampler, introduced in https://github.com/ggml-org/llama.cpp/pull/9639 +# LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy_patterns( +# const struct llama_vocab * vocab, +# const char * grammar_str, +# const char * grammar_root, +# const char ** trigger_patterns, +# size_t num_trigger_patterns, +# const llama_token * trigger_tokens, +# size_t num_trigger_tokens); @ctypes_function( - "llama_sampler_init_penalties", + "llama_sampler_init_grammar_lazy_patterns", [ - ctypes.c_int32, - llama_token, - llama_token, - ctypes.c_int32, - ctypes.c_float, - ctypes.c_float, - ctypes.c_float, - ctypes.c_bool, - ctypes.c_bool, + llama_vocab_p_ctypes, + ctypes.c_char_p, + ctypes.c_char_p, + ctypes.POINTER(ctypes.c_char_p), + ctypes.c_size_t, + ctypes.POINTER(llama_token), + ctypes.c_size_t, ], llama_sampler_p_ctypes, ) +def llama_sampler_init_grammar_lazy_patterns( + vocab: llama_vocab_p, + grammar_str: bytes, + grammar_root: bytes, + trigger_patterns: CtypesArray[bytes], + num_trigger_patterns: int, + trigger_tokens: CtypesArray[llama_token], + num_trigger_tokens: int, + /, +) -> llama_sampler_p: ... + + +# /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first. +# LLAMA_API struct llama_sampler * llama_sampler_init_penalties( +# int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size) +# float penalty_repeat, // 1.0 = disabled +# float penalty_freq, // 0.0 = disabled +# float penalty_present); // 0.0 = disabled +@ctypes_function( + "llama_sampler_init_penalties", + [ctypes.c_int32, ctypes.c_float, ctypes.c_float, ctypes.c_float], + llama_sampler_p_ctypes, +) def llama_sampler_init_penalties( - n_vocab: int, - special_eos_id: int, - linefeed_id: int, penalty_last_n: int, penalty_repeat: float, penalty_freq: float, penalty_present: float, - penalize_nl: bool, - ignore_eos: bool, /, -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # /// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982 # LLAMA_API struct llama_sampler * llama_sampler_init_dry( -# const struct llama_model * model, +# const struct llama_vocab * vocab, +# int32_t n_ctx_train, # float dry_multiplier, # float dry_base, # int32_t dry_allowed_length, @@ -3356,7 +4598,8 @@ def llama_sampler_init_penalties( @ctypes_function( "llama_sampler_init_dry", [ - llama_model_p_ctypes, + llama_vocab_p_ctypes, + ctypes.c_int32, ctypes.c_float, ctypes.c_float, ctypes.c_int32, @@ -3367,15 +4610,31 @@ def llama_sampler_init_penalties( llama_sampler_p_ctypes, ) def llama_sampler_init_dry( - model: llama_model_p, + vocab: llama_vocab_p, + n_ctx_train: int, dry_multiplier: float, dry_base: float, dry_allowed_length: int, dry_penalty_last_n: int, - seq_breakers: CtypesArray[bytes], + seq_breakers, num_breakers: int, /, +) -> llama_sampler_p: ... + + +# LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p( +# float target, +# float decay, +# uint32_t seed); +@ctypes_function( + "llama_sampler_init_adaptive_p", + [ctypes.c_float, ctypes.c_float, ctypes.c_uint32], + llama_sampler_p_ctypes, +) +def llama_sampler_init_adaptive_p( + target: float, decay: float, seed: int, / ) -> llama_sampler_p: + """Initialize an adaptive-p sampler.""" ... @@ -3390,41 +4649,17 @@ def llama_sampler_init_dry( ) def llama_sampler_init_logit_bias( n_vocab: int, n_logit_bias: int, logit_bias: CtypesArray[llama_logit_bias], / -) -> llama_sampler_p: - ... +) -> llama_sampler_p: ... # // this sampler is meant to be used for fill-in-the-middle infilling -# // it's supposed to be used after top_k + top_p sampling -# // -# // 1. if the sum of the EOG probs times the number of candidates is higher than the sum of the other probs -> pick EOG -# // 2. combine probs of tokens that have the same prefix -# // -# // example: -# // -# // - before: -# // "hel": 0.5 -# // "hell": 0.2 -# // "hello": 0.1 -# // "dummy": 0.1 -# // -# // - after: -# // "hel": 0.8 -# // "dummy": 0.1 -# // -# // 3. discard non-EOG tokens with low prob -# // 4. if no tokens are left -> pick EOT -# // -# LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model); +# LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab); @ctypes_function( "llama_sampler_init_infill", - [llama_model_p_ctypes], + [llama_vocab_p_ctypes], llama_sampler_p_ctypes, ) -def llama_sampler_init_infill(model: llama_model_p, /) -> llama_sampler_p: - """This sampler is meant to be used for fill-in-the-middle infilling. - """ - ... +def llama_sampler_init_infill(vocab: llama_vocab_p, /) -> llama_sampler_p: ... # // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise @@ -3434,20 +4669,10 @@ def llama_sampler_init_infill(model: llama_model_p, /) -> llama_sampler_p: [llama_sampler_p_ctypes], ctypes.c_uint32, ) -def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int: - ... +def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int: ... # /// @details Sample and accept a token from the idx-th output of the last evaluation -# // -# // Shorthand for: -# // const auto * logits = llama_get_logits_ith(ctx, idx); -# // llama_token_data_array cur_p = { ... init from logits ... }; -# // llama_sampler_apply(smpl, &cur_p); -# // auto token = cur_p.data[cur_p.selected].id; -# // llama_sampler_accept(smpl, token); -# // return token; -# // Returns the sampled token # LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx); @ctypes_function( "llama_sampler_sample", @@ -3456,8 +4681,7 @@ def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int: ) def llama_sampler_sample( smpl: llama_sampler_p, ctx: llama_context_p, idx: int, / -) -> int: - ... +) -> int: ... # // @@ -3466,8 +4690,6 @@ def llama_sampler_sample( # /// @details Build a split GGUF final path for this chunk. -# /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf" -# // Returns the split_path length. # LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count); @ctypes_function( "llama_split_path", @@ -3487,8 +4709,6 @@ def llama_split_path( # /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match. -# /// llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0" -# // Returns the split_prefix length. # LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count); @ctypes_function( "llama_split_prefix", @@ -3510,16 +4730,31 @@ def llama_split_prefix( # // Print system information # LLAMA_API const char * llama_print_system_info(void); @ctypes_function("llama_print_system_info", [], ctypes.c_char_p) -def llama_print_system_info() -> bytes: - ... +def llama_print_system_info() -> bytes: ... # // Set callback for all future logging events. # // If this is not called, or NULL is supplied, everything is output on stderr. +# // The logger state is global so these functions are NOT thread safe. +# LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data); +@ctypes_function( + "llama_log_get", + [ctypes.POINTER(llama_log_callback), ctypes.POINTER(ctypes.c_void_p)], + None, +) +def llama_log_get( + log_callback: CtypesPointerOrRef[llama_log_callback], + user_data: CtypesPointerOrRef[ctypes.c_void_p], + /, +): + """Get the current logging callback and user data.""" + ... + + # LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data); @ctypes_function( "llama_log_set", - [ctypes.c_void_p, ctypes.c_void_p], + [llama_log_callback, ctypes.c_void_p], None, ) def llama_log_set( @@ -3536,18 +4771,17 @@ def llama_log_set( # // # // Performance utils # // -# // NOTE: Used by llama.cpp examples, avoid using in third-party apps. Instead, do your own performance measurements. -# // - # struct llama_perf_context_data { # double t_start_ms; # double t_load_ms; # double t_p_eval_ms; # double t_eval_ms; -# + + # int32_t n_p_eval; # int32_t n_eval; +# int32_t n_reused; // number of times a ggml compute graph had been reused # }; class llama_perf_context_data(ctypes.Structure): _fields_ = [ @@ -3557,12 +4791,14 @@ class llama_perf_context_data(ctypes.Structure): ("t_eval_ms", ctypes.c_double), ("n_p_eval", ctypes.c_int32), ("n_eval", ctypes.c_int32), + ("n_reused", ctypes.c_int32), ] # struct llama_perf_sampler_data { # double t_sample_ms; -# + + # int32_t n_sample; # }; class llama_perf_sampler_data(ctypes.Structure): @@ -3578,8 +4814,7 @@ class llama_perf_sampler_data(ctypes.Structure): [llama_context_p_ctypes], llama_perf_context_data, ) -def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data: - ... +def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data: ... # LLAMA_API void llama_perf_context_print(const struct llama_context * ctx); @@ -3588,8 +4823,7 @@ def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data: [llama_context_p_ctypes], None, ) -def llama_perf_context_print(ctx: llama_context_p, /): - ... +def llama_perf_context_print(ctx: llama_context_p, /): ... # LLAMA_API void llama_perf_context_reset( struct llama_context * ctx); @@ -3598,8 +4832,7 @@ def llama_perf_context_print(ctx: llama_context_p, /): [llama_context_p_ctypes], None, ) -def llama_perf_context_reset(ctx: llama_context_p, /): - ... +def llama_perf_context_reset(ctx: llama_context_p, /): ... # // NOTE: the following work only with samplers constructed via llama_sampler_chain_init @@ -3609,8 +4842,7 @@ def llama_perf_context_reset(ctx: llama_context_p, /): [llama_sampler_p_ctypes], llama_perf_sampler_data, ) -def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data: - ... +def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data: ... # LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain); @@ -3619,8 +4851,7 @@ def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data: [llama_sampler_p_ctypes], None, ) -def llama_perf_sampler_print(chain: llama_sampler_p, /): - ... +def llama_perf_sampler_print(chain: llama_sampler_p, /): ... # LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain); @@ -3629,7 +4860,101 @@ def llama_perf_sampler_print(chain: llama_sampler_p, /): [llama_sampler_p_ctypes], None, ) -def llama_perf_sampler_reset(chain: llama_sampler_p, /): +def llama_perf_sampler_reset(chain: llama_sampler_p, /): ... + + +# // print a breakdown of per-device memory use via LLAMA_LOG: +@ctypes_function("llama_memory_breakdown_print", [llama_context_p_ctypes], None) +def llama_memory_breakdown_print(ctx: llama_context_p, /): + """Print a breakdown of per-device memory use.""" ... +# // +# // training +# // + +# // function that returns whether or not a given tensor contains trainable parameters +# typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata); +llama_opt_param_filter = ctypes.CFUNCTYPE( + ctypes.c_bool, ctypes.c_void_p, ctypes.c_void_p +) + + +# // always returns true +# LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata); +@ctypes_function( + "llama_opt_param_filter_all", + [ctypes.c_void_p, ctypes.c_void_p], + ctypes.c_bool, +) +def llama_opt_param_filter_all( + tensor: ctypes.c_void_p, userdata: ctypes.c_void_p, / +) -> bool: ... + + +# struct llama_opt_params { +# uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0 + +# llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters +# void * param_filter_ud; // userdata for determining which tensors contain trainable parameters + + +# ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters +# void * get_opt_pars_ud; // userdata for calculating optimizer parameters +# }; +class llama_opt_params(ctypes.Structure): + _fields_ = [ + ("n_ctx_train", ctypes.c_uint32), + ("param_filter", llama_opt_param_filter), + ("param_filter_ud", ctypes.c_void_p), + ( + "get_opt_pars", + ctypes.c_void_p, + ), # ggml_opt_get_optimizer_params - not implemented here + ("get_opt_pars_ud", ctypes.c_void_p), + ] + + +# LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params); +@ctypes_function( + "llama_opt_init", + [llama_context_p_ctypes, llama_model_p_ctypes, llama_opt_params], + None, +) +def llama_opt_init( + lctx: llama_context_p, model: llama_model_p, lopt_params: llama_opt_params, / +): ... + + +# LLAMA_API void llama_opt_epoch( +# struct llama_context * lctx, +# ggml_opt_dataset_t dataset, +# ggml_opt_result_t result_train, +# ggml_opt_result_t result_eval, +# int64_t idata_split, +# ggml_opt_epoch_callback callback_train, +# ggml_opt_epoch_callback callback_eval); +@ctypes_function( + "llama_opt_epoch", + [ + llama_context_p_ctypes, + ctypes.c_void_p, # ggml_opt_dataset_t + ctypes.c_void_p, # ggml_opt_result_t + ctypes.c_void_p, # ggml_opt_result_t + ctypes.c_int64, + ctypes.c_void_p, # ggml_opt_epoch_callback + ctypes.c_void_p, # ggml_opt_epoch_callback + ], + None, +) +def llama_opt_epoch( + lctx: llama_context_p, + dataset: ctypes.c_void_p, + result_train: ctypes.c_void_p, + result_eval: ctypes.c_void_p, + idata_split: int, + callback_train: ctypes.c_void_p, + callback_eval: ctypes.c_void_p, + /, +): ... diff --git a/llama_cpp/llama_grammar.py b/llama_cpp/llama_grammar.py index b95c77ab5e..ba34dda831 100644 --- a/llama_cpp/llama_grammar.py +++ b/llama_cpp/llama_grammar.py @@ -297,7 +297,7 @@ def opt_repetitions(up_to_n, prefix_with_sep=False): if max_items is not None: result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0) else: - item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})' + item_operator = f"({separator_rule + ' ' if separator_rule else ''}{item_rule})" if min_items == 0 and separator_rule: result = f"({item_rule} {item_operator}*)?" @@ -450,9 +450,9 @@ def visit(n: dict): ref = n.get("$ref") if ref is not None and ref not in self._refs: if ref.startswith("https://"): - assert ( - self._allow_fetch - ), "Fetching remote schemas is not allowed (use --allow-fetch for force)" + assert self._allow_fetch, ( + "Fetching remote schemas is not allowed (use --allow-fetch for force)" + ) import requests frag_split = ref.split("#") @@ -475,9 +475,9 @@ def visit(n: dict): raise ValueError(f"Unsupported ref {ref}") for sel in ref.split("#")[-1].split("/")[1:]: - assert ( - target is not None and sel in target - ), f"Error resolving ref {ref}: {sel} not in {target}" + assert target is not None and sel in target, ( + f"Error resolving ref {ref}: {sel} not in {target}" + ) target = target[sel] self._refs[ref] = target @@ -492,7 +492,7 @@ def visit(n: dict): def _generate_union_rule(self, name, alt_schemas): return " | ".join( ( - self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}') + self.visit(alt_schema, f"{name}{'-' if name else 'alternative-'}{i}") for i, alt_schema in enumerate(alt_schemas) ) ) @@ -510,9 +510,9 @@ def _visit_pattern(self, pattern, name): we define sub-rules to keep the output lean. """ - assert pattern.startswith("^") and pattern.endswith( - "$" - ), 'Pattern must start with "^" and end with "$"' + assert pattern.startswith("^") and pattern.endswith("$"), ( + 'Pattern must start with "^" and end with "$"' + ) pattern = pattern[1:-1] sub_rule_ids = {} @@ -566,15 +566,15 @@ def join_seq(): elif c == "(": i += 1 if i < length: - assert ( - pattern[i] != "?" - ), f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/' + assert pattern[i] != "?", ( + f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/' + ) seq.append((f"({to_rule(transform())})", False)) elif c == ")": i += 1 - assert ( - start > 0 and pattern[start - 1] == "(" - ), f"Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}" + assert start > 0 and pattern[start - 1] == "(", ( + f"Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}" + ) return join_seq() elif c == "[": square_brackets = c @@ -586,9 +586,9 @@ def join_seq(): else: square_brackets += pattern[i] i += 1 - assert ( - i < length - ), f"Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}" + assert i < length, ( + f"Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}" + ) square_brackets += "]" i += 1 seq.append((square_brackets, False)) @@ -604,9 +604,9 @@ def join_seq(): while i < length and pattern[i] != "}": curly_brackets += pattern[i] i += 1 - assert ( - i < length - ), f"Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}" + assert i < length, ( + f"Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}" + ) curly_brackets += "}" i += 1 nums = [s.strip() for s in curly_brackets[1:-1].split(",")] @@ -777,13 +777,13 @@ def add_component(comp_schema, is_required): rule_name, '"[" space ' + ' "," space '.join( - self.visit(item, f'{name}{"-" if name else ""}tuple-{i}') + self.visit(item, f"{name}{'-' if name else ''}tuple-{i}") for i, item in enumerate(items) ) + ' "]" space', ) else: - item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item') + item_rule_name = self.visit(items, f"{name}{'-' if name else ''}item") min_items = schema.get("minItems", 0) max_items = schema.get("maxItems") return self._add_rule( @@ -873,17 +873,17 @@ def _build_object_rule( prop_kv_rule_names = {} for prop_name, prop_schema in properties: prop_rule_name = self.visit( - prop_schema, f'{name}{"-" if name else ""}{prop_name}' + prop_schema, f"{name}{'-' if name else ''}{prop_name}" ) prop_kv_rule_names[prop_name] = self._add_rule( - f'{name}{"-" if name else ""}{prop_name}-kv', + f"{name}{'-' if name else ''}{prop_name}-kv", rf'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}', ) required_props = [k for k in sorted_props if k in required] optional_props = [k for k in sorted_props if k not in required] if additional_properties == True or isinstance(additional_properties, dict): - sub_name = f'{name}{"-" if name else ""}additional' + sub_name = f"{name}{'-' if name else ''}additional" value_rule = self.visit( {} if additional_properties == True else additional_properties, f"{sub_name}-value", @@ -908,7 +908,7 @@ def get_recursive_refs(ks, first_is_optional): kv_rule_name = prop_kv_rule_names[k] if k == "*": res = self._add_rule( - f'{name}{"-" if name else ""}additional-kvs', + f"{name}{'-' if name else ''}additional-kvs", f'{kv_rule_name} ( "," space ' + kv_rule_name + " )*", ) elif first_is_optional: @@ -917,7 +917,7 @@ def get_recursive_refs(ks, first_is_optional): res = kv_rule_name if len(rest) > 0: res += " " + self._add_rule( - f'{name}{"-" if name else ""}{k}-rest', + f"{name}{'-' if name else ''}{k}-rest", get_recursive_refs(rest, first_is_optional=True), ) return res diff --git a/llama_cpp/llama_types.py b/llama_cpp/llama_types.py index bbb58afc35..f647822ff5 100644 --- a/llama_cpp/llama_types.py +++ b/llama_cpp/llama_types.py @@ -82,10 +82,28 @@ class ChatCompletionFunction(TypedDict): parameters: Dict[str, JsonType] # TODO: make this more specific +class ChatCompletionTopLogprobToken(TypedDict): + token: str + logprob: float + bytes: Optional[List[int]] + + +class ChatCompletionLogprobToken(ChatCompletionTopLogprobToken): + token: str + logprob: float + bytes: Optional[List[int]] + top_logprobs: List[ChatCompletionTopLogprobToken] + + +class ChatCompletionLogprobs(TypedDict): + content: Optional[List[ChatCompletionLogprobToken]] + refusal: Optional[List[ChatCompletionLogprobToken]] + + class ChatCompletionResponseChoice(TypedDict): index: int message: "ChatCompletionResponseMessage" - logprobs: Optional[CompletionLogprobs] + logprobs: Optional[ChatCompletionLogprobs] finish_reason: Optional[str] @@ -134,7 +152,7 @@ class ChatCompletionStreamResponseChoice(TypedDict): ChatCompletionStreamResponseDelta, ChatCompletionStreamResponseDeltaEmpty ] finish_reason: Optional[Literal["stop", "length", "tool_calls", "function_call"]] - logprobs: NotRequired[Optional[CompletionLogprobs]] + logprobs: NotRequired[Optional[ChatCompletionLogprobs]] class CreateChatCompletionStreamResponse(TypedDict): @@ -214,7 +232,7 @@ class ChatCompletionRequestAssistantMessageFunctionCall(TypedDict): class ChatCompletionRequestAssistantMessage(TypedDict): role: Literal["assistant"] - content: Optional[str] + content: NotRequired[str] tool_calls: NotRequired[ChatCompletionMessageToolCalls] function_call: NotRequired[ ChatCompletionRequestAssistantMessageFunctionCall diff --git a/llama_cpp/llava_cpp.py b/llama_cpp/llava_cpp.py index d9dfaf5fd0..3dc96d2f25 100644 --- a/llama_cpp/llava_cpp.py +++ b/llama_cpp/llava_cpp.py @@ -36,7 +36,11 @@ # Specify the base name of the shared library to load _libllava_base_name = "llava" _libllava_override_path = os.environ.get("LLAVA_CPP_LIB") -_libllava_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _libllava_override_path is None else pathlib.Path() +_libllava_base_path = ( + pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" + if _libllava_override_path is None + else pathlib.Path() +) # Load the library _libllava = load_shared_library(_libllava_base_name, _libllava_base_path) @@ -73,8 +77,7 @@ class llava_image_embed(Structure): ) def llava_validate_embed_size( ctx_llama: llama_cpp.llama_context_p, ctx_clip: clip_ctx_p, / -) -> bool: - ... +) -> bool: ... # /** build an image embed from image file bytes */ @@ -90,8 +93,7 @@ def llava_image_embed_make_with_bytes( image_bytes: CtypesArray[c_uint8], image_bytes_length: Union[c_int, int], /, -) -> "_Pointer[llava_image_embed]": - ... +) -> "_Pointer[llava_image_embed]": ... # /** build an image embed from a path to an image filename */ @@ -103,15 +105,13 @@ def llava_image_embed_make_with_bytes( ) def llava_image_embed_make_with_filename( ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_path: bytes, / -) -> "_Pointer[llava_image_embed]": - ... +) -> "_Pointer[llava_image_embed]": ... # LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed); # /** free an embedding made with llava_image_embed_make_* */ @ctypes_function("llava_image_embed_free", [POINTER(llava_image_embed)], None) -def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /): - ... +def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /): ... # /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */ @@ -132,8 +132,7 @@ def llava_eval_image_embed( n_batch: Union[c_int, int], n_past: "_Pointer[c_int]", /, -) -> bool: - ... +) -> bool: ... ################################################ @@ -146,13 +145,10 @@ def llava_eval_image_embed( @ctypes_function("clip_model_load", [c_char_p, c_int], clip_ctx_p_ctypes) def clip_model_load( fname: bytes, verbosity: Union[c_int, int], / -) -> Optional[clip_ctx_p]: - ... +) -> Optional[clip_ctx_p]: ... # /** free mmproj model */ # CLIP_API void clip_free(struct clip_ctx * ctx); @ctypes_function("clip_free", [clip_ctx_p_ctypes], None) -def clip_free(ctx: clip_ctx_p, /): - ... - +def clip_free(ctx: clip_ctx_p, /): ... diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py new file mode 100644 index 0000000000..f28402775c --- /dev/null +++ b/llama_cpp/mtmd_cpp.py @@ -0,0 +1,396 @@ +from __future__ import annotations + +import os +import warnings +from ctypes import ( + c_bool, + c_char_p, + c_int, + c_uint8, + c_uint32, + c_float, + c_void_p, + c_size_t, + POINTER, + _Pointer, # type: ignore + Structure, + byref, +) +import pathlib +from typing import ( + Union, + NewType, + Optional, + TYPE_CHECKING, +) + +import llama_cpp.llama_cpp as llama_cpp + +from llama_cpp._ctypes_extensions import ( + load_shared_library, + ctypes_function_for_shared_library, +) + +if TYPE_CHECKING: + from llama_cpp._ctypes_extensions import ( + CtypesArray, + ) + + +# Specify the base name of the shared library to load +_libmtmd_base_name = "mtmd" +_libmtmd_override_path = os.environ.get("MTMD_CPP_LIB") +_libmtmd_base_path = ( + pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" + if _libmtmd_override_path is None + else pathlib.Path() +) + +# Load the library +_libmtmd = load_shared_library(_libmtmd_base_name, _libmtmd_base_path) + +ctypes_function = ctypes_function_for_shared_library(_libmtmd) + +################################################ +# mtmd.h types +################################################ + +# Opaque types +mtmd_context_p = NewType("mtmd_context_p", int) +mtmd_context_p_ctypes = c_void_p + +mtmd_bitmap_p = NewType("mtmd_bitmap_p", int) +mtmd_bitmap_p_ctypes = c_void_p + +mtmd_image_tokens_p = NewType("mtmd_image_tokens_p", int) +mtmd_image_tokens_p_ctypes = c_void_p + +mtmd_input_chunk_p = NewType("mtmd_input_chunk_p", int) +mtmd_input_chunk_p_ctypes = c_void_p + +mtmd_input_chunks_p = NewType("mtmd_input_chunks_p", int) +mtmd_input_chunks_p_ctypes = c_void_p + +# Enums +MTMD_INPUT_CHUNK_TYPE_TEXT = 0 +MTMD_INPUT_CHUNK_TYPE_IMAGE = 1 +MTMD_INPUT_CHUNK_TYPE_AUDIO = 2 + + +# Structures +class mtmd_context_params(Structure): + """Context parameters for MTMD initialization. + + `image_marker` is deprecated upstream and kept for compatibility; use + `media_marker` for multimodal prompt placeholders. + """ + + if TYPE_CHECKING: + use_gpu: bool + print_timings: bool + n_threads: int + image_marker: Optional[bytes] + media_marker: Optional[bytes] + flash_attn_type: int + warmup: bool + image_min_tokens: int + image_max_tokens: int + cb_eval: llama_cpp.ggml_backend_sched_eval_callback + cb_eval_user_data: c_void_p + + _fields_ = [ + ("use_gpu", c_bool), + ("print_timings", c_bool), + ("n_threads", c_int), + ("image_marker", c_char_p), + ("media_marker", c_char_p), + ("flash_attn_type", c_int), + ("warmup", c_bool), + ("image_min_tokens", c_int), + ("image_max_tokens", c_int), + ("cb_eval", llama_cpp.ggml_backend_sched_eval_callback), + ("cb_eval_user_data", c_void_p), + ] + + +class mtmd_input_text(Structure): + """Text input passed to `mtmd_tokenize`.""" + + _fields_ = [ + ("text", c_char_p), + ("add_special", c_bool), + ("parse_special", c_bool), + ] + + +################################################ +# mtmd.h functions +################################################ + + +# MTMD_API const char * mtmd_default_marker(void); +@ctypes_function("mtmd_default_marker", [], c_char_p) +def mtmd_default_marker() -> bytes: + """Return the default media marker.""" + ... + + +# MTMD_API struct mtmd_context_params mtmd_context_params_default(void); +@ctypes_function("mtmd_context_params_default", [], mtmd_context_params) +def mtmd_context_params_default() -> mtmd_context_params: + """Return the default MTMD context parameters.""" + ... + + +# MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname, +# const struct llama_model * text_model, +# const struct mtmd_context_params ctx_params); +@ctypes_function( + "mtmd_init_from_file", + [c_char_p, llama_cpp.llama_model_p_ctypes, mtmd_context_params], + mtmd_context_p_ctypes, +) +def mtmd_init_from_file( + mmproj_fname: bytes, + text_model: llama_cpp.llama_model_p, + ctx_params: mtmd_context_params, + /, +) -> Optional[mtmd_context_p]: + """Initialize the MTMD context from a projector file. Returns None on failure.""" + ... + + +# MTMD_API void mtmd_free(mtmd_context * ctx); +@ctypes_function("mtmd_free", [mtmd_context_p_ctypes], None) +def mtmd_free(ctx: mtmd_context_p, /): ... + + +# MTMD_API bool mtmd_decode_use_non_causal(mtmd_context * ctx); +@ctypes_function("mtmd_decode_use_non_causal", [mtmd_context_p_ctypes], c_bool) +def mtmd_decode_use_non_causal(ctx: mtmd_context_p, /) -> bool: + """Check whether MTMD decoding uses non-causal attention.""" + ... + + +# MTMD_API bool mtmd_decode_use_mrope(mtmd_context * ctx); +@ctypes_function("mtmd_decode_use_mrope", [mtmd_context_p_ctypes], c_bool) +def mtmd_decode_use_mrope(ctx: mtmd_context_p, /) -> bool: + """Check whether MTMD decoding uses mRoPE.""" + ... + + +# MTMD_API bool mtmd_support_vision(mtmd_context * ctx); +@ctypes_function("mtmd_support_vision", [mtmd_context_p_ctypes], c_bool) +def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool: + """Check whether the current model supports vision input.""" + ... + + +# MTMD_API bool mtmd_support_audio(mtmd_context * ctx); +@ctypes_function("mtmd_support_audio", [mtmd_context_p_ctypes], c_bool) +def mtmd_support_audio(ctx: mtmd_context_p, /) -> bool: + """Check whether MTMD supports audio.""" + ... + + +# MTMD_API int mtmd_get_audio_sample_rate(mtmd_context * ctx); +@ctypes_function("mtmd_get_audio_sample_rate", [mtmd_context_p_ctypes], c_int) +def mtmd_get_audio_sample_rate(ctx: mtmd_context_p, /) -> int: + """Get the audio sample rate in Hz. Returns -1 if audio is not supported.""" + ... + + +# Deprecated compatibility wrapper for the renamed mtmd_get_audio_sample_rate(). +def mtmd_get_audio_bitrate(ctx: mtmd_context_p, /) -> int: + warnings.warn( + "mtmd_get_audio_bitrate is deprecated; use mtmd_get_audio_sample_rate instead", + DeprecationWarning, + stacklevel=2, + ) + return mtmd_get_audio_sample_rate(ctx) + + +# MTMD_API mtmd_bitmap * mtmd_bitmap_init(uint32_t nx, uint32_t ny, const unsigned char * data); +@ctypes_function( + "mtmd_bitmap_init", [c_uint32, c_uint32, POINTER(c_uint8)], mtmd_bitmap_p_ctypes +) +def mtmd_bitmap_init( + nx: Union[c_uint32, int], + ny: Union[c_uint32, int], + data: CtypesArray[c_uint8], + /, +) -> Optional[mtmd_bitmap_p]: ... + + +# MTMD_API mtmd_bitmap * mtmd_bitmap_init_from_audio(size_t n_samples, const float * data); +@ctypes_function( + "mtmd_bitmap_init_from_audio", + [c_size_t, POINTER(c_float)], + mtmd_bitmap_p_ctypes, +) +def mtmd_bitmap_init_from_audio( + n_samples: Union[c_size_t, int], + data: CtypesArray[c_float], + /, +) -> Optional[mtmd_bitmap_p]: + """Initialize an MTMD bitmap from audio samples.""" + ... + + +# MTMD_API void mtmd_bitmap_free(mtmd_bitmap * bitmap); +@ctypes_function("mtmd_bitmap_free", [mtmd_bitmap_p_ctypes], None) +def mtmd_bitmap_free(bitmap: mtmd_bitmap_p, /): ... + + +# MTMD_API mtmd_input_chunks * mtmd_input_chunks_init(void); +@ctypes_function("mtmd_input_chunks_init", [], mtmd_input_chunks_p_ctypes) +def mtmd_input_chunks_init() -> Optional[mtmd_input_chunks_p]: ... + + +# MTMD_API void mtmd_input_chunks_free(mtmd_input_chunks * chunks); +@ctypes_function("mtmd_input_chunks_free", [mtmd_input_chunks_p_ctypes], None) +def mtmd_input_chunks_free(chunks: mtmd_input_chunks_p, /): ... + + +# MTMD_API size_t mtmd_input_chunks_size(const mtmd_input_chunks * chunks); +@ctypes_function("mtmd_input_chunks_size", [mtmd_input_chunks_p_ctypes], c_size_t) +def mtmd_input_chunks_size(chunks: mtmd_input_chunks_p, /) -> int: ... + + +# MTMD_API const mtmd_input_chunk * mtmd_input_chunks_get(const mtmd_input_chunks * chunks, size_t idx); +@ctypes_function( + "mtmd_input_chunks_get", + [mtmd_input_chunks_p_ctypes, c_size_t], + mtmd_input_chunk_p_ctypes, +) +def mtmd_input_chunks_get( + chunks: mtmd_input_chunks_p, idx: Union[c_size_t, int], / +) -> Optional[mtmd_input_chunk_p]: ... + + +# MTMD_API int32_t mtmd_tokenize(mtmd_context * ctx, +# mtmd_input_chunks * output, +# const mtmd_input_text * text, +# const mtmd_bitmap ** bitmaps, +# size_t n_bitmaps); +@ctypes_function( + "mtmd_tokenize", + [ + mtmd_context_p_ctypes, + mtmd_input_chunks_p_ctypes, + POINTER(mtmd_input_text), + POINTER(mtmd_bitmap_p_ctypes), + c_size_t, + ], + c_int, +) +def mtmd_tokenize( + ctx: mtmd_context_p, + output: mtmd_input_chunks_p, + text: "_Pointer[mtmd_input_text]", + bitmaps: CtypesArray[mtmd_bitmap_p_ctypes], + n_bitmaps: Union[c_size_t, int], + /, +) -> int: ... + + +# MTMD_API size_t mtmd_input_chunk_get_n_tokens(const mtmd_input_chunk * chunk); +@ctypes_function("mtmd_input_chunk_get_n_tokens", [mtmd_input_chunk_p_ctypes], c_size_t) +def mtmd_input_chunk_get_n_tokens(chunk: mtmd_input_chunk_p, /) -> int: ... + + +# MTMD_API enum mtmd_input_chunk_type mtmd_input_chunk_get_type(const mtmd_input_chunk * chunk); +@ctypes_function("mtmd_input_chunk_get_type", [mtmd_input_chunk_p_ctypes], c_int) +def mtmd_input_chunk_get_type(chunk: mtmd_input_chunk_p, /) -> int: ... + + +# MTMD_API const llama_token * mtmd_input_chunk_get_tokens_text(const mtmd_input_chunk * chunk, size_t * n_tokens_output); +@ctypes_function( + "mtmd_input_chunk_get_tokens_text", + [mtmd_input_chunk_p_ctypes, POINTER(c_size_t)], + POINTER(llama_cpp.llama_token), +) +def mtmd_input_chunk_get_tokens_text( + chunk: mtmd_input_chunk_p, n_tokens_output: "_Pointer[c_size_t]", / +) -> Optional["_Pointer[llama_cpp.llama_token]"]: ... + + +################################################ +# mtmd-helper.h functions +################################################ + + +# MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len); +@ctypes_function( + "mtmd_helper_bitmap_init_from_buf", + [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t], + mtmd_bitmap_p_ctypes, +) +def mtmd_helper_bitmap_init_from_buf( + ctx: mtmd_context_p, + buf: CtypesArray[c_uint8], + length: Union[c_size_t, int], + /, +) -> Optional[mtmd_bitmap_p]: ... + + +# MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks); +@ctypes_function("mtmd_helper_get_n_tokens", [mtmd_input_chunks_p_ctypes], c_size_t) +def mtmd_helper_get_n_tokens(chunks: mtmd_input_chunks_p, /) -> int: ... + + +# MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx, +# struct llama_context * lctx, +# const mtmd_input_chunk * chunk, +# llama_pos n_past, +# llama_seq_id seq_id, +# int32_t n_batch, +# bool logits_last, +# llama_pos * new_n_past); +@ctypes_function( + "mtmd_helper_eval_chunk_single", + [ + mtmd_context_p_ctypes, + llama_cpp.llama_context_p_ctypes, + mtmd_input_chunk_p_ctypes, + llama_cpp.llama_pos, + llama_cpp.llama_seq_id, + c_int, + c_bool, + POINTER(llama_cpp.llama_pos), + ], + c_int, +) +def mtmd_helper_eval_chunk_single( + ctx: mtmd_context_p, + lctx: llama_cpp.llama_context_p, + chunk: mtmd_input_chunk_p, + n_past: llama_cpp.llama_pos, + seq_id: llama_cpp.llama_seq_id, + n_batch: Union[c_int, int], + logits_last: Union[c_bool, bool], + new_n_past: "_Pointer[llama_cpp.llama_pos]", + /, +) -> int: ... + + +# MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data); +@ctypes_function( + "mtmd_log_set", + [llama_cpp.llama_log_callback, c_void_p], + None, +) +def mtmd_log_set(log_callback, user_data: c_void_p, /): + """Set the MTMD logging callback.""" + ... + + +# MTMD_API void mtmd_helper_log_set(ggml_log_callback log_callback, void * user_data); +@ctypes_function( + "mtmd_helper_log_set", + [llama_cpp.llama_log_callback, c_void_p], + None, +) +def mtmd_helper_log_set(log_callback, user_data: c_void_p, /): + """Set the MTMD helper logging callback.""" + ... diff --git a/llama_cpp/server/app.py b/llama_cpp/server/app.py index cd3255176f..f776fe159c 100644 --- a/llama_cpp/server/app.py +++ b/llama_cpp/server/app.py @@ -5,9 +5,9 @@ import typing import contextlib -from threading import Lock +from anyio import Lock from functools import partial -from typing import Iterator, List, Optional, Union, Dict +from typing import List, Optional, Union, Dict import llama_cpp @@ -70,14 +70,14 @@ def set_llama_proxy(model_settings: List[ModelSettings]): _llama_proxy = LlamaProxy(models=model_settings) -def get_llama_proxy(): +async def get_llama_proxy(): # NOTE: This double lock allows the currently streaming llama model to # check if any other requests are pending in the same thread and cancel # the stream if so. - llama_outer_lock.acquire() + await llama_outer_lock.acquire() release_outer_lock = True try: - llama_inner_lock.acquire() + await llama_inner_lock.acquire() try: llama_outer_lock.release() release_outer_lock = False @@ -125,9 +125,9 @@ def create_app( server_settings = ServerSettings.model_validate(settings) model_settings = [ModelSettings.model_validate(settings)] - assert ( - server_settings is not None and model_settings is not None - ), "server_settings and model_settings must be provided together" + assert server_settings is not None and model_settings is not None, ( + "server_settings and model_settings must be provided together" + ) set_server_settings(server_settings) middleware = [Middleware(RawContextMiddleware, plugins=(RequestIdPlugin(),))] @@ -155,34 +155,71 @@ def create_app( return app +def prepare_request_resources( + body: CreateCompletionRequest | CreateChatCompletionRequest, + llama_proxy: LlamaProxy, + body_model: str | None, + kwargs, +) -> llama_cpp.Llama: + if llama_proxy is None: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail="Service is not available", + ) + llama = llama_proxy(body_model) + if body.logit_bias is not None: + kwargs["logit_bias"] = ( + _logit_bias_tokens_to_input_ids(llama, body.logit_bias) + if body.logit_bias_type == "tokens" + else body.logit_bias + ) + + if body.grammar is not None: + kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar) + + if body.min_tokens > 0: + _min_tokens_logits_processor = llama_cpp.LogitsProcessorList( + [llama_cpp.MinTokensLogitsProcessor(body.min_tokens, llama.token_eos())] + ) + if "logits_processor" not in kwargs: + kwargs["logits_processor"] = _min_tokens_logits_processor + else: + kwargs["logits_processor"].extend(_min_tokens_logits_processor) + return llama + + async def get_event_publisher( request: Request, inner_send_chan: MemoryObjectSendStream[typing.Any], - iterator: Iterator[typing.Any], - on_complete: typing.Optional[typing.Callable[[], None]] = None, + body: CreateCompletionRequest | CreateChatCompletionRequest, + body_model: str | None, + llama_call, + kwargs, ): server_settings = next(get_server_settings()) interrupt_requests = ( server_settings.interrupt_requests if server_settings else False ) - async with inner_send_chan: - try: - async for chunk in iterate_in_threadpool(iterator): - await inner_send_chan.send(dict(data=json.dumps(chunk))) - if await request.is_disconnected(): - raise anyio.get_cancelled_exc_class()() - if interrupt_requests and llama_outer_lock.locked(): - await inner_send_chan.send(dict(data="[DONE]")) - raise anyio.get_cancelled_exc_class()() - await inner_send_chan.send(dict(data="[DONE]")) - except anyio.get_cancelled_exc_class() as e: - print("disconnected") - with anyio.move_on_after(1, shield=True): - print(f"Disconnected from client (via refresh/close) {request.client}") - raise e - finally: - if on_complete: - on_complete() + async with contextlib.asynccontextmanager(get_llama_proxy)() as llama_proxy: + llama = prepare_request_resources(body, llama_proxy, body_model, kwargs) + async with inner_send_chan: + try: + iterator = await run_in_threadpool(llama_call, llama, **kwargs) + async for chunk in iterate_in_threadpool(iterator): + await inner_send_chan.send(dict(data=json.dumps(chunk))) + if await request.is_disconnected(): + raise anyio.get_cancelled_exc_class()() + if interrupt_requests and llama_outer_lock.locked(): + await inner_send_chan.send(dict(data="[DONE]")) + raise anyio.get_cancelled_exc_class()() + await inner_send_chan.send(dict(data="[DONE]")) + except anyio.get_cancelled_exc_class() as e: + print("disconnected") + with anyio.move_on_after(1, shield=True): + print( + f"Disconnected from client (via refresh/close) {request.client}" + ) + raise e def _logit_bias_tokens_to_input_ids( @@ -267,20 +304,11 @@ async def create_completion( request: Request, body: CreateCompletionRequest, ) -> llama_cpp.Completion: - exit_stack = contextlib.ExitStack() - llama_proxy = await run_in_threadpool( - lambda: exit_stack.enter_context(contextlib.contextmanager(get_llama_proxy)()) - ) - if llama_proxy is None: - raise HTTPException( - status_code=status.HTTP_503_SERVICE_UNAVAILABLE, - detail="Service is not available", - ) if isinstance(body.prompt, list): assert len(body.prompt) <= 1 body.prompt = body.prompt[0] if len(body.prompt) > 0 else "" - llama = llama_proxy( + body_model = ( body.model if request.url.path != "/v1/engines/copilot-codex/completions" else "copilot-codex" @@ -295,41 +323,8 @@ async def create_completion( } kwargs = body.model_dump(exclude=exclude) - if body.logit_bias is not None: - kwargs["logit_bias"] = ( - _logit_bias_tokens_to_input_ids(llama, body.logit_bias) - if body.logit_bias_type == "tokens" - else body.logit_bias - ) - - if body.grammar is not None: - kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar) - - if body.min_tokens > 0: - _min_tokens_logits_processor = llama_cpp.LogitsProcessorList( - [llama_cpp.MinTokensLogitsProcessor(body.min_tokens, llama.token_eos())] - ) - if "logits_processor" not in kwargs: - kwargs["logits_processor"] = _min_tokens_logits_processor - else: - kwargs["logits_processor"].extend(_min_tokens_logits_processor) - - iterator_or_completion: Union[ - llama_cpp.CreateCompletionResponse, - Iterator[llama_cpp.CreateCompletionStreamResponse], - ] = await run_in_threadpool(llama, **kwargs) - - if isinstance(iterator_or_completion, Iterator): - # EAFP: It's easier to ask for forgiveness than permission - first_response = await run_in_threadpool(next, iterator_or_completion) - - # If no exception was raised from first_response, we can assume that - # the iterator is valid and we can use it to stream the response. - def iterator() -> Iterator[llama_cpp.CreateCompletionStreamResponse]: - yield first_response - yield from iterator_or_completion - exit_stack.close() - + # handle streaming request + if kwargs.get("stream", False): send_chan, recv_chan = anyio.create_memory_object_stream(10) return EventSourceResponse( recv_chan, @@ -337,14 +332,29 @@ def iterator() -> Iterator[llama_cpp.CreateCompletionStreamResponse]: get_event_publisher, request=request, inner_send_chan=send_chan, - iterator=iterator(), - on_complete=exit_stack.close, + body=body, + body_model=body_model, + llama_call=llama_cpp.Llama.__call__, + kwargs=kwargs, ), sep="\n", ping_message_factory=_ping_message_factory, ) - else: - return iterator_or_completion + + # handle regular request + async with contextlib.asynccontextmanager(get_llama_proxy)() as llama_proxy: + llama = prepare_request_resources(body, llama_proxy, body_model, kwargs) + + if await request.is_disconnected(): + print( + f"Disconnected from client (via refresh/close) before llm invoked {request.client}" + ) + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="Client closed request", + ) + + return await run_in_threadpool(llama, **kwargs) @router.post( @@ -472,15 +482,8 @@ async def create_chat_completion( # where the dependency is cleaned up before a StreamingResponse # is complete. # https://github.com/tiangolo/fastapi/issues/11143 - exit_stack = contextlib.ExitStack() - llama_proxy = await run_in_threadpool( - lambda: exit_stack.enter_context(contextlib.contextmanager(get_llama_proxy)()) - ) - if llama_proxy is None: - raise HTTPException( - status_code=status.HTTP_503_SERVICE_UNAVAILABLE, - detail="Service is not available", - ) + + body_model = body.model exclude = { "n", "logit_bias_type", @@ -488,41 +491,9 @@ async def create_chat_completion( "min_tokens", } kwargs = body.model_dump(exclude=exclude) - llama = llama_proxy(body.model) - if body.logit_bias is not None: - kwargs["logit_bias"] = ( - _logit_bias_tokens_to_input_ids(llama, body.logit_bias) - if body.logit_bias_type == "tokens" - else body.logit_bias - ) - - if body.grammar is not None: - kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar) - - if body.min_tokens > 0: - _min_tokens_logits_processor = llama_cpp.LogitsProcessorList( - [llama_cpp.MinTokensLogitsProcessor(body.min_tokens, llama.token_eos())] - ) - if "logits_processor" not in kwargs: - kwargs["logits_processor"] = _min_tokens_logits_processor - else: - kwargs["logits_processor"].extend(_min_tokens_logits_processor) - - iterator_or_completion: Union[ - llama_cpp.ChatCompletion, Iterator[llama_cpp.ChatCompletionChunk] - ] = await run_in_threadpool(llama.create_chat_completion, **kwargs) - - if isinstance(iterator_or_completion, Iterator): - # EAFP: It's easier to ask for forgiveness than permission - first_response = await run_in_threadpool(next, iterator_or_completion) - - # If no exception was raised from first_response, we can assume that - # the iterator is valid and we can use it to stream the response. - def iterator() -> Iterator[llama_cpp.ChatCompletionChunk]: - yield first_response - yield from iterator_or_completion - exit_stack.close() + # handle streaming request + if kwargs.get("stream", False): send_chan, recv_chan = anyio.create_memory_object_stream(10) return EventSourceResponse( recv_chan, @@ -530,15 +501,29 @@ def iterator() -> Iterator[llama_cpp.ChatCompletionChunk]: get_event_publisher, request=request, inner_send_chan=send_chan, - iterator=iterator(), - on_complete=exit_stack.close, + body=body, + body_model=body_model, + llama_call=llama_cpp.Llama.create_chat_completion, + kwargs=kwargs, ), sep="\n", ping_message_factory=_ping_message_factory, ) - else: - exit_stack.close() - return iterator_or_completion + + # handle regular request + async with contextlib.asynccontextmanager(get_llama_proxy)() as llama_proxy: + llama = prepare_request_resources(body, llama_proxy, body_model, kwargs) + + if await request.is_disconnected(): + print( + f"Disconnected from client (via refresh/close) before llm invoked {request.client}" + ) + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="Client closed request", + ) + + return await run_in_threadpool(llama.create_chat_completion, **kwargs) @router.get( diff --git a/llama_cpp/server/cli.py b/llama_cpp/server/cli.py index 3dd0076767..171b8db30c 100644 --- a/llama_cpp/server/cli.py +++ b/llama_cpp/server/cli.py @@ -1,8 +1,9 @@ from __future__ import annotations import argparse +import json -from typing import List, Literal, Union, Any, Type, TypeVar +from typing import List, Literal, Union, Any, Type, TypeVar, Dict from pydantic import BaseModel @@ -14,7 +15,9 @@ def _get_base_type(annotation: Type[Any]) -> Type[Any]: elif getattr(annotation, "__origin__", None) is Union: assert hasattr(annotation, "__args__") and len(annotation.__args__) >= 1 # type: ignore non_optional_args: List[Type[Any]] = [ - arg for arg in annotation.__args__ if arg is not type(None) # type: ignore + arg + for arg in annotation.__args__ + if arg is not type(None) # type: ignore ] if non_optional_args: return _get_base_type(non_optional_args[0]) @@ -38,6 +41,17 @@ def _contains_list_type(annotation: Type[Any] | None) -> bool: return False +def _contains_dict_type(annotation: Type[Any] | None) -> bool: + origin = getattr(annotation, "__origin__", None) + + if origin is dict or origin is Dict: + return True + elif origin in (Literal, Union): + return any(_contains_dict_type(arg) for arg in annotation.__args__) # type: ignore + else: + return False + + def _parse_bool_arg(arg: str | bytes | bool) -> bool: if isinstance(arg, bytes): arg = arg.decode("utf-8") @@ -55,6 +69,16 @@ def _parse_bool_arg(arg: str | bytes | bool) -> bool: raise ValueError(f"Invalid boolean argument: {arg}") +def _parse_json_object_arg(arg: str | bytes) -> dict[str, Any]: + if isinstance(arg, bytes): + arg = arg.decode("utf-8") + + value = json.loads(arg) + if not isinstance(value, dict): + raise ValueError(f"Invalid JSON object argument: {arg}") + return value + + def add_args_from_model(parser: argparse.ArgumentParser, model: Type[BaseModel]): """Add arguments from a pydantic model to an argparse parser.""" @@ -66,7 +90,15 @@ def add_args_from_model(parser: argparse.ArgumentParser, model: Type[BaseModel]) _get_base_type(field.annotation) if field.annotation is not None else str ) list_type = _contains_list_type(field.annotation) - if base_type is not bool: + dict_type = _contains_dict_type(field.annotation) + if dict_type: + parser.add_argument( + f"--{name}", + dest=name, + type=_parse_json_object_arg, + help=description, + ) + elif base_type is not bool: parser.add_argument( f"--{name}", dest=name, diff --git a/llama_cpp/server/errors.py b/llama_cpp/server/errors.py index fbf9fd80d5..d0eda5664b 100644 --- a/llama_cpp/server/errors.py +++ b/llama_cpp/server/errors.py @@ -134,8 +134,6 @@ def error_message_wrapper( ] = None, ) -> Tuple[int, ErrorResponse]: """Wraps error message in OpenAI style error response""" - print(f"Exception: {str(error)}", file=sys.stderr) - traceback.print_exc(file=sys.stderr) if body is not None and isinstance( body, ( @@ -149,6 +147,10 @@ def error_message_wrapper( if match is not None: return callback(body, match) + # Only print the trace on unexpected exceptions + print(f"Exception: {str(error)}", file=sys.stderr) + traceback.print_exc(file=sys.stderr) + # Wrap other errors as internal server error return 500, ErrorResponse( message=str(error), diff --git a/llama_cpp/server/model.py b/llama_cpp/server/model.py index c6716f9196..3922ce5df3 100644 --- a/llama_cpp/server/model.py +++ b/llama_cpp/server/model.py @@ -171,19 +171,33 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama: chat_handler = llama_cpp.llama_chat_format.MiniCPMv26ChatHandler( clip_model_path=settings.clip_model_path, verbose=settings.verbose ) + elif settings.chat_format == "qwen2.5-vl": + assert settings.clip_model_path is not None, "clip model not found" + if settings.hf_model_repo_id is not None: + chat_handler = ( + llama_cpp.llama_chat_format.Qwen25VLChatHandler.from_pretrained( + repo_id=settings.hf_model_repo_id, + filename=settings.clip_model_path, + verbose=settings.verbose, + ) + ) + else: + chat_handler = llama_cpp.llama_chat_format.Qwen25VLChatHandler( + clip_model_path=settings.clip_model_path, verbose=settings.verbose + ) elif settings.chat_format == "hf-autotokenizer": - assert ( - settings.hf_pretrained_model_name_or_path is not None - ), "hf_pretrained_model_name_or_path must be set for hf-autotokenizer" + assert settings.hf_pretrained_model_name_or_path is not None, ( + "hf_pretrained_model_name_or_path must be set for hf-autotokenizer" + ) chat_handler = ( llama_cpp.llama_chat_format.hf_autotokenizer_to_chat_completion_handler( settings.hf_pretrained_model_name_or_path ) ) elif settings.chat_format == "hf-tokenizer-config": - assert ( - settings.hf_tokenizer_config_path is not None - ), "hf_tokenizer_config_path must be set for hf-tokenizer-config" + assert settings.hf_tokenizer_config_path is not None, ( + "hf_tokenizer_config_path must be set for hf-tokenizer-config" + ) chat_handler = llama_cpp.llama_chat_format.hf_tokenizer_config_to_chat_completion_handler( json.load(open(settings.hf_tokenizer_config_path)) ) @@ -285,6 +299,21 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama: # Misc verbose=settings.verbose, ) + if settings.chat_template_kwargs: + base_chat_handler = ( + _model.chat_handler + or _model._chat_handlers.get(_model.chat_format) + or llama_cpp.llama_chat_format.get_chat_completion_handler( + _model.chat_format + ) + ) + + def chat_handler_with_kwargs(*args, **kwargs): + return base_chat_handler( + *args, **{**settings.chat_template_kwargs, **kwargs} + ) + + _model.chat_handler = chat_handler_with_kwargs if settings.cache: if settings.cache_type == "disk": if settings.verbose: diff --git a/llama_cpp/server/settings.py b/llama_cpp/server/settings.py index 13c9512419..3c2bb7fd07 100644 --- a/llama_cpp/server/settings.py +++ b/llama_cpp/server/settings.py @@ -2,7 +2,7 @@ import multiprocessing -from typing import Optional, List, Literal, Union, Dict, cast +from typing import Any, Optional, List, Literal, Union, Dict, cast from typing_extensions import Self from pydantic import Field, model_validator @@ -131,6 +131,10 @@ class ModelSettings(BaseSettings): default=None, description="Chat format to use.", ) + chat_template_kwargs: Optional[Dict[str, Any]] = Field( + default=None, + description="Extra keyword arguments forwarded to chat templates at model load time. Matches llama.cpp server `chat_template_kwargs`.", + ) clip_model_path: Optional[str] = Field( default=None, description="Path to a CLIP model to use for multi-modal chat completion.", diff --git a/pyproject.toml b/pyproject.toml index 9983ef777d..b5998dd1c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] @@ -48,7 +49,7 @@ test = [ "huggingface-hub>=0.23.0" ] dev = [ - "black>=23.3.0", + "ruff>=0.15.7", "twine>=4.0.2", "mkdocs>=1.4.3", "mkdocstrings[python]>=0.22.0", @@ -62,6 +63,7 @@ all = [ [tool.scikit-build] wheel.packages = ["llama_cpp"] +wheel.py-api = "py3" cmake.verbose = true cmake.minimum-version = "3.21" minimum-version = "0.5.1" @@ -77,5 +79,16 @@ Issues = "https://github.com/abetlen/llama-cpp-python/issues" Documentation = "https://llama-cpp-python.readthedocs.io/en/latest/" Changelog = "https://llama-cpp-python.readthedocs.io/en/latest/changelog/" +[tool.ruff] +target-version = "py38" +line-length = 88 +required-version = ">=0.15.7" +src = ["llama_cpp", "tests"] +extend-exclude = ["vendor", "examples/notebooks"] + +[tool.ruff.lint] +select = ["E4", "E7", "E9"] +ignore = ["E712"] + [tool.pytest.ini_options] testpaths = "tests" diff --git a/tests/test_llama.py b/tests/test_llama.py index fc182ae20f..23928fff66 100644 --- a/tests/test_llama.py +++ b/tests/test_llama.py @@ -58,14 +58,23 @@ def test_llama_cpp_tokenization(): @pytest.fixture def llama_cpp_model_path(): - repo_id = "Qwen/Qwen2-0.5B-Instruct-GGUF" - filename = "qwen2-0_5b-instruct-q8_0.gguf" + repo_id = "lmstudio-community/Qwen3.5-0.8B-GGUF" + filename = "Qwen3.5-0.8B-Q8_0.gguf" + model_path = hf_hub_download(repo_id, filename) + return model_path + + +@pytest.fixture +def llama_cpp_embedding_model_path(): + repo_id = "CompendiumLabs/bge-small-en-v1.5-gguf" + filename = "bge-small-en-v1.5-q4_k_m.gguf" model_path = hf_hub_download(repo_id, filename) return model_path def test_real_model(llama_cpp_model_path): import os + assert os.path.exists(llama_cpp_model_path) params = llama_cpp.llama_model_default_params() @@ -82,14 +91,19 @@ def test_real_model(llama_cpp_model_path): cparams.n_threads = multiprocessing.cpu_count() cparams.n_threads_batch = multiprocessing.cpu_count() cparams.logits_all = False - cparams.flash_attn = True + cparams.flash_attn_type = llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED context = internals.LlamaContext(model=model, params=cparams) tokens = model.tokenize(b"Hello, world!", add_bos=True, special=True) - assert tokens == [9707, 11, 1879, 0] + assert tokens == [9419, 11, 1814, 0] - tokens = model.tokenize(b"The quick brown fox jumps", add_bos=True, special=True) + tokens = model.tokenize( + b"The quick brown fox jumps over the lazy dog. The quick brown fox jumps ", + add_bos=True, + special=True, + ) + prompt_token_count = len(tokens) batch = internals.LlamaBatch(n_tokens=len(tokens), embd=0, n_seq_max=1) @@ -110,9 +124,12 @@ def test_real_model(llama_cpp_model_path): tokens = [token_id] result += tokens - output = result[5:] + output = result[prompt_token_count:] output_text = model.detokenize(output, special=True) - assert output_text == b" over the lazy dog" + # Low-level sampling output varies across CPU and Metal backends. + assert len(output) == 4 + assert output_text + def test_real_llama(llama_cpp_model_path): model = llama_cpp.Llama( @@ -127,15 +144,14 @@ def test_real_llama(llama_cpp_model_path): ) output = model.create_completion( - "The quick brown fox jumps", - max_tokens=4, + "The quick brown fox jumps over the lazy dog. The quick brown fox", + max_tokens=6, top_k=50, top_p=0.9, - temperature=0.8, - seed=1337 + temperature=0.0, + seed=1337, ) - assert output["choices"][0]["text"] == " over the lazy dog" - + assert output["choices"][0]["text"] == " jumps over the lazy dog." output = model.create_completion( "The capital of france is paris, 'true' or 'false'?:\n", @@ -146,20 +162,19 @@ def test_real_llama(llama_cpp_model_path): seed=1337, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "true" | "false" -""") +"""), ) assert output["choices"][0]["text"] == "true" suffix = b"rot" tokens = model.tokenize(suffix, add_bos=True, special=True) + def logit_processor_func(input_ids, logits): for token in tokens: logits[token] *= 1000 return logits - logit_processors = llama_cpp.LogitsProcessorList( - [logit_processor_func] - ) + logit_processors = llama_cpp.LogitsProcessorList([logit_processor_func]) output = model.create_completion( "The capital of france is par", @@ -168,7 +183,7 @@ def logit_processor_func(input_ids, logits): top_p=0.9, temperature=0.8, seed=1337, - logits_processor=logit_processors + logits_processor=logit_processors, ) assert output["choices"][0]["text"].lower().startswith("rot") @@ -181,10 +196,10 @@ def logit_processor_func(input_ids, logits): max_tokens=4, top_k=50, top_p=0.9, - temperature=0.8, + temperature=1.0, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" -""") +"""), ) number_1 = output["choices"][0]["text"] @@ -193,10 +208,10 @@ def logit_processor_func(input_ids, logits): max_tokens=4, top_k=50, top_p=0.9, - temperature=0.8, + temperature=1.0, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" -""") +"""), ) number_2 = output["choices"][0]["text"] @@ -207,12 +222,28 @@ def logit_processor_func(input_ids, logits): max_tokens=4, top_k=50, top_p=0.9, - temperature=0.8, + temperature=1.0, grammar=llama_cpp.LlamaGrammar.from_string(""" root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10" -""") +"""), ) number_3 = output["choices"][0]["text"] assert number_1 != number_2 assert number_1 == number_3 + + +def test_real_llama_embeddings(llama_cpp_embedding_model_path): + model = llama_cpp.Llama( + llama_cpp_embedding_model_path, + n_ctx=32, + n_batch=32, + n_ubatch=32, + n_threads=multiprocessing.cpu_count(), + n_threads_batch=multiprocessing.cpu_count(), + logits_all=False, + flash_attn=True, + embedding=True, + ) + embedding = model.embed("Hello World") + assert len(embedding) > 0 diff --git a/tests/test_llama_chat_format.py b/tests/test_llama_chat_format.py index f031bf72b7..18c7279cf0 100644 --- a/tests/test_llama_chat_format.py +++ b/tests/test_llama_chat_format.py @@ -10,13 +10,20 @@ from llama_cpp.llama_chat_format import hf_tokenizer_config_to_chat_formatter + def test_mistral_instruct(): chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}" chat_formatter = jinja2.Template(chat_template) messages = [ - llama_types.ChatCompletionRequestUserMessage(role="user", content="Instruction"), - llama_types.ChatCompletionRequestAssistantMessage(role="assistant", content="Model answer"), - llama_types.ChatCompletionRequestUserMessage(role="user", content="Follow-up instruction"), + llama_types.ChatCompletionRequestUserMessage( + role="user", content="Instruction" + ), + llama_types.ChatCompletionRequestAssistantMessage( + role="assistant", content="Model answer" + ), + llama_types.ChatCompletionRequestUserMessage( + role="user", content="Follow-up instruction" + ), ] response = llama_chat_format.format_mistral_instruct( messages=messages, @@ -77,13 +84,11 @@ def test_mistral_instruct(): def test_hf_tokenizer_config_str_to_chat_formatter(): tokenizer_config = json.loads(mistral_7b_tokenizer_config) - chat_formatter = hf_tokenizer_config_to_chat_formatter( - tokenizer_config - ) + chat_formatter = hf_tokenizer_config_to_chat_formatter(tokenizer_config) chat_formatter_respoonse = chat_formatter( messages=[ ChatCompletionRequestUserMessage(role="user", content="Hello, world!"), ] ) - assert chat_formatter_respoonse.prompt == ("[INST] Hello, world! [/INST]" "") + assert chat_formatter_respoonse.prompt == ("[INST] Hello, world! [/INST]") diff --git a/tests/test_llama_speculative.py b/tests/test_llama_speculative.py index b5d450567b..d28c9ca9c6 100644 --- a/tests/test_llama_speculative.py +++ b/tests/test_llama_speculative.py @@ -2,15 +2,20 @@ from llama_cpp.llama_speculative import LlamaPromptLookupDecoding + def test_find_candidate_pred_tokens(): find_candidate_pred_tokens = LlamaPromptLookupDecoding.find_candidate_pred_tokens # Test Case 1: Matching ngram is found input_ids1 = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3]) - result1 = find_candidate_pred_tokens(input_ids1, max_ngram_size=3, num_pred_tokens=2) + result1 = find_candidate_pred_tokens( + input_ids1, max_ngram_size=3, num_pred_tokens=2 + ) assert np.array_equal(result1, np.array([1, 2])) # Test Case 2: Matching ngram is not found input_ids2 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) - result2 = find_candidate_pred_tokens(input_ids2, max_ngram_size=3, num_pred_tokens=2) + result2 = find_candidate_pred_tokens( + input_ids2, max_ngram_size=3, num_pred_tokens=2 + ) assert np.array_equal(result2, np.array([])) diff --git a/vendor/llama.cpp b/vendor/llama.cpp index dc22344088..f49e917876 160000 --- a/vendor/llama.cpp +++ b/vendor/llama.cpp @@ -1 +1 @@ -Subproject commit dc22344088a7ee81a1e4f096459b03a72f24ccdc +Subproject commit f49e9178767d557a522618b16ce8694f9ddac628